diff --git a/README.md b/README.md index 81deb4bdea6c4f9d3bf9c2058e508d27ed43aaaa..6a56552cf369cc8e935049fd74c72b1fb4f75b62 100755 --- a/README.md +++ b/README.md @@ -209,9 +209,15 @@ Gitlab developers ## WHAT'S NEW IN + +### v8.7.0 + +1) Precision about were the cutting is, relatively to the nucleotide position indicated in the files & logos improved for the reverse panels + + ### v8.6.0 -1) Figure 21 and 44 modified. +1) Figure 21 and 44 modified ### v8.5.0 diff --git a/bin/logo.R b/bin/logo.R index 5167259deba5bba602299273e51a437299f1e577..08c1463eca812f564f68b117f8ce76737fd79297 100755 --- a/bin/logo.R +++ b/bin/logo.R @@ -339,21 +339,33 @@ png(filename = paste0("logo_", sub(x = freq, pattern = "\\.stat$", replacement = width <- 7 height <- 2.5 text.size <- 6 -title.text.size <- 5 +title.text.size <- 3 angle <- 90 decal <- -1 # indicate the position occupied by the +1 position of the read (that correspond to first base of coli part of read) after rev complementation. Before rev-comp, it is 201 (as mentioned above). After, it is 400-201+1 = 200, thus -1. Allow to overlay consensus that are not centered on the +1 position tempo.just <- fun_gg_just(angle = angle, pos = "bottom") if(ncol(tempo) > 0){ gg1 = ggseqlogo::geom_logo(data = tempo, method = "bits", seq_type = "dna") # Derived from https://weblogo.berkeley.edu/logo.cgi because the website does not work - title <- paste0(freq, " | CONSENSUS SEQUENCE | POSITION 1 CORRESPONDS TO THE FIRST NUCLEOTIDE OF THE GENOME PART OF THE PLASMID GENOME JUNCTION") + title <- paste0(freq, "\nCONSENSUS SEQUENCE | POSITION 1 CORRESPONDS TO THE FIRST NUCLEOTIDE OF THE GENOME PART OF THE PLASMID GENOME JUNCTION") gg2 <- ggplot2::theme( # axis.text.x = ggplot2::element_text(angle = tempo.just$angle, hjust = tempo.just$hjust, vjust = tempo.just$vjust, size = text.size), #not required anymore because I used annotate() finally panel.background = ggplot2::element_blank() ) gg3 <- ggplot2::annotate("text", x = 0, y = 2.05, hjust = 0, vjust = 0, label = title, size = title.text.size) # y = 2.05 with bits, ggplot2::labs(title = title) to boring to fix the size gg4 <- ggplot2::scale_x_discrete(labels = c((-insertion_dist):(-1), 1:insertion_dist)) #remove the x initial numbers - gg5 <- ggplot2::annotate("text", x = 1:(insertion_dist * 2), y = -0.01, hjust = 1, vjust = 0.5, label = c((-insertion_dist):(-1), 1:insertion_dist), size = text.size, angle = angle) + gg5 <- ggplot2::annotate("text", + x = 1:(insertion_dist * 2), + y = -0.01, + hjust = 1, + vjust = 0.5, + label = if(grepl(x = freq, pattern = "_0.stat$")){ + c((-insertion_dist):(-1), 1:insertion_dist) + }else{ + c((1 + insertion_dist):1, (-1):(-insertion_dist + 1)) + }, + size = text.size, + angle = angle + ) gg6 <- ggplot2::coord_cartesian(clip = "off") gg7 <- ggplot2::theme(text = ggplot2::element_text(size = text.size * 4)) suppressMessages(print(ggplot2::ggplot() + gg1 + gg2 + gg3 + gg4 + gg5 + gg6 + gg7)) diff --git a/bin/motif.R b/bin/motif.R index fa9299e70f4c9dd6e6ccdf5ceec5728bfebbe119..3f63765539b1dcf9dc031fb71ba73dcb7db6e8e8 100755 --- a/bin/motif.R +++ b/bin/motif.R @@ -378,7 +378,7 @@ kableExtra::kable_styling(knitr::kable(head(tempo), row.names = FALSE, caption = fun_report(data = tempo.cat, output = report.rmd, path = "./", overwrite = FALSE) fun_report(data = paste0("\n\nBeginning of the motif positions in the reverse strand:\n\n"), output = report.rmd, path = "./", overwrite = FALSE) options(scipen = 1000) # to avoid writing of scientific numbers in tables, see https://stackoverflow.com/questions/3978266/number-format-writing-1e-5-instead-of-0-00001 -write.table(head(fw), file = paste0("./head.rv.txt"), row.names = FALSE, col.names = TRUE, append = FALSE, quote = FALSE, sep = "\t") +write.table(head(rv), file = paste0("./head.rv.txt"), row.names = FALSE, col.names = TRUE, append = FALSE, quote = FALSE, sep = "\t") options(scipen = 0) tempo.cat <- " \`\`\`{r, echo = FALSE} diff --git a/bin/print_report.R b/bin/print_report.R index 69ab147f6f2014c3224b6d9c89f683fa1374efc1..db1f0b9de7115e81f935f846374cd99e48d2ec46 100755 --- a/bin/print_report.R +++ b/bin/print_report.R @@ -38,7 +38,7 @@ # R version checking if(version$version.string != "R version 4.1.2 (2021-11-01)"){ - stop(paste0("\n\n================\n\nERROR IN plot_read_length.R\n", version$version.string, " IS NOT THE 4.1.2 RECOMMANDED\n\n================\n\n")) + stop(paste0("\n\n================\n\nERROR IN print_report.R\n", version$version.string, " IS NOT THE 4.1.2 RECOMMANDED\n\n================\n\n")) } # other initializations erase.objects = TRUE # write TRUE to erase all the existing objects in R before starting the algorithm and FALSE otherwise. Beginners should use TRUE @@ -148,22 +148,22 @@ for(i in 1:length(param.list)){ ################ import functions from cute little functions toolbox if(length(cute) != 1){ - stop(paste0("\n\n============\n\nERROR IN plot_read_length.R\ncute PARAMETER MUST BE LENGTH 1: ", paste(cute, collapse = " "), "\n\n============\n\n"), call. = FALSE) + stop(paste0("\n\n============\n\nERROR IN print_report.R\ncute PARAMETER MUST BE LENGTH 1: ", paste(cute, collapse = " "), "\n\n============\n\n"), call. = FALSE) }else if(grepl(x = cute, pattern = "^http")){ tempo.try <- try(suppressWarnings(suppressMessages(source(cute, local = .GlobalEnv))), silent = TRUE) if(any(grepl(x = tempo.try, pattern = "^[Ee]rror"))){ - stop(paste0("\n\n============\n\nERROR IN plot_read_length.R\nHTTP INDICATED IN THE cute PARAMETER DOES NOT EXISTS: ", cute, "\n\n============\n\n"), call. = FALSE) + stop(paste0("\n\n============\n\nERROR IN print_report.R\nHTTP INDICATED IN THE cute PARAMETER DOES NOT EXISTS: ", cute, "\n\n============\n\n"), call. = FALSE) }else{ source(cute, local = .GlobalEnv) # source the fun_ functions used below } }else if( ! grepl(x = cute, pattern = "^http")){ if( ! file.exists(cute)){ - stop(paste0("\n\n============\n\nERROR IN plot_read_length.R\nFILE INDICATED IN THE cute PARAMETER DOES NOT EXISTS: ", cute, "\n\n============\n\n"), call. = FALSE) + stop(paste0("\n\n============\n\nERROR IN print_report.R\nFILE INDICATED IN THE cute PARAMETER DOES NOT EXISTS: ", cute, "\n\n============\n\n"), call. = FALSE) }else{ source(cute, local = .GlobalEnv) # source the fun_ functions used below } }else{ - tempo.cat <- paste0("\n\n================\n\nINTERNAL CODE ERROR 3 IN plot_read_length.R: CODE HAS TO BE MODIFIED\n\n============\n\n") + tempo.cat <- paste0("\n\n================\n\nINTERNAL CODE ERROR 3 IN print_report.R: CODE HAS TO BE MODIFIED\n\n============\n\n") stop(tempo.cat, call. = FALSE) } @@ -181,7 +181,7 @@ for(i1 in req.function){ } } if( ! is.null(tempo)){ - tempo.cat <- paste0("ERROR IN plot_read_length.R\nREQUIRED cute FUNCTION", ifelse(length(tempo) > 1, "S ARE", " IS"), " MISSING IN THE R ENVIRONMENT:\n", paste0(tempo, collapse = "()\n")) + tempo.cat <- paste0("ERROR IN print_report.R\nREQUIRED cute FUNCTION", ifelse(length(tempo) > 1, "S ARE", " IS"), " MISSING IN THE R ENVIRONMENT:\n", paste0(tempo, collapse = "()\n")) stop(paste0("\n\n================\n\n", tempo.cat, "\n\n================\n\n"), call. = FALSE) # == in stop() to be able to add several messages between == } # end required function checking diff --git a/example_of_result/20220120_test_1649703168/fastQC1/test.fastq2_trim_fastqc.html b/example_of_result/20220120_test_1649703168/fastQC1/test.fastq2_trim_fastqc.html deleted file mode 100644 index b46355be0b1a60c9f4f904d3498a7b9f8e6d1902..0000000000000000000000000000000000000000 --- a/example_of_result/20220120_test_1649703168/fastQC1/test.fastq2_trim_fastqc.html +++ /dev/null @@ -1,187 +0,0 @@ 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src="data:image/png;base64,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" alt="FastQC"/>FastQC Report</div><div id="header_filename">Sat 9 Apr 2022<br/>test.fastq2_trim.fq</div></div><div class="summary"><h2>Summary</h2><ul><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M0">Basic Statistics</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M1">Per base sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M2">Per tile sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M3">Per sequence quality scores</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M4">Per base sequence content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M5">Per sequence GC content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M6">Per base N content</a></li><li><img src="data:image/png;base64,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" alt="[WARNING]"/><a href="#M7">Sequence Length Distribution</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M8">Sequence Duplication Levels</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M9">Overrepresented sequences</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M10">Adapter Content</a></li></ul></div><div class="main"><div class="module"><h2 id="M0"><img src="data:image/png;base64,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" alt="[OK]"/>Basic Statistics</h2><table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>test.fastq2_trim.fq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>8709</td></tr><tr><td>Sequences flagged as poor quality</td><td>0</td></tr><tr><td>Sequence length</td><td>30-175</td></tr><tr><td>%GC</td><td>54</td></tr></tbody></table></div><div class="module"><h2 id="M1"><img src="data:image/png;base64,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" alt="[OK]"/>Per base sequence quality</h2><p><img class="indented" 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" alt="Per base quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M2"><img src="data:image/png;base64,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" alt="[OK]"/>Per tile sequence quality</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per base quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M3"><img src="data:image/png;base64,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" alt="[OK]"/>Per sequence quality scores</h2><p><img class="indented" 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SlYFjQREREREVFXF6Uvuii8xOjJJytYCpYtSkREREREFI0oecghQcFK33STgqVg2aJERERERETRiOIbbRQUrOzLLytYCpYtSkREREREFIEo+8orQbuKb7hhNxIpWBY0ERERERFRlxalb745vMTowQcrWAqWLUpERERERBSNKHXKKeEJWBdeqGApWLYoERERERFRNKLETjuFJ2A9+qiCpWDZokRERERERBGIcrFYrHfveN++tYmEgqVg2aJEREREREQRiDJ/+lN4idHPfrZ7iRQsC5qIiIiIiKjrilI//nF4idHTT1ewFCxblIiIiIiIKBpR8sADw3s8/+pXCpaCZYsSERERERFFIcrlYoMHBwUr989/KlgKli1KREREREQUgSg3YUJ4idEhQ7qdSMGyoImIiIiIiLqoKH399eEJWIcfrmApWLYoERERERFRNKKaE04IClbqkksULAXLFiUiIiIiIopGlNhhh/AM97FjFSwFyxYlIiIiIiKKQJSbOjXWq1esX7/aVErBUrBsUSIiIiIioghEmT/8IbzE6G67dUeRgmVBExERERERdUVR6oc/DE/A+va3FSwFyxYlIiIiIiKKRpQcOTI8AevOOxUsBcsWJSIiIiIiikKUzcYHDQovMTpxooKlYNmiREREREREEYiyzz8fXmJ08827qUjBsqCJiIiIiIi6nCj9y18GBSv5pS8pWAqWLUpERERERBSNqOZrXwsKVvqyyxQsBcsWJSIiIiIiikYU32aboGBln3xSwVKwbFEiIiIiIqIIRLm33graVWzAgFw6rWApWLYoERERERFRBKLMvfeGJ2DttVf3FSlYFjQREREREVHXEqW+972gYNV897sKloJlixIREREREUUjSu6zT3iJ0d/+VsFSsGxRIiIiIiKiKESZTHzttcNLjE6erGApWLYoERERERFRBKLs00+HlxjdcstuLVKwLGgiIiIiIqIuJEpdcUV4AtaxxypYCpYtSkREREREFI0oecwx4SVGr7pKwVKwbFEiIiIiIqJoRIlPfjI8AevZZxUsBcsWJSIiIiIiikCUe/PN8ASsddetzWYVLAXLFiUiIiIiIopAlPnNb4KCldh33+4uUrAsaCIiIiIioq4iqjn77KBgpc49V8FSsGxRIiIiIiKiaESJ4cPDS4zed5+CpWDZokRERERERBGIcqlUbK21YtXVtW+/rWApWLYoERERERFRBKLs44+HJ2B96lM9QKRgWdBERERERERdQpT++c+DgpU87jgFS8GyRYmIiIiIiKIR1Rx5ZHiJ0WuvVbAULFuUiIiIiIgoGlF8002DgpV98UUFS8GyRYmIiIiIiCIQ5d54I2hXsUGDanM5BUvBskWJiIiIiIgiEKV//evwBKwvfKFniBQsW5SIiIiIiKjzRakzzggvMXr++QqWgmWLEhERERERRSNK7rpreALWgw8qWAqWLUpERERERBSFKJmM9e0b69Ur9+67CpaCZYsSERERERFFkOxjj8WqquJDh/YYkYJlixIREREREXWyKHXxxUHBqvnGNxQsBcsWJSIiIiIiiibJQw8NLzF6440KVr4Fq2qlFPNxBYuIiIiIiKiniuIf/3hQsHJ//7uClVfBWr08rf771j5HwSIiIiIiIqoE0YpkMjwBa/31e9KMOqdgFfpxBYuIiIiIiKinihb9/vfhJUZHjVKwulbBGiciIiLdNv88/PCgYL1y8skeiiBFnYPlCJZ/AxEREREREbUk8/4lRjMPP+wIlpcIbVEiIiIiIqII0rRkSbxPn+BXbTyuYClYtigREREREVEEqXvhhVhVVWLnnXvYjDqnYDV7F6EtSkRERERE1Nw894orwkuMnnqqglVAwWp2HSxblIiIiIiIqPVMO+KI8ASs225TsAorWOX4egoWERERERFR9xQlN9wwvMToa68pWAqWLUpERERERBRBlr/7bngFrE026XkzUrBsUSIiIiIios4RLfztb4OCNe2YYxQsBcsWJSIiIiIiiiYzzzwzKFjzrrlGwVKwbFEiIiIiIqJokv7MZ4KCtbT093hWsBQsW5SIiIiIqCJEjQsWxHr1ivfr17RsmYKlYNmiREREREREEaTu6adjVVXZvfbqkTNSsGxRIiIiIiKiThDNvfTSoGDNPvdcBUvBskWJiIiIiIgiUhxySFCwFv3pTwqWgmWLEhERERERRZGmpsTgwUHBWpHNKlgKli1KREREREQUQerfeiu8BeEWW/TUGSlYtigREREREVG5RQvuvDMoWNO/9jUFS8GyRYmIiIiIiKLJjNNOCwrW/BtuULAULFuUiIiIiIgomqR22CEoWMv+8Q8FS8GyRYmIiIiIiCJI49y5serq+IABTcuXK1gKli1KREREREQUQZY88UR4idERI3rwjBQsW5SIiIiIiKisojkXXRReYvSCCxQsBcsWJSIiIiIiiia5Aw4ICtbiP/9ZwVKwbFEiIiIiIqIo0tCQWHfdoGA1zJihYClYtigREREREVEEqZ80KWhXqa237tkzUrBsUSIiIiIiovKJFtx2W1CwZpx4ooKlYNmiRERERERE0WTGN74RXmL01lsVLAXLFiUiIiIiIoomqW22CS8xOnGigqVg2aJEREREREQRpGHmzKBdJdZdt7mhQcFSsGxRIiIiIiKiCLL4sceCgpXbf/8ePyMFyxYlIiIiIiIqk2j2j38cFKw5F16oYClYtigREREREVE0ye23X1Cwljz+uIKlYNmiREREREREEaRpxYr4gAGx6urGOXMULAXLFiUiIiIiIoogy/75z/ASo9tvXwkzUrBsUSIiIiIionKI5t94Y3iJ0VNPVbAULFuUiIiIiIgomkw/7rigYC244w4FS8GyRYmIiIiIiKJJzSc/GRSs+v/8R8FSsGxRIiIiIiKiCLIilwsvMfqxjzU3NSlYCpYtSkREREREFEEWP/RQULBqDz64QmakYNmiREREREREJRfNPu+8oGDNHT1awVKwbFEiIiIiIqJokv3c58JLjD71lIKlYNmiREREREREEaRp2bL4WmvFevVqXLBAwVKwbFEiIiIiIqIIsvTll2NVVemddqqcGSlYtigREREREVFpRfOuvTYoWDPPOEPBUrBsUSIiIiIiomgy7ctfDgrWwnvuUbAULFuUiIiIiIgomtRsumlQsJa/846CpWDZokRERERERBFkRU1N0K6SG2xQUTNSsGxRIiIiIiKiEooW/eEP4SVGDz9cwVKwbFEiIiIiIqJoRLPOOSe8xOjllytYCpYtSkREREREFI0os/vuQcGqe/55BUvBskWJiIiIiIgiEDXV1cX79In17t24eLGCpWDZokRERERERBGI6l56KVZVldlll0qbkYJlixIREREREZVKNO+qq4KCNes731GwFCxblIiIiIiIKBrRtCOPDArWovvuU7AULFuUiIiIiIgoGlFyo43CS4wmEgqWgmWLEhERERERRSBa/t574SVGhwypwBkpWLYoERERERFRSUQL7703KFjTvvQlBUvBskWJiIiIiIiiEc389reDgjXv6qsVLAXLFiUiIiIiIopGlB42LChYSydMULAULFuUiIiIiIgoAlHjwoWx3r3jffs2LV2qYClYtigREREREVEEorpnnw0vMTp8eGXOSMGyRYmIiIiIiKIXzf3FL8JLjP7gBwqWgmWLEhERERERRSOqPfTQ8BKjDz6oYClYtigREREREVEUoqamxHrrBQVrRTqtYClYtigREREREVEEovopU4J2VbP55hU7IwXLFiUiIiIiIopYtPDuu4OCNf3YYxUsBcsWJSIiIiIiikY08/TTg4I1//rrFSwFyxYlIiIiIiKKRpTeccfwEqOvvqpgKVi2KBERERERUQSixnnzYtXV8f79m+rrFSwFyxYlIiIiIiKKQLTkr3+NVVVl99mnkmekYNmiREREREREUYrm/N//BQVr9vnnK1gKli1KREREREQUjSh34IFBwVr8yCMKloJlixIREREREUUhamxMDBwYFKyGadMULAXLFiUiIiIiIopAVP/mm0G7Sm21VYXPSMGyRYmIiIiIiCITLbj99vASo1//uoKlYNmiRERERERE0YhmnHxyeInRW25RsBQsW5SIiIiIiCgaUepTnwoK1rI33lCwFCxblIiIiIiIKAJRw6xZQbtKrLNO04oVCpaCZYsSERERERFFIFrwq1+FlxgdPtyMFCxblIiIiIiIKBpR5rOfDQpWZpddzEjBskWJiIiIiIgiEDXMmBHv1y9WXb3ogQfMSMGyRYmIiIiIiCIQzbn44lhV1bSjjjIjBcsWJSIiIiIiikDUtGRJcoMNgoK19G9/MyMFyxYlIiIiIiKKQDT/llvC09v32suMylewqv6bNX4wz48rWERERERERF1U1NiY2mab8AbPDz1kRmUqWCu3nzX+fvWCVWhtUrCIiIiIiIg6URT0qvD+g9tsEzQtMypHwWqt+uRZqvJsTgoWERERERFRJ4qye+4Z3h7n1lvNqKwFa/WX/KItWONERESkk/L81VcH7eqdQYOeeOQRj0bxybdgrVyeCn1Z0BEsIiIiIiKiLi6adtRRQcGa83//Z0ad9hKhgmWLEhERERH1JNHyqVNj1dXx/v0bZs40IwXL+ImIiIiIiCIQzTzjjFhV1cwzzzSjshasVcqTdxHaokREREREPUYU3hunf/9Yr17L33nHjDqhYLkOli1KRERERNTzRMXcG0fBKrZglePrKVhERERERETlFRV5bxwFS8GyRYmIiIiIiFZNkffGUbAULFuUiIiIiIjooyn63jgKloJlixIREREREX0kxd8bR8FSsGxRIiIiIiKij6T4e+MoWAqWLUpERERERPS/LJ0wIWhXyQ03bKqrMyMFy/iJiIiIiIgiEEVybxwFS8GyRYmIiIiIiD5IVPfGUbAULFuUiIiIiIjog0R1bxwFS8GyRYmIiIiIiMJEeG8cBUvBskWJiIiIiIjCRHhvHAVLwbJFiYiIiIiIIr43joKlYNmiREREREREEd8bR8FSsGxRIiIiIqKKF0V9bxwFS8GyRYmIiIiIKl0U+b1xFCwFyxYlIiIiIqp0UeT3xlGwFCxblIiIiIiookWluDeOgqVg2aJERERERBUtKsW9cRQsBcsWJSIiIiKqXFGJ7o2jYClYtigRERERUeWKSnRvHAVLwbJFiYiIiIgqVFS6e+MoWAqWLUpEREREVKGi0t0bR8FSsGxRIiIiIqKKFMXjpbs3joKlYNmiRESdIMq99172xRcz999fc9ZZic99LnnCCanRo1NXXJG+9trMLbek77gjM2ZM9oEHMo88khk3Lvfss7nx47OvvpqbOLF2ypRcLJZLp82IiKjIpC6/vHT3xlGwFCxblIioZKJUKvvKK5mHHkrfdFPqJz+pOemk5AEHJIYOjQ0aFDytF/urd+/42msn1lsvuckmNVtumdp++/SwYZnhw7MjRuRGjao9/PBpX/7y9BNOmHHaaTPPOmv2uedO/8Y3svvuO+/KKxvnzDEjIqLabDax5ZaluzeOgqVg2aJERMWlsXFFNrv0lVcWPfjgvGuuqfnmN5OHHZYYNiy+0Uax6upW69GAAfGtt06MGJHYf//k5z6XPOaY1BlnpE45JXncccHvaw4/PDlqVGK//ZJ77ZXcddf4jjvGt902scUW8SFDYoMHB70qaFcdb2bV1akddph5+ukL7757+dSpVh1RZYoyd95Z0nvjKFgKli1KRJRXGmbNWvavfy1+7LH5N988+4ILph9/fHbffWuC0tOnT2tVJvhP8c03Twwfnjz66JrvfCd1+eWZ3/42+8wzubfeikDU0NC0ZEnjnDkNtbUrksnlb79dP2nSstdeq3vppbqnn14ydmzw7/JF990XtKgFt90277rrZp11Vm7EiEzQ/AYMWPmbTG6wwbQjjph35ZVLx49vWrrUqiOqEFHwT5eS3htHwVKwbFGi7iTK3HJLYqedak4/PfXDHxb0a84llxT0a/opp2RHjqw95JDcAQektttulVKyygGh5MYbZ/bYY9oxx8z6/vdTl1yS/vWvM2PHhudLZbNdcEZNy5cv+8c/5l9//fRjj63ZdNOPNMK+fbN77jn73HODctbw3y9h1RH1PFH2z38OF/z665fu3jgKloJlixJ1J1F8p50iOHupQ78Sgwend9659otfnPntb8+9/PKFv/td3YsvLo/Hm+rru/WMViSTi37/+1lnn5357GdXeeUxtdVW0084IX3llblnny1pWbSPiMosSh58cLjCzzvPjBQs4yciqs3985+xPn1ivXp14AjW3NGjC/o149RTcyNHzj7//CVPPVU/ZUrjokWVMKPGhQvrnnkm4NcedFDio2fixwcOTI4cGTyS2QcfzL37rn1E1H1FuQkTwvMj11orN3myGSlYxk9EVJs65ZTgJ33Nl79sRuUQNTbWv/nmgttuCx7wxBZbrPIGxsSnPx2MI3PrrbnXXrOPiLqXqObEE8Nnkm98w4wULAWLiKg2N3FirF+/WK9e2ZdeMqPyi3KTJmXuvDN15pnhqcF9+37k4NaQITWHH56+9NLs44/nUin7iKgri3L//ndsrbWCZ5Lc3/5mRgqWgkVEVJt6/4asNUccYUadL0oms48+mr7wwuSoUfH11//Iwa3+/RO77JIYOjR9111mRNQFRalzzw3fPHvwwWakYClYRES1ucmTYwMGxKqrw7OtzaiLiXITJmSuv77m+OMTn/rU/y791atXzTe+kXvjDTMi6kKieDy+3nrh1dsfe8yMFCwFi4iotua73w3/0XnQQWbU1UVTpqSvvTa8Wn2vXmHN6tev5vTTc5MmmRFRVxC13BsnudtuZqRgKVhERLW1b78dX3fd8B+dTzxhRt1FlPvb35JHHfVBzRowIHX22e1eXtWMiEor+u+9cTJ33mlGCpaCRURUm/rhD8N/dH7+82bU7UTZ559PHnroB+fCr7tu6txza6dONSOiThG13Bsn6FgrX9TNjBQs4yeqUFHunXdiH/tYePjqz382o24qyj71VPKAAz44N2vQoNSPf5x77z0zIiqzqOXeOKkrrjAjBUvBIiKqTf30p+Hhq733NqPuLsqOHZvcd98Pjmatv37q4otrEwkzIiqP6MN741h1CpaCRUT0/lt+NtggPHz1xz+aUc8QZR56KLHHHh9eQCt9+eW1NTVmRFRq0Yf3xjEjBUvBIiKqTV1ySXjOxEpv+TGjHnI2zP33Jz772Q9q1qabpn/5y1w6bUZEJRKtfG8cM1KwFCyiihclk/EhQ8K3/Pzud2bUI0WZe+6JDx36wb20t9xy4ZgxzQ0NZkQUuWjle+OYkYKlYBFVuqjlijWJz3zGjHqyKJdL3357fNttW2pWaocdFj34YHNTkxkRRbbEPnpvHDNSsBQsoooW5VKp+GabhYev7r7bjHq+KJtN33RTapttWmpWeuedF//5z2ZEFElWuTeOGSlYChZRRYvS11wTHr7aYYfaXM6MKkTUtGLFgjvuqNlii5aaldl99yV//asZERWV1e6NY0YKloJFVMGiTOaDCy7/6ldmVGmipvr6+TffnNxkk5aald1nn7oXXjAjog4evlrt3jhmpGApWESVK0rfdFP45rKtt175gstmVFGiprq6eddck9xww5aaldt//6Uvv2xGRIW+9Lz6vXHMSMFSsIgqVZTNtpzynL7hBjOqcFHjokVzL7ss8f5LPMGv2sMOW/b662ZElGfWeG8cM1KwFCyiChWlf/3r8Dlxiy1y6bQZEYU1a/78ORdfnBg4sKVmTTv66PrJk82IqN2s8d44ZqRgKVhEFSnK5VoujJS++mozIlo5DbNnz77ggvjaa4c1q7p6+nHHLZ861YyIWn15sJV745iRgqVgEVWiKPPb34bPiZtsUptKmRHRGmrW9Omzvve9+FprhTWrd+/UttsunTDBjIjWcPiqlXvjmJGCpWARVaKo5fYp6V/8woyI2siKTGbmmWfGevVqqVmzzzuvcc4cMyL636Hw1u+NY0YKloJFVHGizP33h4evNtqo7UP6ZkTUkqUTJoTXJg1+jlZVJQYPnnfVVU11dWZEVNvmvXHMSMFSsIgqTpTYY4/wkP5FF5kRUf5ZNnFi7UEHtZz/XrP55gt/85vmxkYzqmRR2/fGMSMFS8EiqixR5qGHwsNX662Xe+89MyIqNHXPPJN5/y1j4UvMO+20ZNw4M6pYUdv3xjEjBUvBIqosUXLffcPDVxdcYEZEHUxT06Lf/z611VYfXJt05Mhlr71mRhUnau/eOGakYClYRBUkyo4dGx6+GjQoN3WqGREV1bLq6+ffcENygw1aatb0Y49d/t57ZlQ5onbvjWNGCpaCRVRBouQBB4SHr37wAzMiiiSNCxbM+dnP4gMGhMW9b99Z3/lOw4wZZtTzRXncG8eMFCwFi6hSRNmnngp/Cq6zTu6tt8yIKMKsyOVmfvObsd69w7cZrrvu3EsvbVy82Ix6sCife+OYkYKlYBFViih56KHh4auzzzYjolKk/q23ph15ZMsrhskhQxb86lfR3oXJjLqOKJ9745iRgqVgEVWEKPv88+F1jPr3z735phkRlS5Lx4/P7rVXS82Kb7115q67zKiHifK8N44ZKVgKFlFFiJJHHRVevuj0082IqAyWxQ8/nNpuuw+OZu22W/bRR82ox4jyvDeOGSlYChZRzxctf+ed8G4nffvm/vUvMyIqD6dpxYr0VVfFP/7xD2rWqFHZF180o+4tmjIlmGk40H792r03jhkpWAoWUc8XzTj55DxvZ2FGRNGKcrFY6kc/iq+zTsvdDGuOPz43caIZdSNRbsKEzHXXBYOLb7tty+2SwtPbd9rJjBQsBYuo0kXLE4l4nz7Br9xrr5kRUaeIcv/+d+rUU+N9+4Y/nvv3T51zTrRXYjOjKEWJRPbRR9M/+1ly1KiWS4n+71f//slddgnaVeb2281IwVKwiCpdNPOMM8LDV1/7mhkRda4o9/LLNUcc8cH57+utl7700tpUyoy6gig3cWLmzjtTZ5yR3HXXD3rwf3/FhwwJphYMK/vEE7ki5mVGCpbxE/Uo0YpMJt6vX553YzUjojKIso8/nvzc5z54pWmLLTK33lqby5lRuUWNjfWTJi247bbkMccEU/jIYarevROf/nTq1FOD0eT+8Q/7SMFSsCxoojVk1jnnhLcxOf54MyLqUqLMvfcmdtjhg5r1mc9kH3zQjEotaly4sO7pp+eOHp0bNSoxaNBHDlMNHJgcOTL1ox8Fg8i9+659pGApWBY0UVtpmD49vIdJdXV9ge/3MSOicoiy2cz118c32eSDtxmOHJl95hkzila0IplcdN99s84+Oz1sWPhW4pVKVWqrrWaceGL6yitzzz2X/wXZ7SMFS8GyoImaZ//oR8HT6LSjjzYjoq4rSibTF14YazmgUl2dGDo0PXp0duzYgq4CYEYfpmn58mWvvTb/+uunf+UrNZtu+pHDVP36Zffcc/Z55y1+6KGGadPsIwVLwbKgiTp0+Gr27MT7741f9sYbZkTU1UVTpqTOPLPlbob/KwSDBiWGDUsedVTq3HPTN97YRuuq5BkFjap+8uS5P/95du+9M8OGtdx4+8NfyQ03nHbEEfOuumrp+PFNS5dadQqWgmVBExWbORdeGDy91h52mBkRdRdR5o47EjvumDz44KBXxT96klDbrauCZtTYuPy99xY/+ujcX/xi+te+lv70p1d501+sujo9dOjM009f+JvfLJ861apTsBQsC5ooyjTOn5/42MeCZ9ulr7xiRkTdVJT7z3+C/pS+6aagSyW/9KU2Wlew2jO77z79uOPmXHzxwjFjlr78csPMmT1jRivS6SVPPDHv6qtnnHRSZtddVzlAFf7q1Su17bbZ/fbL7rNP8GmNc+ZYdQqWgmVBE5VKNPfnPw+eeXMHHGBGRD1M1Lmtq9Sjyf3735k//jH9i1/MPOOM7N57J9ZEq/nEJ2oPPXT2+ecHqGVvvNFUV2fVKVgKlgVNVA5R46JFifXXD56I61580YyIKkHUMGtW0J8W3ntv0KWmH3980KtajuC21rpy+++f3W+/Wd/5zoK77lr0pz8teeqppa++uvztt1fkco2LF5dNlJs6NfuXv6SvvrrmtNOS++wT32CD1b/h5Mc/Hny3s845Z8Gvf730739vnD/fqlOwFCwLmqhzRPOuvjp4Xs7uu68ZEVWyKP/Wteqv3r0T661Xs+WW6Z13zo4YUXv44dO//vWZZ501+yc/Sf30p6krrkjffHNmzJjMI49kn3km++qrtVOm5NLp9r/LeDz75JPpG25InXVW8gtf+PD6FKueYbb77jUnnjj/ppvqnn++RC90WnUKloJlQRMVnKa6uuSQIcEzdfCPcjMiIlpj65r9s59l99tv+le/OuO006Z9+cu5UaMyw4endtghuckm8bXXzquErf5rwID4xz8e32abxC67JEaMSH7xi8njjksecURyzz0Tw4fHt9xylctQtdzaL7HzzjVf/Wrq4oszv/997vXXzYhIwTJ+oi4qmn/jjcETd2aPPcyIiKiD/0pZsaJxzpzliUT9pEl1L720ZOzYRffdN//WW+ddeWXNd79bc/LJyWOOSY4aldhzz/iOO8Y/8YnY4MGrXGBize9/7Ns3EXS4I49MXXBB5je/yb38chuX9zQjIgXL+Im60g+G+vqazTYLnsoXP/aYGRERlVOUi8VyEyfmxo/PjBuXfeCB9B13ZK67rua00xJ77ZU677zsCy8UdKdkMyIqd8Gqej+rfyT/jytYRD1YtOD224N2lR42rLmpyYyIiIiIFKyiCtYa61FrH1ewiHqqqGnFipottwwK1qI//tGMiIiIiBSsfAtWSwFauQYVWrYULKIeLFp4zz3h4auhQ5sbG82IiIiISMHKq2CtsTxFW7DGiXTbPD527JT3b+w6/oc/9GiIiEgXKliOYBF1X9Gi++8P2lVqm22aGxrMiIiIiMgRrLwKVmulSsGyoIneP/2qKf3pTwcFa8Fdd5kRERERkYJVQMFaJQqWBU30YRY//HB4h7IttmhavtyMiIiIiBSsAs7B6vBRK+8iJOrxoswuuwQFa/6tt5oRERERkYIVWcFyHSwLupJFS8aNC+8Iu8kmTcuWmRERERGRgtXBglXCr6dgEXVDUXavvYKCNe+668yIiIiISMFSsCxooghEdc88Ex6+2mijpiVLzIiIiIhIwVKwLGiiCES5kSPDw1dXXmlGRERERAqWgmVBE0UgWjp+fNCuEuut17hwoRkRERERKVgKlgVNFIGo9qCDgoI1d/RoMyIiIiJSsBQsC5ooAtGy114LD18NHNg4d64ZERERESlYCpYFTRSBaNoRRwQFa85Pf2pGRERERAqWgmVBE0Ugqp80KWhX8bXXbpg504yIiIiIFCwFy4ImikA0/StfCQrW7HPPNSMiIiIiBUvBsqCJIhDVT5kS69Ur3r9/QyF/3IyIiIiIFCwFy4Imav3w1QknxKqqZp19thkRERERKVgKlgVNFIEovPZVdXW8T58VqZQZERERESlYCpYFTRSBKLXVVrGqqtS225oRERERkYKlYFnQRBGI6p57LnzzYJ8+S//2NzMiIiIiUrAULAuaqFhR04oV6R13DC/dfsUVZkRERESkYClYFjRRBKJ5110Xvjj4qU811debEREREZGCpWBZ0ETFihqmTUsMGhQUrCVPPGFGRERERAqWgmVBE0UgmnHSSUG7mnbkkWZEREREpGApWBY0UQSipX//e3hphv79l8fjZkRERESkYClYFjRR0aLGxswuu4T3db74YjMiIiIiUrAULAuaKALRgl/9KmhXNZ/8ZFNdnRkRERERKVgKlgVNVKyoYfbsxPrrBwVr8cMPmxERERGRgqVgWdBEEYhmnnlm0K5yo0aZEREREZGCpWBZ0EQRiJa9/nqsV694377L337bjIiIiIgULAXLgiYqWtTUlN1rr1hV1ezzzzcjIiIiIgVLwbKgiSIQLbznnvDc9k03bVy40IyIiIiIFCwFy4ImKlbUOH9+csiQoGAtuu8+MyIiIiJSsBQsC5ooAtGs738/aFfZESPMiIiIiEjBUrAsaKIIRPWTJ8f79In17l3/5ptmRERERKRgKVgWNFEEotznPx+rqpr13e+aERERERGRgmVBE0UgWvTAA0G7Sm60UeO8eWZERERERKRgWdBExYoaFy+u2XzzoGAtuOsuMyIiIiIiUrAsaKIIRLN/8pOgXWX22KO5qcmMiIiIiIgULAuaqFjR8nffjffrF6uuXvaPf5gREREREZGCZfxEEYhqDzkkVlU18/TTzYiIiIiISMEyfqIIRIsfeyxoV4nBgxtmzjQjIiIiIiIFy/iJihU1LV2a2mqroGDNv/lmMyIiIiIiUrCMnygC0dxLLw3aVXrnnZsbGsyIiIiIiEjBMn6iYrOipiY+YEBQsJaOH29GREREREQKlvETRZBpRx8dtKvpJ5xgRkRERERECpbxE0WQzB/+EJ7bPnDgilzOjIiIiIiIFCzjJyo2uVQqvs02QcGad/XVZkREREREpGAZP1EESV10UdCuUjvs0LR8uRkRERERESlYxk9U9OGriRPj66wTFKy6p582IyIiIiIiBcv4iSJI8v1z25OHHWZGREREREQKlvETRZDMI48E7SrWv3+uNLcdNCMiIiIiBUvBsqArTJTJxIcODc++Ov98MyIiIiIiUrCMnyiCpC+7LLw0wyc/WZtMmhERERERkYJl/ETFJjd5cmzQoKBgZX77WzMiIiIiIlKwjJ8ogiSPOy48t33//c2IiIiIiEjBMn6iCJJ9/PFYdXWsb9/c3/9uRkRERERECpbxExVfr7KJz342PLf9nHPMiIiIiIhIwTJ+ogiSvuaaoF3FN9kkF4uZERERERGRgmX8REXn7bfj668fFKz07bebERERERGRgmX8RBEkdeqp4bnt++xjRkRERERECpbxE0WQ3LPPxnr3jvfpk33hBTMiIiIiIlKwjJ8ogiSGD49VVdV861tmRERERESkYBk/UQTJ3HJLeG77Rhvl3nnHjIiIiIiIFCzjJyr6xcF3340PGRKe237DDWZERERERKRgGT9RBEmddVZ428HddqvN5cyIiIiIiEjBMn6iog9fjR8f79s31qtX9qmnzIiIiIiISMEyfqIIRIkRI8Jz2086yYyIiIiIiBQs4yeKQJS5886gXcUGD66dMsWMiIiIiIgULOMnKlqUSMQ32yw8t/2qq8yIiIiIiEjBUrCIIhClfvCD8Nz2z3ymNps1IyIiIiIiBUvBIipWlH3llVi/frHq6uzYsWZERERERKRgKVhEEYiSo0aF57Z/9atmRERERESkYClYRBGIMr/7XXjd9oEDc2++aUZERERERAqWgkVUtKimJr7lluG57ZdeakZERERERAqWgkUUgSj1k5+E57Zvv30unTYjIiIiIiIFS8EiKlaUffLJWO/eQcHK/OlPZkREREREpGApWERFi3K5lps6xzfbzIyIiIiIiBQsBYsoAlFq9OiwXa21Vtu3HTQjIiIiIiIFy/iJ8jt69eyz4YWvqqoyY8aYERERERGRgqVgERUtSiQSn/pU0K5Sp5xiRkRERERECpaCRRSBqObkk8N3Dm63XdC0zIiIiIiISMFSsIiKFWXGjAnaVaxfv9yzz5oREREREZGCpWARFSvKTZoUX3/98MXB0aPNiIiIiIhIwVKwiIoW5XLJkSODdpX8/OeD35sREREREVF3LVhVK6WYjytYRMWLPrguwwYb5CZNMiMiIiIiou5asFauPqt0pg9/v3rBKrQ2KVhE+YiKvC6DGRERERERddGXCAstVXk2JwWLqH1R0ddlMCMiIiIiosoqWONE2ss/DzssaFdTttjiiUce8WiIiEiXTcEFa42vDzqC5V8MZRBFcl0GMyIiIiIi6nJHsNpuTgqWBV06UVTXZTAjIiIiIqKuVbBWL0AKlgVdJlF012UwIyIiIiKiLlSwWms/3kVoQZdBFOF1GcyIiIiIiKirFKyq1bLG/7RKW3IdLAs6ElG012UwIyIiIiKirvUSYcm/noJFtLoo6usymBERERERkYJl/JUuqjn55KBdJbbbLmhaZkREREREpGApWBZ0saJSXJfBjIiIiIiIFCzjr1xRia7LYEZERERERAqW8VeqqGTXZTAjIiIiIiIFy/grVFS66zKYERERERGRgmX8lSiqnzSpdNdlMCMiIiIiIgXL+CtO1FRXlx46tHTXZTAjIiIiIiIFy/grTjTzrLNKel0GMyIiIiIiUrCMv7JEi//yl/DUq7XWKt11GcyIiIiIiEjBMv4KEjVMm5bccMOgYM277jozIiIiIiJSsBQsC7roNDXVHnRQ0K5qDz44/L0ZEREREREpWAqWBV1k5l13XXjVq402apg2zYyIiIiIiBQsBcuCLjb1kybF11orKFiL//IXMyIiIiIiUrAULAu66NcG/3tdhllnn21GREREREQKloJlQUeQlusypHfcMWhaZkREREREpGApWBZ0sfnwugz1kyaZERERERGRgqVgWdDFZuXrMpgREREREZGCpWBZ0EXno9dlMCMiIiIiIgVLwbKgi80q12UwIyIiIiIiBUvBsqCLyurXZTAjIiIiIiIFS8GyoIt4bXBN12UwIyIiIiIiBUvBsqA7njVel8GMiIiIiIgULAXLgu5gWrsugxkRERERESlYCpYF3ZG0cV0GMyIiIiIiUrAULAu68LR5XQYzIiIiIiJSsBQsC7rgtH1dBjMiIiIiIlKwFCwLurC0e10GMyIiIiIiUrAULAu6kNcG87gugxkRERERESlYCpYFXUDyuS6DGRERERERKVgKlgWdb/K8LoMZEREREREpWAqWBZ1X6idPbjn1qt3rMpgREREREZGCpWBZ0O2nccGCxHrrBe2qZuON270ugxkRERERESlYCpYF3U6ali7NjRwZtKvEwIH1//mPGRERERERKVgKlgVdnKihYdpRR4XHrjbbbEVNjRkRERERESlYCpYFXZyoqWnGqaeGx67WX7/+rbfMiIiIiIhIwVKwLOhiRbN/9KOwXa2zztJXXzUjIiIiIiIFS8GyoIsVzbvqqvCiDP36LXnqKTMiIiIiIlKwFCwLuljRgjvvDNpVrFevRQ88YEZERERERAqWgmVBFyta/PDDsd69g4K14LbbzIiIiIiISMFSsCzoYkV1zz3XckHRuT//uRkRERERESlYCpYFXaxo2euvJwYODO/l/L3vmRERERERkYKlYFnQxYqWT52a3HDDoF1NP+GEQi/XbkZERERERAqWgmVBrypakcnUbLFF0K5qDzusacUKMyIiIiIiUrAULAu6KFHD7NnpoUODdpXdZ5+mujozIiIiIiJSsBQsC7ooUeOiRZnhw4N2ld5558Z588yIiIiIiEjBUrAs6KJETfX1uQMPDNpVauutG6ZNMyMiIiIiIgVLwbKgixM1Nk4/9tigXSU33nh5LGZGREREREQKloJlQRcrmnnmmeGtBgcPrn/zTTMiIiIiIlKwFCwLutjMufDC8FaDAwYsnTDBjIiIiIiIFCwFy4IuNulLLw3bVZ8+S8aNMyMiIiIiIgVLwbKgi25XN90Uq64Ofi28914zIiIiIiJSsBQsC7rYZMaMiffpE6uqmn/DDWZERERERKRgKVgWdLHJPvporH//8KIMP/iBLUpEREREpGApWBZ0sck9+2x80KCgXdWcdJItSkRERESkYClYFnTRx65efjm+0UZhuzryyNps1hYlIiIiIlKwFCwLurhjVxMnJt6/kXNiv/1qUylblIiIiIhIwVKwLOjiMnVq4v0bOSd33TUXi9miREREREQKloJlQReXeDyxxx7hsavttsu99ZYtSkRERESkYClYFnRxrwymUskDDggvKLrZZrk33rBFiYiIiIgULAXLgi6yXuWSxxwTtqsNNsj97W+2KBERERGRgqVgWdDFpua008J2te662SeftEWJiIiIiIgULAu62KR+9KOgXcX69cv86U+2KBERERERkYJlQRfdrq64ImxXvXtn7r7bFiUiIiIiIlKwjL9YUeb222O9egUFK33ttbYoERERERGRgmX8xYoy998f79s3bFcXXWSLEhERERERKVjGX6woM3ZsfO21wxs5n322LUpERERERKRgGX+xouwLL8QGDw4v137ccbYoERERERGRgmX8xYpyr70W33jjsF0dckhtJmOLEhERERERKVjG30FRbvLk7Nix6csui7ccu9p779qaGluUiIiIiIhIwTL+vEQfdKkbb0yde27yqKMSO+8cHzQovBbDf3/FP/7x3Dvv2KJEREREREQKlvGvIe12qf/9GjQoMWxY8tBDE9ttlxkzxhYlIiIiIiJSsIy/uWHmzKUvv7xwzJg5F188/bjjMrvtlvjYx9rqUl/6UtC60jfdFDSw3H/+Y4sSEREREREpWBU9/s7tUrYoERERERGRgtW9x9/Speb87GfZESNyBxzQRpdKDB6c2X336ccfH7SuhffeG/yphlmzbFEiIiIiIiIFq6ILVv7HpdbYpWxRIiIiIiIiBauiC1ahXar2wAOzI0bMufDCNrqULUpERERERKRgVUrByv99fPkfl7JFiYiIiIiIenjBqlopFV6wOrdL2aJEREREREQ9qmCVugaVrWDl0ulcLFY7ZUpu4sTsq6/mxo/PPftsZty4zCOPZB94IDNmTPqOOzK33JK+9trUFVekRo+uOfHExF57Jfbdt/3rS330fXwWNBERERERkYLVasFapfqUqAkV+tfWvfDCnEsumf2Tn8w+99yZZ50147TTpp9wwrQvf7n28MNzo0ZlR4zIDB+eHjYstf32NVtumdxkk8R668XXXjvWu/eaG1L+v/K+JoIFTUREREREpGB1csEaV2Be/frXO9aQ3uvV673+/d8ZOHDq+uu/PWTIW5tv/p+ttvrP9ttP3mmnN3fZZdLw4RP32eeNz3/+9VGjXv/iF//5pS/96wtf+PdOO7321a8+d801f73//nEiIiIibaYLFaxCU/fii3NHj5535ZXzrrtuwW23Lbz77kX33bf4oYeWjB1b9/TTdS+9tOy11+onTVr+9tsrksmG2trGOXOalixpbmhoFhEREamQI1giIiIiCpaIiIiIdLRgNZflXYQiIiIiFVewSn0dLBEREZHKKlid/920Uu+qVkuJvlDkLbNsX6iNv7A8X6gU1bxzReX8Qiv/1zLMqDyicq66UuzZHr/qPDN4ZvDMUEEF68OHpshP6PAXKtGJaK2tsAi/yupra/X/VOov1Bzpi8utfaG2v4ESfaFIaO1+5xFWhNKttEJXXXkeulJ/obIthpI+M7Q2l1J/odI9M7SmiPyZoY2Hq3RfqKTPDM2lPCkon1VXnoeuRMAeW7BKt1HLXLBKOv7SPReUGVL+ndNZX6jl/5biASzbuZXl/0KlXnvl30eeGTwzeGbo+s8MCpaC5Wm023yhkj4BrXJ0pHTHzFdRlOELKVieGTwzeGZQsKIpWJG/kL/GD0Y+/nZfIiydq6RPc+0Wxx7wHFqi54LOOla/xhVYilVXnuVdnnZVumeGsj10azxJpUT/mGzt5e/IH7SSvlicjyjyHxNtnHRRBlF5Vl15lndXaFcKVqcdwVplTZThBIVS7NJO+TlX/n+hlvqhi/akhK4mKsOqK/8BmDL866tELwxVwhGsHvPPvNI9M3QFUdn+haxgdaHDFWUrWOX5QmUW9ZjzHso8oxK9i8eq88xQaQWrJ70WWeq3wvX4gtXpF5xSsDrnabQU/zRp93Erw0+F8hyAKc8TTSkWQyeWnuYSvM0qz1VX6oeu/C9Blk4U+TND5byLsNQroRPfRVjqGZXtDZiRPzOUbdX1hILVRlsv3WVUSnqmRT5fqBSi1q4IUrovVNLLEbXxhSK8KFpnPXSleBrtdFE5v1Dpnhk696ErBap0z3XNnX2NpR55UbRS/NOrci7z1vmVpllEREREFCwRERERBUtEREREwRIRERERBUtEREREwRIRERFRsEREREREwRIRERFRsEREREQULBERERFRsESkJz+pRXT3pFJ/ldL9bSUii4iCJdKFfrSX+udW5Pf26vQf80VCVr+FbaF/YT630ivbfc3LNlwFS0TBEunS/aCcBwa64P1NI7EUA1n9zxZZsNpub2Wr5mVo6rawiIIl0i0LVkF3mG/5fdsHMNr4oZjP12qtLrT7PbfxveUPzLMVdex7W/2b/PD/FtTG2i5YbT8yHRhEocMtaBW1OwsdS0TBEum6x2BW/xm8etnKp9zk85pU2y8tFfT7dr+H1n7fxue3+weLKVjtfnyNnxlhwWr3EejAIDow3EIn1cbXVbBEFCyRblmw8vzxWejnt3uGUPFlpWMfzx/SsSNYHShYHfhuI3mESzfcSL4fBUtEwRLp6gWr7WMMrf3ILOhluzy/gfz/zraP03T472kXXmTBavt7K10dLOiRLMVwI/9+FCwRBUukGxesIn9UF/kzuDmiI1JFVpZ8/sLmkr1EuPJHylOwCvrjZShYBU1BRBQskS5UsNptXc2tnyFU0I/GYs4Qau1r5XmuVUHNMp93CLb2Ofk8PgUdwWpu792dhX63ze0dMcp/EIUOtwNn1+XzOIuIgiXSbQpWcx5vKGuO6DSd5sLfvNbaW+0KOgOpuaPvImzjz+bzvTW3dwSroJfDIixYzUW8izDPBVPQpNr+O21hEQVLRErVCCvku2r3IgiVthIULBEFS0R6csFqdlFNEVGwRETB6l5k7UpEFCwRERERBUtERESk4vP/23BObWpPtZgAAAAASUVORK5C" alt="Per Sequence quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M4"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per base sequence content</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per base sequence content" width="800" height="600"/></p></div><div class="module"><h2 id="M5"><img src="data:image/png;base64,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" alt="[OK]"/>Per sequence GC content</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per sequence GC content graph" width="800" height="600"/></p></div><div class="module"><h2 id="M6"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAFlUlEQVR42s2XCUzTVxzHybIs0IgCLYqyQ9RtmhjCNKKbikajYVc22NThNjYjeC0yWBESxAV1Ckw8QEUF5ChnS4FyFETucwWU+7ClHKKiImLI4nCz9bv3/pS7ra1HtM1Lm/b/fp/ve+93PQMABq9y6D/Bz+ANNvethWxPw3Xs3wx/YAb9Tn6j/700AeZc1qccrhGPw2UNkk8Fx9voIceX9Tcz6Hf62/B/PPrsCxNgzjW043iyGjheRkOWQRzFt2IH/Nl4DDFyHjJ6siDoSoZ/YwB2VuyEffpGzA5iK+izdA6d+8wCyHa+SYyEsj2N/jU/aaL0vbIf0sF2tA92oOVBG+ruN6CqrwZldypR0FuMnBuXkX49C5GyaGwq3ASLU2ZKOpfaoLb0EmDibmLC8TQq5/iw/vlIaA1xjxiyQTmaH7Sitr8ekr5qlN6pQP6tImTfyIXoeiYEnSmIlSfiooyHc20R8G84htUZq0FtUFvUpk4ChldO4H6sRzvKXdEw0ISmgRZc7a/DXwRccrscebcKIb5xCWndGeB3CsGTJyBCGoPQtnCcajlLjugk/qgPxO+1h7Eqyw7UFrWpbiemCKBbRlW7lrmgcaAZV/prUXm3CsW3y3D5VgGyenKQ2p2OpM5k8NoTEC6Nxtm2MJxsPoPAxhM4XB+AA1cPwbvmADyqvPFLpQdsM1aodoIVqlUAdRp6bgv5HzBbXHFXgqLbpci9mY/MnmykdIuQ2CFATHs8wqRRONN6ASeaTyOg8TgO1fnDl4C9anzhLvHCnkp3uJTvgXOJC7YUOWN+0nww/jTJMSevvsHs+DRlSOsZFPaW4NLNPOLpYgi70pBAwFHtcbhwLRKnW8/jeHMIOecgHKw7iv1XD2Jf9X78KtmHyNZoiOQi/FiyHZsJ+Ov87/DZZUesEq8H+4SxkjLUCmDinISPY74jcm4Oe3RyVyriO/iIksUS8EWEtJ5DUFMwjhIH86s7Ap8rfvCs9oGbxBO7KtwQ3hKBrgdd6H94D3ypgIAdsD7nc6wWb8DyzDWYy58HyhifJ8ZWT5NMoLGCJ48jsZ2COHkSCSkezl+LQHBLKI40BGJLsRPW5qzDskxbLE5bDBuRDWwzV5DV2SGwNhCdAx0Yed172AdeWyyWpa/ColQbzE1+H7MTLcEOmKagrAkCaAqlWWwZf6kqlMY8+gDZ3jXZazEz0Rym8TPUDg+yetl9Kca/HikeIUUmgEXizAnPml2YDsoaSduMAJrHaSrdWvw9AYcR8LBHu0m4eJtvqRGsDS4k8DlJFlPnxM0AZVHmmABSTGg+31GxW+XRAfi51AWztKxaI/zxELNytXDVoCzKHBNAKhotKrsr9zIe7Vq+G5wEs9EJi1I+hFDKx7zkuTrAk7XCGQGERZlTBLiSuN1Ltn2BcMEEeGF3PoaI8dzObFgJ3nsuuHoBqiNwLNgMu+x1ow9SByrsyoNC+ZiBPFb8h5LrhWrhqTKhTnD1R6BywuWZn0zYejq2FTuj/k7tKEz5RPlccLVOOBKG7DATtZO+yXNATW81npD3ZHhaux5wTWE4kohoktA0cWPOelT3SkZFDMNT9ICbwCJplvpEND4Vm8ZqNrJBJWJIT7h5Ioek5a+wRLRUcyoeX4y0GaMiaEjqCn9X8A62le0ADXGLYLbmYjS+HJtGTdf5TLUNa5E1Kc8+8K09iPkJVk8vx+MbEm1H8bTBTjDFF3lfMlmVNipLREt0a0gmt2TPIsKKZEv3Ki5TU2gZt8+1168lm9yU6noc1MO3FDkxYNojhpH+YW2Gnf5Nqbq2nHHMWPVbbZNuA6firQghpZt2TRHSSGwn/eScYPNnb8s1XUxoDFtGWpD6sAgrxSvhSi4juypJ31f6ExwLSNsl/BicAOMXczF5ba5mr83l9GWN/wE1f784zfWjGwAAAABJRU5ErkJg" alt="[OK]"/>Per base N content</h2><p><img class="indented" 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" alt="N content graph" width="800" height="600"/></p></div><div class="module"><h2 id="M7"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEpklEQVR42s1XfUyUdRz/URrktNpaW/aHW1LLNdN0+U9WSzdfcq31/uLrWvZP+Y/2T+mc54GplSCOt4CBgqicpICGwokvSYkc3B3nQJxmBSo0K6fsiJe759Pn+zzccRx3xyGwvO07fvye5/f9fH7f90cBUP+nDP+QSd1XZ1LT6hPU/FqzWi4ia9mTZ2NGwLZZvUag/Dqzul2XoDzOROVu3KI6RGQte/JM3pF3R41AbaJ6pT5RNdgT1L+tyXGezpJFgM0MNGYDV34ALhUAtZugWZeh0/IiWpNiPfKunJGzd03glEmNo6L0erPqbkse59WqvwD+bgJuXQL+cgF/2oAbPwPXTgJ/HAOulgKXLYArHdqRxWjfGeuVs6JDdA2LgMOkHrGbVbVri+rsyZ9K5YeBfy4CNxsIXEvgaqC1Cvi9nM9KCFwENNMSjTk6ATiSgfMb0XtgJkSH6BKdURHQb84DzVtVl5gVN+0UJ9B+Hrh+lsAnjBsL+K8Ev3KQbtgLXMgkcBJQt5Xgm4Fz6wFaTds/A6JLdIayxCACYjJhrVV+RGAHgWsI/BPQYgV+O0rQQ1xXAJ4uwNvNv5TeTuBiHoFNwC9fAWfXAWfWACc/BayroBVOMyxB3REJ6AFHv/XkTuZN6dvrpwlWSeAjfQG3j0C7DRKaB/6frF2pBF4LnP4cqFpN4JXA8Q+AH98CypagN+dRiO7gwBxAQCK3bYfywv4NzcxbXi0jcDGBC4GmPMPMDSmGv4MJ1G8n8CdA5XLg2PsEfpPAzMbD84Hil4B9z6E9KcYrGCEJSO5K+mhlC/oimr5tpm+bcgmcATh3EoTEbAnG/4EEvL1AzUYCvwccfQMoXUzgeQSeCxS9oIOj4BloWQ9DMALrRP/tWUBavovx6PkdGNHOZON25zZQ+RLAMpumfYegPQEEuLby5haCWeYYoPtnAoXPAvlPAbmPAwRHZixavlUewRpIQMorq5h7z9NGgXGl9UX0Nj2SYZnFw+OBdGVI8Rwj+Hw/Wcue73kEuZOiIFi+sq0TkDoupRTlNJ9jR18qmQyfZk8crGgEBDSKXrald/gISDOReo7KpQTexFT6kv58l7d+ILSiERAQESzB9BOQjiZNBdYVRipVsAZk3B9eyQgJCJZgDiYgwFUfM2gei6xktAn4XVDCFCx6fmglo+0CfxCyZEY0/VgFoS8N7+yKie4WAtZ1iz3AbYisR5KG/kLEIhEVgawJLDLxA0X2hjqXGRemEAWUYm9adH7Ui9PlA4bIOtK7GezCRS9D2xMfvhQPaEbRWKCjpT8GZB3OAlmT2JRYU8pXGFNSuGYU2I67U4cgUPrq4G4oe8Hv7Z4CnPiMLXoderKjaMeBA0lEV+Q8BLjb+gnIWvb8Jmd8FROnmlW15mt4C+KjG0iCR7KIJKRty8AiIms/OY5+Vk5CNvYURwY0tuRhjWTBQ+mQ7vDJ93H65AMbu2gDO6p9F4fS2cMfSkOO5QzMkNYQU+c/SeDX+W3AmeECpyZnKrTjH6I95cG7H8vDfZhIDnekTYCW9wSnnOnsmOyeFWxg5W9DK10I997pzPOY0fkwuWc+ze6Zj9Oxkv8AsrpUzUM9ceYAAAAASUVORK5C" alt="[WARN]"/>Sequence Length Distribution</h2><p><img class="indented" src="data:image/png;base64,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" alt="Sequence length distribution" width="800" height="600"/></p></div><div class="module"><h2 id="M8"><img src="data:image/png;base64,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" alt="[FAIL]"/>Sequence Duplication Levels</h2><p><img class="indented" src="data:image/png;base64,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" alt="Duplication level graph" width="800" height="600"/></p></div><div class="module"><h2 id="M9"><img src="data:image/png;base64,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" alt="[FAIL]"/>Overrepresented sequences</h2><table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>2628</td><td>30.17568033069239</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGAT</td><td>1004</td><td>11.528304053278218</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG</td><td>139</td><td>1.59605006315306</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGGT</td><td>110</td><td>1.2630612010563786</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGCT</td><td>76</td><td>0.872660466184407</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTA</td><td>74</td><td>0.8496957170742909</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAGTTCAAGCGTT</td><td>74</td><td>0.8496957170742909</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGG</td><td>72</td><td>0.826730967964175</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTA</td><td>54</td><td>0.6200482259731313</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAGTTCAAGCGAT</td><td>38</td><td>0.4363302330922035</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGACGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>36</td><td>0.4133654839820875</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCG</td><td>35</td><td>0.40188310942702954</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGG</td><td>28</td><td>0.3215064875416236</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTAT</td><td>26</td><td>0.29854173843150766</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGTCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>22</td><td>0.25261224021127565</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTGATTCAAGCGTT</td><td>21</td><td>0.24112986565621772</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGATC</td><td>18</td><td>0.20668274199104375</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAGGCGTT</td><td>16</td><td>0.18371799288092777</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAG</td><td>15</td><td>0.17223561832586978</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTG</td><td>14</td><td>0.1607532437708118</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGGGCGGCTTAATTCAAGCGTT</td><td>14</td><td>0.1607532437708118</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGGTTAATTCAAGCGTT</td><td>14</td><td>0.1607532437708118</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTG</td><td>13</td><td>0.14927086921575383</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGC</td><td>13</td><td>0.14927086921575383</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGG</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTGAAGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTAG</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTA</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGGGGCGCGGCTTAATTCAAGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGGCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCACGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCGTAATTCAAGCGTT</td><td>11</td><td>0.12630612010563783</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGACGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGAT</td><td>11</td><td>0.12630612010563783</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGATA</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGC</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGGGGCTTAATTCAAGCGTT</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTGAATTCAAGCGTT</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCTGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGATA</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGAG</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCGAGCGTT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGGGGCTTAATTCAAGCGAT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTGATTCAAGCGAT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr></tbody></table></div><div class="module"><h2 id="M10"><img src="data:image/png;base64,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" alt="[OK]"/>Adapter Content</h2><p><img class="indented" 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src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAADfUlEQVR42rWXT0hUURTGj5ZpaZqDmYNaCZkuMjXFiiTThZEyoiRlZGmCBYlWMBU5kW+cmRwhKEQXEkRFEBkGQUbpQmnjwk0S4TKiFuWmFqlhf17fbe7Qm8e9z/dmpoEfDr57z/nueeeec4ZUVaVw6CNKdRM1KEQ3wWMwCSbAQ9AHakDSSnYsO4bRDDAAloC6Al9Bbz/R+qgIgLFy8MmEYz1zHqI8qQB8NoCtoAgcAPWgFZzXOC8D38NwHuQzyApLgJ8oBZs/Sgw/ZbngJcpmp8T34/jfa8naaXiLsfwKYNQlMejXG+TRSgJjoj2wVWtJADbF8vDpjb0ROdfsSweLgn3PLQnoJSqWnL7ZhPhBwb6FYaI4fQ4kgy2gEFRocwAh65AIyDAhoE6yt9S0ACx2CwwsG4VfI6BIkgcOKzkwIDDyXninaWQV6s5uIuUSuJdEzpeSCLRaEXBDYGA+1PFwHALVAafvgBokhS4Ia0IJOWaxvtaUANkVHCCKDzj3ZsPZtNZxkM3UJhSQSe3s+ZTZJDwmMoKik0vks8PQB5FzRhHVCwXg1ZgX4COyi4z0UCweKTMS5wvAc5ES3+r3dZFtia+ZMl0JWQnVG7pKq7/F0xWRc4jy5GLNfpHwaxTbiTWNYMhKKXaIjJ0h+99E0zhfBFl4Vg2+SNpzcljtGBtHRSIu01q1ifLVCqpkf1mkXoGfgrW/tfdfVIgyQT4oA1U8D06As2wNGypgZCaCdtxtNA+kghxQrGnHLeAc6NFEIQHcZqex4JgNMEeiNpIxtlHzgwYqVJ0Y+VZwPg/SojoTBiKm9AcTz0Zdag61qAXUyFqvXyBi6H8IcErufzqfiPXNx2UoAJ9EkAZQVmk7L0h7QCWoAYdBM2jnAuokAqpYl4TT+4JItBkJsPGZcCco506bwGmA07K5hG6BO1wAZkjll0DABHvOBg44HNcJ+KEfx8J+BVzEmDgKbge/MWv4bwgttn/7vdkRCugtkbyGZYhwsdlAInwXeAYmIxLAjXllnZDPBiOgGyD6yiMwq3ke0g3X8WKUwbsiS8QCUAr28cp4iBeoo+BkYJ8aA0N3DUQYMSVKwh1gL6jmmc9aMroX4RTs9ygNAjikJ6GRcHfyRmRFwHjEryBUxPWNMIrboswZOGU35wXADVMSoiogVIxvE6JSGej77lPgIGaEvKBTLX8AEZuD+ek/sxoAAAAASUVORK5C" alt="FastQC"/>FastQC Report</div><div id="header_filename">Sat 9 Apr 2022<br/>test.fastq2_5pAtccRm.fq</div></div><div class="summary"><h2>Summary</h2><ul><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M0">Basic Statistics</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M1">Per base sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M2">Per tile sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M3">Per sequence quality scores</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M4">Per base sequence content</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M5">Per sequence GC content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M6">Per base N content</a></li><li><img src="data:image/png;base64,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" alt="[WARNING]"/><a href="#M7">Sequence Length Distribution</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M8">Sequence Duplication Levels</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M9">Overrepresented sequences</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M10">Adapter Content</a></li></ul></div><div class="main"><div class="module"><h2 id="M0"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAFlUlEQVR42s2XCUzTVxzHybIs0IgCLYqyQ9RtmhjCNKKbikajYVc22NThNjYjeC0yWBESxAV1Ckw8QEUF5ChnS4FyFETucwWU+7ClHKKiImLI4nCz9bv3/pS7ra1HtM1Lm/b/fp/ve+93PQMABq9y6D/Bz+ANNvethWxPw3Xs3wx/YAb9Tn6j/700AeZc1qccrhGPw2UNkk8Fx9voIceX9Tcz6Hf62/B/PPrsCxNgzjW043iyGjheRkOWQRzFt2IH/Nl4DDFyHjJ6siDoSoZ/YwB2VuyEffpGzA5iK+izdA6d+8wCyHa+SYyEsj2N/jU/aaL0vbIf0sF2tA92oOVBG+ruN6CqrwZldypR0FuMnBuXkX49C5GyaGwq3ASLU2ZKOpfaoLb0EmDibmLC8TQq5/iw/vlIaA1xjxiyQTmaH7Sitr8ekr5qlN6pQP6tImTfyIXoeiYEnSmIlSfiooyHc20R8G84htUZq0FtUFvUpk4ChldO4H6sRzvKXdEw0ISmgRZc7a/DXwRccrscebcKIb5xCWndGeB3CsGTJyBCGoPQtnCcajlLjugk/qgPxO+1h7Eqyw7UFrWpbiemCKBbRlW7lrmgcaAZV/prUXm3CsW3y3D5VgGyenKQ2p2OpM5k8NoTEC6Nxtm2MJxsPoPAxhM4XB+AA1cPwbvmADyqvPFLpQdsM1aodoIVqlUAdRp6bgv5HzBbXHFXgqLbpci9mY/MnmykdIuQ2CFATHs8wqRRONN6ASeaTyOg8TgO1fnDl4C9anzhLvHCnkp3uJTvgXOJC7YUOWN+0nww/jTJMSevvsHs+DRlSOsZFPaW4NLNPOLpYgi70pBAwFHtcbhwLRKnW8/jeHMIOecgHKw7iv1XD2Jf9X78KtmHyNZoiOQi/FiyHZsJ+Ov87/DZZUesEq8H+4SxkjLUCmDinISPY74jcm4Oe3RyVyriO/iIksUS8EWEtJ5DUFMwjhIH86s7Ap8rfvCs9oGbxBO7KtwQ3hKBrgdd6H94D3ypgIAdsD7nc6wWb8DyzDWYy58HyhifJ8ZWT5NMoLGCJ48jsZ2COHkSCSkezl+LQHBLKI40BGJLsRPW5qzDskxbLE5bDBuRDWwzV5DV2SGwNhCdAx0Yed172AdeWyyWpa/ColQbzE1+H7MTLcEOmKagrAkCaAqlWWwZf6kqlMY8+gDZ3jXZazEz0Rym8TPUDg+yetl9Kca/HikeIUUmgEXizAnPml2YDsoaSduMAJrHaSrdWvw9AYcR8LBHu0m4eJtvqRGsDS4k8DlJFlPnxM0AZVHmmABSTGg+31GxW+XRAfi51AWztKxaI/zxELNytXDVoCzKHBNAKhotKrsr9zIe7Vq+G5wEs9EJi1I+hFDKx7zkuTrAk7XCGQGERZlTBLiSuN1Ltn2BcMEEeGF3PoaI8dzObFgJ3nsuuHoBqiNwLNgMu+x1ow9SByrsyoNC+ZiBPFb8h5LrhWrhqTKhTnD1R6BywuWZn0zYejq2FTuj/k7tKEz5RPlccLVOOBKG7DATtZO+yXNATW81npD3ZHhaux5wTWE4kohoktA0cWPOelT3SkZFDMNT9ICbwCJplvpEND4Vm8ZqNrJBJWJIT7h5Ioek5a+wRLRUcyoeX4y0GaMiaEjqCn9X8A62le0ADXGLYLbmYjS+HJtGTdf5TLUNa5E1Kc8+8K09iPkJVk8vx+MbEm1H8bTBTjDFF3lfMlmVNipLREt0a0gmt2TPIsKKZEv3Ki5TU2gZt8+1168lm9yU6noc1MO3FDkxYNojhpH+YW2Gnf5Nqbq2nHHMWPVbbZNuA6firQghpZt2TRHSSGwn/eScYPNnb8s1XUxoDFtGWpD6sAgrxSvhSi4juypJ31f6ExwLSNsl/BicAOMXczF5ba5mr83l9GWN/wE1f784zfWjGwAAAABJRU5ErkJg" alt="[OK]"/>Basic Statistics</h2><table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>test.fastq2_5pAtccRm.fq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4391</td></tr><tr><td>Sequences flagged as poor quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1-127</td></tr><tr><td>%GC</td><td>52</td></tr></tbody></table></div><div class="module"><h2 id="M1"><img src="data:image/png;base64,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" alt="[OK]"/>Per base sequence quality</h2><p><img class="indented" 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" alt="Per base quality graph" width="1020" height="600"/></p></div><div class="module"><h2 id="M2"><img src="data:image/png;base64,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" alt="[OK]"/>Per tile sequence quality</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per base quality graph" width="1020" height="600"/></p></div><div class="module"><h2 id="M3"><img src="data:image/png;base64,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" alt="[OK]"/>Per sequence quality scores</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per Sequence quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M4"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per base sequence content</h2><p><img class="indented" 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" alt="Per base sequence content" width="1020" height="600"/></p></div><div class="module"><h2 id="M5"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per sequence GC content</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per sequence GC content graph" width="800" height="600"/></p></div><div class="module"><h2 id="M6"><img src="data:image/png;base64,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" alt="[OK]"/>Per base N content</h2><p><img class="indented" 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" alt="N content graph" width="1020" height="600"/></p></div><div class="module"><h2 id="M7"><img src="data:image/png;base64,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" alt="[WARN]"/>Sequence Length Distribution</h2><p><img class="indented" src="data:image/png;base64,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" alt="Sequence length distribution" width="800" height="600"/></p></div><div class="module"><h2 id="M8"><img src="data:image/png;base64,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" alt="[OK]"/>Sequence Duplication Levels</h2><p><img class="indented" src="data:image/png;base64,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" alt="Duplication level graph" width="800" height="600"/></p></div><div class="module"><h2 id="M9"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEkklEQVR42s2XV2hUaxSF/4nRiaIoiKA+CDYMdsQnS8CAFREVsV4VUR9sWMECVuzYsETsGnvvivVaImhyU+Hq9VUfzKtKkptk5nz3rPkz42RyJskE5Xpgw8yZ8++1Zu+1yzGA+T8t8UPGJP1lTGquMenZxvwh02fd02+/jECOMSNdoEwX6KtrgQJjSv425rtMn3VPv+kZPfvTCLj/MM11WphnTNnnlJRA6fDhsGkTHDsG16/D2bOwfj3OtGmUDhjAZ78/oGd1RmcbTOBPY5JdRxmuo/IvyclBZ/lyeP8ePn6EoiLIyYE3b+D5c3j4EG7fhitXICMDZ8QIiv3+oM7Kh3wlRCDfmFbuwawiY0orOnWCmzfhwwcoLITsbMjKgmfP4MEDuHULLl+2kTh+PESAvXth7Voq+/RBPuRLPutFoOqfZ/1jzL8KK3l5UFAA797B69fw9Cncv29JXboEmZk2HYcOwZ49sG0bbNwIa9aAGzWnd2/kSz69IlGDgEIm1s6UKZCfD2/fwqtX8OQJ3LsHN27AxYtw5gwcPQoHDsDu3bB1K2zYAKtXw7JlsHAhzJ0LM2fipKaGI5FRK4EqwZVXtGtnc/viBTx+DHfvWsFduACnT8ORI7B/P+zaBVu2hETIqlUWeMECmDMHZsyASZNg3DgYNYrK1q2R71hhViMg5X4xJsjOnfDoEdy5A9euwfnzcOqUTUNurgXevBnWrYOVK2HJEli0yGpDaZk4EcaOhZFuNaanw6BB0KsXxT6fhFnoSUC1q/Jxhg61ir56Fc6dg5Mn4fBhq/jKSggE7GcBL14M8+bZUEsfwSCUl8OJEzBkCAwcCP37h8Dp1g2nZUuEEd0nov995iefLxASVKyiX76Eigoil4jouY4doUcPmyIRC18isW8fdO8OXbpA27bgguP388ltWMKqTsC2168lXbtWV/T27bBihRWdQKMvERI5kY0G16XvSllSkoWIsm+uCSvctkME1MfVShkzxipapSRFz54NzZtbR1J7LIlwSmLBVZrJyTXAZY4lEAjNjjABDRP1c6ZO/aHoCROgSZMfh+OR8AJv3NgTPGzCEmaEgCaahgrTp8PSpaAe0KhRzcMicfCgNwmBKx11gMuEJcyaBAQ8axa0aeN9WGFVbmPDHk6HNOHzJU4gkgKVYN++iYNHC1PirYNEjRREROi2TM/QxwPXd6/qkJDjkPAUYbgMv3kdUt7VjOKVmpcmRELd0sOfZxlGGpGi4BU2db2ysprgioxXdZSW2qEU6yclJU4jimrFwXi5C5OIBvcqUYGrjGNTOHgwTufO8VtxtWFUGwmlw6vJiIQmZCx4ixa2p7glXrUlFdY5jstrKyOP9hqx2Jx36ADz54fGdEV9xnH0QhKso5api2Ramu2q7qISdENfr4UkdiVrEIlWrex4Vim6Y9xxR3JCK1nsUlpeX2BX4dp8Qo1IE9XVQ2W/fokvpZ5ruSvMYLxQaycYPRp27Ij0BWfyZIqbNm34Wh7vxUQ1/L1ZM5z27aFnT0LTUwNs/HicYcMoce9pqfkpLya/zavZb/Ny+qvsPyB3ge/Rlc06AAAAAElFTkSuQmCC" alt="[FAIL]"/>Overrepresented sequences</h2><table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>TTAGACGGATCCACTAGTTCTAGAGCGGCCGCCACCGCGGTGGAGCTCCA</td><td>146</td><td>3.3249829196082894</td><td>No Hit</td></tr><tr><td>TT</td><td>92</td><td>2.0951947164654974</td><td>No Hit</td></tr><tr><td>TTATACAGCAAGCGCGCACCGACTCTGACAATCGGCATATCCGGTTCGCC</td><td>86</td><td>1.958551582782965</td><td>No Hit</td></tr><tr><td>TTAAAACCTCTTCAAATTTGCCGTGCAAATTTGGTAGGCCTGAGTGGACT</td><td>83</td><td>1.890230015941699</td><td>No Hit</td></tr><tr><td>TTA</td><td>74</td><td>1.6852653154179003</td><td>No Hit</td></tr><tr><td>TTGGCGAAGTAATCGCAACATCCGCATTAAAATCTAGCGAGGGCTTTACT</td><td>50</td><td>1.1386927806877705</td><td>No Hit</td></tr><tr><td>TTTACCAGCATTAAGGAACAGCTGCTTACGGTCGGCGTGGGTTGCCAGCA</td><td>32</td><td>0.7287633796401731</td><td>No Hit</td></tr><tr><td>ATCACAGGCGTAAACGTCGCCGTTGTGCTCAACAATCACCGAGCGCCCAC</td><td>30</td><td>0.6832156684126622</td><td>No Hit</td></tr><tr><td>TTAT</td><td>26</td><td>0.5921202459576406</td><td>No Hit</td></tr><tr><td>AT</td><td>23</td><td>0.5237986791163743</td><td>No Hit</td></tr><tr><td>GTACGATGTCACTGTGCACGACGATGGTCACTTCCAAGGCGCGGAGTGCC</td><td>19</td><td>0.43270325666135273</td><td>No Hit</td></tr><tr><td>ATCATCGTACGCAAGTGACCAACGCTGTCGATGGTGTCTTTGATGCCAAC</td><td>17</td><td>0.38715554543384195</td><td>No Hit</td></tr><tr><td>TTATCCAGCGCGAGCACTCGCTCTATCACCATTTCACGAGTTTCAAGGTT</td><td>16</td><td>0.36438168982008656</td><td>No Hit</td></tr><tr><td>ATGAGGAGCGAAGGCATGAAACCATATCAGCGCCAGTTTATTGAATTTGC</td><td>16</td><td>0.36438168982008656</td><td>No Hit</td></tr><tr><td>ATATAGAGCATTTTTGCCTCCTTTCGCGCCACAAGAAATTAACTGTAGCG</td><td>15</td><td>0.3416078342063311</td><td>No Hit</td></tr><tr><td>TTAGGCGACAACGTGATGGTTGCGCCGGTGTTCACTGAAGCGGGCGATGT</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTG</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTATCAATACTGCCTTCAATCAGTACATTGGTGGCAGGAACATCATTGAG</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTATCCGAAGCGATGAGAGTTATCCCGTAACCGGGTCAGCCACTGCATAG</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTACGCATAGTCATTTCTCCTTCTAAGAAGCGAGTAAGTACCTGCAAATC</td><td>13</td><td>0.2960601229788203</td><td>No Hit</td></tr><tr><td>TTACGCATAATCAATAGCTCCTGAAATCAGCGAGAATGTAAGACCTTCCA</td><td>12</td><td>0.2732862673650649</td><td>No Hit</td></tr><tr><td>TTAG</td><td>12</td><td>0.2732862673650649</td><td>No Hit</td></tr><tr><td>TTATCCGGCCAGGCGGGAACTGCAGTTCGGAGAGTGGCAGCGCAAAGACA</td><td>10</td><td>0.2277385561375541</td><td>No Hit</td></tr><tr><td>TTAACCTTCACCAGCGTGCGACCCTGGATCTGGTTATTAATGATGGCCTC</td><td>10</td><td>0.2277385561375541</td><td>No Hit</td></tr><tr><td>ATA</td><td>10</td><td>0.2277385561375541</td><td>No Hit</td></tr><tr><td>TTATCCAGATAGTTCGCCAGCTCTTCATTATTGAGTTTTTTCTTAAGCAC</td><td>9</td><td>0.20496470052379867</td><td>No Hit</td></tr><tr><td>TTAATTGGCATCAACACTGCAATCCTTGCGCCTGGCGGCGGGAGCGTCGG</td><td>9</td><td>0.20496470052379867</td><td>No Hit</td></tr><tr><td>TTATCCGGCGTGTAGGCATCGTCATGACGTAAACGGCGATCGGCGGTATA</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>ATCTACCGCGAAGGTTTTACCGGACTGGATCTGGCTTCGTCTGCCGCACA</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>TTAGGGATTAGCGTCTTAAGCTGGCGCGAGGACCAACGTATCAGCCAGGC</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>TTAA</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>ATC</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>T</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTT</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTATGGCTACTGGCGGTGCGGGTCGCGTTTATCGTTACAACACCAACGGC</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTATCCGGCGTTGCAACCTGTGAGCTGTAGATCATATCGGTGATAGCCTG</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>ATG</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTAAA</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTC</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTAGCATGACTCACGCCGGGCGTCCAGTTTTTAGCGACGGGGCACCCGAA</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTAGTGCGCTGGTTAGCGTGCGGGATAACGCCTGTCAGGATTATCTCGCG</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTATCCATCAGGGAGTTACTGTAAGCGAGAATATATTTATCACTCAATGC</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>ATAACCAGCATCAGCATTGGCGCGTAGAGAAAGGTAAAGCCCAGCAGCAA</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTATGCACCGCATCGTGAGCATCTTTCCCCCAGGCGAACGGCCCGTGCTG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTATA</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>GT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTAAGCTGCACCACACCGATACCGAGCGTAGTGGCAATACCGAAGATAGT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTAAATACCGTCGGCGCGTTAATCGGCCCAACTGCGCCACCAACACCAAT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>ATCA</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTGATCGTCAAAACCAACATTGCGACCGACGGTGGCGATAGGCATCCGGG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTATATACCAGGCTTAGCTGGGGTTGCCCCTTAATCTCTGGAGAATAACG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTGAGTTTCAGCAGCCGCGGTTCCGCCAGCACTTTACTGAAACTGCCTTT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>ATCTACCGCGAGGTTAAGCTGCTGTTCAATCTGGGCGACGCTCAGTTCGG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr></tbody></table></div><div class="module"><h2 id="M10"><img src="data:image/png;base64,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" alt="[OK]"/>Adapter Content</h2><p><img class="indented" 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a/example_of_result/20220120_test_1649703168/reports/nf_dag.png b/example_of_result/20220120_test_1649703168/reports/nf_dag.png deleted file mode 100644 index f292ac5f76c0cb831a343933530558edf31ff912..0000000000000000000000000000000000000000 Binary files a/example_of_result/20220120_test_1649703168/reports/nf_dag.png and /dev/null differ diff --git a/example_of_result/20220120_test_1649703168/reports/nf_trace.txt b/example_of_result/20220120_test_1649703168/reports/nf_trace.txt deleted file mode 100644 index b2512ff2b0363a35369247c90c6b59b1bdb2119f..0000000000000000000000000000000000000000 --- a/example_of_result/20220120_test_1649703168/reports/nf_trace.txt +++ /dev/null @@ -1,55 +0,0 @@ -task_id hash native_id name status exit submit duration realtime %cpu peak_rss peak_vmem rchar wchar -2 25/7b2ed6 281 Nremove (1) CACHED 0 2022-04-09 17:15:31.366 5.4s 1.3s 37.7% 11.9 MB 70.6 MB 16.8 MB 14.5 MB -3 f3/638b7c 308 report1 CACHED 0 2022-04-09 17:15:31.415 4.7s 85ms 6.6% 0 0 104.4 KB 684 B -4 e9/33684c 1241 trim (1) CACHED 0 2022-04-09 17:15:36.960 10.5s 7.3s 44.5% 63.5 MB 5.6 GB 16.4 MB 12 MB -5 d5/d15b46 2771 kraken (1) CACHED 0 2022-04-09 17:15:47.569 311ms 34ms 81.6% 0 0 150.8 KB 220 B -7 f8/e48213 2908 fivep_filtering (1) CACHED 0 2022-04-09 17:15:47.765 8.2s 6.2s 27.1% 11.6 MB 67.3 MB 28 MB 15.3 MB -6 59/2dbb6e 2819 fastqc1 (1) CACHED 0 2022-04-09 17:15:47.681 19s 17s 52.0% 165.2 MB 3.1 GB 13.9 MB 1.2 MB -9 c8/fa65a5 4175 cutoff (1) CACHED 0 2022-04-09 17:15:56.282 3.4s 988ms 14.5% 9.8 MB 61.3 MB 7 MB 3.9 MB -8 b4/13acb3 4147 fastqc2 (1) CACHED 0 2022-04-09 17:15:56.193 14.7s 12.8s 69.2% 176.1 MB 3.1 GB 12.2 MB 1.2 MB -11 96/3945da 5033 bowtie2 (1) CACHED 0 2022-04-09 17:16:00.685 13.5s 9.3s 40.6% 114.8 MB 239.5 MB 35 MB 16.2 MB -13 50/34788a 6971 Q20 (1) CACHED 0 2022-04-09 17:16:15.154 2.7s 707ms 17.8% 4.5 MB 42.8 MB 3.2 MB 2.2 MB -14 a8/f98ebd 11174 coverage (1) CACHED 0 2022-04-09 17:17:18.784 4.6s 2s 18.3% 43.4 MB 80.1 MB 479.7 KB 91 KB -17 41/75981b 7462 no_soft_clipping (1) CACHED 0 2022-04-09 17:16:18.863 4.3s 1.2s 14.5% 5.1 MB 57.7 MB 2.1 MB 1.5 MB -15 8f/71ec44 6997 multiQC CACHED 0 2022-04-09 17:16:15.192 23.2s 23s 36.5% 70.7 MB 81.1 MB 28.3 MB 2.3 MB -18 da/6b7a6c 11388 coverage (2) CACHED 0 2022-04-09 17:17:23.376 4.3s 1.9s 19.1% 43.6 MB 80.1 MB 335 KB 82.4 KB -16 0a/40301f 7482 duplicate_removal (1) CACHED 0 2022-04-09 17:16:18.891 9.7s 6.6s 24.6% 12.4 MB 85.1 MB 12.9 MB 6.6 MB -23 91/2fd95b 9315 insertion (1) CACHED 0 2022-04-09 17:16:29.566 4.3s 2.3s 19.6% 7.8 MB 65.6 MB 2.5 MB 1.7 MB -24 22/3c6c1a 9335 insertion (2) CACHED 0 2022-04-09 17:16:29.595 4.5s 2.5s 20.4% 9.2 MB 65.7 MB 3 MB 2.2 MB -22 d5/03b2f8 11599 coverage (3) CACHED 0 2022-04-09 17:17:27.665 4.2s 1.8s 19.8% 43.5 MB 80.1 MB 310.2 KB 82.1 KB -21 88/636f95 14201 plot_coverage (2) CACHED 0 2022-04-09 17:18:29.257 26.4s 24.3s 49.3% 215.2 MB 353.8 MB 18.3 MB 434.3 KB -29 37/f53840 15016 plot_coverage (3) CACHED 0 2022-04-09 17:18:55.680 25.9s 23.9s 49.5% 216.1 MB 354.4 MB 18.3 MB 434.9 KB -19 9d/64d05b 13378 plot_coverage (1) CACHED 0 2022-04-09 17:18:02.485 26.7s 24.5s 49.2% 210.1 MB 346.8 MB 18.3 MB 440.1 KB -27 13/37b94c 11809 final_insertion_files (1) CACHED 0 2022-04-09 17:17:31.877 15.1s 13s 39.8% 121.6 MB 246.8 MB 17.2 MB 269.2 KB -28 23/48d601 12520 final_insertion_files (2) CACHED 0 2022-04-09 17:17:46.998 15.5s 13s 39.8% 121.6 MB 246.8 MB 17.2 MB 278.8 KB -31 08/101dfa 23264 report3 (1) CACHED 0 2022-04-11 00:56:04.071 1.5s 65ms 9.5% 0 0 298.7 KB 43.6 KB -20 c6/bef5aa 560 motif CACHED 0 2022-04-09 17:15:32.488 46.5s 42.5s 42.6% 200.7 MB 325.6 MB 47.6 MB 39.7 MB -12 2f/6c72d5 10356 plot_read_length (1) CACHED 0 2022-04-09 17:16:50.093 28.7s 26.5s 53.9% 198.8 MB 337.6 MB 18.7 MB 691.5 KB -10 51/09f085 7550 plot_fivep_filtering_stat (1) CACHED 0 2022-04-09 17:16:18.958 31.1s 28.5s 42.9% 210.2 MB 346.7 MB 18.2 MB 792.5 KB -32 61/3abc05 17353 seq_around_insertion (2) CACHED 0 2022-04-09 17:20:01.464 14.8s 12.7s 40.7% 121.7 MB 246.8 MB 17.2 MB 143 KB -30 16/ef3227 15833 seq_around_insertion (1) CACHED 0 2022-04-09 17:19:21.578 15.1s 12.9s 40.7% 121.8 MB 246.8 MB 17.2 MB 208.5 KB -36 73/f0b62e 18913 extract_seq (1) CACHED 0 2022-04-09 17:20:44.033 5.2s 2.8s 19.6% 17.6 MB 62.4 MB 9.1 MB 4.5 MB -35 52/691666 27108 extract_seq (2) CACHED 0 2022-04-09 17:26:36.849 3.7s 1.5s 17.8% 6 MB 50.4 MB 9 MB 4.5 MB -41 49/575b96 27342 goalign (1) CACHED 0 2022-04-09 17:26:40.735 6.3s 2.9s 13.2% 13.6 MB 700.2 MB 128.1 KB 347.8 KB -37 35/ec05ea 19358 base_freq (1) CACHED 0 2022-04-09 17:20:49.698 4.8s 396ms 10.0% 0 0 279.8 KB 16.8 KB -45 b4/c8c939 27489 base_freq (8) CACHED 0 2022-04-09 17:26:41.036 4.8s 201ms 9.6% 0 0 180.5 KB 7 KB -38 ac/24a1a8 19211 base_freq (2) CACHED 0 2022-04-09 17:20:49.332 4.8s 399ms 11.1% 0 0 281.9 KB 18.9 KB -42 e7/c17b46 27448 base_freq (5) CACHED 0 2022-04-09 17:26:41.008 4.6s 197ms 9.7% 0 0 182.5 KB 8.9 KB -40 f0/df4b99 19278 base_freq (4) CACHED 0 2022-04-09 17:20:49.523 4.8s 432ms 11.3% 0 0 282.1 KB 19 KB -44 24/882b89 27418 base_freq (7) CACHED 0 2022-04-09 17:26:40.971 4.8s 172ms 9.8% 0 0 176.4 KB 2.8 KB -39 3c/c2beee 19315 base_freq (3) CACHED 0 2022-04-09 17:20:49.606 4.8s 426ms 11.9% 0 0 282.7 KB 19.7 KB -43 63/faaafa 28010 base_freq (6) CACHED 0 2022-04-09 17:26:45.691 3s 109ms 11.6% 0 0 178.7 KB 5.1 KB -50 71/fcd5ac 28454 report2 CACHED 0 2022-04-09 17:26:49.697 2.7s 45ms 5.6% 0 0 132.1 KB 2.4 KB -34 34/a909b8 18724 random_insertion (1) CACHED 0 2022-04-11 19:50:56.918 9.5s 8.5s 57.3% 351.5 MB 487.6 MB 30 MB 1.2 MB -46 12/8d4218 30389 logo (4) CACHED 0 2022-04-09 17:27:35.457 16.9s 14.8s 44.2% 120.1 MB 293.4 MB 13.8 MB 1002.1 KB -47 49/98147b 27326 logo (1) CACHED 0 2022-04-09 17:26:40.631 21.1s 17.7s 38.5% 168.5 MB 306.9 MB 13.8 MB 852.7 KB -48 20/686354 28980 logo (2) CACHED 0 2022-04-09 17:27:01.775 16.5s 14.5s 44.4% 123.9 MB 296.8 MB 13.8 MB 882.9 KB -49 d7/09132f 29683 logo (3) CACHED 0 2022-04-09 17:27:18.258 17.2s 15.1s 43.2% 118 MB 258.9 MB 13.8 MB 873.5 KB -51 6d/f8b929 31807 global_logo (1) CACHED 0 2022-04-09 17:28:10.164 17.6s 15.6s 46.3% 116.7 MB 254.6 MB 13.8 MB 847 KB -52 e9/9bbb1e 31094 global_logo (2) CACHED 0 2022-04-09 17:27:52.403 17.7s 15.6s 46.4% 116.5 MB 255.3 MB 13.8 MB 967.6 KB -1 bd/8b5bfa 137 init COMPLETED 0 2022-04-11 20:52:50.952 1.8s 17ms 5.3% 0 0 104 KB 659 B -25 86/7a76f6 226 backup COMPLETED 0 2022-04-11 20:52:51.343 1.8s 11ms 6.3% 0 0 104.2 KB 494 B -26 ec/876577 262 workflowVersion COMPLETED 0 2022-04-11 20:52:51.403 2.3s 744ms 14.3% 5 MB 38.4 MB 133.5 KB 2.1 KB -33 8b/74bcbf 403 dup_insertion_and_logo (1) COMPLETED 0 2022-04-11 20:52:52.046 12.1s 11s 64.5% 270.8 MB 406.8 MB 16.7 MB 586.2 KB -53 f8/d685f3 1420 plot_insertion (1) COMPLETED 0 2022-04-11 20:53:04.174 4m 4m 35.7% 394.9 MB 546.2 MB 44.8 MB 22.6 MB -54 ac/8ffc76 7745 print_report (1) COMPLETED 0 2022-04-11 20:57:05.043 10.2s 9.3s 45.4% 266.5 MB 1 TB 51.9 MB 26.3 MB diff --git a/example_of_result/20220120_test_1657729206/fastQC1/test.fastq2_trim_fastqc.html 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{ + margin-bottom:-0.5em; + margin-top:0.5em; + } + + div.main { + background-color: white; + } + + div.module { + padding-bottom:1.5em; + padding-top:1.5em; + } + + div.footer { + background-color: #EEE; + border:0; + margin:0; + padding: 0.5em; + font-size: 100%; + font-weight: bold; + width:100%; + } + + + a { + color: #000080; + } + + a:hover { + color: #800000; + } + + h2 { + color: #800000; + padding-bottom: 0; + margin-bottom: 0; + clear:left; + } + + table { + margin-left: 3em; + text-align: center; + } + + th { + text-align: center; + background-color: #000080; + color: #FFF; + padding: 0.4em; + } + + td { + font-family: monospace; + text-align: left; + background-color: #EEE; + color: #000; + padding: 0.4em; + } + + img { + padding-top: 0; + margin-top: 0; + border-top: 0; + } + + + p { + padding-top: 0; + margin-top: 0; + } +</style></head><body><div class="header"><div id="header_title"><img src="data:image/png;base64,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" alt="FastQC"/>FastQC Report</div><div id="header_filename">Wed 13 Jul 2022<br/>test.fastq2_trim.fq</div></div><div class="summary"><h2>Summary</h2><ul><li><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAFlUlEQVR42s2XCUzTVxzHybIs0IgCLYqyQ9RtmhjCNKKbikajYVc22NThNjYjeC0yWBESxAV1Ckw8QEUF5ChnS4FyFETucwWU+7ClHKKiImLI4nCz9bv3/pS7ra1HtM1Lm/b/fp/ve+93PQMABq9y6D/Bz+ANNvethWxPw3Xs3wx/YAb9Tn6j/700AeZc1qccrhGPw2UNkk8Fx9voIceX9Tcz6Hf62/B/PPrsCxNgzjW043iyGjheRkOWQRzFt2IH/Nl4DDFyHjJ6siDoSoZ/YwB2VuyEffpGzA5iK+izdA6d+8wCyHa+SYyEsj2N/jU/aaL0vbIf0sF2tA92oOVBG+ruN6CqrwZldypR0FuMnBuXkX49C5GyaGwq3ASLU2ZKOpfaoLb0EmDibmLC8TQq5/iw/vlIaA1xjxiyQTmaH7Sitr8ekr5qlN6pQP6tImTfyIXoeiYEnSmIlSfiooyHc20R8G84htUZq0FtUFvUpk4ChldO4H6sRzvKXdEw0ISmgRZc7a/DXwRccrscebcKIb5xCWndGeB3CsGTJyBCGoPQtnCcajlLjugk/qgPxO+1h7Eqyw7UFrWpbiemCKBbRlW7lrmgcaAZV/prUXm3CsW3y3D5VgGyenKQ2p2OpM5k8NoTEC6Nxtm2MJxsPoPAxhM4XB+AA1cPwbvmADyqvPFLpQdsM1aodoIVqlUAdRp6bgv5HzBbXHFXgqLbpci9mY/MnmykdIuQ2CFATHs8wqRRONN6ASeaTyOg8TgO1fnDl4C9anzhLvHCnkp3uJTvgXOJC7YUOWN+0nww/jTJMSevvsHs+DRlSOsZFPaW4NLNPOLpYgi70pBAwFHtcbhwLRKnW8/jeHMIOecgHKw7iv1XD2Jf9X78KtmHyNZoiOQi/FiyHZsJ+Ov87/DZZUesEq8H+4SxkjLUCmDinISPY74jcm4Oe3RyVyriO/iIksUS8EWEtJ5DUFMwjhIH86s7Ap8rfvCs9oGbxBO7KtwQ3hKBrgdd6H94D3ypgIAdsD7nc6wWb8DyzDWYy58HyhifJ8ZWT5NMoLGCJ48jsZ2COHkSCSkezl+LQHBLKI40BGJLsRPW5qzDskxbLE5bDBuRDWwzV5DV2SGwNhCdAx0Yed172AdeWyyWpa/ColQbzE1+H7MTLcEOmKagrAkCaAqlWWwZf6kqlMY8+gDZ3jXZazEz0Rym8TPUDg+yetl9Kca/HikeIUUmgEXizAnPml2YDsoaSduMAJrHaSrdWvw9AYcR8LBHu0m4eJtvqRGsDS4k8DlJFlPnxM0AZVHmmABSTGg+31GxW+XRAfi51AWztKxaI/zxELNytXDVoCzKHBNAKhotKrsr9zIe7Vq+G5wEs9EJi1I+hFDKx7zkuTrAk7XCGQGERZlTBLiSuN1Ltn2BcMEEeGF3PoaI8dzObFgJ3nsuuHoBqiNwLNgMu+x1ow9SByrsyoNC+ZiBPFb8h5LrhWrhqTKhTnD1R6BywuWZn0zYejq2FTuj/k7tKEz5RPlccLVOOBKG7DATtZO+yXNATW81npD3ZHhaux5wTWE4kohoktA0cWPOelT3SkZFDMNT9ICbwCJplvpEND4Vm8ZqNrJBJWJIT7h5Ioek5a+wRLRUcyoeX4y0GaMiaEjqCn9X8A62le0ADXGLYLbmYjS+HJtGTdf5TLUNa5E1Kc8+8K09iPkJVk8vx+MbEm1H8bTBTjDFF3lfMlmVNipLREt0a0gmt2TPIsKKZEv3Ki5TU2gZt8+1168lm9yU6noc1MO3FDkxYNojhpH+YW2Gnf5Nqbq2nHHMWPVbbZNuA6firQghpZt2TRHSSGwn/eScYPNnb8s1XUxoDFtGWpD6sAgrxSvhSi4juypJ31f6ExwLSNsl/BicAOMXczF5ba5mr83l9GWN/wE1f784zfWjGwAAAABJRU5ErkJg" alt="[PASS]"/><a href="#M0">Basic Statistics</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M1">Per base sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M2">Per tile sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M3">Per sequence quality scores</a></li><li><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEkklEQVR42s2XV2hUaxSF/4nRiaIoiKA+CDYMdsQnS8CAFREVsV4VUR9sWMECVuzYsETsGnvvivVaImhyU+Hq9VUfzKtKkptk5nz3rPkz42RyJskE5Xpgw8yZ8++1Zu+1yzGA+T8t8UPGJP1lTGquMenZxvwh02fd02+/jECOMSNdoEwX6KtrgQJjSv425rtMn3VPv+kZPfvTCLj/MM11WphnTNnnlJRA6fDhsGkTHDsG16/D2bOwfj3OtGmUDhjAZ78/oGd1RmcbTOBPY5JdRxmuo/IvyclBZ/lyeP8ePn6EoiLIyYE3b+D5c3j4EG7fhitXICMDZ8QIiv3+oM7Kh3wlRCDfmFbuwawiY0orOnWCmzfhwwcoLITsbMjKgmfP4MEDuHULLl+2kTh+PESAvXth7Voq+/RBPuRLPutFoOqfZ/1jzL8KK3l5UFAA797B69fw9Cncv29JXboEmZk2HYcOwZ49sG0bbNwIa9aAGzWnd2/kSz69IlGDgEIm1s6UKZCfD2/fwqtX8OQJ3LsHN27AxYtw5gwcPQoHDsDu3bB1K2zYAKtXw7JlsHAhzJ0LM2fipKaGI5FRK4EqwZVXtGtnc/viBTx+DHfvWsFduACnT8ORI7B/P+zaBVu2hETIqlUWeMECmDMHZsyASZNg3DgYNYrK1q2R71hhViMg5X4xJsjOnfDoEdy5A9euwfnzcOqUTUNurgXevBnWrYOVK2HJEli0yGpDaZk4EcaOhZFuNaanw6BB0KsXxT6fhFnoSUC1q/Jxhg61ir56Fc6dg5Mn4fBhq/jKSggE7GcBL14M8+bZUEsfwSCUl8OJEzBkCAwcCP37h8Dp1g2nZUuEEd0nov995iefLxASVKyiX76Eigoil4jouY4doUcPmyIRC18isW8fdO8OXbpA27bgguP388ltWMKqTsC2168lXbtWV/T27bBihRWdQKMvERI5kY0G16XvSllSkoWIsm+uCSvctkME1MfVShkzxipapSRFz54NzZtbR1J7LIlwSmLBVZrJyTXAZY4lEAjNjjABDRP1c6ZO/aHoCROgSZMfh+OR8AJv3NgTPGzCEmaEgCaahgrTp8PSpaAe0KhRzcMicfCgNwmBKx11gMuEJcyaBAQ8axa0aeN9WGFVbmPDHk6HNOHzJU4gkgKVYN++iYNHC1PirYNEjRREROi2TM/QxwPXd6/qkJDjkPAUYbgMv3kdUt7VjOKVmpcmRELd0sOfZxlGGpGi4BU2db2ysprgioxXdZSW2qEU6yclJU4jimrFwXi5C5OIBvcqUYGrjGNTOHgwTufO8VtxtWFUGwmlw6vJiIQmZCx4ixa2p7glXrUlFdY5jstrKyOP9hqx2Jx36ADz54fGdEV9xnH0QhKso5api2Ramu2q7qISdENfr4UkdiVrEIlWrex4Vim6Y9xxR3JCK1nsUlpeX2BX4dp8Qo1IE9XVQ2W/fokvpZ5ruSvMYLxQaycYPRp27Ij0BWfyZIqbNm34Wh7vxUQ1/L1ZM5z27aFnT0LTUwNs/HicYcMoce9pqfkpLya/zavZb/Ny+qvsPyB3ge/Rlc06AAAAAElFTkSuQmCC" alt="[FAIL]"/><a href="#M4">Per base sequence content</a></li><li><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAFlUlEQVR42s2XCUzTVxzHybIs0IgCLYqyQ9RtmhjCNKKbikajYVc22NThNjYjeC0yWBESxAV1Ckw8QEUF5ChnS4FyFETucwWU+7ClHKKiImLI4nCz9bv3/pS7ra1HtM1Lm/b/fp/ve+93PQMABq9y6D/Bz+ANNvethWxPw3Xs3wx/YAb9Tn6j/700AeZc1qccrhGPw2UNkk8Fx9voIceX9Tcz6Hf62/B/PPrsCxNgzjW043iyGjheRkOWQRzFt2IH/Nl4DDFyHjJ6siDoSoZ/YwB2VuyEffpGzA5iK+izdA6d+8wCyHa+SYyEsj2N/jU/aaL0vbIf0sF2tA92oOVBG+ruN6CqrwZldypR0FuMnBuXkX49C5GyaGwq3ASLU2ZKOpfaoLb0EmDibmLC8TQq5/iw/vlIaA1xjxiyQTmaH7Sitr8ekr5qlN6pQP6tImTfyIXoeiYEnSmIlSfiooyHc20R8G84htUZq0FtUFvUpk4ChldO4H6sRzvKXdEw0ISmgRZc7a/DXwRccrscebcKIb5xCWndGeB3CsGTJyBCGoPQtnCcajlLjugk/qgPxO+1h7Eqyw7UFrWpbiemCKBbRlW7lrmgcaAZV/prUXm3CsW3y3D5VgGyenKQ2p2OpM5k8NoTEC6Nxtm2MJxsPoPAxhM4XB+AA1cPwbvmADyqvPFLpQdsM1aodoIVqlUAdRp6bgv5HzBbXHFXgqLbpci9mY/MnmykdIuQ2CFATHs8wqRRONN6ASeaTyOg8TgO1fnDl4C9anzhLvHCnkp3uJTvgXOJC7YUOWN+0nww/jTJMSevvsHs+DRlSOsZFPaW4NLNPOLpYgi70pBAwFHtcbhwLRKnW8/jeHMIOecgHKw7iv1XD2Jf9X78KtmHyNZoiOQi/FiyHZsJ+Ov87/DZZUesEq8H+4SxkjLUCmDinISPY74jcm4Oe3RyVyriO/iIksUS8EWEtJ5DUFMwjhIH86s7Ap8rfvCs9oGbxBO7KtwQ3hKBrgdd6H94D3ypgIAdsD7nc6wWb8DyzDWYy58HyhifJ8ZWT5NMoLGCJ48jsZ2COHkSCSkezl+LQHBLKI40BGJLsRPW5qzDskxbLE5bDBuRDWwzV5DV2SGwNhCdAx0Yed172AdeWyyWpa/ColQbzE1+H7MTLcEOmKagrAkCaAqlWWwZf6kqlMY8+gDZ3jXZazEz0Rym8TPUDg+yetl9Kca/HikeIUUmgEXizAnPml2YDsoaSduMAJrHaSrdWvw9AYcR8LBHu0m4eJtvqRGsDS4k8DlJFlPnxM0AZVHmmABSTGg+31GxW+XRAfi51AWztKxaI/zxELNytXDVoCzKHBNAKhotKrsr9zIe7Vq+G5wEs9EJi1I+hFDKx7zkuTrAk7XCGQGERZlTBLiSuN1Ltn2BcMEEeGF3PoaI8dzObFgJ3nsuuHoBqiNwLNgMu+x1ow9SByrsyoNC+ZiBPFb8h5LrhWrhqTKhTnD1R6BywuWZn0zYejq2FTuj/k7tKEz5RPlccLVOOBKG7DATtZO+yXNATW81npD3ZHhaux5wTWE4kohoktA0cWPOelT3SkZFDMNT9ICbwCJplvpEND4Vm8ZqNrJBJWJIT7h5Ioek5a+wRLRUcyoeX4y0GaMiaEjqCn9X8A62le0ADXGLYLbmYjS+HJtGTdf5TLUNa5E1Kc8+8K09iPkJVk8vx+MbEm1H8bTBTjDFF3lfMlmVNipLREt0a0gmt2TPIsKKZEv3Ki5TU2gZt8+1168lm9yU6noc1MO3FDkxYNojhpH+YW2Gnf5Nqbq2nHHMWPVbbZNuA6firQghpZt2TRHSSGwn/eScYPNnb8s1XUxoDFtGWpD6sAgrxSvhSi4juypJ31f6ExwLSNsl/BicAOMXczF5ba5mr83l9GWN/wE1f784zfWjGwAAAABJRU5ErkJg" alt="[PASS]"/><a href="#M5">Per sequence GC content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M6">Per base N content</a></li><li><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEpklEQVR42s1XfUyUdRz/URrktNpaW/aHW1LLNdN0+U9WSzdfcq31/uLrWvZP+Y/2T+mc54GplSCOt4CBgqicpICGwokvSYkc3B3nQJxmBSo0K6fsiJe759Pn+zzccRx3xyGwvO07fvye5/f9fH7f90cBUP+nDP+QSd1XZ1LT6hPU/FqzWi4ia9mTZ2NGwLZZvUag/Dqzul2XoDzOROVu3KI6RGQte/JM3pF3R41AbaJ6pT5RNdgT1L+tyXGezpJFgM0MNGYDV34ALhUAtZugWZeh0/IiWpNiPfKunJGzd03glEmNo6L0erPqbkse59WqvwD+bgJuXQL+cgF/2oAbPwPXTgJ/HAOulgKXLYArHdqRxWjfGeuVs6JDdA2LgMOkHrGbVbVri+rsyZ9K5YeBfy4CNxsIXEvgaqC1Cvi9nM9KCFwENNMSjTk6ATiSgfMb0XtgJkSH6BKdURHQb84DzVtVl5gVN+0UJ9B+Hrh+lsAnjBsL+K8Ev3KQbtgLXMgkcBJQt5Xgm4Fz6wFaTds/A6JLdIayxCACYjJhrVV+RGAHgWsI/BPQYgV+O0rQQ1xXAJ4uwNvNv5TeTuBiHoFNwC9fAWfXAWfWACc/BayroBVOMyxB3REJ6AFHv/XkTuZN6dvrpwlWSeAjfQG3j0C7DRKaB/6frF2pBF4LnP4cqFpN4JXA8Q+AH98CypagN+dRiO7gwBxAQCK3bYfywv4NzcxbXi0jcDGBC4GmPMPMDSmGv4MJ1G8n8CdA5XLg2PsEfpPAzMbD84Hil4B9z6E9KcYrGCEJSO5K+mhlC/oimr5tpm+bcgmcATh3EoTEbAnG/4EEvL1AzUYCvwccfQMoXUzgeQSeCxS9oIOj4BloWQ9DMALrRP/tWUBavovx6PkdGNHOZON25zZQ+RLAMpumfYegPQEEuLby5haCWeYYoPtnAoXPAvlPAbmPAwRHZixavlUewRpIQMorq5h7z9NGgXGl9UX0Nj2SYZnFw+OBdGVI8Rwj+Hw/Wcue73kEuZOiIFi+sq0TkDoupRTlNJ9jR18qmQyfZk8crGgEBDSKXrald/gISDOReo7KpQTexFT6kv58l7d+ILSiERAQESzB9BOQjiZNBdYVRipVsAZk3B9eyQgJCJZgDiYgwFUfM2gei6xktAn4XVDCFCx6fmglo+0CfxCyZEY0/VgFoS8N7+yKie4WAtZ1iz3AbYisR5KG/kLEIhEVgawJLDLxA0X2hjqXGRemEAWUYm9adH7Ui9PlA4bIOtK7GezCRS9D2xMfvhQPaEbRWKCjpT8GZB3OAlmT2JRYU8pXGFNSuGYU2I67U4cgUPrq4G4oe8Hv7Z4CnPiMLXoderKjaMeBA0lEV+Q8BLjb+gnIWvb8Jmd8FROnmlW15mt4C+KjG0iCR7KIJKRty8AiIms/OY5+Vk5CNvYURwY0tuRhjWTBQ+mQ7vDJ93H65AMbu2gDO6p9F4fS2cMfSkOO5QzMkNYQU+c/SeDX+W3AmeECpyZnKrTjH6I95cG7H8vDfZhIDnekTYCW9wSnnOnsmOyeFWxg5W9DK10I997pzPOY0fkwuWc+ze6Zj9Oxkv8AsrpUzUM9ceYAAAAASUVORK5C" alt="[WARNING]"/><a href="#M7">Sequence Length Distribution</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M8">Sequence Duplication Levels</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M9">Overrepresented sequences</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M10">Adapter Content</a></li></ul></div><div class="main"><div class="module"><h2 id="M0"><img src="data:image/png;base64,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" alt="[OK]"/>Basic Statistics</h2><table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>test.fastq2_trim.fq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>8709</td></tr><tr><td>Sequences flagged as poor quality</td><td>0</td></tr><tr><td>Sequence length</td><td>30-175</td></tr><tr><td>%GC</td><td>54</td></tr></tbody></table></div><div class="module"><h2 id="M1"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAFlUlEQVR42s2XCUzTVxzHybIs0IgCLYqyQ9RtmhjCNKKbikajYVc22NThNjYjeC0yWBESxAV1Ckw8QEUF5ChnS4FyFETucwWU+7ClHKKiImLI4nCz9bv3/pS7ra1HtM1Lm/b/fp/ve+93PQMABq9y6D/Bz+ANNvethWxPw3Xs3wx/YAb9Tn6j/700AeZc1qccrhGPw2UNkk8Fx9voIceX9Tcz6Hf62/B/PPrsCxNgzjW043iyGjheRkOWQRzFt2IH/Nl4DDFyHjJ6siDoSoZ/YwB2VuyEffpGzA5iK+izdA6d+8wCyHa+SYyEsj2N/jU/aaL0vbIf0sF2tA92oOVBG+ruN6CqrwZldypR0FuMnBuXkX49C5GyaGwq3ASLU2ZKOpfaoLb0EmDibmLC8TQq5/iw/vlIaA1xjxiyQTmaH7Sitr8ekr5qlN6pQP6tImTfyIXoeiYEnSmIlSfiooyHc20R8G84htUZq0FtUFvUpk4ChldO4H6sRzvKXdEw0ISmgRZc7a/DXwRccrscebcKIb5xCWndGeB3CsGTJyBCGoPQtnCcajlLjugk/qgPxO+1h7Eqyw7UFrWpbiemCKBbRlW7lrmgcaAZV/prUXm3CsW3y3D5VgGyenKQ2p2OpM5k8NoTEC6Nxtm2MJxsPoPAxhM4XB+AA1cPwbvmADyqvPFLpQdsM1aodoIVqlUAdRp6bgv5HzBbXHFXgqLbpci9mY/MnmykdIuQ2CFATHs8wqRRONN6ASeaTyOg8TgO1fnDl4C9anzhLvHCnkp3uJTvgXOJC7YUOWN+0nww/jTJMSevvsHs+DRlSOsZFPaW4NLNPOLpYgi70pBAwFHtcbhwLRKnW8/jeHMIOecgHKw7iv1XD2Jf9X78KtmHyNZoiOQi/FiyHZsJ+Ov87/DZZUesEq8H+4SxkjLUCmDinISPY74jcm4Oe3RyVyriO/iIksUS8EWEtJ5DUFMwjhIH86s7Ap8rfvCs9oGbxBO7KtwQ3hKBrgdd6H94D3ypgIAdsD7nc6wWb8DyzDWYy58HyhifJ8ZWT5NMoLGCJ48jsZ2COHkSCSkezl+LQHBLKI40BGJLsRPW5qzDskxbLE5bDBuRDWwzV5DV2SGwNhCdAx0Yed172AdeWyyWpa/ColQbzE1+H7MTLcEOmKagrAkCaAqlWWwZf6kqlMY8+gDZ3jXZazEz0Rym8TPUDg+yetl9Kca/HikeIUUmgEXizAnPml2YDsoaSduMAJrHaSrdWvw9AYcR8LBHu0m4eJtvqRGsDS4k8DlJFlPnxM0AZVHmmABSTGg+31GxW+XRAfi51AWztKxaI/zxELNytXDVoCzKHBNAKhotKrsr9zIe7Vq+G5wEs9EJi1I+hFDKx7zkuTrAk7XCGQGERZlTBLiSuN1Ltn2BcMEEeGF3PoaI8dzObFgJ3nsuuHoBqiNwLNgMu+x1ow9SByrsyoNC+ZiBPFb8h5LrhWrhqTKhTnD1R6BywuWZn0zYejq2FTuj/k7tKEz5RPlccLVOOBKG7DATtZO+yXNATW81npD3ZHhaux5wTWE4kohoktA0cWPOelT3SkZFDMNT9ICbwCJplvpEND4Vm8ZqNrJBJWJIT7h5Ioek5a+wRLRUcyoeX4y0GaMiaEjqCn9X8A62le0ADXGLYLbmYjS+HJtGTdf5TLUNa5E1Kc8+8K09iPkJVk8vx+MbEm1H8bTBTjDFF3lfMlmVNipLREt0a0gmt2TPIsKKZEv3Ki5TU2gZt8+1168lm9yU6noc1MO3FDkxYNojhpH+YW2Gnf5Nqbq2nHHMWPVbbZNuA6firQghpZt2TRHSSGwn/eScYPNnb8s1XUxoDFtGWpD6sAgrxSvhSi4juypJ31f6ExwLSNsl/BicAOMXczF5ba5mr83l9GWN/wE1f784zfWjGwAAAABJRU5ErkJg" alt="[OK]"/>Per base sequence quality</h2><p><img class="indented" 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" alt="Per base quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M2"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAFlUlEQVR42s2XCUzTVxzHybIs0IgCLYqyQ9RtmhjCNKKbikajYVc22NThNjYjeC0yWBESxAV1Ckw8QEUF5ChnS4FyFETucwWU+7ClHKKiImLI4nCz9bv3/pS7ra1HtM1Lm/b/fp/ve+93PQMABq9y6D/Bz+ANNvethWxPw3Xs3wx/YAb9Tn6j/700AeZc1qccrhGPw2UNkk8Fx9voIceX9Tcz6Hf62/B/PPrsCxNgzjW043iyGjheRkOWQRzFt2IH/Nl4DDFyHjJ6siDoSoZ/YwB2VuyEffpGzA5iK+izdA6d+8wCyHa+SYyEsj2N/jU/aaL0vbIf0sF2tA92oOVBG+ruN6CqrwZldypR0FuMnBuXkX49C5GyaGwq3ASLU2ZKOpfaoLb0EmDibmLC8TQq5/iw/vlIaA1xjxiyQTmaH7Sitr8ekr5qlN6pQP6tImTfyIXoeiYEnSmIlSfiooyHc20R8G84htUZq0FtUFvUpk4ChldO4H6sRzvKXdEw0ISmgRZc7a/DXwRccrscebcKIb5xCWndGeB3CsGTJyBCGoPQtnCcajlLjugk/qgPxO+1h7Eqyw7UFrWpbiemCKBbRlW7lrmgcaAZV/prUXm3CsW3y3D5VgGyenKQ2p2OpM5k8NoTEC6Nxtm2MJxsPoPAxhM4XB+AA1cPwbvmADyqvPFLpQdsM1aodoIVqlUAdRp6bgv5HzBbXHFXgqLbpci9mY/MnmykdIuQ2CFATHs8wqRRONN6ASeaTyOg8TgO1fnDl4C9anzhLvHCnkp3uJTvgXOJC7YUOWN+0nww/jTJMSevvsHs+DRlSOsZFPaW4NLNPOLpYgi70pBAwFHtcbhwLRKnW8/jeHMIOecgHKw7iv1XD2Jf9X78KtmHyNZoiOQi/FiyHZsJ+Ov87/DZZUesEq8H+4SxkjLUCmDinISPY74jcm4Oe3RyVyriO/iIksUS8EWEtJ5DUFMwjhIH86s7Ap8rfvCs9oGbxBO7KtwQ3hKBrgdd6H94D3ypgIAdsD7nc6wWb8DyzDWYy58HyhifJ8ZWT5NMoLGCJ48jsZ2COHkSCSkezl+LQHBLKI40BGJLsRPW5qzDskxbLE5bDBuRDWwzV5DV2SGwNhCdAx0Yed172AdeWyyWpa/ColQbzE1+H7MTLcEOmKagrAkCaAqlWWwZf6kqlMY8+gDZ3jXZazEz0Rym8TPUDg+yetl9Kca/HikeIUUmgEXizAnPml2YDsoaSduMAJrHaSrdWvw9AYcR8LBHu0m4eJtvqRGsDS4k8DlJFlPnxM0AZVHmmABSTGg+31GxW+XRAfi51AWztKxaI/zxELNytXDVoCzKHBNAKhotKrsr9zIe7Vq+G5wEs9EJi1I+hFDKx7zkuTrAk7XCGQGERZlTBLiSuN1Ltn2BcMEEeGF3PoaI8dzObFgJ3nsuuHoBqiNwLNgMu+x1ow9SByrsyoNC+ZiBPFb8h5LrhWrhqTKhTnD1R6BywuWZn0zYejq2FTuj/k7tKEz5RPlccLVOOBKG7DATtZO+yXNATW81npD3ZHhaux5wTWE4kohoktA0cWPOelT3SkZFDMNT9ICbwCJplvpEND4Vm8ZqNrJBJWJIT7h5Ioek5a+wRLRUcyoeX4y0GaMiaEjqCn9X8A62le0ADXGLYLbmYjS+HJtGTdf5TLUNa5E1Kc8+8K09iPkJVk8vx+MbEm1H8bTBTjDFF3lfMlmVNipLREt0a0gmt2TPIsKKZEv3Ki5TU2gZt8+1168lm9yU6noc1MO3FDkxYNojhpH+YW2Gnf5Nqbq2nHHMWPVbbZNuA6firQghpZt2TRHSSGwn/eScYPNnb8s1XUxoDFtGWpD6sAgrxSvhSi4juypJ31f6ExwLSNsl/BicAOMXczF5ba5mr83l9GWN/wE1f784zfWjGwAAAABJRU5ErkJg" alt="[OK]"/>Per tile sequence quality</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per base quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M3"><img src="data:image/png;base64,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" alt="[OK]"/>Per sequence quality scores</h2><p><img class="indented" 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" alt="Per Sequence quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M4"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per base sequence content</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per base sequence content" width="800" height="600"/></p></div><div class="module"><h2 id="M5"><img src="data:image/png;base64,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" alt="[OK]"/>Per sequence GC content</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per sequence GC content graph" width="800" height="600"/></p></div><div class="module"><h2 id="M6"><img src="data:image/png;base64,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" alt="[OK]"/>Per base N content</h2><p><img class="indented" 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kKhUCgUisASWBQKhUKhUASWwKJQKBQKhUIRWA4NhUKhUCgUgSWwKBQKhUKhCCyBRaFQKBQKhSKwHBoKhUKhUCgCS2BRKBQKhUIRWAKLQqFQKBSKwBJYDg2FQqFQKBSBJbAoFAqFQqEILIFFoVAoFApFYAksh4ZCoVAoFIrAcmgoFAqFQqEILIFFoVAoFApFYAksCoVCoVAoFIHl0FAoFAqFQhFYAotCoVAoFIrAElgUCoVCoVAElsByaCgUCoVCoQgsgUWhUCgUCkVgCSwKhUKhUCgCS2A5NBQKhUKhUASWwKJQKBQKhSKwBBaFQqFQKBSBJbAcGgqFQqFQKALLoaFQKBQKhSKwBBaFQqFQKBSBJbAoFAqFQqFQBJZDQ6FQKBQK5UUCa/rv1k9ZP11gUSgUCoVCEVjtgVWbTQKLQqFQKBSKwHoTRvd5tBdbAotCoVAoFIrAOg6szStVbYF1MzMzM7v0HhBYrmBRKBQKhUJxBWs3qgQWhUKhUCgUgdUeWIsJLAqFQqFQKAKr92uwDq9a+S5CCoVCoVAoAistsPwcLAqFQqFQKAKrK7ASbldgUSgUCoVCEVgCi0KhUCgUisASWI4mhUKhUCgUgeXQUCgUCoVCEVgCi0KhUCgUisASWBQKhUKhUCgCy6GhUCgUCoUisAQWhUKhUCgUgSWwKBQKhUKhCCyB5dBQKBQKhUIRWAKLQqFQKBSKwBJYFAqFQqFQBJbAcmgoFAqFQqEILIeGQqFQKBSKwBJYFAqFQqFQBJbAolAoFAqFQhFYDg2FQqFQKBSBJbAoFAqFQqEILIFFoVAoFAqFIrAcGgqFQqFQKAJLYFEoFAqFQhFYAotCoVAoFIrAElgODYVCoVAoFIElsCgUCoVCoQgsgUWhUCgUCkVgCSyHhkKhUCgUisByaCgUCoVCoQgsgUWhUCgUCkVgCSwKhUKhUCgUgeXQUCgUCoVCEVgCi0KhUCgUisASWBQKhUKhUASWwHJoKBQKhUKhCCyBRaFQKBQKRWAJLAqFQqFQKAJLYDk0FAqFQqFQBJbAolAoFAqFIrAEFoVCoVAoFIElsBwaCoVCoVAoAsuhoVAoFAqFIrAEFoVCoVAoFIElsCgUCoVCoVAElkNDoVAoFArl8oE13S3ydIFFoVAoFApFYJUCax1V698LLAqFQqFQKAKr/SHCvagKlpPAolAoFAqFIrCSA+tmZmZmdulVBNb6a61cwaJQKBQKheIKlocIKRQKhUKhUASWQ0OhUCgUCuWSgeW7CCkUCoVCoQis/CtYfg4WhUKhUCgUgTXwIcLe2xVYFAqFQqFQBJbAolAoFAqFIrAElqNJoVAoFApFYDk0FAqFQqFQBJbAolAoFAqFIrAEFoVCoVAoFIrAcmgoFAqFQqEILIFFoVAoFApFYAksCoVCoVAoAktgOTQUCoVCoVAElsCiUCgUCoUisAQWhUKhUCgUgSWwHBoKhUKhUCgCy6GhUCgUCoUisAQWhUKhUCgUgSWwKBQKhUKhUASWQ0OhUCgUCkVgCSwKhUKhUCgCS2BRKBQKhUKhCCyHhkKhUCgUisASWBQKhUKhUASWwKJQKBQKhSKwBJZDQ6FQKBQKRWAJLAqFQqFQKAJLYFEoFAqFQhFYAsuhoVAoFAqFIrAcGgqFQqFQKAJLYFEoFAqFQhFYAotCoVAoFApFYDk0FAqFQqFQBJbAolAoFAqFIrAEFoVCoVAoFIElsBwaCoVCoVAoAktgUSgUCoVCEVgCi0KhUCgUisASWA4NhUKhUCgUgSWwKBQKhUKhCCyBRaFQKBQKRWAJLIeGQqFQKBSKwHJoKBQKhUKhCCyBRaFQKBQKRWAJLAqFQqFQKBSB5dBQKBQKhUK5fGBNd4s8XWBRKBQKhUIRWKXAuk+iRUt9/b3AolAoFAqFIrDaHyLci6pgOQksCoVCoVAoAis5sG5mZmZml151YG0+PugKFoVCoVAoFFewGq9glYtKYFEoFAqFQhFYdYG1DiOBRaFQKBQKRWC1B9ZeFfkuQgqFQqFQKAKrJbCm1Tb/SGBRKBQKhUIRWHUPEabdrsCiUCgUCoUisAQWhUKhUCgUgSWwHE0KhUKhUCgCy6GhUCgUCoUisAQWhUKhUCgUgSWwKBQKhUKhUASWQ0OhUCgUCkVgCSwKhUKhUCgCS2BRKBQKhUIRWALLoaFQKBQKhSKwBBaFQqFQKBSBJbAoFAqFQqEILIHl0FAoFAqFQhFYDg2FQqFQKBSBJbAoFAqFQqEILIFFoVAoFAqFIrAcGgqFQqFQKAJLYFEoFAqFQhFYAotCoVAoFApFYDk0FAqFQqFQBJbAolAoFAqFIrAEFoVCoVAoFIElsBwaCoVCoVAoAktgUSgUCoVCEVgCi0KhUCgUisASWA4NhUKhUCgUgeXQUCgUCoVCEVgCi0KhUCgUisASWBQKhUKhUCgCy6GhUCgUCoUisAQWhUKhUCgUgSWwKBQKhUKhCCyB5dBQKBQKhUIRWAKLQqFQKBSKwBJYFAqFQqFQBJbAcmgoFAqFQqEILIFFoVAoFApFYAksCoVCoVAoAktgOTQUCoVCoVAElkNDoVAoFApFYAksCoVCoVAoAktgUSgUCoVCoQgsh4ZCoVAoFMq1A2v6ss1aKvypwKJQKBQKhSKwSlew9gKrNpsEFoVCoVAoFIG1G0aLpwTLSWBRKBQKhUIRWMmBdTMzMzO79FzBolAoFAqF4gqWhwgdGgqFQqFQKAJLYFEoFAqFQhFY0cCafRchhUKhUCgUgdUWWNPbLWrJz8GiUCgUCoUisBqvYKXdrsCiUCgUCoUisAQWhUKhUCgUgSWwHE0KhUKhUCgCy6GhUCgUCoUisAQWhUKhUCgUgSWwKBQKhUKhUASWQ0OhUCgUCkVgCSwKhUKhUCgCS2BRKBQKhUIRWALLoaFQKBQKhSKwBBaFQqFQKBSBJbAoFAqFQqEILIHl0FAoFAqFQhFYDg2FQqFQKBSBJbAoFAqFQqEILIFFoVAoFAqFIrAcGgqFQqFQKAJLYFEoFAqFQhFYAotCoVAoFApFYDk0FAqFQqFQBJbAolAoFAqFIrAEFoVCoVAoFIElsBwaCoVCoVAoAktgUSgUCoVCEVgCi0KhUCgUisASWA4NhUKhUCgUgeXQUCgUCoVCEVgCi0KhUCgUisASWBQKhUKhUCgCy6GhUCgUCoUisAQWhUKhUCgUgSWwKBQKhUKhCCyB5dBQKBQKhUIRWAKLQqFQKBSKwBJYFAqFQqFQBJbAcmgoFAqFQqEILIFFoVAoFApFYAksCoVCoVAoAktgOTQUCoVCoVAElkNDoVAoFApFYAksCoVCoVAoAktgUSgUCoVCoQgsh4ZCoVAoFMrlA2u6m8CiUCgUCoUisHICqzmPBBaFQqFQKBSBtQysRRJ1Ntbmi//+66//+vnnvV9/++mn9a+/fvjw52+++cuHD5t/unk7v/3ww9+//fa3H3+kUCgUCoVyeeWPX79//PgSgXXb2p++//7TNPnll19++eWXX37l/vrcGLfT94DA2tznuvz3L79U/fqcvf/47rvP/+tFvIgX8SJexIt4ES+y9+tVrmCZmZmZvewElpmZmdmwwJpTv4vQzMzMTGD9r6uyfg6WmZmZmcAazDR1W0PwNTdi/EWmtxt0J0yrvc97bP1SkVe78Kcplb93I5EbLz9P1o/hvb+Rqr/otjeq+S80eOOD7rG994KqM7Z+tgucsdr3oEHvlbn35LXflub3/drbPOdtSf976fwQ9NKB9fWeanvm4J3b82VkVRHT+bY33MKgN3/xSSvl77Sq7dJfn/L9cHjjh/fhoMDqjLPOe6xwPiM3Xn5bEj8y9rylizjrP2N7NxI5Y+Xn6fxYtHnjPe+Vhdsc+rOp+62q2zzn52w3v+/XRtvQt6X2Q0TPbT71Vy6938Dqf9nav+9xgTXip7ZmBVb6m5b14S/rPerwE2Ht/ZPyt9nzQbbwslmVHL/xwytb6X+PDTc4+h6rOmPl5+n8WFRbe4m3OfqjXNYn8ge+Lf3v+6d9TGj4CJ94xoZ+RhBYp2ZN7ftt2yN3c+vjpM8YWPHHlZ4osHIvFDU8RBi5MtGTC5vX/IO5sHdtf8TfY8P74LMEVsPHovgrlv5e+cDAan5bHhhYPe/7c8fVykGB1fYQYcNtCqzhgTXuS53m7scdGh6Ja36otCr+al//EfEXLIbE16dwDOLvq+Nyoerxo/gH6M57LPhYyeHjWXt3+JmPuUSC5jL3WNt/ojS8VxZCJ+W9dY5dCu1/W8rJPvRt6Xnfnzseuh0RWIe3n/WRP/dtEVhnZ1nnl0adcKGo+bpr7QW5QZcJ1//dGfnoP+KhpcP/uG/7D8TmazBV91i5FDvvsdpLFAUx9x6r/UxZLuPcM7Z5I8EzdviyQwOr9r2yfMkt6721v5OCH2GGnorIfVj7vt9wm6e9Lc91xgRWWl3NNV/l3fN9CkMDqyfFzrm344GV+7KJgVX1sbvn+1mCN3L4rU+1L5seWPHLG1nfzeSMDfrkd85t9vwtj/jbH30qDr9wIuvvZdzHhEueMYGV+fyDuIZvb5x3rqs/PLBGXMCL3z/N/53XfD/0fxfhPPhLtoPfop97jxXOZ9V3ER5eCBx0PeCxZ6zhuwizPpG0XXZNfCgq8Z0ifnKy3pb0U1F7ya3/bRn6MSF4Hg4fpky8zfnZdvbPwXqHPwiq7cLSCT9uqvlrwtpescS/09qfUZTy+kRegf6fUTQisGr/4l7qHoscoeaXTbnH2l6xns+mbe99Ke+Vo89e4Yumq36SWc9tjv5Ml/j3ctrHhGc/Yy9xBcvMzMzsFSawzMzMzASWmZmZmcAyMzMzE1hmZmZmJrDMzMzMBJaZmZmZwDIzMzMzgWVmZmYmsMzMzMwElpnZhT4gFv+Jj/LzFG7zSf+hDzMTWGZW9/m+81/42quNEf9Q4zsJrJ5XT2CZCSwzu35gJX7uv1g6CCwzE1hmlhxY8X/Q/o/fr5//8NYWL1h+DePPvHgdym9F4embSvD5y7ejscwElpm9YmCtG2WzmfZ+E7+1w6tBtc+8rpzyWxF5PQ8Dq+rNFFhmAsvMrh9YhWtLhZaK/Gnz0/ufOf4ig4i218TMBJaZXSSwIk/ffPDrKQKr6iE8gWVmAsvMHhNYhZh451ewOqtIYJmZwDKz3sA652uwRgfW4ZW2+BdObSoNX4OlrswElpm9aGDN9d9FuP6j4HcRjgus4FsR/+6/8tsVvB2BZSawzMxsOz3bytVPcjcTWGZm1h5h7gQzE1hmZgLLzASWmZmZmcAyMzMze839BxjgPj6hQyZuAAAAAElFTkSuQmCC" alt="N content graph" width="800" height="600"/></p></div><div class="module"><h2 id="M7"><img src="data:image/png;base64,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" alt="[WARN]"/>Sequence Length Distribution</h2><p><img class="indented" src="data:image/png;base64,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" alt="Sequence length distribution" width="800" height="600"/></p></div><div class="module"><h2 id="M8"><img src="data:image/png;base64,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" alt="[FAIL]"/>Sequence Duplication Levels</h2><p><img class="indented" src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAyAAAAJYCAIAAAAVFBUnAABj1klEQVR42uzdB3hT5dvH8ZRVZCOyQZYiIKBYkCEIiKAMARUBEdkiIKIsFf+oKMOBAgoow4GCshWhDBmCKMgQkSEze9GWQlsoLZ2899P6ItI2TdLTNmm/99XLq5Y2Oedz7uc5v5wkT3TXKIqiKIqiKE1LBwFFURRFURQBi6IoiqIoioBFURRFURRFwKIoiqIoiqIIWBRFURRFUQQsiqIoiqIoAhZFURRFURRFwKIoiqIoiiJgURRFURRFEbAoiqIoiqIoAhZFURRFURQBi6IoiqIoioBFURRFURRFwKIoiqIoiqIIWBRFURRFUQQsiqIoiqIoAhZFURRFURRFwKIoiqIoiiJgURRFURRFEbAoivKlAZxSOOQYpqfaXh+dXDms9BJFEbAoSvsz6/XKrW3gdOjR3mmy+9dvxM1by/mA5d0taPtXGY0ON0dN2t9J96aYiCgCFkXl8XO5v1w84JyU8/7+cgVLw4CV0ei4MSS5uLubclW62ZFOpghYFJXvAla6j9GvnyduOtm48/j+xr9N++fuX0Jw/SceXW/I6AJeppuUEYILQE+hMhJz5xeuuXFt8qa/dY3petdc/77rmJLp3nl0WLO+te4MFncelrj+IQGLImBRVD4NWGlPWmm/z+ihvPt/6841AI8uG2R0+55+n+lp0gVCprfjTvJwU8zFL2T0O+77e+HmzsFy//fd/NdMf+5pZ2oYsDLKmjdd0KIoAhZF5cGA5foqQtYfu3t6RvTipnJse9xBcD9geXp5Iyshw7uAlRW3rP/co8s8bu67R0QZXTxzM7+6DnNpv6EoAhZF5dkrWBkFL0/P3J4+VaRVwHJxOvRoX9x5itCj23fxvYs/9NmAlZXjq2HAcnMzvO5kFyA3PfnraQq/lvGrsiiKgEVReTxgeXHxRpMrIln8k6wHCE9/6NHzldrmJ1+4gpV9V7xc32MWL01lMWBlsUl4MRZFwKKofH0Fy52zlxevHPL0cpSL+3In03j6QiV3XoPlqZWbL+LWMGDlzGuwvPDMjtdgZXoFK4tEnh5Z1x1IwKIIWBSVfwPWtcze4Jb21zz9W9fvQ/R0M7zYnmtZexehRxuZ6VOE2l7Buub2uwjd74GsvC8vo93U6l2ErtspK+8izPRvM03ebv4acxFFwKIoKt9lTXYNcIqiCFgURXG+v+bpGgSAUxRFwKIoivO9W3vky08/EbAoioBFURRFURRFEbAoiqIoiqIIWBRFURRFUQQsiqIoiqIoKuOA5eaHybvzc4qiKIqiKALWfzJTugEr3X9l+V2KoiiKoihvAlbWP4adoiiKoiiKgKVBwAqmKIqiqDxUuygqTeVCwPKpC1pOXyqfytfIIIMMMsi4Wa7PplQ+LAIWEx8yyCCDDDIELIqAxfBGBhlkkEGGgEXl7YB1zat3ERKwmPiQQQYZZAhYFAHrP4taubnelYt1sAhYTHzIIIMMMgQsioClcRGwmPiQQQYZZAhYFAGLgMXEhwwyyCCDDAGLImAx8SGDDDLIIEPAoghYDG8mPmSQQQYZAhZFwCJgMfEhgwwyyCCTywFL7+0pVZ8b52J9vvnoPAIWEx8yyCCDDDJaBizJEDfFCBepIvWXr3/lSsDKyfslYBGwmPiQQQYZZJDx+GyaNrW4jhTuRzFfu4JFwCJgMfEhgwwyyCDjHwHrpp9kdHEr3eteGWW1jC6SpfuHmd6diw3zaGszupLn+n692H0XW5vp/Xq0MQQsJj5kkEEGGWRyKGBlesHGRcBKG2gy/d7T23H9m5nebNqc4f73LsJQju2+O/fr/p3etAEELCY+ZJBBBhlksuU1WGm/8TRg5czPPXoxljtXjNz/Nfd/mPMsXmwMAYuJDxlkkEEGmewKWGmfhPIoW2T01F4uBiw3N8PTZ+5cP9Gm4e5n5XZc3whPETLxIYMMMsggk6MBy8344v4VrOy7tKPJxRtPt9bTK2Ra7b53F6U8urZHwGLiQwYZZJBBxrcCVtpLIxldkkn3+3T/VsPXYGV6Bcv9rXWx19m3++5f8XJ/YwhYTHzIIIMMMsjkUMByHT4yffYt3X91569u/El2vIsw3Zv1emtdP0Wo1e5fc/kuQhfRlncRMryRQQYZZJDxxddg+Ujl7aWnfHbvCFhMfMgggwwyyBCwCFgELIY3MsgggwwyBCwCFgGLgMXEhwwyyCBDwKLyVRGwmPiQQQYZZJAhYFEELIY3MsgggwwyBCyKgEXAYuJDBhlkkCFgUQQsAhYTHzLIIIMMMgQsioDF8EYGGWSQQYaARRGwGN5MfMgggwwyBCyKgEXAYuJDBhlkkEHGDwKWTqcn3xCwGN5MfMgggwwyeSpgpX42nTuJ578fqadP+4feJScCFgGL4c3EhwwyyCCTpwLW9XCT9pscS04ELAIWw5uJDxlkkEGGgJXO76S9ppXuVa6Mfuj6gpnrO0r36lq6t+zmlri+fY82hoBFwGLiQwYZZJAhYHl5zSltrso032Qa6dJNYC7uyKMNcOdvXfy++3fqRxmLgMXEhwwyyCCDTLa8BivtN+4HLPeTkxcBy9M7yu6fZ2WvCVgELCY+ZJBBBpl8EbDSPkGWaVDwOmC580SeiyfaMnqJvReBKSu34/pGeIqQgMXEhwwyyCBDwMowTGgesLy+5ONOFMv6FaksXsFy5wYJWAQsJj5kkEEGGQKWBwHL09dgZXoFK9PXQl3z/OVQ7tyOiyte7m8MAYuAxcSHDDLIIEPAchWY3Lww4+YzZTf+xIunCK95+C7CjO7xmnvvZ8z0WT/eRUjAYuJDBhlkkEHG3bOpvxcLaxGwmPiQQQYZZJAhYBGwCFgMbyY+ZJBBBhkCFgGLgEXAYuJDBhlkkEEm7dmUom4qAhYTHzLIIIMMMsggk3MyBCyaGBlkkEEGGWSQIWDRxMgggwwyyCCDDAGLQ0UTI4MMMsgggwwByycCVswvv1x48035L4eKJkYGGWSQQQYZPw5YuhvKnZ9na8CSdKXX6eS/HCqaGBlkkEEGGWT8NWDdmJ/SBixPY1PWA1bUwoUSsMKGDuVQ0cTIIIMMMsgg48cBy51Q5WZyynrAuhIcLAHL2bkzh4omRgYZZJBBBhkClqrgLNe2jz+WgHW8Zs1giqIoiqIo3ytvniLM9StYiaGhErBM5cqRhXmUgAwyyCCDDDL+egXr2g0vZveFpwivJScbihSRjJUcG8uhoomRQQYZZJBBxl8Dlk+9BkvKXKOGBKx4g4FDRRMjgwwyyCCDjN+/BssX3kUoZW/VSgJW7O7dHCqaGBlkkEEGGWTywlOE6f48hwNWyFNPScC6vHw5h4omRgYZZJBBBpm88BRhFkuTgHX+5ZclYEV89BGHiiZGBhlkkEEGGQKWNgEr4oMPJGCFjxvHoaKJkUEGGWSQQYaApU3AuvzttxKwQvr04VDRxMgggwwyyCBDwNImYMXs2iUBy966NYeKJkYGGWSQQQYZApY2ASv+7FkJWJZatThUNDEyyCCDDDLIELC0CVjJMTESsAyBgRwqmhgZZJBBBhlkCFjaBCwpY9mykrESz5/nUNHEyCCDDDLIIEPA0uaWrQ0bSsCKO3KEQ0UTI4MMMsgggwwBS5tbdnbqJAHryqZNHCqaGBlkkEEGGWQIWNrccujgwRKwohYv5lDRxMgggwwyyCBDwNLmli9MniwB6+KUKRwqmhgZZJBBBhlkCFja3HLUZ59JwAp77jkOFU2MDDLIIIMMMgQsbW45ev16CVjOLl04VDQxMsgggwwyyBCwtLnlq4cOScCy3nMPh4omRgYZZJBBBhkClja3nBgSIgHLVL48h4omRgYZZJBBBhkClka3nJRkKFRIHxCQHBdHE9PEyCCDDDLIIEPA0qbM1avrdboEk4kmpomRQQYZZJBBhoClTdlbtJCAFfvbbzQxTYwMMsgggwwyBCxt6tyTT0rAurxyJU1MEyODDDLIIIMMAUubOj9mjASsiFmzaGKaGBlkkEEGGWQIWNpUxPvvS8AKHz+eJqaJkUEGGWSQQYaApU1dXrZMAlbI00/TxDQxMsgggwwyyBCwtKmYn3+WgOV48EGamCZGBhlkkEEGGQKWNhV/+rQELEudOjQxTYwMMsgggwwyBCxtKik6WgKWoWhRmpgmRgYZZJBBBhkClmZlLF1aMlbShQs0MU2MDDLIIIMMMgQsbcraoIEErLijR2limhgZZJBBBhlkCFjalKNjRwlYVzZvpolpYmSQQQYZZJAhYGlToYMGScC69MUXNDFNjAwyyCCDDDIELG3qwuuvS8C6+M47NDFNjAwyyCCDDDIELG0qcv58CVhhzz9PE9PEyCCDDDLIIEPA0qai162TgOXs1o0mpomRQQYZZJBBhoClTV09eFAClq1JE5qYJkYGGWSQQQYZApY2leh0SsAyVahAE9PEyCCDDDLIIEPA0qiSkvQFC+oDApLj42limhgZZJBBBhlkCFjalLlaNb1Ol2Cx0MQ0MTLIIIMMMsgQsLQp2/33S8CK3buXJqaJkUEGGWSQQYaApU2de/xxCViXV6+miWliZJBBBhlkkCFgaVPnR4+WgBU5Zw5NTBMjgwwyyCCDDAFLm4p4910JWOETJ9LENDEyyCCDDDLIELC0qUvffCMBK6RfP5qYJkYGGWSQQQYZApY2FbNjhwQsR9u2NDFNjAwyyCCDDDIELG0q/tQpCViWO++kiWliZJBBBhlkkPGngKW7odz5eU4GrKRLlyRgGYoVo4lpYmSQQQYZZJDxm4CVNlSl/T4XA5aUsWRJyVhJERE0MU2MDDLIIIMMMv4dsFwErxwOWJZ69SRgxR0/ThPTxMgggwwyyCCTHwNWcDbUkXvukYC1c+rUYIqiKIqiqNyuLL0Gy3euYIUOGCAB69KXX/IogUcJyCCDDDLIIMNThNrUhUmTJGBdnDqVJqaJkUEGGWSQQYaApU1FzpsnASts5EiamCZGBhlkkEEGGf8OWNd85l2E0d9/LwHrXPfuNDFNjAwyyCCDDDL+EbCu+fY6WFJX9++XgGULCqKJaWJkkEEGGWSQ8ZuApWFlR8BKsNslYJkqVaKJaWJkkEEGGWSQIWBpVImJ+oIF9QUKJCck0MQ0MTLIIIMMMsgQsLQpc5Uqep0uwWqliWliZJBBBhlkkCFgaVO2Zs0kYMX+/jtNTBMjgwwyyCCDDAFLmzrXs6cErOi1a2limhgZZJBBBhlkCFja1PlRoyRgRX7yCU1MEyODDDLIIIMMAUubujh9ugSs8FdfpYlpYmSQQQYZZJAhYGlTl5YskYAV2r8/TUwTI4MMMsgggwwBS5uK2bZNApajfXuamCZGBhlkkEEGGQKWNhV34oQELEvdujQxTYwMMsgggwwyBCxtKikqSgKWsUQJmpgmRgYZZJBBBhkClmYl6UoyliQtmpgmRgYZZJBBBhkCljZlqVtXAlbciRM0MU2MDDLIIIMMMgQsbcrRvr0ErJht22himhgZZJBBBhlkCFjaVGj//hKwLi1ZQhPTxMgggwwyyCBDwNKmwl99VQLWxenTaWKaGBlkkEEGGWQIWNpU5CefSMA6P2oUTUwTI4MMMsgggwwBS5uKXrtWAta5Hj1oYpoYGWSQQQYZZAhY2lTsvn0SsGzNmtHENDEyyCCDDDLIELC0qQSrVQKWuUoVmpgmRgYZZJBBBhkCljaVnJCgL1BAX7DgtcREmpgmRgYZZJBBBhkCljZlqlRJr9Ml2O00MU2MDDLIIIMMMgQsbcoWFCQB6+r+/TQxTYwMMsgggwwyBCxt6lz37hKwor//niamiZFBBhlkkEGGgKVNhY0cKQErcu5cmpgmRgYZZJBBBhkCljZ1cdo0CVgXJk2iiWliZJBBBhlkkCFgaVOXvvxSAlbogAE0MU2MDDLIIIMMMgQsberKTz9JwHJ06EAT08TIIIMMMsggQ8DSpuKOH5eAZalXjyamiZFBBhlkkEGGgKVNJUVESMAylixJE9PEyCCDDDLIIEPA0qwMxYpJxkq6dIkmpomRQQYZZJBBhoClTVnuvFMCVvzJkzQxTYwMMsgggwwyBCxtytGunQSsmB07aGKaGBlkkEEGGWQIWNpUSL9+ErAuffMNTUwTI4MMMsgggwwBS5sKnzhRAlbEu+/SxDQxMsgggwwyyBCwtKnIOXMkYJ0fPZompomRQQYZZJBBhoClTV1evVoC1rnHH6eJaWJkkEEGGWSQIWBpU7F790rAst1/P01MEyODDDLIIIMMAUubSrBYJGCZq1aliWliZJBBBhlkkCFgaVPJ8fH6gAB9wYLXkpJoYoY3MsgggwwyyBCwtClThQp6nS7RjZ2niZFBBhlkkEEGGQKWW2Vr0kQC1tWDB2lihjcyyCCDDDLIELA0OgDduknAil63jiZmeCODDDLIIIOMjwYsXZpK9598J2CFPf+8BKzI+fNpYoY3MsgggwwyyPjHFaybApansSkHAtbFd96RgHXh9ddpYoY3MsgggwwyyPhBwEo3XXmUnHIgYF364gsJWKEDB9LEDG9kkEEGGWSQyRcBKzj7a2fKFawjTZoEUxRFURRF5Xh5FrBcJyrfuYIVd/SoBCxrgwY8SuDxEzLIIIMMMsj4+hUsfwlYSRcuSMAyli5NEzO8kUEGGWSQQcanA1baYOSzAUvKcMstkrGSoqNpYoY3MsgggwwyyPhTwLrmq+8ilLLUqSMBK/70aZqY4Y0MMsgggwwyPhqwMkpFvrkOlpTjwQclYMX8/DNNzPBGBhlkkEEGGd+9gqVV5UzACnn6aQlYl5YupYkZ3sgggwwyyCBDwNKmwidMkIAV8f77NDHDGxlkkEEGGWQIWNpUxKxZErDOjxlDEzO8kUEGGWSQQYaApU1dXrlSAta5J5+kiRneyCCDDDLIIEPA0qZif/tNApa9RQuamOGNDDLIIIMMMgQsbSrBZJKAZa5enSZmeCODDDLIIIMMAUubSo6L0wcEGAoVupaURBMzvJFBBhlkkEGGgKVNmcqX1+t0iefO0cQMb2SQQQYZZJAhYGlTtnvvlYB19dAhmpjhjQwyyCCDDDIELI0OQ5cuErCi16+niRneyCCDDDLIIEPA0qbCnntOAlbUZ5/RxAxvZJBBBhlkkCFgaVMXp0yRgHVh8mSamOGNDDLIIIMMMgQsbSpq8WIJWKGDB9PEDG9kkEEGGWSQIWBpU1c2bZKA5ezUiSZmeCODDDLIIIMMAUubijtyRAKW9e67aWKGNzLIIIMMMsgQsLSpxPBwCVjGsmVpYoY3MsgggwwyyBCwNCtDYKBkrOSYGJqY4Y0MMsgggwwyBCxtylKrlgSs+LNnaWKGNzLIIIMMMsgQsLQpe+vWErBidu2iiRneyCCDDDLIIEPA0qZC+vSRgHX5229pYoY3MsgggwwyyBCwtKnwceMkYEV88AFNzPBGBhlkkEEGGQKWNhXx0UcSsM6/9BJNzPBGBhlkkEEGGQKWNnV5xQoJWCFPPUUTM7yRQQYZZJBBhoClTcXu3i0By96qFU3M8EYGGWSQQQYZApY2FW8wSMAy16hBEzO8kUEGGWSQQYaApU0lx8ZKwDIUKXItOZkmZngjgwwyyCCDDAFLmzKVKycZKzE0lCZmeCODDDLIIIMMAUubsjZuLAHr6p9/0sQMb2SQQQYZZJAhYGl0MDp3loB1ZcMGmpjhjQwyyCCDDDIELG0qbNgwCVhRCxfSxAxvZJBBBhlkkCFgaVMX3nxTApb8lyZmeCODDDLIIIMMAUubilq4UAJW2NChNDHDGxlkkEEGGWQIWNrUleBgCVjORx+liRneyCCDDDLIIEPA0qauHj4sAcvaqBFNzPBGBhlkkEEGGQKWNpUYFiYBy3jrrTQxwxsZZJBBBhlkCFgaVXKyoUgRyVjJsbE0McMbGWSQQQYZZAhY2pS5Zk0JWPEGA03M8EYGGWSQQQYZApY2ZW/VSgJW7O7dNDHDGxlkkEEGGWQIWNpUyFNPScC6vHw5TczwRgYZZJBBBhkCljZ1/uWXJWBFfPghTczwRgYZZJBBBhkCljYVMXOmBKzwsWNpYoY3MsgggwwyyBCwtKnL330nASukd2+amOGNDDLIIIMMMgQsbSrml18kYNkfeIAmZngjgwwyyCCDjG8FLN3/V7o/dD825XzAitfrJWBZatWiiRneyCCDDDLIIONDAevGVJTu974csJJjYiRgGQIDaWKGNzLIIIMMMsj4SsDKKBKlvZrlmwFLyli2rGSsxPPnaWKGNzLIIIMMMsj4UMBK+1SgdwErODfq7xo1JGBtnzs3mKIoiqIoKpvL3YB141OBGT0t6MtXsJyPPCIB68rGjTxK4PETMsgggwwyyPjiU4T+GLBChwyRgBW1aBFNzPBGBhlkkEEGGQKWNnXhjTckYF146y2amOGNDDLIIIMMMj4RsG4KVX73LkKpqAULJGCFPfccTczwRgYZZJBBBhkfClj+uw6WVPT69RKwnF260MQMb2SQQQYZZJDxlYClYeVKwLp66JAELOs999DEDG9kkEEGGWSQIWBpU4khIRKwTLfdRhMzvJFBBhlkkEGGgKVRJScbCheWjJV89SpNzPBGBhlkkEEGGQKWNmW+/XYJWPFGI03M8EYGGWSQQQYZApY2ZW/ZUgJW7K+/0sQMb2SQQQYZZJAhYGlTIb16ScC6vHIlTczwRgYZZJBBBhkCljZ1fswYCVgRs2bRxAxvZJBBBhlkkCFgaVMR778vASt8/HiamOGNDDLIIIMMMgQsberysmUSsEL69qWJGd7IIIMMMsggQ8DSpmJ27pSAZW/ThiZmeCODDDLIIIMMAUubij9zRgKWpXZtmpjhjQwyyCCDDDIELG0q+coVCViGokVpYoY3MsgggwwyyBCwNCtjmTKSsZIuXKCJGd7IIIMMMsggQ8DSpqwNGkjAijt6lCZmeCODDDLIIIMMAUubcnTsKAHryubNNDHDGxlkkEEGGWQIWNpU6KBBErCiPv+cJmZ4I4MMMsgggwwBS5u68L//ScC6+PbbNDHDGxlkkEEGGWQIWNpU1KefSsAKGz6cJmZ4I4MMMsgggwwBS5uK/vFHCVjOrl1pYoY3MsgggwwyyBCwtKmrf/whAcvWpAlNzPBGBhlkkEEGGQKWNpXodErAMlWoQBMzvJFBBhlkkEGGgKVRJSUZChXSBwQkx8XRxAxvZJBBBhlkkCFgaVPmatX0Ol2C2UwTM7yRQQYZZJBBhoClTdmbN5eAFbtnD03M8EYGGWSQQQYZApY2de6JJyRgXV61iiZmeCODDDLIIIMMAUubOv/iixKwImfPpokZ3sgggwwyyCBDwNKmIt57TwJW+MSJNDHDGxlkkEEGGWQIWNrUpW++kYAV0q8fTczwRgYZZJBBBhkCljYVs2OHBCxH27Y0McMbGWSQQQYZZAhY2lT8qVMSsCx33EETM7yRQQYZZJBBhoClTSVdviwBy3DLLTQxwxsZZJBBBhlkCFialbFUKclYSRcv0sQMb2SQQQYZZJAhYGlT1vr1JWDFHTtGEzO8kUEGGWSQQYaApU05Hn5YAtaVn36iiRneyCCDDDLIIEPA0qZCBwyQgHXpyy9pYoY3MsgggwwyyBCwtKkLkyZJwLo4dSpNzPBGBhlkkEEGGQKWNhU5b54ErLARI2hihjcyyCCDDDLIELC0qegffpCAde6xx2hihjcyyCCDDDLIELC0qasHDkjAst13H03M8EYGGWSQQQYZApY2leBwSMAyVaxIEzO8kUEGGWSQQYaApVElJuoLFtQXKJCckEATM7yRQQYZZJBBhoClTZmrVNHrdAlWK03M8EYGGWSQQQYZApY2ZWvWTAJW7O+/08QMb2SQQQYZZJDJnYCl+2/dlJbS/bmPB6xzPXtKwIpes4YmZngjgwwyyCCDTK4FrEzTkn8FrPMvvCABK/Ljj2lihjcyyCCDDDLI+FbASns1y18C1sUZMyRghb/yCk3M8EYGGWSQQQYZ33qK0LuAFewD9eu4cRKwDrVrF0xRFEVRFKV1uRWw0r7oyt+vYMVs3y4By9G+PY8SePyEDDLIIIMMMrlzBcvN1135UcCKO3FCApalbl2amOGNDDLIIIMMMgQsbSopKkoClrF4cZqY4Y0MMsgggwwyufYarHSfIrzmt+8ilDKWKKGeJTx1iiZmeCODDDLIIINM7lzBymPrYElZ7rpLApZ91y6amOGNDDLIIIMMMrn/FGEWy0cCluOhh1TAWrGCJmZ4I4MMMsgggwwBS5sKffZZCVjW2bNpYoY3MsgggwwyyBCwtKnw115TbyR87TWamOGNDDLIIIMMMgQsbSryk09UwBo0iCZmeCODDDLIIIMMAUubil67VgKW6ZFHaGKGNzLIIIMMMsgQsLSp2H371FJY99xDEzO8kUEGGWSQQYaApU0l2GwSsAwVK9LEDG9kkEEGGWSQIWBpU8kJCfoCBeTLabPRxAxvZJBBBhlkkCFgaVOmypXVYu6HDtHEDG9kkEEGGWSQIWBpU7amTdVaoxs30sQMb2SQQQYZZJAhYGlT57p3l4Bl++ILmpjhjQwyyCCDDDIELG0qbORItZj79Ok0McMbGWSQQQYZZAhY2tTFadMkYJlHj6aJGd7IIIMMMsggQ8DSpi599ZUKWL160cQMb2SQQQYZZJAhYGlTMVu3qsXcW7emiRneyCCDDDLIIEPA0qbi/v5brTVapw5NzPBGBhlkkEEGGQKWNpUUGakCVokSNDHDGxlkkEEGGWQIWJqVoVgxtdbomTM0McMbGWSQQQYZZAhYGgWsWrVUwNq9myZmeCODDDLIIIMMAUubMrVqpRZzX7WKJmZ4I4MMMsgggwwBS6OA9cQTaq3Rjz+miRneyCCDDDLIIEPA0qbMo0ZJwLJMmkQTM7yRQQYZZJBBhoClTVmnTlUBa/BgmpjhjQwyyCCDDDIELG3KtnixWmu0c2eamOGNDDLIIIMMMgQsbcq+YYMELGOTJjQxwxsZZJBBBhlkCFjalOPgQbXWaKVKNDHDGxlkkEEGGWQIWBoFLItFHxCgL1jQabPRxAxvZJBBBhlkkCFgaXOoDOXLq7VGDx+miRneyCCDDDLIIEPA0uZQGRs1UmuNbtpEEzO8kUEGGWSQQYaApc2hMnXsKAHL9uWXNDHDGxlkkEEGGWQIWNocKvOAAWoprBkzaGKGNzLIIIMMMsgQsLQ5VJaJE1XAGjOGJmZ4I4MMMsgggwwBS5tDZZs1SwKWuXdvmpjhjQwyyCCDDDIELI0C1nffqbVG27ShiRneyCCDDDLIIEPA0uZQ2X/+WQWsO++kiRneyCCDDDLIIEPA0uhQnTypFnMvWZImZngjgwwyyCCDDAFLs0OlL1pUrTV69ixNzPBGBhlkkEEGGQKWNofKULOmCli//UYTM7yRQQYZZPKAzAcfWJs2Ncl/kSFg5eahMrVoodYaXb2a4c3EhwwyyCCTB2QkXel0+mbNTMgQsHI1YPXsKQHLOncuw5uJDxlkkEEmD8jUr2+UgNWggREZAlZuHirLyJEqYP3vfwxvJj5kkEEGGX+XsdudxYsbJGDJf+V7ZAhYuXaorG+/rdYaHTKE4c3EhwwyyCDj7zI7dtglXRUqJGc2/c8/25EhYOVewFq4UNrQ1KULw5uJDxlkkEHG32VmzrRKtKpWTV3E+vBDKzJ+ELB0KZX2J2l/7l8By75+vQpY993H8GbiQwYZZJDxd5mnnzZLtOrcWf1XvkfGXwOWp7HJBwOW48ABtdZolSoMbyY+ZJBBBhl/l7nrLvUK948+ssh/5XtkfD1gpQajG+NRRmHL7wKW02LRBwQYChVyZv3VgDQxMsgggwwyuSdz+rSjQAF9kSL6s2edhQvr5Xv5CTK+G7DSvVLlXcAK9sk6Xbq0XqfbsnRpMEVRFEX5bU2fvjPlwtVx+V7+K9/LT2DJ+cqFgOWLV7CcTuPdd0vAsm/ZwuMnHlkigwwyyPivzKuvqmcGhw1TL72S/8r38hNkfPQKVkahKi8FLNPDD6vF3JcsYXgz8SGDDDLI+K9Mx45qDffPPrPJ959+apPv5SfI+G7AuqnyXsAy9++v1hp9912GNxMfMsggg4z/ypQrp1Zn2L9fve5q3z61IJb8BBnffQ1Wplet/PpdhFKWCRMkYFleeonhzcSHDDLIIOOnMr//rhLVbbf9m6jke/mJ/Jye8b+AlQfWwZKyfvSRWgqrTx+GNxMfMsggg4yfysyfr54T7NTJdNMzhp9+aqNnfD1gZb18M2DZli1TAattW4Y3Ex8yyCCDjJ/KDBmiXtU+adK/r2p/7TX1mvehQ830DAErdw6VY8cOCVjGu+5ieDPxIYMMMsj4qUyTJmqJ0VWr/r1eJd/LT+Tn9AwBK5cC1t9/q0/FLFWK4c3EhwwyyCDjjzJm8z8ri5458+/KovK9/ER+bjbTMwSsXDpU+sBAyVhOg4HhzcSHDDLIION3Mhs2qFe4169/88WqevXUZa0NG2z0DAErdw6VsUYNCViOPXsY3kx8yCCDDDJ+J/P221YJUv363XypSn4iP5d/pWcIWLkUsJo3V2uNrlnD8GbiQwYZZJDxO5nu3U0pn/F8c5CSn8jP5V/pGQJW7hwqU48eKmDNn8/wZuJDBhlkkPE7mWrV1JJXO3fevOTVzz+rpw6rVzfQMwSs3DlUluefV2uNTp7M8GbiQwYZZJDxL5m//nJIiipZ0mBPs6So/KRECZW9jhxx0DMErNwIWG+9JQHLPGwYw5uJDxlkkEHGv2S++kotx9C6dfrLMcjP5V/ld+gZAlYuHCrbggUqYHXrxvBm4kMGGWSQ8S+Z0aPVK9lfesmS7r+OGaOWG33xRQs9Q8DKhUNlX7dOrTUaFMTwZuJDBhlkkPEvmVat1Cvcv/46/WtU8nP51wceMNEzBKzcCFj79knAMlStyvBm4kMGGWSQ8SMZm81ZvLh6ldWxY+m/ykp+Lv8qv2O30zMErJw/VGazCliFCzsdDoY3Ex8yyCCDjL/I7Nih8lONGq4+D0f+VX5HfpOeIWDlwqEy3HqrWmv06FGGNxMfMsggg4y/yHzwgVrp6vHHXT0D2LOneg5x5kwrPUPAyo2AVb++BCz71q0MbyY+ZJBBBhl/kenTR4WnadNchaepU1UI69vXRM8QsDKs/fuvTpwYLv/V/FCZHnpIrTX69dcMbyY+ZJBBBhl/kalbVz39t3GjqxdYyb/K78hv0jMErAxL0pV0ySuvhGt+qMz9+knAsr7/PsObiQ8ZZJBBxi9kTp1yBAToixTRW1wuwiD/Kr9ToID+9GkHPUPASr927IiRgNW4sVXzQ2UZP14t5j52LMObiQ8ZZJBBxi9kVqxQl6aCgjK/NBUUpJ5JXLnSTs8QsNKvuLjk4sXV5VCnM1HbQ2WdOVMClqlvX4Y3Ex8yyCCDjF/IvPKKWkT0uefMmf6m/E7K8z8WeoaAlWF16+ZMWfX/kraHyrZ0qQpY7doxvJn4kEEGGWT8Qubhh9V1qQULMv8YHPkd+U35fXqGgJVhzZsXKV3Sp0+ItofKsX27Wsy9fn2GNxMfMsggg4xfyNx6q1pi9MCBzF9ZtX+/Wi5Lfp+eIWBlWHp9fEqXGJOSNA1Yx45JwNKXKcPwZuJDBhlkkPF9mb171QuwKlRwNzOVL6/SmPwVPUPAyrDq1FHPOu/bF6vloXI49IULS8ZyGo0MbyY+ZJBBBhkfl5k3Tz3r98gj7j7rJ78pvy9/Rc8QsDKsF144L13y9tsXtT1UhurV1Vqje/cyvJn4kEEGGWR8XGbwYPW69ddfd3d99kmT1LWJIUPM9AwBK8Navz5auqRlS7u2h8rYrJlaa/T77xneTHzIIIMMMj4uc8896j31a9a4e0Vq9Wp1xevee430DAErw4qOTipSxFCwoP7ixSQND5W5e3cVsD79lOHNxIcMMsgg48syJpOzcGF1Hjx71t21Q+U3CxTQy1+ZTPQMASvjeugh9YaIVasua3ioLMOHq7VG33iD4c3EhwwyyCDjyzLr16tXuNev79m7AuvXVxe9Nmyw0zMErAzr/fcjUp5LDtUyYL35pgpYw4czvJn4kEEGGWR8Weatt9QLqvr3N3u0p888o162NWWKhZ4hYGVYR47ESZdUrWrW8FDZPv1UApa5e3eGNxMfMsggg4wvy3TvrqLSrFk2j/ZUfl/+Sv6WniFguarKldU7To8di9MsYH3/vVprtFkzhjcTHzLIIIOML8tUraoWtdq1y7PPFpTfT7k2YaBnCFiuatCgUGmUmTMjtDpU9r17JWAZqldneDPxIYMMMsj4rMzhw+pVyKVKGRwOz/bUbneWLKmS2V9/OegZAlaGtWLF5ZRPVnJodqhMJrWYe+HCTk97lokPGWSQQQaZnJL58kv1TF+bNt4siy1/JX8rt0DPELAyrAsXkgoU0AcGGq5cSdbqUOnLlJGM5Th2jOHNxIcMMsgg45syL7ygXoD18ssWL3ZW/kr+Vm6BniFguarmzdXTycHBV7Q6VMb69dVi7tu2MbyZ+JBBBhlkfFOmZUv1EuRvvrF5sbNff62ufrVqZaJnCFiu6q23LkijvPjiea0Olal9e7XW6NKlDG8mPmSQQQYZH5Sx2ZzFiqnXUR0/7s2rWeSv5G/lFmw2eoaAlXHt3RsrjXLnnRbNAlbfvhKwrDNnMryZ+JBBBhlkfFBm+3aVkGrWNHq9v/K3cgtyO/QMASvDSky8VrasahSDIV6TQ2UZO1atNTp+PMObiQ8ZZJBBxgdl3n/fKme9J54web2/jz+unmH84AMrPUPAclVPPRUijfLpp1GaHCrr+++rtUb79WN4M/EhgwwyyPigTJ8+6hXu06dbvd7fadNUROvTx0TPELBc1RdfXEpZl/acJofK9vXXErBMDz3E8GbiQwYZZJDxQZk771TP22zaZPd6f+VvU15dY6RnCFiuym5PkEYpUcIYH5+c9UNl37pVrTVavz7Dm4kPGWSQQcbXZE6dcgYE6AMD9RaL9+s1yt/KLcjtnDrloGcIWK6qYUN1tXPnzpisHyrH0aMqYJUty/Bm4kMGGWSQ8TWZ5cvVIgtNmxqzuMtBQeoy2IoVdnqGgOWqJkwIl0Z59dVwDQ6Vw2EoXFgyltNsZngz8SGDDDLI+JTMhAlqmdDnn7dkcZeHD1e3M3GihZ4hYLmq7dtjpFHuvdemyaEyVK2q1hrdt4/hzcSHDDLIIONTMh06qDcALlhgzeIuyy3I7cit0TO5GbB0N5Q7P8/5gHX1anKxYoaAAP25c4lZP1TGoCAVsNatY3gz8SGDDDLI+JRM2bJqidGDB7P6gbkHDqjFtOTW6JlcC1hpQ1Xa73M9YEl17eqUXvn660tZP1Tmbt3UYu4LFjC8mfiQQQYZZHxHZs8elYoqVjRostcVKqistnevg57xiacIMwpVbian7AtYn3wSKY3y9NMhGgSsYcPUWqNvvcXwZuJDBhlkkPEdmblz1fN6jz5q0mSv5Xbk1uQ26Zm8ELCCs60WLdoqjVKq1OkNGzZm8ab2Dh4sAetAz57BFEVRFOUz1a3bH3KmGzTod01uTW5Hbk1uE1jNy4OAlfa1Vr52BUuqdm31nogDB65mMQvb5s9Xa412787jJx5ZIoMMMsj4jkzjxmpthbVrbZrs9Zo1asWHe+4x0jM8RZhJjRwZJr3yzjsXsxqw1q6VgGVs3pzhzcSHDDLIIOMjMkajs3BhQ8GCer3eocley+3Ircltmkz0DAHLZf34Y7QErFat7Fk8VI49e1TAqlGD4c3EhwwyyCDjIzI//qg+36ZBA4OGOy63Jrcpt0zP5ELA8pd3EUpdvpyUmu4jIpKydKgMBglY+sBAhjcTHzLIIIOMj8i8+aZ6Gcyzz2q5CHb//upzo996y0LP5M4VLN9fB+t6tWun3sK6evXlLB4qfalSkrEcf//N8GbiQwYZZJDxBZlu3VQYmj3bquGOz56tXob12GNmeib3nyLMYmV3wHrvvQjplWHDwrJ4qIx33aUC1o4dDG8mPmSQQQYZX5CpUkU9nffLL3YNd1xuTW5TbpmeIWBlUocPX5VeqVbNnMVDZWrbVq01umwZw5uJDxlkkEEm12UOH3akLEWkdzi03HG5tVKlVG6T26dnCFiuKjn5WqVKauW048fjshSw+vSRgGX98EOGNxMfMsggg0yuy3z+uXour21bk+b7/uCDaumHL76w0TMErExq4MBQ6ZWPPorIyqGyvPyyWsx9wgSGNxMfMsggg0yuy4wapV6ANXasRfN9l9uUW5bbp2cIWJnUd99dll7p2NGRlUNlffddCVjm/v0Z3kx8yCCDDDK5LtOihXpyZulSm+b7Lrcptyy3T88QsDKp8PDEAgX0gYGGmJhkrw+VbckStZj7ww8zvJn4kEEGGWRyV8Zqddxyi1o+6O+/HZrvu9ym3HKxYgabjZ4hYGVWzZqpPL5p0xWvD5V9yxa11ujddzO8mfiQQQYZZHJXZts29V6/mjUN2bT7csty+3Iv9AwBK5N6440L0itjxpz3+lA5/vpLApahXDmGNxMfMsggg0zuyrz3nlVOak8+ac6m3ZdbltuXe6FnCFiZ1J49sdIrd91l8f5Q2e2GQoX0AQFOi4XhzcSHDDLIIJOLMr17qwA0fXp2nY+mT1cBrk8fMz1DwMqkEhOvlSmj3ndqMiV4fagMVaqotUb372d4M/EhgwwyyOSiTJ066im8LVvs2bT7mzerpyDvuMNAzxCwMq9evUKkXT77LMrrQ2UKCpKAZV+/nuHNxIcMMsggk1syJ086AwL0gYF6i8WRTbsvtyy3L/dy6hQ9Q8DKrD7/PEoCVs+e57wPWF26qLVGFy5keDPxIYMMMsjklsx336m3bTVrZsxWgaZN1dM+y5fb6BkCViZlsyVIr5QsaYyPT/buUJmHDFEB6+23Gd5MfMgggwwyuSUzfrxaCHTEiOx9QfDzz6t7mTDBQs8QsDKvu++2pnwuZox3h8r6v/+pxdxHjmR4M/EhgwwyyOSWzEMPqSVGFy60ZquA3L7ci9wXPUPAyrzGjQuXdpk06YKXAWvuXLXWaM+eDG8mPmSQQQaZ3JIpU0YtMfrHH45sFZDbl3uR+6JnCFiZ19atMdIuTZrYvDtUttWrVcBq0YLhzcSHDDLIIJMrMr/9pnJPxYqGHECQe5H72rPHQc8QsDKpq1eTixUzBAToQ0ISvThUjt9+U4u516zJxMfEhwwyyCCTKzIff6yeuevSxZQDCJ07q+ciP/nESs8QsDKvzp2d0i7ffHPJm4Cl16vLskWLMvEx8SGDDDLI5IrMoEHqteeTJ1tzAEHuRe5L7pGeIWBlXh9/HCnt0q9fiHeHylCypGQstQgJEx8THzLIIINMjss0aqRWT/j+e1sOIKxdq9aDaNzYSM8QsDKv06fjpV3KlzclJ3tzqIx33qnWGv35ZyY+Jj5kkEEGmRyWMRqdhQoZChbUG3LiJVhOvd4h9yX3aDTSMwQsN6pmTfURTgcPXvUmYLVpIwHL9t13THxMfMgggwwyOSyzbp36BJu77zbmmIPcl9yj3C89Q8DKvEaMCJN2mTbtoheHyty7twpYs2Yx8THxIYMMMsjksMwbb6gXYA0YYM4xB7kvuUe5X3qGgJV5/fBDtLRL69Z2Lw6VZcwYtdboxIlMfEx8yCCDDDI5LNO1q3pb35w51hxzmD1bvc69WzczPUPAyrwuXUoqXNhQqJAhMjLJ44A1Y4YELPOzzzLxMfEhgwwyyOSwTOXKamGq3bsdOeYg9yX3KPdLzxCw3Kq2bVXHrF0b7emhsn31lVprtGNHJj4mPmSQQQaZnJT580915ipVSu/IuXzllPuSe5T7lXunZwhYmdeMGRelXZ57LszTQ2XftEmtNdqoERMfEx8yyCCDTE7KLF6sFk1o186UwxRt26rnJT//3EbPELAyrz//vCrtcvvtZk8PlePwYQlYhvLlmfiY+JBBBhlkclJm5Ej1Cvfx4y05TDFunLrfUaMs9AwBK/NKTr5WsaKK5CdOxHl2qGw2fcGC+oAAh8XCxMfEhwwyyCCTYzLNm6sVE5Yts+Uwhdyj3K/cOz1DwHKrnn02VDpm1qwITw+VoVIlvU7nOHiQiY+JDxlkkEEmZ2SsVsctt+hTrgvkNIXco9yv3LtsAz1DwMq8vv32snTMI484PT1UxiZN1GLuGzYw8THxIYMMMsjkjMxPP6klRmvXNuSKRq1a6t2LW7fa6RkCVuYVFpYYEKAvWtQQE5Ps0aEyde4sAcu6aBETHxMfMsggg0zOyLz7rlqPqlcvc65oyP3Kvcs20DMELLeqaVP1vPLmzVc8OlTmwYNVwJo6lYmPiQ8ZZJBBJmdknnpKRZwZMyy5oiH3K/cu20DPELDcqsmTL0jHvPzyeY8OlWXSJLXW6KhRTHxMfMgggwwyOSNTu7Z6ku6nn+y5orFli3qCsk4dAz1DwHKrfv01VjqmXj2LR4fK+vHHaq3RJ55g4mPiQwYZZJDJgTpxwpnympZ/Xmae8yX3K/cu23DyJD1DwHKjEhKSS5dW73o1mxPcP1T2VatUwGrViomPiQ8ZZJBBJgfq22/VC1ruv9+YiyBy77INsiX0DAHLrXriiXPSMQsXRrl/qBy7d6u1RmvVYuJj4kMGGWSQyYEaP169BGrkSEsugowY8c8yp/QMAcutWrQoSjrm8cfPeRCwzpzRpywJwsTHxIcMMsggkwPVrp1aGXvxYlsugixapN7G2L69iZ4hYLlVFktCymdnGhMSkt0/VIYSJSRjOU+dYuJj4kMGGWSQydZKTr5WurR6XH/okCMXQeTeZRtkS5KT6RkClntVv75K5bt3x3oQsO64Q601umsXEx+nBGSQQQaZbK1Tp+LlJFWpkiHXTWQbZEtOn46nZwhYbtXYseHSMa+/fsH9Q2Vq3VoFrBUrmPg4JSCDDDLIZGstWXJJTlJdu5py3aRLF/VM5ddfX6JnCFhu1U8/XZGOCQqyuX+ozL16qbVGZ89m4uOUgAwyyCCTrTVyZJicpCZPtuS6iWxDymvtw+gZApZbFRubfMsthoAAfVhYorsBa/RoCViWV19l4uOUgAwyyCCTrdWkiVqj4Ycf7LluItsgWyLbQ88QsNytRx9VHxW+bNllNxGt06erxdwHDmTi45SADDLIIJN9deVKcqFCBvky5P5LsJyyDakbI1tFz2RXwNLdUO783McD1uzZkRKw+vcPdRPR9sUXaq3RTp2Y+DglIIMMMshkX/3yS4ycnho1MvoIS8OGxutvC6NntA9YN0aim7LU9e/9K2CdPKneo1Ghgsnh3ntg7Rs3SsAyNm7MxMcpARlkkEEm++qDDyLk9DRwoNlHWGRLZHtkq+iZnHiKMKNQ5WZy8oWAJVWjhtn9z9F0/PmnWsy9YkUmPk4JyCCDDDLZV6kfN/Lxx1YfYZkzRy1s9OST5+gZPwhYwb5Rjz76hzTNgAG/u/PLG9evPxsQIF/BP/4YTFEURVHZU7feeirl89y2+sj2LFiwVbanXLlTHBpPy+OAle7zg/54Bev776OlaZo3d/d5bkPFinqdznHoEI8secyNDDLIIJMdZbOpzxopW9bocPgKi2yJbI9slWwbPZONV7BcJyr/ClhRUUmpb444fdqtRjbec48ELFtwMBMfpwRkkEEGmeyo1asvS5R59FGfgnGmvu9eto2eya6AlTYY+XXAkmrTRq3w8cUXbn2apumRR1TA+vxzJj5OCcgggwwy2VHjx6sPGpky5aJPybz11gXZqgkTwumZbAlYGaUiP30XYWpNn35RmubZZ916s4Zl0CC1mPu0aUx8nBKQQQYZZLKjWrdWD/s3b77iUzKyPbJVsm30jPYBS5em0v0nvwtYf/xxVZqmWjW3VnOzvPaaWmv0hReY+DglIIMMMshoXgkJ/3zKyMWLST4lI9sjWyXbJltIz2TLU4Rale8ErOTka7fdZkhZQi3zl2FZ58xRAatXLyY+TgnIIIMMMtn0mP+uuyw+KFO3rvpQwkOHrtIzBCx368kn1WpY77yT+Yoj9hUr1GLurVsz8XFKQAYZZJDRvObNi0xZYjTUB2UGDAiVbZs/P5KeIWC5W3PnqiXU2rc3ZR6wdu1Sa43WqcPExykBGWSQQUbzevZZFWI+/TTKB2Vkq1JeshxKzxCw3K2jRx0BAfqiRfWmzCKW49QpFbCKF2fi45SADDLIIKN53Xmnehruzz+v+qDMoUPq6cu6dS30DAHLg0PVqJFaQm358swXazAUK6bWGj19momPUwIyyCCDjIZ14UKSnImKFfvnheS+JiNbJdsmWyjbSc8QsNw9VGPGqAcNw4dbMg9YtWtLwLL/8gsTH6cEZJBBBhkNa9MmtRRCmzZ2n5VJXTlStpOeIWC5e6h++MGecuUz88/MMT3wgApYK1cy8XFKQAYZZJDRsN58Uy3mOXFiuM/KyLbJFsp20jMELHcPldXqKFlSXfn8449MFmswP/mkWmv044+Z+DglIIMMMshoWJ06qY+jWbs22mdl1qxRH+D7yCNOeoaA5cGh6tzZJH0zc2YmizWYR42SgGWZNImJj1MCMsggg4xWlZx8rUwZ9Wpguz3BZ2Vk22QLZTuTk+kZApbbh+qDD9RiDV27ZvJOQuvUqSpgDR7MxMcpARlkkEFGqzp5Mj7lY0XMPi4jWyjbKVtLzxCw3D1UBw44pGlKlTJYra6eJbQtXqzWGs22Dzpn4kMGGWSQyYcyX311Sc5BvXqF+LjMk0+ek+1csuQSPUPA8uBQ3XGHehnWjz/aXQWsDRskYBnvvZeJj1MCMsggg4xW9fzzYXIC+vDDCB+XmTkzQrZzxIgweoaA5cGhGjZMXfl8+WVXizU4/vhDrTVasSITH6cEZJBBBhmt6t57bXIC+vXXWB+XkS2U7ZStpWcIWB4cqm+/Vf19zz2uFmtwWK36AgX0BQs6bTYmPk4JyCCDDDJZr+jopIIF9YUKGWJikn1cRrZQtlO29sqVZHqGgOXuoTIanYGBKj4dP+7qZViGChXUYu6HDzPxcUpABhlkkMl67doVIw/vg4JsfiFz333qYsQvv8TQMwQsDw5V27amlE8Ld3V1ytiokVprdNMmJj5OCcgggwwyWa/331cvbBo16rxfyMh2ytbKNtMzBCwPDtWUKZaU93GYXWiaOnaUgGX78ksmPk4JyCCDDDJZr8cfV2/N++abS34hI9spWyvbTM8QsDw4VLt2qc/MKV/e4Mj4SULzgAFqKazp05n4OCUggwwyyGS9KldWT56cORPvFzKnT6slu6pUMdMzBCzPDlWVKmqxhq1bM1yswfLKKypgvfgiEx+nBGSQQQaZLJbFopZHv/VWox/JyNbKNlutCfQMAcuDQ/XMM2qxhtdfz/Azc2yzZ0vAMj/1FBMfpwRkkEEGmSzWqlWX5aTTubPTj2Rka2WbZcvpGQKWB4dq8WL1/oiWLTP8zBzb8uVqrdHWrZn4OCUggwwyyGSxxo0Ll5PO229f9COZKVMuyjaPHx9OzxCwPDhUp045ChUyFC5sOHMm/ddh2XfuVAHrzjuZ+DglIIMMMshksVq1Uq/9/emnK34ks2XLFdnmBx6w0zMELM8O1f33q2eXv/oqg8UaTp5Ui7mXLMnExykBGWSQQSYrFR+fXLSoISBAHxGR5EcyFy8myTbLlsv20zMELA8O1SuvqMUaBgzIcLEG/S23qLVGz55l4uOUgAwyyCDjdR08eFVON/XqWfxORrZZtly2n54hYHlwqDZvVhdsb789w8/MMdSsqQLWr78y8XFKQAYZZJDxuubOjZTTzaBBoX4nM3BgqGz5vHmR9AwBy4ND5XA4b71VLdbw22/pvwzL1LKlWmt01SomPk4JyCCDDDJeV//+KqZ89lmU38nINsuWy/bTMwQszw7V44+rZd+mTk1/sQbT449LwLJ+8gkTH6cEZJBBBhmv64471BNthw9f9TsZ2WbZctl+eoaA5dmh+uQTq7ROhw7pL9ZgGTVKBazXX2fi45SADDLIIONdhYcnyommeHFjYqL/ycg2FyumnuqRvaBnCFgeHKojRxwBAeq17Ob0XulufecdtdbokCFMfJwSkEEGGWS8q+BgtdhB27YOP5V58EGHbP/GjVfoGQKWZ4eqYUO1WMOKFel8Zo514UIJWKYuXZj4OCUggwwyyHhXb7xxQc4yr7wS7qcysuWy/bIX9AwBy7ND9eKL6qnxESMsaf/Jvn69Clj33cfExykBGWSQQca76thRXQH6/vtoP5VZuzZatr9TJyc9Q8Dy7FCtXas+M+euu9JZrMFx4IBaa7RyZSY+TgnIIIMMMl5UcvK10qXV8yROZ6Kfyjgc6mOqZS+Sk+kZApYnh8picZQooV7Bd+jQzYs1OCwWfUCAoVAhp93OxMcpARlkkEHG0zpxIk7OL9Wrm/1aRrZf9kL2hZ4hYHl2qB55RC3W8NFH6SzWYLjtNrXW6OHDTHycEpBBBhlkPK0vv7wk55enngrxaxnZftkL2Rd6hoDl2aF67z21WEO3bum8k9DYsKEELPvmzUx8nBKQQQYZZDyt4cPDUh7AR/i1zIcfRshePP98GD1DwPLsUO3fr16BWKqU3pbmc59NHTuqxdyXLGHi45SADDLIIONp3XOPNeXzQmL9Wka2X/ZC9oWeIWB5fKhq11Yvw1q//ubXWpn791drjb77LhMfpwRkkEEGGY8qOjqpYEF94cKG2Nhkv5aJiUmWvZB9kT2iZwhYnh2qoUPVK/jGjr15sQbLhAkSsCwvvcTExykBGWSQQcaj2rkzRs4sTZva8oBMUJB6x/2uXTH0DAHLs0O1dKlqnSZNbl6swfrRR2ox9z59mPg4JSCDDDLIeFTvvqteujR69Pk8IPPCC+dlX957L4KeIWB5dqgMBmeRIvoCBfR///2fxRps334rAcv44INMfJwSkEEGGWQ8qp49z0koWbr0Uh6Qkb2QfZE9omcIWB4fqgcfVGvBffbZf17obt+xQwWsunWZ+DglIIMMMsh4VJUqqTWAzp6NzwMyZ87Ey75UrmyiZwhYHh+qN99Un5nTu/d/F2s4cUIt5l6qFBMfpwRkkEEGGffLbFYLoJcrZ8ozMrIvskcWSwI9Q8Dy7FDt3GmX1qlQwXDTz/WBgWqtUb2eiY9TAjLIIIOMm7Vy5WU5p3Tp4swzMrIvskeyX/SMNwFL9/+Vblpy8a95IGBJVa6sFmvYvv0/L8My1qihAtaePUx8nBKQQQYZZNyssWPD5YTyzjsX84yM7EvK2+3D6Rnvr2BlFLA8jU1+F7Ceflot1vC///3nM3OMzZurtUbXrGHi45SADDLIIONmtWypnhXZujUmz8j89NMV2aNWrez0jJYB66afuJmc/C5gLVqkltx94AHTjT809eihAta8eUx8nBKQQQYZZNypuLjkwEBDQIA+MjIpz8hERCTJHhUtaoiPT6ZncjlgBftbrVy5uUCBs4UK6des2XT9hwcef1wC1u+DBwdTFEVRlBs1e/YOebhevfqJPLZfskeyX7J3HOIbiytYblXTpmqxhiVL/l2swTJlilprdOhQHlnymBsZZJBBxp365JNIOZUMHhyax2QGDQqV/Zo7N5Ke4SlCjw/VxIlqsYZBg/79zBzrggUSsExduzLxcUpABhlkkHGn+vULkVPJwoVReUxmwYIo2a9nngmhZwhYHh+qjRvVyxJr1Pj3M3PsP/6o1hoNCmLi45SADDLIIONO1amjHqv/9VdcHpORPZL9kr2jZzQLWNfyx7sIVZyyO8uWVYs17Nnzz2IN9n371FqjVasy8XFKQAYZZJDJtMLCEuUkUry4MTExr8nIHsl+yd6dP59Iz3gWsHT/rZvSUp5fByu1evZUi9VOn/7/izWYzSpgFS7sdDiY+DglIIMMMsi4rg0b1HIG7do58qRM27YO2bvg4Cv0jDdXsLQqPw1Yc+aoxRoefvjfxRoMt96q1ho9coSJj1MCMsggg4zrmjz5gpxEXn01PE/KyH7J3sk+0jMELI8P1V9/OQIC9MWKGcz//7GEhgYNJGDZf/qJiY9TAjLIIIOM63r4YXWN54cfovOkjOxXyjUIBz1DwPLmUDVooF6GtWqVPfV/TR06qLVGv/6aiY9TAjLIIIOMi0pKulaqlHqV0rlziXlSxulUrzArXdqYnEzPELA8P1QvvKA+M2fkyH8WazA/84wELOt77zHxcUpABhlkkHFRx4/HpbwV3ZyHZW6/XZ0i//47jp4hYHl8qNassUn31K//z2INlvHjJWBZxo5l4uOUgAwyyCDjor744pKcPnr3DsnDMrJ3so+yp/QMAcvjQ2WxOIoXV88S/vmneuegdeZMtdZo375MfJwSkEEGGWRc1HPPhcm5Y9asiDws89FHEbKPw4eH0TMELG8OVadOppRBoj4zx7Z0qQpY7dox8XFKQAYZZJBxUY0aqfeh790bm4dl9uyJlX1s3NhKzxCwvDlUM2aodXi7d1fvJHRs364Wc69Xj4mPUwIyyCCDTEZ1+XJSgQL6woUNsbHJeVhG9k72sWBBvewvPUPA8vhQ/f67PeWNEnqbzek4flyf8j9MfJwSkEEGGWQyqp9/jpFzRbNmtjwvI/soeyr7S88QsLw5VLVqqZdhbdigniXUFykiGctpNDLxcUpABhlkkEm3Zsy4KGeNF188n+dlRo8+L3v67rsR9AwBy5tDNXiweifq+PFqsQbj7berxdz37mXi45SADDLIIJNu9ehxTs4ay5ZdzvMyso+yp7K/9AwBy5tD9c036hLoffepz8wx3n+/Wmv0+++Z+DglIIMMMsikWxUrqndH6fXxeV5G9lH2VPaXniFgeXOo9HpHkSL6AgX0J044zd27q4D16adMfJwSkEEGGWTSlsmUIJnjtttM+URG9lT212xOoGcIWN5U69bqEw8WLLBZhg9Xa42+8QYTH6cEZJBBBpm0tXy5etasWzdnPpHp2tUp+7tixWV6hoDlTb3xhlqsoU8fk+XNNyVgmZ97jomPUwIyyCCDTNp6+WX1uu+pUy/mExnZU9lf2Wt6hoDlTe3YYU95mtlg++wzFbAee4yJj1MCMsggg0zaatFCnS+2bYvJJzJbt6o1KVq2tNMzBCwvq1IltVjDttmb1VqjTZsy8XFKQAYZZJC5qeLikgMDDQUK6KOikvKJTGRkUkCAXvZa9p2eIWB5U337qtfx/W/0EQlYhmrVmPg4JSCDTPbJLFpka9DAOG2a9eef7RYLMn7TM/v3X5UzRYMG1nwlI/srey37Ts8QsLypBQtUA7VuZVCLuRcu7HQ4OCVwskQGGc1ljh51DBliLlBAzTSpX4UKGerWNT72mHnCBMvnn9t+/dVhs9EzPtozH38cKYdsyJDQfCUj+yt7LftOzxCwvKmTJ50FC0qy0h8rXUmtNXrsGKcETpbIIKOhzJkzjnHjLMWLq1cjSMCqX9/Ytq2pVi3DjWEr9atIEX2DBoYnnjBNmmT5+mvbvn12F4/46JmclHn66RA5QIsWReUrmYULo2Sv+/ULoWcIWF5WUJBarOHz6gNlhrNv28YpgZMlMshoImM2O6dMsdx6qyE1P3XqZPr5Z/v1fzWZnFu32j/5xPrCC+aHHzZVq2YICLg5chUrZrj3XmOfPuY337R8953t0CEHPZMrPVOrlnrL+dGjcflK5siRONnr2rUt9AwBy8saP16NnAFVF6u1Rr/5hpMlJ0tkkMmijM3mnD3bWrXqP9GqeXPj+vX2TP/q7FlHcLBt1izb8OGWtm1NFSsabspb8lWqlKFpU2P//uaPP47csSMmNDSRnsnunhFkkS9RwpiUlL9kEhOvyV7LvoeFJdIzBCxvSmY0aaAaxX5XKzWMGOGwWjlZcrJEBhmvZb780la3rjE1DzVoYFi61Ob17p886Vy3zv7ee9bBg82tWpnKlUsnct12m6ldO8cLL5z/7LOo3btjL15Mome0rfXro8W5fXtHPpSR1pJ937DhCj1DwPKm7HZnmTJqnvpZpz7yWV+6tKlnT9v8+Wpu42RJjEAGGbdl1qyxBQWZUnNPjRrG+fNtWrxt5j919Khj1SrbtGnW4cPDHnjAXqaMMW3kqlLF3LGjY+zY8M8/j9q3L/bSpSR6Jiv1+usXRHXSpAv5UOa118LVG+3/d4GeIWB5Wd27qznx7dtfN1St+u8sVbCgqUULy+TJ9l9+4WRJjEAGGRfbuXWrvV27f6JV+fKG6dMtFosjZ2Ts9oQtW658+GHE4MGhzZrZihe/OXIFBEjaM3ft6nzllfBvvrl06NDVmJhkesb96tBBXcVZty46H8rIXsu+iwDzDAHLy5o9Wy3W0LGjSV3Q2rvX+s47xjZtDIULX5+ijDVrmocNs69c6XRj7RpOlsggk39k9u519OhhSn19eqlShldftej1jlyUSU6+ZjTGr18f/e67Ec88E3LvvbaiRW9+YrFAAf0dd1jatLHL16RJF3bujDGZEhIT6Zl0KinpWsmSKrOGhCTmQ5lz5xJTGtub158RsAhYqg4fdqS+YefG+OQ4fdq6aJG5d29DuXLXZyZDiRLmbt2sc+a4WNCBkyUyyOQHGZk3BgwwFyqk4ktgoH7kSMuJE74oI8np9On4tWuj3377Yu/eIQ0aWAsXTue1XLIjtWpZ2rd3DBkSOnXqxaVLL/32W6zDkZCcnK975tgx9U46eYidb0dTjRpmETh+PI55hoDlZdWooWacl16ypLPCst1u37DBMmaMoX79Gx8DmoKCLK+95tixg5MlMQKZfCVz6pRz9GjzLbekvpRA//TT5hsXUPB9mfj4ZMkNU6ZcbNPG/sgjztat7dWqmdOuy5X6FRhoqFvXIr/2/PNh770XsXLl5f37r970trI83DOff67WgurTJyTfjibZd7WS0edRzDMELC+raVOjOyssOw4etMyYYXroIfWI9fplrapVzQMH2pYtUyvbcLIkRiCTd2WMRufkydbSpf8Z/V27mnbvduQNmbi45DNn4rdujVm4MOq118L79g1p3txeoYIp3dQlX8WLG+++29qtm/PFF89PmWL54gvbtm32U6cceaxnhg0Lk52dPTsy344m2XcREAfmGQKWl/XBB1bJWC1bur3Cst5g++orc79+hooV//29W24xdeoUtWhRgsPByZIYgUxekklISJZZ4vraVK1bGzdtsucHmStXkv/+Oy44+MrcuZHjx4c/8cS5Jk1sZcsaMwpepUrpGzY0duliev55y/Tp1m++se3caTcY/FWmYUP1Ct3ff4/Nt6Np795YEWjUyMo8Q8DSoDxdYfnb6bv3DX/T2Lix/vrvBQTYgoIuvPXW1YMHryUnc7IkRiDjvzIygleuvFy3riV1cDdubFy50o5MZGTS4cNXf/ghetasiCFDzB07murVM6Z+IlC6X7fdZmjSxNi9u2n0aPP771uXL7ft2ePI4ideZ7fMpUtJ8ni7SBHD1avJ+XY0yb6LgDhou94HASufBqy05e4Ky/ec7tdi+9SGM5cFttuv++el8abKlcOGDYtety4pOpqTJTECGf+S2bo1JijIljrGa9c2LFxodTiQcdUzx487Nm2yL1hgnTzZOmCAuV07k7gVKZJ+6pLHpJUqGe6/39ihgykoyDhtmtVqdfiOzI4dMbKR999vy+ejSQTEQTSYZwhYOVHurLB8a9FjLYqu7a+b8o6u33Jdsz8DKzg7d46cPz/BYuFkSYxAxsdl9u+/+tBDjtSxXLWqedGiqKyf+/Ntz0gqPXzY8eOP9nnzbK+8Yunb19S6ten2242p78G88at0aX3Pnqb5823ervSspcz06Rdlk8aMOZ/PR9OLL54XhxkzLjLPELByp9xZYbmCbm9r3ZLBuv99UG3s1kGzw7b/fi0piZMlMQIZn5I5eTL+iSfOpY7ZsmWNH3wQkboyJzKa94zN5jxwwLFmjW3cOPUq2Ouf3pj69swWLUyTJ1t++cWeWzKPPaba4LvvLufz0fTtt5fFoXv3c8wzBCxfOVQ3r7Bc7ObHagG6s1UL/tax2uaXe+z5emEoKyzTM8jkrozNljB0aJic2lNfZzlp0oWIiCRkcrJn9u61v/OOtU0b442LddWsaRw2zLxypd2d12xpuF+pb6I0GOLz+WgSAXWBoIKJeYaA5aOH6voKyzOmhfft8FfD8vsCA07cvMJywNk6NfSpKyxPnBgeHHzlr7/izp9P5GRJjEAmWys8PHH8+PDURc/l1D5yZJjTmYhMLvbM6dOORYusvXubb3wBRokShm7dzHPmWI8dc2S3jEzXKR98ZGI0SYmDaIgJ8wwByz8OVWLitb83n/pmwLcTanzYJWDuHbothXSnMlrrr1YtS+vW9r59Q+Q0MGuWWu5vz55YszkhPj6ZkyUxAhmvKzo6aerUi6VKGVNfcy1DTK+PR8Z3esZud27YYB8zxlK/vuHGT/sJCjK99pplxw5HNsl89516Xuyxx84xmqS6dXOKxvLll5lnCFj+d6iSLl68/N13tj7PbCnVdIxuTDPd8va6xQ/qvrxLt7G07lBG73xOPSVUqGC67z6bTAQjRoTJqeKrry5t3Rrz999xkZFJnBKIEchkVPLgZN68yIoV/1lL89FHnYcPX0XGl3vm4EHHjBmWhx4y3bDSs75qVcPAgeZly2wpKz1rJvPSS+qV3dOmXWQ0SYlDyoednGeeIWD588SXmBg5d669VStnly721q3N1arJg7XjuqI7dDW+093/ka7HK7rhA3VvPKqbd2+BHyoX2lco4IyL+FWsmKFOHUPr1qZevcyjR5unT7d8+aVt0yb74cMOu51TAifLfCqTlHRt2bLLtWv/s7RVixb2XbtikPGjnjEYnF99ZevXz3zjojm33KLv1Mm0aFGUw5GQ9Z1q3twut7l9ewyjSUocRENMmGcIWHlq4kuOi4s/cyZm69aohQvDX3stpG9fe/PmpgoVUieVM7oCe3UVftA1+kzXcYqu/wjdhCcKzW5V/Ps6JfYWL3zSRfYqWFBfubLhvvtMXbqYhgwxT55snTfPtmaNWv0vm5ZdJkYg4wsywcFXGje2po6CBg2s69ZFI+O/PeNwOLdssY8fb2nc2HjDSs/6oCDbW29dOHjwqncrPWu1umae6ZmoKLXmamCgIS4umXmGgJX3J77kK1fi/v77SnBw5Ny54ePHn3viCVuTJsayZW+MUX/pSmzR3bFE1/p93ZPjAsc+c+u8DhXWNbxtz20ljqddnv6m1Wjq1TO2a2fq0MHUsqWxTx+TTGFZ/JL5LotfAweGtm1rHzEibP78yCVLLq1efXnjxiu7dsXINHriRJzFkhAenhgbm0zPMJrSrd9+i23Txp7a4bffbv7qq0seLZNCz/i4zOHDjg8/tHbvfq7YDW/ZrlzZNGxYmMTo6GgPDvbvv6vPh2nY0Mpoul53360eluzbF8s8Q8DKvxNfUmTk1cOHo3/4IWLWLPOQIaaOHY316hmKF78pQ53SFfpFV3WV7r65JXtPrjr5uTqfP3bnxvtr7bu9wt9FCp91kb18/0seaZUsaaxUyVS7tqVRI2uLFvYOHRwy7T79dIhMtS+9dP711y+8+qrl7betM2da58+3ffWVbeVK+4YN9h07HHv32v/6y3H69L+f8M3JMg+MpmPH4lLXNEr5zBbT7NmRXnz4iTc7YLc7jh+379plW7vWunChnOqNLVqYhw2T7+Un8nP5V6dXa8PTMy5k5FHWpk1XRo06LzH6+rRQtKihc2enPDaTR2KZ7tGcOeoTjocODWM0XS/REBORYZ4hYPHI8mYZNdFv2mRN+QwL84ABpnbtDLVr6zP4DIuDAbcG39bmy7rDh1ef17xU8FN1Vo99cM3Y9j+M7fDj2E7BYx/dNK7rT2Mf2zau58/jntg17qlfx/XZM67fvnH9D4wfeGjc4MPjhh4ZP/z4+BEnxr9wevyLZ8e/bBw/zjxlysUsfg0aFNqunaNv3xCZOgcODO3VK0RmzAcfdAQF2erVs1Svbi5XzpT6TntNvsSmTBl9lSqGO+4wNG4sZ0bTQw+ZunY1PfWUeeBAc48e5pYtjb17c20vl0eT7Jfsneyj7Knsr+y17LsIiINoTJp0oXFja+onuJcoYRQrr5/0Sef5KUniEsnXr7d9+aWkdcurr5qHDjX16GFq3dpYv76hfHl96pparr8KFpTflN9Xq5tLVw0dKrcjtya3Kbcsty/3QsDyumeOHImbPv1iy5b21B5I/ZLHXfIoa+/e2IwuYcokI7+2eHEU56brtWhRlJjIw1TO2ukHLN0NRcDiakTqSxgchw/bf/zRNm+e5ZVXTCmfYWG8/XZDoULZcWXJEBhoLFnSeOutpkqVzNWrW+rUsdSrZ23UyBYUZG/RwiFZqUMHZ+fO57p3D5H09PTToQMHhg0bFjZy5PmXXgqfMCH0mWfsbduGDhsW8cEHkfJIav78qEWLLi1Zcvnbby+vXh29bt2VTZtitm27vHN32PbfLVsOnQw++scPJ3ev1m/51vzDEtuyRecWzQ2dPfP89KnhY8ZYhg0z9+tn7tnT1KmTqU0bY1CQOifWrGmsUMFQooTBnTMj1/ayUnI7cmuHD6tbltuXe5H7knuU+5V7l22QLZHtka2SbZMtlO2UrZVtli2X7Ze9kH258azpOsOMGXM+LMyDJeWSY2ISzOarBw9e2bjx0ldfRbz/vmXECPNTT5natzc2amSoXDmjByc3v8u3TBlJ6MbmzVU2f/RRo6TyRx+V7+Un8nMV4V0/W///YV/uUe5X7l22QbZEtke2SrZNtlC2U7aWGdj1DCxHXzK3zCupS3KkfpUvbxowIFTieFTUf6JWzZrq0texY3Gcm67X0aNxYlKrloWzdoYBS6t4RMDK45fubTbHgQO2NWsso0ebWrY09+plGTXKPGyYZdAglUp69zY9/ri5WzfTI4+YHnrI2KaNqUULlVAaNzbUr2+8805jzZqGqlUNElXKlpW0og8M1Lt5JszJrCEnLdk2Of/JdlaporZZToSy/Y0bm4KCZI9Otmr/Z5vH9j7YZ0e7oevbjV7V/pUl7d76tN17H7X9eGqbBc/UWtqidHCvWqvGtlqRxa9X232fxa++d6xtWWZjz9rrh7bc+vT9u3o02dOx4f4H7vqzSa0jdaser3bbyVtLnSla5GxOXtvr0cMk38tP5Ofyr/I78pvy+/JX8rfuhBM3v4oWNZQrZ6pe3VyvniUoyCYpvXNnp5xHBw4MHTXq/PDhYffea1ux4j/r9yTHxyc6nXF//RWzdevlZcsiZ8++MGlS2NCh5x57zN68uaV2baM0hhv3bShe3FijhuqWTp1kXFhefFGCoSREiYoOyYwSHq3WzB/jWK3qN3fskL+Sv5VbkNuRW1PBX8ZUjRppn99P90u2WbZcHqjIXsi+yB7JfsneyT7Knsr+yl4zA19LWZ5j27YYCe7y+O66X+HCBonvs2dH6vXxISGJ8hOJ71n/JLO8dG4SDTERmdDQRM7aNwesmyJRFhMSAYvXRngsk5SUfPVq0qVLSRcuJJ47l2C1xstkdvJk3JEjV//4I/b332N++SVm+/YrmzZF//jjZXlQ+d13l5YsiVq8OHL+/Mg5cyJmzgwdNszRrl1o//7hEyeelwly1KiwYcNCBw4M6dcvpFevcz16ODt3djz8sOPBB+0tW9qCgqyNG1vr17fUqWO+/XZTpUqmcuWMJUsaihb1ubSXU1+ndQWP6Ir/riv/s+72YF291bomX+seWKjrMFv32Axd78m6QeN1I0cGTBhU8K0+hWc+VmT+w4FLHghccV/g+npFtt5e5LfyhQ4WL3i8oMuVQdz/ktspXuRk+eJHa5T5s375/fdV2dO6xq5Od2zvUW9z30Ybhtz3/ejmqyY/tHrGoyvmPLZ00ZNLvu27+PtnF2wZOm/3iDl/jPnw5Pj3LJOmhb35tutnT0MGDbK3bevs0ePck0/a27Sx3HWXeiOIGxeNDIGB5urVpYucjz4aOmBA+IQJlsmTrXPm2JYutW/e7Dh40Jm67FLOlMkk9yj3K/dunT1btkRd0B0wQLZNtlC203DjwlAZX06TfRcBcRANMVHXgwcNupDlp6gtWX6aXB6zGVu2NPXunfWb8mjL970wa0rHVa1q7L2xq6sU+1P+e+9tu/OzTLpfjcr9JjKP1/rxlbZrs/j1csvlmnytfmdLHgxYwRTlz7Vxw4aNP/ywafXqzcuXb1m69KclS35avHjbggXb583bPmfO9g8/3PHeezunT985ZcquN974ZdKk3RMn/jp27K8vvvjbyJF7hw/fO2TIH488crRRo0MPP7yvX79c/5LNkI35s337A08+eaB79z+6dj30yCOHOnT4s127ww888FeLFkeaNj1y773HGjY8Vq/e8TvuOF6z5onq1U9WrnyyfPlTZcueLlnyzC23nC1S5Kw7T1fpdH/rAv/QlflVV/knXZ11uobLdc2+0LWd93/t3UGLHEUYxvEmH0D3I+TkTSIiRMkhVy/m4NEQSMBrIB8gCPGkEoiS454FWXJWDAh7EHIQBQ8GIR8hwYsgBGTHEkXi7kx19VRV9zuZ359hWWZ6ut966p2pZ6rfrh7e/Wx4/85w9YPh44vDl+lv+j89k55Pr6Zt0pZp+/Su9N60hwWN5pNz5349OPjl/PmfL1z46fLlpNija9e+v3nz+Pbt7+7efXh4+PXR0c6ldIo5RZ7iT61IbUkt+uHKldS61MbU0tTeJ/v6u6Lk8ePwavqZ8d5w77+Fnd8YHpDl1OOt4atoQX34+mHvT9YCBgvAy0mricbr13+rv66h+pHCSME8vXXr96OjP46Pnz9+/OezZ6uTk33s2ZOT1PakQNIhqZE0+bubbtyI000RcubpR3c+fef+m688/PzSPcqcenzy9v2LB99cfe1BfVVDq8e3Xzxa8CPFYAEAADBYAAAAu2KwVk2vIgQAAGCw/vVVrdbBAgAAywztkQbxsI6id2BDzDaHMnmhfGdAExwhmOH/SONNyoSKx6epMGHmjzOTM/MEs4Ua/QLbQo3ZumzTURZJ6U1CzZ+3hWmzjwYrlOc91RnLRhWzTi7IsB02e/2UjJnAL+ZtqJ8rmSfnHKhGX5ohmElq9AtsCzVm7rI4BivC10552jBYogodzD8xMFjxUzdgN+2b2yv5KTI6UraKMx9M4UhZE0xbNSoDa6tG81W7K4VqmNv1Qi2uxpxmi8EykDcIwCnCtaYzWjkjg7V4MKNZOpvBygdTeK6nyWDZRI36wBqq0bzLaoTqesJ0qlBNgumRNp0+/gzW7oUUqmokpu8Mde4p4MnuIFY4Tp4sEs+m2p1FjOBo8cpsbm8pg9VQja5dNkmopSrA1grVZYqoRdr0U4nBYvgaHF1nhZ2nCRVGNGVe/HZmsEqmBBis1fS6q6UM1mxnvUuEmjNvJ6VN32s1GKwddVerGDXCMZf2YLBiJnDk1YydIiwRZz9PES5usLYWquuk0VSh2pYPNkwbM1giCT0+hSruCXVVY7STuRJ4U87M5q626Kke5+LLq5U71dM0V6MmsOZqNOyyyrLuHvNnU4VaXI3VWGH7HtVghV22xzJCkcdvCyzF/4Ww2p2lp0J97y0iWpB1sIIsPRV/HaxMAf7iaTNbMFPTZn+vIgQAANhRGCwAAAAGCwAAgMECAABgsAAAAMBgAQAAMFgAAAAMFgAAABgsAAAABgsAAIDBAoClv8WGQTMBMFgAagfagPejrGlF/W2DayxIj1tQd7odnuQHGCwAvXzJyzHoNmxIK38W3AwxWACDBWAmX3LqpdHbyG+a7Dl1V/lNt53P778kmExDRmPbNOm19o2FUeWVmSpFvpvKeyHfTAAMFoCZDNZZ0zD6f2Zoz/+f2X70jfUGK3/cklfrDzfqe9a27uwe1h66sMsAMFgAGnus0emNGldR83x+43qDlX9+khdpLkVh66YGz2ABDBaABczWWddVOFPVYz/5nSxusFpJtJ3Byh99ahcAYLAAzGGw8hts5x6m7qfcCsxvsHpP5hW2brTVk+q6ADBYAFo6qrOjckmNVKaUe3Q/o8etqVLatP2kerLyGqzKcrTtDFZJG8u7AACDBaCxx5p04V7m4ru11daTarxWFVcRZrZZG1vDqwjX7jazk0Ip8q3Ld9CoegwWwGABiGvOBA8ADBYAHoXBAsBgAeBRBA/A9y0JAAAAGCwAAIDQ/AXZS70byryYBwAAAABJRU5ErkJg" alt="Duplication level graph" width="800" height="600"/></p></div><div class="module"><h2 id="M9"><img src="data:image/png;base64,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" alt="[FAIL]"/>Overrepresented sequences</h2><table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>2628</td><td>30.17568033069239</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGAT</td><td>1004</td><td>11.528304053278218</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG</td><td>139</td><td>1.59605006315306</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGGT</td><td>110</td><td>1.2630612010563786</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGCT</td><td>76</td><td>0.872660466184407</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTA</td><td>74</td><td>0.8496957170742909</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAGTTCAAGCGTT</td><td>74</td><td>0.8496957170742909</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGG</td><td>72</td><td>0.826730967964175</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTA</td><td>54</td><td>0.6200482259731313</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAGTTCAAGCGAT</td><td>38</td><td>0.4363302330922035</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGACGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>36</td><td>0.4133654839820875</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCG</td><td>35</td><td>0.40188310942702954</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGG</td><td>28</td><td>0.3215064875416236</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTAT</td><td>26</td><td>0.29854173843150766</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGTCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>22</td><td>0.25261224021127565</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTGATTCAAGCGTT</td><td>21</td><td>0.24112986565621772</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGATC</td><td>18</td><td>0.20668274199104375</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAGGCGTT</td><td>16</td><td>0.18371799288092777</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAG</td><td>15</td><td>0.17223561832586978</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTG</td><td>14</td><td>0.1607532437708118</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGGGCGGCTTAATTCAAGCGTT</td><td>14</td><td>0.1607532437708118</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGGTTAATTCAAGCGTT</td><td>14</td><td>0.1607532437708118</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTG</td><td>13</td><td>0.14927086921575383</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGC</td><td>13</td><td>0.14927086921575383</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGG</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTGAAGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTTAG</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTA</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGGGGCGCGGCTTAATTCAAGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGGCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCACGCGTT</td><td>12</td><td>0.13778849466069584</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCGTAATTCAAGCGTT</td><td>11</td><td>0.12630612010563783</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGACGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGAT</td><td>11</td><td>0.12630612010563783</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGATA</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGC</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGGGGCTTAATTCAAGCGTT</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTGAATTCAAGCGTT</td><td>10</td><td>0.11482374555057985</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCTGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGTT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>AATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGATA</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCGAG</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCGAGCGTT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGGGGCTTAATTCAAGCGAT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr><tr><td>CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTGATTCAAGCGAT</td><td>9</td><td>0.10334137099552188</td><td>No Hit</td></tr></tbody></table></div><div class="module"><h2 id="M10"><img src="data:image/png;base64,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" alt="[OK]"/>Adapter Content</h2><p><img class="indented" 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src="data:image/png;base64,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" alt="FastQC"/>FastQC Report</div><div id="header_filename">Wed 13 Jul 2022<br/>test.fastq2_5pAtccRm.fq</div></div><div class="summary"><h2>Summary</h2><ul><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M0">Basic Statistics</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M1">Per base sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M2">Per tile sequence quality</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M3">Per sequence quality scores</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M4">Per base sequence content</a></li><li><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEkklEQVR42s2XV2hUaxSF/4nRiaIoiKA+CDYMdsQnS8CAFREVsV4VUR9sWMECVuzYsETsGnvvivVaImhyU+Hq9VUfzKtKkptk5nz3rPkz42RyJskE5Xpgw8yZ8++1Zu+1yzGA+T8t8UPGJP1lTGquMenZxvwh02fd02+/jECOMSNdoEwX6KtrgQJjSv425rtMn3VPv+kZPfvTCLj/MM11WphnTNnnlJRA6fDhsGkTHDsG16/D2bOwfj3OtGmUDhjAZ78/oGd1RmcbTOBPY5JdRxmuo/IvyclBZ/lyeP8ePn6EoiLIyYE3b+D5c3j4EG7fhitXICMDZ8QIiv3+oM7Kh3wlRCDfmFbuwawiY0orOnWCmzfhwwcoLITsbMjKgmfP4MEDuHULLl+2kTh+PESAvXth7Voq+/RBPuRLPutFoOqfZ/1jzL8KK3l5UFAA797B69fw9Cncv29JXboEmZk2HYcOwZ49sG0bbNwIa9aAGzWnd2/kSz69IlGDgEIm1s6UKZCfD2/fwqtX8OQJ3LsHN27AxYtw5gwcPQoHDsDu3bB1K2zYAKtXw7JlsHAhzJ0LM2fipKaGI5FRK4EqwZVXtGtnc/viBTx+DHfvWsFduACnT8ORI7B/P+zaBVu2hETIqlUWeMECmDMHZsyASZNg3DgYNYrK1q2R71hhViMg5X4xJsjOnfDoEdy5A9euwfnzcOqUTUNurgXevBnWrYOVK2HJEli0yGpDaZk4EcaOhZFuNaanw6BB0KsXxT6fhFnoSUC1q/Jxhg61ir56Fc6dg5Mn4fBhq/jKSggE7GcBL14M8+bZUEsfwSCUl8OJEzBkCAwcCP37h8Dp1g2nZUuEEd0nov995iefLxASVKyiX76Eigoil4jouY4doUcPmyIRC18isW8fdO8OXbpA27bgguP388ltWMKqTsC2168lXbtWV/T27bBihRWdQKMvERI5kY0G16XvSllSkoWIsm+uCSvctkME1MfVShkzxipapSRFz54NzZtbR1J7LIlwSmLBVZrJyTXAZY4lEAjNjjABDRP1c6ZO/aHoCROgSZMfh+OR8AJv3NgTPGzCEmaEgCaahgrTp8PSpaAe0KhRzcMicfCgNwmBKx11gMuEJcyaBAQ8axa0aeN9WGFVbmPDHk6HNOHzJU4gkgKVYN++iYNHC1PirYNEjRREROi2TM/QxwPXd6/qkJDjkPAUYbgMv3kdUt7VjOKVmpcmRELd0sOfZxlGGpGi4BU2db2ysprgioxXdZSW2qEU6yclJU4jimrFwXi5C5OIBvcqUYGrjGNTOHgwTufO8VtxtWFUGwmlw6vJiIQmZCx4ixa2p7glXrUlFdY5jstrKyOP9hqx2Jx36ADz54fGdEV9xnH0QhKso5api2Ramu2q7qISdENfr4UkdiVrEIlWrex4Vim6Y9xxR3JCK1nsUlpeX2BX4dp8Qo1IE9XVQ2W/fokvpZ5ruSvMYLxQaycYPRp27Ij0BWfyZIqbNm34Wh7vxUQ1/L1ZM5z27aFnT0LTUwNs/HicYcMoce9pqfkpLya/zavZb/Ny+qvsPyB3ge/Rlc06AAAAAElFTkSuQmCC" alt="[FAIL]"/><a href="#M5">Per sequence GC content</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M6">Per base N content</a></li><li><img src="data:image/png;base64,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" alt="[WARNING]"/><a href="#M7">Sequence Length Distribution</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M8">Sequence Duplication Levels</a></li><li><img src="data:image/png;base64,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" alt="[FAIL]"/><a href="#M9">Overrepresented sequences</a></li><li><img src="data:image/png;base64,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" alt="[PASS]"/><a href="#M10">Adapter Content</a></li></ul></div><div class="main"><div class="module"><h2 id="M0"><img src="data:image/png;base64,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" alt="[OK]"/>Basic Statistics</h2><table><thead><tr><th>Measure</th><th>Value</th></tr></thead><tbody><tr><td>Filename</td><td>test.fastq2_5pAtccRm.fq</td></tr><tr><td>File type</td><td>Conventional base calls</td></tr><tr><td>Encoding</td><td>Sanger / Illumina 1.9</td></tr><tr><td>Total Sequences</td><td>4391</td></tr><tr><td>Sequences flagged as poor quality</td><td>0</td></tr><tr><td>Sequence length</td><td>1-127</td></tr><tr><td>%GC</td><td>52</td></tr></tbody></table></div><div class="module"><h2 id="M1"><img src="data:image/png;base64,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" alt="[OK]"/>Per base sequence quality</h2><p><img class="indented" 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" alt="Per base quality graph" width="1020" height="600"/></p></div><div class="module"><h2 id="M2"><img src="data:image/png;base64,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" alt="[OK]"/>Per tile sequence quality</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per base quality graph" width="1020" height="600"/></p></div><div class="module"><h2 id="M3"><img src="data:image/png;base64,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" alt="[OK]"/>Per sequence quality scores</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per Sequence quality graph" width="800" height="600"/></p></div><div class="module"><h2 id="M4"><img src="data:image/png;base64,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" alt="[FAIL]"/>Per base sequence content</h2><p><img class="indented" 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" alt="Per base sequence content" width="1020" height="600"/></p></div><div class="module"><h2 id="M5"><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAACAAAAAgCAYAAABzenr0AAAEkklEQVR42s2XV2hUaxSF/4nRiaIoiKA+CDYMdsQnS8CAFREVsV4VUR9sWMECVuzYsETsGnvvivVaImhyU+Hq9VUfzKtKkptk5nz3rPkz42RyJskE5Xpgw8yZ8++1Zu+1yzGA+T8t8UPGJP1lTGquMenZxvwh02fd02+/jECOMSNdoEwX6KtrgQJjSv425rtMn3VPv+kZPfvTCLj/MM11WphnTNnnlJRA6fDhsGkTHDsG16/D2bOwfj3OtGmUDhjAZ78/oGd1RmcbTOBPY5JdRxmuo/IvyclBZ/lyeP8ePn6EoiLIyYE3b+D5c3j4EG7fhitXICMDZ8QIiv3+oM7Kh3wlRCDfmFbuwawiY0orOnWCmzfhwwcoLITsbMjKgmfP4MEDuHULLl+2kTh+PESAvXth7Voq+/RBPuRLPutFoOqfZ/1jzL8KK3l5UFAA797B69fw9Cncv29JXboEmZk2HYcOwZ49sG0bbNwIa9aAGzWnd2/kSz69IlGDgEIm1s6UKZCfD2/fwqtX8OQJ3LsHN27AxYtw5gwcPQoHDsDu3bB1K2zYAKtXw7JlsHAhzJ0LM2fipKaGI5FRK4EqwZVXtGtnc/viBTx+DHfvWsFduACnT8ORI7B/P+zaBVu2hETIqlUWeMECmDMHZsyASZNg3DgYNYrK1q2R71hhViMg5X4xJsjOnfDoEdy5A9euwfnzcOqUTUNurgXevBnWrYOVK2HJEli0yGpDaZk4EcaOhZFuNaanw6BB0KsXxT6fhFnoSUC1q/Jxhg61ir56Fc6dg5Mn4fBhq/jKSggE7GcBL14M8+bZUEsfwSCUl8OJEzBkCAwcCP37h8Dp1g2nZUuEEd0nov995iefLxASVKyiX76Eigoil4jouY4doUcPmyIRC18isW8fdO8OXbpA27bgguP388ltWMKqTsC2168lXbtWV/T27bBihRWdQKMvERI5kY0G16XvSllSkoWIsm+uCSvctkME1MfVShkzxipapSRFz54NzZtbR1J7LIlwSmLBVZrJyTXAZY4lEAjNjjABDRP1c6ZO/aHoCROgSZMfh+OR8AJv3NgTPGzCEmaEgCaahgrTp8PSpaAe0KhRzcMicfCgNwmBKx11gMuEJcyaBAQ8axa0aeN9WGFVbmPDHk6HNOHzJU4gkgKVYN++iYNHC1PirYNEjRREROi2TM/QxwPXd6/qkJDjkPAUYbgMv3kdUt7VjOKVmpcmRELd0sOfZxlGGpGi4BU2db2ysprgioxXdZSW2qEU6yclJU4jimrFwXi5C5OIBvcqUYGrjGNTOHgwTufO8VtxtWFUGwmlw6vJiIQmZCx4ixa2p7glXrUlFdY5jstrKyOP9hqx2Jx36ADz54fGdEV9xnH0QhKso5api2Ramu2q7qISdENfr4UkdiVrEIlWrex4Vim6Y9xxR3JCK1nsUlpeX2BX4dp8Qo1IE9XVQ2W/fokvpZ5ruSvMYLxQaycYPRp27Ij0BWfyZIqbNm34Wh7vxUQ1/L1ZM5z27aFnT0LTUwNs/HicYcMoce9pqfkpLya/zavZb/Ny+qvsPyB3ge/Rlc06AAAAAElFTkSuQmCC" alt="[FAIL]"/>Per sequence GC content</h2><p><img class="indented" src="data:image/png;base64,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" alt="Per sequence GC content graph" width="800" height="600"/></p></div><div class="module"><h2 id="M6"><img src="data:image/png;base64,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" alt="[OK]"/>Per base N content</h2><p><img class="indented" 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" alt="N content graph" width="1020" height="600"/></p></div><div class="module"><h2 id="M7"><img src="data:image/png;base64,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" alt="[WARN]"/>Sequence Length Distribution</h2><p><img class="indented" src="data:image/png;base64,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" alt="Sequence length distribution" width="800" height="600"/></p></div><div class="module"><h2 id="M8"><img src="data:image/png;base64,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" alt="[OK]"/>Sequence Duplication Levels</h2><p><img class="indented" src="data:image/png;base64,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" alt="Duplication level graph" width="800" height="600"/></p></div><div class="module"><h2 id="M9"><img src="data:image/png;base64,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" alt="[FAIL]"/>Overrepresented sequences</h2><table><thead><tr><th>Sequence</th><th>Count</th><th>Percentage</th><th>Possible Source</th></tr></thead><tbody><tr><td>TTAGACGGATCCACTAGTTCTAGAGCGGCCGCCACCGCGGTGGAGCTCCA</td><td>146</td><td>3.3249829196082894</td><td>No Hit</td></tr><tr><td>TT</td><td>92</td><td>2.0951947164654974</td><td>No Hit</td></tr><tr><td>TTATACAGCAAGCGCGCACCGACTCTGACAATCGGCATATCCGGTTCGCC</td><td>86</td><td>1.958551582782965</td><td>No Hit</td></tr><tr><td>TTAAAACCTCTTCAAATTTGCCGTGCAAATTTGGTAGGCCTGAGTGGACT</td><td>83</td><td>1.890230015941699</td><td>No Hit</td></tr><tr><td>TTA</td><td>74</td><td>1.6852653154179003</td><td>No Hit</td></tr><tr><td>TTGGCGAAGTAATCGCAACATCCGCATTAAAATCTAGCGAGGGCTTTACT</td><td>50</td><td>1.1386927806877705</td><td>No Hit</td></tr><tr><td>TTTACCAGCATTAAGGAACAGCTGCTTACGGTCGGCGTGGGTTGCCAGCA</td><td>32</td><td>0.7287633796401731</td><td>No Hit</td></tr><tr><td>ATCACAGGCGTAAACGTCGCCGTTGTGCTCAACAATCACCGAGCGCCCAC</td><td>30</td><td>0.6832156684126622</td><td>No Hit</td></tr><tr><td>TTAT</td><td>26</td><td>0.5921202459576406</td><td>No Hit</td></tr><tr><td>AT</td><td>23</td><td>0.5237986791163743</td><td>No Hit</td></tr><tr><td>GTACGATGTCACTGTGCACGACGATGGTCACTTCCAAGGCGCGGAGTGCC</td><td>19</td><td>0.43270325666135273</td><td>No Hit</td></tr><tr><td>ATCATCGTACGCAAGTGACCAACGCTGTCGATGGTGTCTTTGATGCCAAC</td><td>17</td><td>0.38715554543384195</td><td>No Hit</td></tr><tr><td>TTATCCAGCGCGAGCACTCGCTCTATCACCATTTCACGAGTTTCAAGGTT</td><td>16</td><td>0.36438168982008656</td><td>No Hit</td></tr><tr><td>ATGAGGAGCGAAGGCATGAAACCATATCAGCGCCAGTTTATTGAATTTGC</td><td>16</td><td>0.36438168982008656</td><td>No Hit</td></tr><tr><td>ATATAGAGCATTTTTGCCTCCTTTCGCGCCACAAGAAATTAACTGTAGCG</td><td>15</td><td>0.3416078342063311</td><td>No Hit</td></tr><tr><td>TTAGGCGACAACGTGATGGTTGCGCCGGTGTTCACTGAAGCGGGCGATGT</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTG</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTATCAATACTGCCTTCAATCAGTACATTGGTGGCAGGAACATCATTGAG</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTATCCGAAGCGATGAGAGTTATCCCGTAACCGGGTCAGCCACTGCATAG</td><td>14</td><td>0.31883397859257573</td><td>No Hit</td></tr><tr><td>TTACGCATAGTCATTTCTCCTTCTAAGAAGCGAGTAAGTACCTGCAAATC</td><td>13</td><td>0.2960601229788203</td><td>No Hit</td></tr><tr><td>TTACGCATAATCAATAGCTCCTGAAATCAGCGAGAATGTAAGACCTTCCA</td><td>12</td><td>0.2732862673650649</td><td>No Hit</td></tr><tr><td>TTAG</td><td>12</td><td>0.2732862673650649</td><td>No Hit</td></tr><tr><td>TTATCCGGCCAGGCGGGAACTGCAGTTCGGAGAGTGGCAGCGCAAAGACA</td><td>10</td><td>0.2277385561375541</td><td>No Hit</td></tr><tr><td>TTAACCTTCACCAGCGTGCGACCCTGGATCTGGTTATTAATGATGGCCTC</td><td>10</td><td>0.2277385561375541</td><td>No Hit</td></tr><tr><td>ATA</td><td>10</td><td>0.2277385561375541</td><td>No Hit</td></tr><tr><td>TTATCCAGATAGTTCGCCAGCTCTTCATTATTGAGTTTTTTCTTAAGCAC</td><td>9</td><td>0.20496470052379867</td><td>No Hit</td></tr><tr><td>TTAATTGGCATCAACACTGCAATCCTTGCGCCTGGCGGCGGGAGCGTCGG</td><td>9</td><td>0.20496470052379867</td><td>No Hit</td></tr><tr><td>TTATCCGGCGTGTAGGCATCGTCATGACGTAAACGGCGATCGGCGGTATA</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>ATCTACCGCGAAGGTTTTACCGGACTGGATCTGGCTTCGTCTGCCGCACA</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>TTAGGGATTAGCGTCTTAAGCTGGCGCGAGGACCAACGTATCAGCCAGGC</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>TTAA</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>ATC</td><td>8</td><td>0.18219084491004328</td><td>No Hit</td></tr><tr><td>T</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTT</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTATGGCTACTGGCGGTGCGGGTCGCGTTTATCGTTACAACACCAACGGC</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTATCCGGCGTTGCAACCTGTGAGCTGTAGATCATATCGGTGATAGCCTG</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>ATG</td><td>7</td><td>0.15941698929628786</td><td>No Hit</td></tr><tr><td>TTAAA</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTC</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTAGCATGACTCACGCCGGGCGTCCAGTTTTTAGCGACGGGGCACCCGAA</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTAGTGCGCTGGTTAGCGTGCGGGATAACGCCTGTCAGGATTATCTCGCG</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTATCCATCAGGGAGTTACTGTAAGCGAGAATATATTTATCACTCAATGC</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>ATAACCAGCATCAGCATTGGCGCGTAGAGAAAGGTAAAGCCCAGCAGCAA</td><td>6</td><td>0.13664313368253245</td><td>No Hit</td></tr><tr><td>TTATGCACCGCATCGTGAGCATCTTTCCCCCAGGCGAACGGCCCGTGCTG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTATA</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>GT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTAAGCTGCACCACACCGATACCGAGCGTAGTGGCAATACCGAAGATAGT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTAAATACCGTCGGCGCGTTAATCGGCCCAACTGCGCCACCAACACCAAT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>ATCA</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTGATCGTCAAAACCAACATTGCGACCGACGGTGGCGATAGGCATCCGGG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTATATACCAGGCTTAGCTGGGGTTGCCCCTTAATCTCTGGAGAATAACG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>TTGAGTTTCAGCAGCCGCGGTTCCGCCAGCACTTTACTGAAACTGCCTTT</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr><tr><td>ATCTACCGCGAGGTTAAGCTGCTGTTCAATCTGGGCGACGCTCAGTTCGG</td><td>5</td><td>0.11386927806877704</td><td>No Hit</td></tr></tbody></table></div><div class="module"><h2 id="M10"><img src="data:image/png;base64,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" alt="[OK]"/>Adapter Content</h2><p><img class="indented" 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rename to example_of_result/20220120_test_1657729206/report.html index 2eb4046e36c999d386e3b887fa072232e0a5e93d..d6c85abe3f4cbba792d5119b19689a5d8d67d812 100644 --- a/example_of_result/20220120_test_1649703168/report.html +++ b/example_of_result/20220120_test_1657729206/report.html @@ -1737,7 +1737,7 @@ div.tocify { </div> <div id="trim-of-the-read-for-the-primer-parts" class="section level3"> <h3>Trim of the read for the primer parts</h3> -<p>AlienTrimmer main options: -k 10 -l 30 -m 5 -q 20 -p 0 (Phred+33) / 26 alien sequence(s) / 810 k-mers (k=10) <br />[00:02] 8,932 reads processed: 4,767 trimmed 223 removed <br /><br />AlienTrimmer also removes reads according to quality criteria</p> +<p>AlienTrimmer main options: -k 10 -l 30 -m 5 -q 20 -p 0 (Phred+33) / 26 alien sequence(s) / 810 k-mers (k=10) <br />[00:00] 8,932 reads processed: 4,767 trimmed 223 removed <br /><br />AlienTrimmer also removes reads according to quality criteria</p> <p>Number of sequence before trimming: 8,932</p> <p>Number of sequences after trimming: 8,709</p> <p>Ratio: 0.98</p> @@ -1827,7 +1827,7 @@ div.tocify { </div> <div id="bowtie2-alignment" class="section level3"> <h3>Bowtie2 alignment</h3> -<p>Time loading reference: 00:00:00 <br />Time loading forward index: 00:00:00 <br />Time loading mirror index: 00:00:00 <br />Multiseed full-index search: 00:00:01 <br />3742 reads; of these: <br /> 3742 (100.00%) were unpaired; of these: <br /> 1240 (33.14%) aligned 0 times <br /> 2308 (61.68%) aligned exactly 1 time <br /> 194 (5.18%) aligned >1 times <br />66.86% overall alignment rate <br />Time searching: 00:00:01 <br />Overall time: 00:00:01</p> +<p>Time loading reference: 00:00:00 <br />Time loading forward index: 00:00:00 <br />Time loading mirror index: 00:00:00 <br />Multiseed full-index search: 00:00:00 <br />3742 reads; of these: <br /> 3742 (100.00%) were unpaired; of these: <br /> 1240 (33.14%) aligned 0 times <br /> 2308 (61.68%) aligned exactly 1 time <br /> 194 (5.18%) aligned >1 times <br />66.86% overall alignment rate <br />Time searching: 00:00:00 <br />Overall time: 00:00:00</p> <p><br /><br /></p> </div> <div id="multiqc" class="section level3"> @@ -1894,29 +1894,29 @@ div.tocify { <p><br /><br /></p> </center> <div class="figure"> -<img 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pNaMSydum5+a4ev3uv15tedVH8jnfeiQ9OU09UsN1SxRx5iqP1dUDaR1BESqkAMUgldNLqcV64nGoDOBY+n9sqbgBeYSu3ran7a3NCo0V03bLOL/boBAADQKMFUAAAAAAD+T2vrB1o6laaptKNRjmDVlBMT6vdUPBWyEf/XOZUw6mIaDakCEFzAeOr4djo/ETR6Vi02ldrXcjY1+EfZ5s458lphv26df8sAAADIg2AqAAAAAAC9Xo3CuH5BWIWnx7B0KlCs1nL49LUcSZ0QIWuzJeKpQAtSj6SOs6IXQCoGB+qGlnZc8LUafZUWuA7WzjsQ8PONZEAS9uu2e/P6SPoFAABA0gRTAQAAAACoGNAariSrE09VkwfQnJZXpBnXXBC0WuBz8rOaaK14Kkxp+6aN5ayrXF9+edRxVvQCSEVDCdVhyZ34nL+6kl8kdUSo9YqjHWitm9/adRMAAACYlmAqAAAAAEDRai6UOv6gpVOB+NVcLtXxakpNBMyajncObz9s+48++2LZVCCI5JI5NUWbmgBg3MjhuqhzllNVDELtcvF/mnMbthX1/QJy0u3hK/4jPABAWgRTAQAAAADKFTaVOvinapu1dCpAQzpZNDVsqrOTSOfgRUP1xdKpQE0t1+9OWbDbTqvEUwFSNH7cziZK55QUoXJSqX1Bsqm7N69Ppb9AKiI/1y/ZPEdFAICZCKYCAAAAAJSoiUjqyN9YOhWIk+VSm5ZBJHXBNgSMp8bQKWB6MXxnGy3trVl3u+DTG2qw7ARA6hY7jEcYYnHGSUtpqdS+tFoL5CfC03d9451ysAUAmEAwFQAAAACgOI2mUof/2NKpVBM21damGIIr0K2A39/YvlAB46myqcBMmij2bbqydnj7Yds/ZTZ1+6aN6Q4pAQok8kEdZaZSATrRWhi1wjG5uRskDf9PJwsAgGGCqQAAAAAABamzTmCFsGidbGq1VyQVueYEJvRLCI1uVT4g9015y4CMU6kDkk7QKF+xEcHLajupoB28aKju9LfTTl/cSgCApLW/ll26cZ10Ww7QtFRmppM3G3ZCuuTLAQAUQjAVAAAAAKAU7SyUuuBzLZ1auLAJkw6zAXU6Mv5cIYdO1Aln9gTmJwr1TY//qxEkOCfpBCwpbO1vDCWz/TYErAae3KlQOefkjthBep1WlwEK1376dIIJjWloNBJV9wml5bsrAgsKeICNZ0I6UL93bd4yCQAgWoKpAAAAAEBW1s1v7boJMeq8lMfSqQXKMqI20piafRw8Pao+wmLauVlAUV+H5JJOQJsyqwAeNrdhm2xqc6w5DFCCRKOYI82ObYhCJGreUKznejKEEOpEE/OhPtSNk8RTAYDCCaYCAAAAAGSuk4VSF9xa5ZZYOjUhRS3QNGhnkIRqKr1OWv3qxqTVOQ4vqbQYTKikE8CCMk6l9oVdOnWy/hAryErXvegHbFneGgaAvkTDqBMM9yjOEQstC3vJQjwVKgtyxknlwB4wnppKlwEAwhJMBQAAAADIVucLpS64TUun5qrkOvgU21wmx5CG3oGSv/5A9to/NGWfSg1ryvLfgEun9qI8YTkXA2QpvzDqYoRUC9fcXbTc7rDX662b39p1E0hGCQulLii5BgMAxEMwFQAAAAAgT7GlUoc3bulUFqMUHoqV3Nc/uQZDKgpfkbioVOrchm1t5m1CLZ06spFuTwdhvyxObcC4+XUndd2EtFXOg7Vziqw8Wmi0ef2Nxz+SmVXMMeNu3+3mUqnD23dJGVqT3wEcAIAJBFMBAAAAAHJTv5qn6XqgOtQSAUSl5PwYsXEDCxhIpRQ4SDZ1ykVT+wbZy1Dnr5HtNJrtbOicK48KEI+GsovBBwYLbjBs43ONpzKszUvQLinDkoIcxh23AQBKI5gKAAAAAJCVmDOlAUmeAOREJAagFy7OoRR4GsOnnoCBz4Tu1+DkCxCV4JHU9scDw68YqjviqbmqfAV7+IJwhY2IpwIAAIQlmAoAAAAAJCmGet8IC3kLSaX2KSSKzfZNG+t/MY8+++IIv1kAQJlSHJYkl9yIocENhVQjlOIuDXRr3fzWDl99ft1J9TfSbRemETaSGsOJtTfUjCC9m2l1dCIXJJI6/Ei1eKpLypCuhlYXr8+pCgAok2AqAAAAABCp+AuCJ7ew5ZLfoiKpwxQSRSVUNrWXVNF8qINVQl2GgfhP1kCKgowokhNtcW1pRoZkSe+KhpcA8Qs4AIgzD9NvVf1uTsimzm3YlsQ4qokPKImODwuYSh3+J0unAgAAdEUwFQAAAACIQtL1vgtasEcNVQbXSaXGU39TuRcKiaIy2MlrfqmHnx5nSX3Ao1acHaRRdfYfOwwAi4kzkZK0xU67sU1gDQ8AUhQqVRj/ACBIdrTpdVOty9qoJiKpI38jnrqYhgLMvi8AAEBPMBUAAAAA6ErThbz1C3ObaGHwrF3NhVKjKrupdnv7gbSWTl03v7XrJjQu4GJTbca8p29ATcIDWWonozLTq9jTAKAJ1c6wk0/iztoARSknldrXaDY11KKpsqlNaDSSOvL31V4roXhqVGvkVmuMr1j2HEgBAEqzbP/+/V23AQAgvNjuVN2npAAAAIKP1bsaZoftSOVe5LFQ6rhc+8W4OOfvwbkgkJ/Ud92A+2Sot8LXZFjNu070Ej8b5nTTjb75dSfV30gJt7QYEcORts1DU2nRFACYRsbjKKf+JhT4rkbe5a6u8bYWhW1OVAHUdiT0vctb5EeV5gTpeHK9BgAIQjAVAEhADAUonUilIK9+qdyIeC73AwAQUMbBlU66ll9mY0T2HWR6kV8WiPCgRBMi3w9rqrMbZ3x+75Bgap2nR9j3jAMVTev82CuYCgDdynUc5bzfnIBxviTe3pj3pc7ToZ03YHoNxVDb3IeDdyGJL2CuYj6wNEcwFQCgshVdNwAA4P8ErDKJsJStQu8WfEoMXQueRJ28/QiLyQAAmF4JeZVB22p2tv/0aXpawoKiO8/bUqebay86LZWesqSYv/7krfNAVGuGe+obB5AuVbAAUA7n/QX135Yg+aL+RrzPFUSSCK18ebn/rKavLYdNcna+o443oGYHB0/vvGsFCnUgdRQFACiEYCoA0LEgFX5JFMwNN7JOrwfPbbPXTfxyMP025VQBANJVzoC/b/umjfW7fPTZF0/ocn5riE3Qb23lLrdTQgTkp6E8anOns7AN7uTSE2E59wEAQLQaWhqRYXMbtoV6n6XjZhLb7RTrXF5u7tpyIctRDpoXJKEaeWezFDae2vMhAgDkSzAVAOhMaRXqw+Jvdgv3sBz5SzlVAID8lDnmb7TBRaVSByydCrQjbLyzzfPX+GsF6cv063gDZMkBEAAgXQGXTu2LMFsVVcg5tkjq+PZjiKcWEkkdkVZrGRF8GerhzQIAkAfBVAAAer0IficY3shMjZFTBQCIU5mp1KYVO9yNs+MNranYFV83Shbw6xzJV2nQjCDrePei6RcApKWFe4ACUCaRnimFWrNx2PimWvs4okqiDqt5O8WaT29BkFsfBvn4fPfpxPCOFzykutgLNSfaYykAQKIEUwEAChXw4n5UvxPIqQIAxCCzwB5lamI3bi3ZVaHxk58ik0auQn3To/2O9BsWJJ4abR+BjG3ftNHMIjbRlvDGVqMfJJ7hBw4ACKWJhOpAtAOkKdUZR0VVK9IowzPoCx5SHZbu4TS2CSkAQJsEUwGAzgQpjEt00YZOFo+q+ZNAJFfYZ+2FnCoAADCNzMJpizWjcjdHnhhJN6GOzL71kwVJdsmmAkVxxKOOUAkN+QcAOhRtOqhm+KfRhGpC6meoyomkDqs8PEtof4u2qYJ/ERr/UKLdf8KyNwIADBNMBQA6NlzeEapAthdl1Uj79Y55JFFHjLRKThUAAKijk9sGdSihplaT5USYJhSVSu1rLptqPUMAGGgioSGeCgBNGIkVlRCmCpWkyub6W+WOGJ7BYiYcZ9I6zIqeAgBMTzAVAIhIwBVd8qiHK3NN1JnIqQIAAJWVlkrNWNg1qXqmh0wnua9/cg0GgIQ0vWjY2otOM0aFacyvO6nrJqRt3fzWrpsA3chs3b/mIlV1xjyxDWb67akTT52+R3MbtiW9R0F9dY5L0399BEoBANonmAoAJGDWsrloU6lN1//Fdh2/fd4BgNIUe9IHoL5oTyJMr7nqfwnVvAmlx2nB5VgBLMtM/JqOpI68kAEqTNZtrjJILFY0FCKxWLopnnhhy/mrnCKpw3aet6WdpVODZFP7W2j0o29i4/F8a0iUuCkAQMwEUwGADCliA4AkBCwtjeTsP+hR/a5F0iOoTPl4cFb8qG+kstNemq5qtWKDErGZni6hmh9f/CY4ogKZMSVnSpUzDNWGpsNPMToFCiGmu5jdm9dL6XSlwHe+5m044h+31F86tTddN/s7TxLxVIC8pR6YdwoAICqCqQAAAEDjClmWKv4WQpuCZFQsnkaj+ntXzR21//S0dtRQ+bGWe10zjzryyKxbk1CFFiR33i9kmgMkREalTUFGp5VTEOKpAOkKsmoitCP7SOqwROOpw9sEKFPYkVU8R9QJ/Zq1y/F0CoAsLdu/f3/XbQCABFiapj53MwWAYinXhpIVeATIssuhJsXxTwzzW817IN2u1V+HqtvtE5tEg9lJKPC9be6kX1Tt77j8uh9kKBX/OKpRLS/L3MmBKFQZZdO1htEGadossmxoAGlcChlw0mdKqZz3gwvS8eR6naia87JeykOU9uekDY2xY/iyBOxaDN0BYlDsOAoAoiWYCgAJKKcCGADIWJZJrSWV2WsYUU5MJeOempa2nIWIR4d7Y/t1+ZIAhcj4WB2Doka/je5L+SUzZ5Jf92VU6mtzMNbhUajYqEZCHY/2ninTvwTQKCd9ppfQ6S+gMnsNFUR7O5iu+OIDfamkUmM+jDuiAhDciq4bAABA2tq5kuKaCEAGJld2TlljWq0UNWBRaaO1sKmU4MOs+vt2/a/PYAsRflnEnLI300cTeYo18t2sw3ToYAuztmHw98IAEMTRZ18c+ZGq59QPRGz35vUupzehnWFq/4+rvdbai04zHAUojfM+5Gqxr3bMSaf6HNA6F+oWpcVye5EWzG3YFuRI2PQgqomN530KACBpgqkAAMVp7TpFhYssi7UtbJtdzgaIUCoF2am0EyI0+PoETKiObLllYcOHDi858WlWENVqpZXzABKqsH3TxiDnx/5Goj2cSqVC+0IdXiIXqrySsNpfxbTmcNRYFMhPVBcNQkklVgFEZdbve4fzC4cmoCELHl4qHO4mPMURDABmsmz//v1dtwEAWEKoG5JlcF+ucmoy6l/gCPVeJXGpJUhnF+yp2wHWlMFhBwBaEGTIkehpt4Xi8sqRj3YK31NJpJiW0pA6Vf5JiLkEtlgihS0IeA6N8H1up3c1D4+pH3zy637JA/6AChmfF3Vhf6C5K/w1tR9Jja0BAB0KfsUgzgNjwPqHJM7+0Z7062himNr5oBSgfS6e0LJcy1BjGykBUAIrpiZpz5493/jGN771rW/dddddd91115133tnr9f6///XUpz71hBNOmJub67qZS8ijFwAEVEi9xZTdnPxni/WxkPdwXHMN7vaSpauuAED2RipsmijiiW1hJUVF0CsgjzpgDdUIFbLmXrf6J7ssl07NO3MLxMDiafGIYXW+ykun9qyeCqSp0csFwxuP5/DYP1+HOvv3EvyhP06dXzeYqQFmlwBQgVETAIRixdTEfO9737vqqqu2bt26a9euCX+2atWqE0444dRTT33qU5/aWtuml0cvAKiv2BRlc1K5w2i0txyLbV8STAWAQjjpT9B5DVBN2VQFWTGVILJZ8SmbjhTLoqmtCXse7/YNbzmSmt+SoTMpvPtM0MLsIIZje4E/ncT200acg704WwVQX6gbAcRwQ4E6gv+AHudIILaTfuoXnyeLYWQLMD2/lgIAJEowNRm7du366Ec/etVVV830kf3u7/7uG97whoMOOqi5hs0kj14AEEpsvzosJq0UZYE1K32p7E4zcdUVAArhpF9Ht8VD5RT3CKZSR6419HJTSQty+kjuLNBJr5s4U7f5zgdv/5SNL/wIU3j3maDpwX88B/bSrvPHc3k/8oFr5M0DmF6jIdKkE6pNVCbEMx6IYYTT5sXkJceWLV/Z7mqsG229zTTi+fpACfxaCrCYFuY4SU+jgM4Jpqbhm9/85gc+8IGf//znFZ778Ic//I//+I9PPPHE4K2aVR69ACCsGH576ERrZRZhr/LH/D7HU7kCAAD5EUylgnLq5uMPUCVdAjgs7LS9tHVTu+1vo+W2YT+CSJoa/4GlUYV3nwnKCab2gp6+47/uHcPl/ZpHnlQ4QgLdar/WOdHq6kZn8S0PDGKI2nZ1v6GwwvaichfyW9q3qFE3RE4wFShEHr+fuoYPDBNMTcC11177//7f/9u3b1/lLTzkIQ85//zzf+d3fidgq2aVRy8AaEd+17LHxVBmMSybEtVZRbhvALCkFE9bzjhA6gRTF5TiKWmyzpeZ6iX+S2QTfY/tAsJiov06TO57OdnUSHra7Trn3Zr1rSv5WNorvvtM1tyRJMKDeTlV8t0OeAqJpA5znIRZRTvfGRbtoT6SaGiixd8t73uz7kVdfTWmbGckE+EmtNa1Ym80D3RCMBUAIFGCqbG7/vrr3/nOd+7du7fmdlasWHHhhRf+9m//dpBWzSqPXgAQuSR+EZyS6/JAbKr9YD/8U339LZANVZWzirybQCFKC6Zmdr+kck6+1BTVpZU2d7aMa1V7QRNcoTpYWjy12vtWeDKz8O4zWVHB1F7os3O0Y7kOg6kFplIHHC3JWyq3+MlV5CnQyJu3oKgm7F2RRx3RaE/9wpWQ+C+zxP9tIhKCqQAAiRJMjdott9zypje9ac+ePQv+64EHHvjsZz/7sMMOO/TQQ3u93o4dO2655Zb//u//vu+++xb8+1WrVv3DP/zDYx/72AZbvJA8egEAAOlSX0XnlAEBpC7vYGqZhVbOzjAiwgBnTfH3KP7SyTpqvmmFJzML7z6TlRZM7WV3z5QFdTI0TTGVtCDHTBiRymw32pyhBainJ6Tagll3yCBjxWiHhQtqrssOpzWF6nveF096qX3jaFSBwdQmjmBtTvlLuF4xosC9FACmIZgatb/927+99tprxx8/4IADzjzzzA0bNqxevXrkn+65554rr7zyU5/61IJB0Gc961kXXHBBI21dXB69AOhKE7/f+I0ZAGhfmZmfXjq/3AOUyVEaGNFEtV9rBXbpNj6nIstQ71jhKaPYup/TLjqQdO1vuoe7ylKvVZ2s/UtG2URSh8V25ITItZBiaugwG1vLczr4pHt2iDaVV03l7045C6W2r8Af+Lq6aNzo3DP4vl3gPYNomshfWK2NEBI6vNeX5V5qEAVAfYKp8frOd77zl3/5l+OPP/ShD73wwguPO+64Cc/dtm3bO97xjgUXHX33u9+9bt26YK1cSh69AKiv8s8YTfyGEVVjgAkKLLECSOIHkvbrPIr6OQegTakEU6MtMXSGImNt5tCWnKq3H4rr6upBuvG/4O9YTiX+FdS/XeOU70DYXS6Sy27xr5ZcX7FXTRsaE3Y1outkZZXsj67ppqqAtOR9tMngZBHtdaQRYQchBa6VGoN2drbg49Womp3lDC7LTtGCLCN/ZMZeCgALEkyN1P79+9/85jf/4Ac/GHn8gAMOeNe73vWkJz1pyS18//vfv+CCC8ZXHD3mmGPe+973BmvoRHn0AmBYBj9CVFZy3ymcG4MBQAmUreSntTwDqUslmxpcsR2HCtJNS84qtsFMzO980+9V4RdjmxvIFTjsz/XaZpah4mnEtlDerBpt/+SWl3NcLaenQMvKPLzk1+s2M6ttXjXKddBbppaT1V1d3ixnclpOT6lP5A8AWpNKqUC0911SJjFCMDVSV1999fve977xx1/zmtf8/u///pQb+eQnP/nxj398/PHzzz//5JNPrtW+6eTRCwCAMn/K8gsBAJCi+jGGcRHWkNGQgD9sxP9TRCq/NkGEYk5LziqtaXuH73z7b1R+FfAzaSKY6kpXZgpfgaeTcpxpBn7d1gkt1sIyj6hl9hqAYpX5az4pKnNfLbPXABTO77BAUVZ03QAW9rnPfW78wcMPP/ylL33p9Bs544wzrr766ltuuWV84+1EOvPoBQCUJo/yyrCXpMu8wF1mrwGAFDURRl1w+2pzszfh571Zfz5s7efG2O4SGuSm5iUbv6G7t7SmJu6RPz5fTuJaSgbT/MldqPMpZPDmQGm2b9qYxLG3If1RYsvjwNiGncMmDJuLzWf2W165+/0nptt9AIAIFXu/pBTbDAA1Bc+UxnxpbkkStpA9wdQY7dixY35+fvzxs846a8WKGT6yFStWvPrVr37Pe94z8vi2bdvuuOOOgw8+uFYrl5JHLwDIWJDKziaqG0Mp56aDC/Z01u7H300AAEZULrEdLq6daSMSqiUL8oNZJ+nWcY3++NftNDnLmb63NAnNBSZDtaEE3gEmC5JjPPrsi+1pmUn6Ax0M6pKuS6tpyZFt4XO3wrsPAAAAkAfZTiBmgqkx+upXvzr+4Jo1a0444YRZN3XiiSeuWbPmnnvuGX5w//7911xzzcte9rLqTZxCHr0AgLSUeYfF5BoMAEAdQfKo4w9WS6guttnYZFyqntaPcGm1FnplZEpb4MIFRGL4y1j5Our4E+P8jpdw18LCF00d1skCqt0yrgYAIC0mL0BYYX8tbW6bAEAJBFNjdMMNN4w/+JznPGflypWzbmrlypXPec5ztmwZHfNdf/31TUc68+gFAKTFjf8BAMhV07+GVkuo9uJYRjV4DXqHdd5h1xRVsA5AiioPe5hewIWOMygvLvZqcGYdHx76ZhlSNbYHYhDw8pS6fwgo1C1LVEoAkIomxoTGmQBANcv279/fdRv4Dbt27TrrrLP27ds38vi73vWuY489tsIGb7zxxre+9a0jD65cufJf//VfV61aVbGVS8mjFwAVtPlj/6y/wbfTNpUB0QpbINXh7zHZdASA1syvO6n+RiwXBl3psEov5gLBUPO7VGZwQfqbSmepw0kfyEP9YGrqZWSFvANTXud0AXOyOpeLS3hvU0+oGsMDTSj8JiBJDJOgmlCFBGmNEoP0Os4u5/eBZvxhAbQmv7MDAGRAMDU6N9xww9vf/vaRB1esWHHppZeuWFFlhdu9e/e+4hWveOCBB0Yef+c73/lbv/VbFVu5lDx6AQAAQAZkVIhKy6VviZaa1XmXmuhyPAlV+UyYzEkfyEMhscwJvAPMpFpJYpkFiPHnVM1WADKTwdry47odRWT5lvaCvqtF5VU6Dzq2s0NWbmELzZu1bZ1/ZBCn4D+eui4EANCmKhFBGvXDH/5w/MEnPvGJ1fKcvV5v5cqVT3ziE7dv3z7+Qs1FOvPoBQBAQO3nGeJJUAB0S7yE+mr+IDp8bu3wPBuwFw2JLY86vvFZWzj89/VbGH8ROQBAEDvP21L4UmbMZPumjbOWdxdbyb1g7LOTiYYAKkDS8g7+Ldm7Jta9L/wtnfJvpuxd/8/qv6WDLUT4rgbMW07Tu3SzlBVedNbOLvb3i710hcnLYi8a4Z65mHR3IZpQv5Bpmi0s+DdKoYD45Xobmp5TOeROMDU6C0Y6jznmmDrbPOaYY8YjnT/60Y/qbHOyPHoBABBQ+5c4XVQFgFDyOKtG24sgqYP4owuDFlb+IOY2bEslmxptO5W/A2SgnZN+tAMnoD5VUCOmHCRPHuQnOtJuaObS9LuRaLOBdOWdmRzXTjuLiml1m0gMmFAd2XLLwqYUluxFaV/8gRYaHGrnjDk73VfsXsSwJu6nP/5PU77K+J+5AAjExlkPSJRganRuvvnm8Qef/OQn19nmgk9f8IVCyaMXAAAAADQk/ihpQ+okVINkU/tbaLTmuImNRxt2nUa1vb2/h9R5LlBB6kmP4O3vKqPSRNXaTNtf7A/aOcA6jPe8Ccxo+nWHVHdVlmhqMdSZMaruR9UYgCk5B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alt="Figure 9: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16" width="600" /> +<img 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alt="Figure 9: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16" width="600" /> <p class="caption">Figure 9: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16</p> </div> </center> <p><br /><br /></p> </center> <div class="figure"> -<img 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alt="Figure 10: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16" width="600" /> -<p class="caption">Figure 10: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16</p> +<img 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alt="Figure 10: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0" width="600" /> +<p class="caption">Figure 10: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0</p> </div> </center> <p><br /><br /></p> </center> <div class="figure"> -<img 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alt="Figure 11: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0" width="600" /> -<p class="caption">Figure 11: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0</p> +<img 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9WhH9V+tOjtByEsDaiXlho8ScwoXxaMRQfniQkxE0BlQQBVIi27TgzVnRiqCzFUQL20VAyVjAAqI2Ko+kiYk012xowZs88++7R/fv/99ycrsOOG++23X+KM/F5UoxVAd0888cSBBx547bXXru+tvxvWtddee9hhh3UciCmvW2+99fDDD1+wYEH+P/3Tn/70wAMP7O/vz/+ns/PrX//60EMPfe6550a11U033XTxxRcP/v9Lly4944wzVq1aNdyXN9hgg4022qh7gXffffcXvvCFUdWhLN72tre1fPKb3/zmHe94x/nnn79s2bJCqlRGH/nIR/7qr/4qwYbf/va3/+f//J8jfm3ChAlTpkwZ8WvnnXfe7bffnqAalfHpT3/61ltvHfFrfX19G2+88bhx47p/beHChTNnzly6dGmg2lE18+bNO+iggx5//PFRbfXss88edthhg/2E9evXn3POOStWrEhch/Xr11944YX/+I//mLiEeCxfvvyUU05Jecb99re/PfrooxctWhSqVtCRGCo4MVRYYqhMCaCCEEDFQwxFnsRQYYmhoJ6c+1lwh6Kq9Pa7EJZmyuBJKEb5smAsOjhPTIiZACoLAigqTAzVhRgqU2KoIARQGRFD1UpfIXEd3d1zzz3tt9LtttvuW9/6VoLSPv7xj8+fP7/lw/PPP//II49MWL/eVKMVwHAeeeSR97znPYsXLx7xmxtttNF222233XbbbbvtttOmTZs8efLEiRMnTZo0ceLEcePGrVy5cuXKlStWrFixYsUbb7yxcOHCBf/ulVdeGbHwLbfc8o477th7771DtKlgP/jBD84888y1a9cO94W3vOUt22677eabb75q1aqXXnppYGDg1Vdfbf/annvu+ed//ufLly9//fXXB3fp008//fTTTz/77LNder2DNtlkkx/96Efvec970jYmAq+//vr+++8/MDCQbPNbbrnl+OOP/9CHPvR3f/d3Lf80ceLEM844Y+bMme94xzs233zzMWPGrFy5cv78+f/0T/90880333HHHe2l9fX13Xnnne9+97uTVSYS7W+p/NWvfvXII4986lOfev3111v+aeedd/72t799zDHH5FW7svre97539tlnD/evY8aMuf/++w844ID2f3ryySf33HPPNWvWtP/ThAkTTjrppHe9612HHHLIdtttN3ny5EajsWLFihdeeOG+++676667brzxxo6jtBtvvPGvfvWrXXbZJUWDCtZ+lD744IP777//iBveeOONJ5988nD/euihhx577LGHH3747rvvPnXq1LFjxzYajd///ve/+93v7r333nvuuecnP/nJ6tWr2zc86qij7rrrrsHvl85vfvObG2+8MetfaRlM2WeffU488cQRt/r85z+fVYVy8cYbb+y///5PPvlkss3PPPPMa6655oYbbjjllFPa/3XKlClHHnnkbrvtttlmm40dO3bx4sXz58//xS9+MdwQz5vf/OZHH3102223TVaZSHz0ox/9m7/5m47/dMQRR7zvfe87+OCDp02btn79+oGBgYGBgVtuueWuu+7q+P13vetdd95556jef7RgwYL2HWikhY7EUMGJocISQ4UlgMqCACo4MVRAYqjsiKGCE0NBPTn3g3OHCkU/KiN6+1kQloZl8CQjRvmyYCw6OE9MiJwAKjgBVEBiqIyIobIghgpLDJUFAVRGxFC1k+8CrfTk1Vdffe9733t8m6eeemq0RT399NPt5Zxwwgm///3vs6h5s2q0Auho0aJFm222WZebyx577HHuueded911zzzzTOJfeeGFF26++eaLL774yCOP7DI6M3369ApcDZ599tnh3oay9957/+3f/u3zzz/fssmaNWt++ctfnnPOOePHj2/Z5IMf/ODatWvbv//www9ffvnlxxxzzMSJE4fbnxMmTHj00UfzaneGzjrrrOHaOG7cuD322OPoo48+/PDD3/KWt3T8ztZbb33PPfe0H3inn376/Pnzu/zu3LlzO64Zvs8+++TW9oy0N+rBBx9cv379M888c9hhh3XcjSeccEKC+359zJ8/f9KkSR133QEHHPCXf/mX7Sf+kI6DfWPGjLngggteeuml7r/70ksvXXjhhRtssEHHP1noVuZquKO0u1WrVg13KXjf+9730EMPjVjC/Pnz/8t/+S8db1Xf/OY3Q7SsAD/84Q877pMYFL1v0upyhxrU/R1pfX19Dz74YPuFd6eddrr22mtXrVrV8UcffvjhU045peNR+v73vz/nPRDWwMBAx3btu+++995773BbPfDAAzNnzuy4hy+99NJRVaDjq5RTN4sKEkMFJ4YKTgwVVnuLBFApCaCyMNyB2p0YqiMxVHbEUGGJoaCenPtZcIcKRT8qI+3N0dtPSVga3HBHqcGTlIzyBWcsOjhPTIicACoLAqiAxFAZaW+OGColMVRwwx2lYqg0BFBZEEPVULk7ARV26aWXtqdiXnHFFaMt52tf+1p7Of/tv/23LOrcrhqtANqdfvrpHe/fm2666bnnnttLPDZaCxYsuOKKK2bMmNHxd0877bTgv5indevWHXXUUe3t2nLLLW+++eYRN//d73539NFHt2z7gQ98oMsmr7766le/+tUddtih4/6cMWPGsmXLwrWvAI8//nj7y4EajcY+++xzww03/OEPf2j+8nPPPXfZZZdtvPHGLV9u791efvnlvfz6G2+80TEwvvXWW7Npbk7aWzR0sq9du/byyy9v32ODu/HCCy987bXXiq18nM4777z2PbbxxhvfcMMN3Te8//772zfcbLPN7rvvvt5//b777ttmm23ayxlVIbHpcpR28a1vfat9w8mTJ3/ve98b1a/fcccdm2++eUs5W2yxRcs1pywMZGfkN7/5Tcc71Pjx4z/0oQ/95Cc/WbRo0bp169avX79s2bKnn376m9/85n777dfy5V133bXlk+OOO66XI+2ee+5pf8o7ZsyY/v7+7JueldmzZ7fvz5NPPnn58uUjbnvZZZe1b7vBBhs88sgjvVegks8FyYIYKiwxVHBiqODamyOASkkAlYUuB2oXYqiOxFAZEUMFJ4aCenLuB+cOFZB+VEbam6O3n5KwNLguR6nBk8SM8gVnLDoLnpgQOQFUcAKosMRQGWlvjhgqJTFUcF2OUjFUMgKoLIih6qncnYAKmzdvXnsq5vHHH//YY4/1Xshjjz3WsZAe+9Nr1669fxivvvpqWVoBBPeP//iPHe/c//k//+dFixZl+tNr1qy58sorO75F48Ybb8z0pzP1ve99r71FhxxyyIsvvthjCWvWrPnoRz/aUsJ3v/vdEbe66qqrJk+e3P7rH/3oR1M3q0jt77Dp6+v7whe+MDiG1dHzzz+/9957dzy2B1144YW9V2DlypUHHHBASwmHH354iMYVpn2ftAy+/Ou//mvHV/g0Go0tt9zyO9/5zpo1a4qqfIRefvnl9leC7bHHHr28oepjH/tYy4ZTpkx54IEHRluHJ554on3s9d3vfneiBkVhxKO0o5133rllq7Fjx95xxx0JKvDQQw9tuOGGLaWV9HUqBrIzcvbZZ7e36J3vfGeXd6StW7fu85//fJcdctBBB61evbrHCjz99NNTp05tKeHss88O1L68vfbaaxMmTGhpzrve9a7e7zjXXHPN2LFjW0o4+OCDu/QZWlTvuSBZEEMFJ4YKTgwVXPsOEUClIYDKyIgHakdiqI7EUBkRQ4UlhoJ6cu5nwR0qIP2ojLQ3R28/DWFpFkY8Sg2eJGCULzhj0cF5YkLkBFBZEECFJYbKSHtzxFBpiKGyMOJRKoYaLQFUFsRQ9VTuTkC1feYzn2nPxvz4xz++atWqXjZftWrVxz72sfYSLr300h4rsHz58o4Zoccff3zvKxoX3goguHe+850tN+xx48b18hKLUObPn9/+BqxSRwtHHHFES3P22muvpUuXjqqQtWvXHnfccc2FbLLJJgsWLBhxwyeffLL9HWON3uLqOL366qvjxo1rac4Xv/jFXjbcaaed2ndFo9HYZ599eh/MGvRv//ZvLS/CGTNmzJIlS5I2q3i9HCSrV6/+i7/4i4kTJ3bcjdOnT7/mmmtGuyerqn2UcNy4cY8//ngv226//fYt2379619PVo177rmnpaixY8e+8soryUorXC9HaYv+/v72rb7xjW8krsN1113XUtpOO+2UuLQCGcjOQseHWH/yJ3/Sy6jfJZdc0nFvjBs3bmBgYFTVaH/cu+GGG65cuTJps4rU3pZNN910tA+tv/zlL7fv2BEHs4ZU77kgWRBDBSeGCksMlYVejhABVO8EUBnp5UBtIYYajhgqC2Ko4MRQUE/O/eDcocLSj8pIe3P09tMQlmahl6PU4MmoGOXLgrHo4DwxIXICqOAEUMGJoTLS3hwxVBpiqCz0cpSKoXongMqIGKqe+tZ3ukgRg0cfffTSSy9t/3y//fb77Gc/23Gt7SErVqz4H//jfzz66KPt//TlL395991376UCK1asOOWUUzr+02WXXdY9139I4a2gzu66666rr7666Fp0dsMNNxRdhYReeOGF7bbbbt26dUOfjBkz5gc/+MFpp52WZzXmz59/6KGHzp8/f+iTvr6+Z599drh12GM2f/78HXbYofl2PGHChHnz5k2fPn20RS1ZsuQtb3nLH/7wh6FPzjjjjPZgtd3SpUsPPvjggYGB5g/POeecv/7rvx5tHWLw05/+9L3vfW/zJwcffPDcuXP7+vpG3Pbee+9t7xM3Go2bbrrpfe9732hr8md/9mff/e53mz+54YYb/uRP/mS05USifQc++OCD+++/f/s3n3766dmzZ//zP/9zx3J23nnnz33uc7NmzWp/QWCttB8eF110Ucex6Rbz5s3ba6+9mj/Zeeed+/v724PkHp122mk/+tGPmj/5+7//+9NPPz1ZacXq/Sgd8pd/+Zfnn39+8ye77777r3/96w022CBxNQ444IAHH3yw+ZPf/OY3peu7Llmy5KyzzrrtttuKrkgH5Y1hb7/99v/4H/9j8yfTp09/9NFHO76pq8WKFSv23HPPZ555puXzk08++frrrx9tTf7Df/gPd911V/Mn995772GHHTbacgp38cUXt1w5v/jFLw73dGo469atO/roo3/+8583fzht2rSnnnqqlz/NggULtt1225YPy3uUiqGyIIYKTgwVnBgqCwKosARQGRFDBSSGyoIYKjgxVFgCqODs0ow494NzhwpLPyojevthCUuzYPAkOKN8wRmLDs4Tk7DEUFkQQAUngApODJURMVRYYqgsiKHCEkBlQQxVW7W+mkRun3322XfffR955JGWzx9++OHLLrvs/PPP33TTTTtuuGTJkiuuuOKJJ55o/6eDDjoo595JNVpBSf3bv/3bjTfeWHQtquaHP/xh8/hgo9H4wAc+/Jgz/wAAIABJREFUkPP4YKPR2G677b7zne8ce+yxQ5+sX79+zpw5f/7nf55zTdL74Q9/2BKQf/KTn0zQA2s0GpttttknPvGJyy67bOiTm2666fXXX58yZUr3DadOnXrbbbe9/e1vf+2114Y+/Pu///uvfe1rkyZNSlCTYrW/5eizn/1sL6FCo9E4/PDDjzjiiF/84hfNH2611VYnnnhigpp89KMfbYkW7r777pJGC6Myffr0e+655+/+7u8uuOCCpUuXtvzrM888c/bZZ1922WWf/vSnZ82a1cs4YyU9/PDDzf85YcKEz33uc71s+NRTT7V8cuqppyYeeWk0Gp/61KdaBl/+9//+3yUdfEmg5UlAo9G4+OKL04wPNhqNiy666OSTT27+5Lbbbitd93WzzTa79dZb//qv//qCCy5Yvnx5+xd23HHHHXfcMeWv/OxnP2v+z6222mq33XZLWWbMWu4vjUbjkksu6fEyOHHixHPPPfeTn/xky+dnnnlmgppccMEFLU9cfvazn5Xxicv999/f/J99fX0f+chHRlvImDFjvv/977/tbW9r7ggtXLjwK1/5yhe+8IX0lSwXMVQWxFDBiaGCE0MVSwDVCwFUPMRQwxFDZUEMFZwYKiwBVHB2aUac+8G5Q4WlHxUPvf0uhKXFMnjSI6N8wRmLDs4Tk7DEUFkQQAUngApODBUPMVQXYqhiiaF6IYDKghiqvvJbnJXRe+21184+++zjOznppJP+9m//9ne/+93Qcs/Lly8fGBi48sorTzzxxI6bfOQjH3n99dd7//Xly5d3LOf4449/9NFHy9IK6uyqq64q+hI7rKL3TXLvf//7mxvS19f3+OOPF1WZlqWbTznllKJqksapp57acng899xziUtrj8p+8IMf9LjtRRdd1LLtddddl7gmBWoZMJoyZcqqVat63/wb3/hGy36YNWtWspqsXbv2TW96U3NRRx11VLKiYtB+KXvwwQe7b/Liiy+eeeaZXUK1TTbZ5L/+1//65JNP5tOEqLS83fDII4/sccP21/bce++9KSuz3XbbNRd4xBFHpCywKAmO0re//e3N399ggw1eeeWVlNV47bXXWl4nduKJJ6Yss0CPP/74vvvu275vJ02a9M1vfnPdunVpCm8p86yzzgpV7Ti9853vbG7vhhtuuHLlyt43b7/LNxqNl19+OUFNVqxY0TLO8p/+039KUE7hWgbf3/72tycu6utf/3rLvp08efKCBQtG3PD5559v/7skrkbhxFBZEEMFJ4YKTgyVhfbrmAAqDQFURhIcqGKoEYmhAhJDBSeGCksAFZxdmhHnfnDuUBnRjwqrfU/q7achLM1CgqPU4El3RvmCMxYdnCcmYYmhsiCACk4AlR0xVFjte1IMlYYYKgsJjlIxVBcCqCyIoWprzHBXGWKw8cYbX3zxxePHj2//p1WrVv34xz/++Mc/ftJJJ334wx+eNWvWKaeccv755999991r165t//7EiRM/97nPFfJKg2q0AhjUMvCxxx57zJgxo6jKtLwzb/78+UXVJI0XXnih+T/f/va3b7/99olLmz59esubRX7605/2uO25557bEtP+n//zfxLXpECLFy9u/s+DDjpoVC9MOvzww1s+OeCAA5LVZMyYMS2jD4sWLUpWVEltueWW11xzzf3333/wwQd3/MJrr7125ZVX7rbbbscee+wtt9yyZs2anGtYoCVLljT/50477dTjhi+++GLLJ+nfctfyxGXhwoUpCyyRl156qfk/991336lTp6Ysc+ONN95///2bP2n/q5XIjBkz7r///gsvvHDMmD8KHpcvX37uueced9xxLTcyumi5Cxx22GEdo6Th7LzzzhtvvHHzJ9tvv33LsFSPJkyY0HJ3azkXyuLll19u/s+99torcVHnnHPOHnvs0fzJG2+8cckllyQuEIaIoYITQwUnhoqEAKoLAVQ8xFAjEkMFJIYKTgwF9eTcD84dKiP6UYXT2+9CWBoJgyfdGeULzlh0cJ6YED8BVHACqOyIoQonhupCDBUJMVQXAqgsiKFqS2Jq7KZPn/6xj32spdfYbP369YsWLWpeaLjdBhts8KlPfarlfQ95qkYrKJ399tvvmGOOGVUvgRG1DBHuvPPORdWk/ddLOkS4YMGC5v/cc889UxbY8ua25557rscNt91225bhsN63jUpLTLvNNtuMavP272+xxRaJK9OybUskUxMHHnjg3Llzv//972+99dYdv7B+/fo77rjjhBNOmDZt2jnnnPPzn/983bp1OVcyfxtuuGHzf06YMKHHDTfYYIOWTzbffPOUlWk57Es6npVMSwwfqqvZUk7Zd+n48eOvuOKKu+++u+Vtdo1G44477thrr71uvPHGQipWOi0PsVrGTUbU19fXskma07/lxG+5e5bF0qVLm/9zyy23TFzU2LFjr7zyypYPr7nmml//+teJyywjMVQWxFDBiaGCE0NFRQDVkQAqHmKoXoihQhFDBSeGCksAFZxdmhHnfnDuUNnRjyqW3n4XwtKoGDwZjlG+4IxFB+eJSVhiqCwIoIITQGVKDFUsMVQXYqioiKE6EkBlQQxVWxJTS+Doo4++7LLLEr9FY4sttrj88suHe89BbqrRCsrlgAMOuPPOOxcvXnzttdceeuihRVenIlr6YVtttVVRNWk0Gi3hdEn7YS1vB5k2bVrKAltGxEb1dp+3vOUtzf9Z0k7Y73//++b/3GijjUa1+SabbNLySct710al5Q1tr776auKiSq2vr2/WrFlPPvnkF77whfY9PGTJkiVXXXXVUUcdtd12233yk5/85S9/2XEF9WrYYYcdmv+z94cc7VeJ/8vevcdZVRb6458ZYEQuASGCBIiK4OUoJqFgRlxSQzQVoToehS7HTE1TvCRFqS8hK/GkYUcPdRAqj3a8kEZSQmoYBkqGpNwJQWBgkjsoCDPz+4PX4be/ew/Dmr2fta/v91/tNXuv/ayPs2evz7N6Fu+//36GgyninA8r6SJNJrMDiZJmxIpjPmvQoEGLFi36/Oc/n7R9y5YtI0eOHDVqVMN3maGsrGzLli2JD9u3b9/YPST9ijbwF/Wwkt69QK+4JE1kZ3jfviFDhlx22WWJW2pra2+99dZM9llwdKg46FDB6VDB6VD5RoFKpUDlDx0qOh0qczpUcDpUWApUcCKNic9+cL6h4uY8Klec7TdALc03Jk/qZZYvOHPRwbliEpYOFQcFKjgFKgt0qFzRoRqgQ+UbHSqVAhUHHapkNT38U8gDp5122k9+8pP7779/4cKFjXph//79b7zxxpYtW8Y0sEYpjqOg4LRp02bUqFGjRo167bXXrr/++gULFtT7tEGDBvXq1SvLYytEHTt2TGwISXe2yLKkxpXJLcpyqHnz5nv27Dn4cN++fRnuMKnuNqqXJt3BpUBPwtq2bZs4X9zYiZXUE/pMTvGT3j2pPJSaVq1a3XnnnTfeeON//Md/PPjggzt37jzUMzds2PDAAw888MADbdq0GTx48Pnnn3/++ecnlYRC171797/97W8HH/7jH/+I+MLUqrZp06a07/1xQNIf88be/KmgffSjH921a9fBh5n/ET6gvLw88eGRRx4ZZLc5165du1//+tcXXnjhDTfckPQR/uUvf/mnP/1p2rRpAwcOzNHoCkCzZs0+/PDDgw8Tf/ciSrpMUllZmfZgkn7bC/RGd506dUr8mn7nnXcy3OH9998/c+bMxHOzWbNm/e53vxs2bFiGey4sOlRYOlRwOlRwOlR+UqASKVD5Q4dqFB0qQzpUcDpUHBSo4EQanM9+cL6hssB5VE4422+AWpqfTJ4kMcsXnLno4FwxiYMOFZYCFZwClR06VE7oUA3QofKTDpVIgYqDDlWyLEwtGG3atLn77rvfeOONWbNmzZ8/v+HbD1RWVvbr1++8887r3bt32u/YvHnz5557Lu2X1yv7RwEHnXXWWfPnz7/66qunTJmS+tPRo0ePHj06+6MqOF27dk2cIly5cmUOB7NixYrEh127ds3VSDKRNJ+VdLOQNGzatCnxYaN6adINYAp08uWoo45KbAvRb7Z0wNq1a5O2JEXaKEmvTbqdVWlq167dPffcc9NNN02cOHHSpEm7d+9u4Mnbt2+fPn369OnTy8rKjj/++AEDBvTt27dv376nn376EUccka0hxyLprmDLli1bunTpSSeddNgXptb7v/71r1FeeCi1tbVvvPFG4pbOnTunvbeC87GPfSzxU5/J5z1R0h0fiyzS0aNHf+pTn7rqqqteffXVxO1r164dMmTImDFjxo8fX+if0Ji0b98+8Y9eY7+hyoJONyf9lmZ+f7Kc6NSp09KlSw8+XLVqVYY7PO6442655ZYJEyYkbrz++usHDhxYmndK0qGC0KGC06GC06HymQJ1gAKVP3SoNOhQadOhgtOhYqVABSfSUHz2g/MNlTXOo7LM2X4D1NJ8ZvLkILN8wZmLDs4Vk1jpUEEoUMEpUNmkQ2WZDtUAHSqf6VAHKFBx0KFKVkWuB0AjlJeX9+nT54477pg2bdrVV1993nnnnXnmmV27dm3RokWLFi26det25plnXnDBBddee+20adNuvfXW/FzPWRxHQYGqqKj4+c9/fuGFF+Z6IAWsW7duiQ+XLl3617/+NVeDeeKJJxIfFugUYadOnRIfvvbaaxnucPny5Q3sv2GLFy9OfHj00UdnOJicSLqL4fz58/fu3Rv95a+88krSlrT/o9TW1r7++uuJWwq3LQTXvn37e++999133/3Rj36U9IflUP7xj39MnTr1+uuvP+uss1q3bt2nT59rrrkm7nHG54ILLkh8WFdX99BDD0V54SmnnNKjR4/ELb/73e8yGcmcOXOSyltJ3RWsf//+iQ/XrVsXZLdLlixJfFh8c9nHH3/8nDlz7r777qZN/59bHdXW1k6cOLFv376LFi3K1djyWdKNPNO4iBXwikvS7FiB/pYmnecsXLgw8wvYY8eOTTqrXLNmzbhx4zLcbeHSoTKnQwWnQwWnQ+U/BUqByh86VHp0qPToUMHpUHFToIITaRA++8H5hsom51HZ5Gy/AWpp/jN5UmaWLwbmooNzxSRuOlTmFKjgFKgs06GySYdqgA6V/3QoBSoOOlTpqgMoMStWrKisrEz6Yzh16tRcj6sw/OxnP0uKbvjw4TkZybPPPps0knvvvTcnI8nQ1VdfnXQgb775Ztp7S70Fy6BBgyK+dt26dRUV/88dK/r375/2SHLojjvuSArhiSeeiP7ys88+O+nlRx99dE1NTRojmT9/ftKurrvuujT2kyeSTyLLyhYsWBBkz/v373/yySc/+clPpr7FYQUZQE7U1NQkzTs3b948YqTf/va3E1945JFHrlu3Lu2RDB06NCnVyZMnp7233ErjtzTp26S8vHzt2rUZDmPbtm1NmjRJ3O1VV12V4T7z1l/+8pcTTjghNfnKysr77rvvsH88k171pS99KTvDzpXUS4ArVqxo1B7efvvtGQnmz5+f3kjef//9Zs2aJY5k5MiR6e0qt+68886kSG+55ZbMd/v0008n7ba8vHzWrFn1PrneSwuZjyHf6FCZ0KGC06GC06HikPzdoEBlRoGKSRq/qDpUhnSoRtGhgtOhskOBCk6kGfLZD843VE44j2qs1Kyc7WdCLY1DGr+lEZXm5EmdWb4YmIsOzhWT7NChMqFABadA5YoO1VipWelQmdCh4pDGb2lEpdmhFKg46FAly7+YCpScHj16DBs2LNejKFQjRow44ogjErc888wzDz74YJaHsWzZsq997WuJWyoqKq688sosDyOI1N/GH/7wh2nv7dFHH03acvrpp0d87U033ZT079Qn3dKpUAwePDhpy/e///39+/dHee0f/vCH1FP86urqp556Ko2R/OQnP0nact5556Wxn6LXpEmTESNG/PnPf/7b3/520003Jd2IqFhVVFR85StfSdyyZ8+eSy+9dOPGjYd97RVXXJFYmT744IPUmfGIpk+fPnPmzMQtlZWVI0aMSG9vhWjo0KGJd1Gqq6v73//93wz3+Ytf/KKmpiZxS79+/TLcZ97q16/fwoULv/zlLydt//DDD2+77bZBgwatWbMmJwPLT+eee27SlqlTpzZqD6eccsqwBGeddVZ6I5k9e/a+ffsStxToHRaHDx+etGXy5MmZ37N2+PDhSRPTdXV1o0aNqqqqynDPhUuHyoQOFZwOFZwOVVgUqAMUqFzRoTKkQzWKDhWcDpUdClRwIs2Qz35wvqFywnlUFjjbb4BaWlhKc/KkzCxfDMxFB+eKSXboUJlQoIJToHJFh8oCHaoBOlRhKc0OpUDFQYcqXblbEwuQM0888UTSH0N3BYvusssuS0qvvLx80qRJWRvAm2++2blz56QxDB06NGsDCGv37t3NmzdPOpyXX345jV1t3LixdevWSbt64YUXorx2xowZqScJzz//fBrDyLndu3e3bNky6VjGjBlz2BdWVVUdar7p1FNP3bt3b6OG8eabbybdbaVJkyZbt25N97ByLzWWUPdbSrJ///7nn3/+i1/84pFHHlnvf45EcQwga9asWZN00aWsrOz444+Pcm+/b37zm0kvnDZtWmMHsGDBgtQ/GpdccklaR5MX0vstveuuuxJf0qlTp/feey/tMezcubN79+6JOywvL3/33XfT3mGheOqppz760Y+m/if4yEc+0sAvZ9KTi/4Oi3/5y1+SDrl169bLly/P/khSr/1Mnz49+8MIomfPnknHctppp+3evTvD3a5atSr1DO2ss856//33k55ZZDesbYAOlQkdKiwdKjgdKg6pmShQGVKg4pDeL6oOFYQOFYUOFQcdKjsUqOBEmiGf/bB8Q+WW86iIUiNytp8htTS49H5L01A6kydm+YIzFx0HV0yyQ4fKhAIVlgKVczpURKkR6VAZ0qGCS++3NA0l0qEUqDjoUCWrgP8WAKTtn//8Z9KXjcmX6BYsWNCkSZPUL+yBAwcuWbIk1rfesWPHmDFjmjZtmvruzz33XKxvHavLL7886XDat2/f2DDff//9c845J2k/rVq1inKO++STT7Zp0ybptUcddVTqzFehSO2lZWVlN95444cffniol7z99tvHH3986qsOuv7666MPYNu2bSeeeGLSHoYMGRLi4HImNZOYau1BO3fufOqpp0aPHt2hQ4dD/XeJdQBZUO+9P5s1a3bPPffs3LmzgRfu2rXruOOOS3xVZWXlY489Fv2tX3jhhaOOOir13Qu6faUezkknnTRixIg77rjjv//7v+fMmbNhw4bUV23bti1ppmDkyJFpjyHpZm9lZWV9+/bN4JgKybp164YMGVLvR/Xyyy+vd+I16WlFP5G9b9++du3aJR117969s/yFO23atKQxtGnTZs+ePdkcQ0Df/va3U3/lBg8eXFVVleGe6/0TPXjw4F27diU+rciuCzZAh8qEDhWcDhWcDhVcaiAKVOYUqOBSj0iHyiYd6rB0qDjoUNmhQAUn0gz57IflGyrnnEdFkRqOs/3MqaVhpR6OyZPMmeULzlx0cK6YZIcOlQkFKiwFKh/oUFGkhqNDZU6HCiv1cHSoDClQcdChSlNh/y0ASFvSOavJl0a55ZZb6j2datq06SWXXDJ9+vQGzsnSM2/evOuvv77eWzeVlZV9+ctfDvt2WfbWW28l3e+krKysY8eOs2fPjriHjRs31jt3cNgT3B07dnz961+vN9Xvf//7GR9ZzqxZs6beqeQTTjhh8uTJiROFNTU18+bNu/baa5s1a5b05NS7towdO7a2tvaw715VVXX22WenvvusWbPiPOjYpR5R3LX2oJqamrlz537rW98644wzkj4s2RlAfGpra4cOHVrvZ7Bt27Zjx45t4J5n8+bNa9u2bdKrrrnmmo0bNzb8phs3brzxxhvLy8tT33T06NGBjzC76k0yScuWLc8444wRI0aMHTt2ypQpc+bMqaqq+vWvf530tO9+97uNffeampp6pyp+8IMfxHGw+am2tnbixImVlZWpORxzzDEzZ85Men7Sc0phIvt73/teajinnXbaokWLsjOAl156KfULrqCTX7NmTerd0crKyo4++uinnnqqpqYmk51ffPHFqXs+9dRT582bd/A5RXZdsGE6VCZ0qLB0qOB0qOBSD0eBypwCFVy9YSbRoWKlQx2WDhWcDpU1ClRwIs2Ez35wvqFyznnUYaUmk8rZfmOppWGlHpHJk8yZ5QvOXHQcXDHJDh0qbQpUcApUPtChDis1mVQ6VGPpUGGlHpEOlSEFKg46VGkq7L8FAGkbOXJk4veNyZdG2b17d48ePer95j6gffv2l1xyyb333vviiy+m9w/K7969e/78+ZMmTbryyiuTZsqSnHLKKbt37w5+jFn25S9/OfXQKioqvva1r61ataqBF+7evfvhhx+u9240LVu2PFQH27Vr1xNPPHH55ZcfeeSR9aZ6zDHH7NixI55jzZLbbrvtUL8z5eXlnTp1+pd/+ZcePXq0bNmy3ucce+yxc+fOTb1N42c/+9m33nrrUG9aW1v7P//zPx/72MdSd3jWWWdl8/DjkHpQWau1ibZs2fKb3/zmpptuOlBxsz+A4DZt2pR047Qkp5122i233DJz5sx169Yl9dVFixZ17tw56flHHnnk17/+9RkzZiTdV2zz5s1PPfXUV77yldQmfEDXrl23bduW3aMPrIEYG9aqVavUjWPGjIn+1suXLx8wYEDqTjp37tzwDd6K0sKFC0899dR6o77uuusSv7WTfloKE9lbtmyp9yLWEUcc8e///u/PP/98lNt6pWfPnj3f//73651N+8Mf/hDTm2bHww8/fKhP93HHHXfzzTc/++yzK1euTOMa4XvvvdelS5fU3VZUVNxwww3vvPNOXTFeF2yADpUJHSo4HSo4HSqs1CNSoIJQoMJqIMmG6VBh6VAN0KHioENlhwIVnEgz5LMflm+oPOE8qgGH+sgflrP9hqmlAaUelMmTIMzyBWcuOjhXTLJDh8qEAhWWApU/dKgGHOpTf1g6VMN0qIBSD0qHypwCFQcdqgSV12XwVQpQuL73ve/dc889Bx9OnTp19OjRORxPwVm7du3gwYNXrVoV5cmtW7fu2rVrly5dOnfu3KpVq+b/p7Kyct++fXv27NmzZ8/evXt37969cePGdevWrVu3bvPmzVH23KVLlxdeeOHkk0/O7Ghyr6qqqk+fPlVVVak/atKkyaBBgz7zmc984hOf6NixY9u2bXfu3Ll58+bFixe/8sorzz///JYtW+rd51133XXnnXfW+6MJEyaMGzfuUINp0qTJiy++WG/XLSD79u375Cc/+frrr6fx2oqKilmzZg0ePPjaa6995JFHUn/62c9+dtiwYf369evUqVPr1q3fe++9tWvXzp49++mnn16yZEnqDps0afLyyy+fe+656RxJ3ki9idSCBQv69OmTk8EcsGXLlkPd0rKwvPPOOxdddNHbb7992Ge2aNHixBNPPOGEE9q1a9eyZctWrVq99957kydPrvfJ5eXl7dq169ChQ01NTXV19Y4dOxrYc7t27X73u9/1798/zWPID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alt="Figure 11: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16" width="600" /> +<p class="caption">Figure 11: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16</p> </div> </center> <p><br /><br /></p> </center> <div class="figure"> -<img 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alt="Figure 12: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0" width="600" /> -<p class="caption">Figure 12: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0</p> +<img 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alt="Figure 12: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0" width="600" /> +<p class="caption">Figure 12: test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0</p> </div> </center> <p><br /><br /></p> @@ -1930,6 +1930,7 @@ div.tocify { </div> <div id="final-insertion-site-files" class="section level3"> <h3>Final insertion site files</h3> +<p>Warning: in these files, the position indicated is the first nucleotide of the genomic part of the read (the W of the 5’GWT3’ consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position.</p> <p>See the <a href="./files/test.fastq2_q20_nodup_annot.pos">test.fastq2_q20_nodup_annot.pos</a> and <a href="./files/test.fastq2_q20_nodup_annot.freq">test.fastq2_q20_nodup_annot.freq</a> files</p> <p>Number of total positions without duplicates: 1,660</p> <p>Number of different positions without duplicates: 1,145</p> @@ -1939,6 +1940,7 @@ div.tocify { <h3>Motif selected for the random insertions</h3> <p>The forward motif is: G[AT]T</p> <p>The reverse motif is: A[AT]C</p> +<p>Warning: the position indicated is the first nucleotide of the genomic part of the read (the W of the 5’GWT3’ consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position.</p> <p>Beginning of the motif positions in the forward strand:</p> <table class="table table-striped table-bordered table-responsive table-condensed" style="font-size: 10px; width: auto !important; "> <thead> @@ -2017,50 +2019,50 @@ Sequence <tbody> <tr> <td style="text-align:right;"> -9 +12 </td> <td style="text-align:left;"> -GAT +ATC </td> </tr> <tr> <td style="text-align:right;"> -126 +28 </td> <td style="text-align:left;"> -GTT +ATC </td> </tr> <tr> <td style="text-align:right;"> -136 +42 </td> <td style="text-align:left;"> -GAT +ATC </td> </tr> <tr> <td style="text-align:right;"> -189 +75 </td> <td style="text-align:left;"> -GTT +ATC </td> </tr> <tr> <td style="text-align:right;"> -192 +121 </td> <td style="text-align:left;"> -GAT +AAC </td> </tr> <tr> <td style="text-align:right;"> -225 +156 </td> <td style="text-align:left;"> -GTT +AAC </td> </tr> </tbody> @@ -2910,11 +2912,11 @@ Reverse </tr> <tr class="even"> <td align="left">Git info<br />(empty means no .git folder where the main.nf file is present)</td> -<td align="left">v8.5.0-dirty</td> +<td align="left">v8.6.0-dirty</td> </tr> <tr class="odd"> <td align="left">Cmd line</td> -<td align="left">nextflow run main.nf -resume</td> +<td align="left">nextflow run main.nf -c nextflow.config</td> </tr> <tr class="even"> <td align="left">execution mode</td> @@ -2926,7 +2928,7 @@ Reverse </tr> <tr class="even"> <td align="left">result path</td> -<td align="left">/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1649703168</td> +<td align="left">/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1657729206</td> </tr> <tr class="odd"> <td align="left">nextflow version</td> @@ -2990,7 +2992,7 @@ Reverse <tr class="odd"> <td align="left">out_path</td> <td align="left">output folder path</td> -<td align="left">/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1649703168</td> +<td align="left">/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1657729206</td> </tr> <tr class="even"> <td align="left">in_path</td> diff --git a/example_of_result/20220120_test_1649703168/reports/base_freq_report.txt b/example_of_result/20220120_test_1657729206/reports/base_freq_report.txt similarity index 100% rename from example_of_result/20220120_test_1649703168/reports/base_freq_report.txt rename to example_of_result/20220120_test_1657729206/reports/base_freq_report.txt diff --git a/example_of_result/20220120_test_1649703168/reports/bowtie2_report.txt b/example_of_result/20220120_test_1657729206/reports/bowtie2_report.txt similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/bowtie2_report.txt rename to example_of_result/20220120_test_1657729206/reports/bowtie2_report.txt index 9643a93492f14c10093fd39c40d463dfcd686d17..579219706fa6be5eee5e4f189569c32d37082335 100644 --- a/example_of_result/20220120_test_1649703168/reports/bowtie2_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/bowtie2_report.txt @@ -41,7 +41,7 @@ Building DifferenceCoverSample Building sPrime Building sPrimeOrder V-Sorting samples - V-Sorting samples time: 00:00:00 + V-Sorting samples time: 00:00:01 Allocating rank array Ranking v-sort output Ranking v-sort output time: 00:00:00 @@ -67,7 +67,7 @@ Getting block 1 of 1 No samples; assembling all-inclusive block Sorting block of length 4641652 for bucket 1 (Using difference cover) - Sorting block time: 00:00:01 + Sorting block time: 00:00:00 Returning block of 4641653 for bucket 1 Exited Ebwt loop fchr[A]: 0 @@ -108,7 +108,7 @@ Headers: reverse: 0 Total time for call to driver() for forward index: 00:00:01 Reading reference sizes - Time reading reference sizes: 00:00:01 + Time reading reference sizes: 00:00:00 Calculating joined length Writing header Reserving space for joined string @@ -129,7 +129,7 @@ Building DifferenceCoverSample Ranking v-sort output Ranking v-sort output time: 00:00:00 Invoking Larsson-Sadakane on ranks - Invoking Larsson-Sadakane on ranks time: 00:00:00 + Invoking Larsson-Sadakane on ranks time: 00:00:01 Sanity-checking and returning Building samples Reserving space for 12 sample suffixes @@ -189,7 +189,7 @@ Headers: ebwtTotSz: 1547264 color: 0 reverse: 1 -Total time for backward call to driver() for mirror index: 00:00:02 +Total time for backward call to driver() for mirror index: 00:00:01 <br /><br /> @@ -200,15 +200,15 @@ Total time for backward call to driver() for mirror index: 00:00:02 Time loading reference: 00:00:00 Time loading forward index: 00:00:00 Time loading mirror index: 00:00:00 -Multiseed full-index search: 00:00:01 +Multiseed full-index search: 00:00:00 3742 reads; of these: 3742 (100.00%) were unpaired; of these: 1240 (33.14%) aligned 0 times 2308 (61.68%) aligned exactly 1 time 194 (5.18%) aligned >1 times 66.86% overall alignment rate -Time searching: 00:00:01 -Overall time: 00:00:01 +Time searching: 00:00:00 +Overall time: 00:00:00 <br /><br /> diff --git a/example_of_result/20220120_test_1649703168/reports/cov_report.txt b/example_of_result/20220120_test_1657729206/reports/cov_report.txt similarity index 100% rename from example_of_result/20220120_test_1649703168/reports/cov_report.txt rename to example_of_result/20220120_test_1657729206/reports/cov_report.txt diff --git a/example_of_result/20220120_test_1649703168/reports/dup_insertion_and_logo_report.txt b/example_of_result/20220120_test_1657729206/reports/dup_insertion_and_logo_report.txt similarity index 97% rename from example_of_result/20220120_test_1649703168/reports/dup_insertion_and_logo_report.txt rename to example_of_result/20220120_test_1657729206/reports/dup_insertion_and_logo_report.txt index 2333f378db67e4cb8800b08b446d1ef24aad6f11..1b4a53da61dad0ddd3ac1084d3cf30512108f4bd 100644 --- a/example_of_result/20220120_test_1649703168/reports/dup_insertion_and_logo_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/dup_insertion_and_logo_report.txt @@ -12,7 +12,7 @@ -2022-04-11 18:52:56 +2022-07-13 16:22:01 @@ -31,7 +31,7 @@ -END TIME: 2022-04-11 18:53:03 +END TIME: 2022-07-13 16:22:08 @@ -121,7 +121,7 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-11 18:53:03 +TIME: 2022-07-13 16:22:08 TOTAL TIME LAPSE: 7S diff --git a/example_of_result/20220120_test_1649703168/reports/dup_report.txt b/example_of_result/20220120_test_1657729206/reports/dup_report.txt similarity index 100% rename from example_of_result/20220120_test_1649703168/reports/dup_report.txt rename to example_of_result/20220120_test_1657729206/reports/dup_report.txt diff --git a/example_of_result/20220120_test_1649703168/reports/extract_seq_report.txt b/example_of_result/20220120_test_1657729206/reports/extract_seq_report.txt similarity index 100% rename from example_of_result/20220120_test_1649703168/reports/extract_seq_report.txt rename to example_of_result/20220120_test_1657729206/reports/extract_seq_report.txt diff --git a/example_of_result/20220120_test_1649703168/reports/final_insertion_files_report.txt b/example_of_result/20220120_test_1657729206/reports/final_insertion_files_report.txt similarity index 96% rename from example_of_result/20220120_test_1649703168/reports/final_insertion_files_report.txt rename to example_of_result/20220120_test_1657729206/reports/final_insertion_files_report.txt index ac94373ff67008ecc1c8055672594159070d64a0..d5bddfa4e17100abb37744c0e2af909667471e55 100644 --- a/example_of_result/20220120_test_1649703168/reports/final_insertion_files_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/final_insertion_files_report.txt @@ -1,6 +1,6 @@ -################################################################ final_insertion_files PROCESS WITH FILE test.fastq2_q20_nodup.pos +################################################################ final_insertion_files PROCESS WITH FILE test.fastq2_q20_dup.pos @@ -12,7 +12,7 @@ -2022-04-09 15:17:45 +2022-07-13 16:21:05 @@ -25,7 +25,7 @@ -HEAD OF THE INITAL FILE test.fastq2_q20_nodup.pos +HEAD OF THE INITAL FILE test.fastq2_q20_dup.pos V1 V2 @@ -39,7 +39,7 @@ HEAD OF THE INITAL FILE test.fastq2_q20_nodup.pos -HEAD OF THE MODIFIED FILE test.fastq2_q20_nodup.pos +HEAD OF THE MODIFIED FILE test.fastq2_q20_dup.pos Sequence Position names fork orient @@ -54,7 +54,7 @@ HEAD OF THE MODIFIED FILE test.fastq2_q20_nodup.pos NUMBER OF OBS POSITIONS: -1,660 +2,182 @@ -64,12 +64,12 @@ NUMBER OF OBS POSITIONS: -END TIME: 2022-04-09 15:17:46 +END TIME: 2022-07-13 16:21:05 -TOTAL TIME LAPSE: 1S +TOTAL TIME LAPSE: 0S @@ -97,12 +97,12 @@ erase.objects TRUE erase.graphs TRUE script final_insertion_files run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/final_insertion_files.R,--args,test.fastq2_q20_nodup.pos,2320711 2320942,4627368 4627400,6,test.fastq2_q20_nodup,cute_little_R_functions.R,final_insertion_files_report.txt -pos test.fastq2_q20_nodup.pos +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/final_insertion_files.R,--args,test.fastq2_q20_dup.pos,2320711 2320942,4627368 4627400,6,test.fastq2_q20_dup,cute_little_R_functions.R,final_insertion_files_report.txt +pos test.fastq2_q20_dup.pos ori_coord 2320711 2320942 ter_coord 4627368 4627400 nb_max_insertion_sites 6 -file_name test.fastq2_q20_nodup +file_name test.fastq2_q20_dup cute cute_little_R_functions.R log final_insertion_files_report.txt @@ -151,15 +151,15 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:17:46 +TIME: 2022-07-13 16:21:05 -TOTAL TIME LAPSE: 1S +TOTAL TIME LAPSE: 0S -################################################################ final_insertion_files PROCESS WITH FILE test.fastq2_q20_dup.pos +################################################################ final_insertion_files PROCESS WITH FILE test.fastq2_q20_nodup.pos @@ -171,7 +171,7 @@ TOTAL TIME LAPSE: 1S -2022-04-09 15:18:01 +2022-07-13 16:20:59 @@ -184,7 +184,7 @@ TOTAL TIME LAPSE: 1S -HEAD OF THE INITAL FILE test.fastq2_q20_dup.pos +HEAD OF THE INITAL FILE test.fastq2_q20_nodup.pos V1 V2 @@ -198,7 +198,7 @@ HEAD OF THE INITAL FILE test.fastq2_q20_dup.pos -HEAD OF THE MODIFIED FILE test.fastq2_q20_dup.pos +HEAD OF THE MODIFIED FILE test.fastq2_q20_nodup.pos Sequence Position names fork orient @@ -213,7 +213,7 @@ HEAD OF THE MODIFIED FILE test.fastq2_q20_dup.pos NUMBER OF OBS POSITIONS: -2,182 +1,660 @@ -223,12 +223,12 @@ NUMBER OF OBS POSITIONS: -END TIME: 2022-04-09 15:18:01 +END TIME: 2022-07-13 16:20:59 -TOTAL TIME LAPSE: 1S +TOTAL TIME LAPSE: 0S @@ -256,12 +256,12 @@ erase.objects TRUE erase.graphs TRUE script final_insertion_files run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/final_insertion_files.R,--args,test.fastq2_q20_dup.pos,2320711 2320942,4627368 4627400,6,test.fastq2_q20_dup,cute_little_R_functions.R,final_insertion_files_report.txt -pos test.fastq2_q20_dup.pos +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/final_insertion_files.R,--args,test.fastq2_q20_nodup.pos,2320711 2320942,4627368 4627400,6,test.fastq2_q20_nodup,cute_little_R_functions.R,final_insertion_files_report.txt +pos test.fastq2_q20_nodup.pos ori_coord 2320711 2320942 ter_coord 4627368 4627400 nb_max_insertion_sites 6 -file_name test.fastq2_q20_dup +file_name test.fastq2_q20_nodup cute cute_little_R_functions.R log final_insertion_files_report.txt @@ -310,9 +310,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:18:01 +TIME: 2022-07-13 16:20:59 -TOTAL TIME LAPSE: 1S +TOTAL TIME LAPSE: 0S diff --git a/example_of_result/20220120_test_1649703168/reports/global_logo_report.txt b/example_of_result/20220120_test_1657729206/reports/global_logo_report.txt similarity index 74% rename from example_of_result/20220120_test_1649703168/reports/global_logo_report.txt rename to example_of_result/20220120_test_1657729206/reports/global_logo_report.txt index 6836194b4d346992571f4b370ab7539449e5d038..2a77a0bccf705d8dedc3946fe2104057758e07a8 100644 --- a/example_of_result/20220120_test_1649703168/reports/global_logo_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/global_logo_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:28:04 +2022-07-13 16:26:42 @@ -31,12 +31,12 @@ -END TIME: 2022-04-09 15:28:09 +END TIME: 2022-07-13 16:26:44 -TOTAL TIME LAPSE: 5S +TOTAL TIME LAPSE: 2S @@ -64,8 +64,8 @@ erase.objects TRUE erase.graphs TRUE script global_logo run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/global_logo.R,--args,test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.stat,test.fastq2,20,cute_little_R_functions.R,global_logo_report.txt -freq test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.stat +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/global_logo.R,--args,test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat,test.fastq2,20,cute_little_R_functions.R,global_logo_report.txt +freq test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat file_name test.fastq2 insertion_dist 20 cute cute_little_R_functions.R @@ -115,9 +115,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:28:09 +TIME: 2022-07-13 16:26:44 -TOTAL TIME LAPSE: 5S +TOTAL TIME LAPSE: 2S diff --git a/example_of_result/20220120_test_1649703168/reports/goalign_report.txt b/example_of_result/20220120_test_1657729206/reports/goalign_report.txt similarity index 100% rename from example_of_result/20220120_test_1649703168/reports/goalign_report.txt rename to example_of_result/20220120_test_1657729206/reports/goalign_report.txt diff --git a/example_of_result/20220120_test_1649703168/reports/insertion_report.txt b/example_of_result/20220120_test_1657729206/reports/insertion_report.txt similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/insertion_report.txt rename to example_of_result/20220120_test_1657729206/reports/insertion_report.txt index 86af5b1e22cbde8e20d077e7483f66c45a671221..5892f0ca1c875428284b39bc4eb7d2feff6fed2f 100644 --- a/example_of_result/20220120_test_1649703168/reports/insertion_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/insertion_report.txt @@ -1,6 +1,6 @@ -######## test.fastq2_q20_nodup.bam file +######## test.fastq2_q20_dup.bam file >0 197823 @@ -33,7 +33,7 @@ Header is the 1) sens of insersion (0 or 16) and 2) insertion site position Final fasta file -Positions of reverse reads (16) use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0) +Positions of reverse reads (16) use the 3' end of the read as insertion site and not the 5' part as with forward reads (0) >0 197823 @@ -83,7 +83,7 @@ Final pos file 0 595937 -######## test.fastq2_q20_dup.bam file +######## test.fastq2_q20_nodup.bam file >0 197823 @@ -116,7 +116,7 @@ Header is the 1) sens of insersion (0 or 16) and 2) insertion site position Final fasta file -Positions of reverse reads (16) use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0) +Positions of reverse reads (16) use the 3' end of the read as insertion site and not the 5' part as with forward reads (0) >0 197823 diff --git a/example_of_result/20220120_test_1649703168/reports/logo_report.txt b/example_of_result/20220120_test_1657729206/reports/logo_report.txt similarity index 92% rename from example_of_result/20220120_test_1649703168/reports/logo_report.txt rename to example_of_result/20220120_test_1657729206/reports/logo_report.txt index df916e5a47ff224b96064d1312599649f3f44ba3..cce224d16260e601e00c5cf578c7f6c3e0c868f4 100644 --- a/example_of_result/20220120_test_1649703168/reports/logo_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/logo_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:26:56 +2022-07-13 16:26:18 @@ -31,12 +31,12 @@ -END TIME: 2022-04-09 15:27:00 +END TIME: 2022-07-13 16:26:20 -TOTAL TIME LAPSE: 4S +TOTAL TIME LAPSE: 2S @@ -64,8 +64,8 @@ erase.objects TRUE erase.graphs TRUE script logo run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/logo.R,--args,test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat,20,cute_little_R_functions.R,logo_report.txt -freq test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/logo.R,--args,test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat,20,cute_little_R_functions.R,logo_report.txt +freq test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat insertion_dist 20 cute cute_little_R_functions.R log logo_report.txt @@ -114,9 +114,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:27:00 +TIME: 2022-07-13 16:26:20 -TOTAL TIME LAPSE: 4S +TOTAL TIME LAPSE: 2S diff --git a/example_of_result/20220120_test_1649703168/reports/motif_report.txt b/example_of_result/20220120_test_1657729206/reports/motif_report.txt similarity index 96% rename from example_of_result/20220120_test_1649703168/reports/motif_report.txt rename to example_of_result/20220120_test_1657729206/reports/motif_report.txt index 40e8ace6d127a25904559c694dce227e31f07fad..d55e7e260f24b061dac5254f1e2059434c7c6410 100644 --- a/example_of_result/20220120_test_1649703168/reports/motif_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/motif_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:15:57 +2022-07-13 16:20:17 @@ -78,12 +78,12 @@ fork and orient -END TIME: 2022-04-09 15:16:18 +END TIME: 2022-07-13 16:20:25 -TOTAL TIME LAPSE: 21S +TOTAL TIME LAPSE: 8S @@ -168,9 +168,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:16:18 +TIME: 2022-07-13 16:20:25 -TOTAL TIME LAPSE: 21S +TOTAL TIME LAPSE: 8S diff --git a/example_of_result/20220120_test_1649703168/reports/multiqc_report.html b/example_of_result/20220120_test_1657729206/reports/multiqc_report.html similarity index 99% rename from example_of_result/20220120_test_1649703168/reports/multiqc_report.html rename to example_of_result/20220120_test_1657729206/reports/multiqc_report.html index 963c2a9f7e63151caf02557d7beaf8c0480122a7..4aa3115d1cadf1fbe12b100848df95c69e1d4c74 100644 --- a/example_of_result/20220120_test_1649703168/reports/multiqc_report.html +++ b/example_of_result/20220120_test_1657729206/reports/multiqc_report.html @@ -6259,12 +6259,12 @@ function findPos(obj) { <div id="analysis_dirs_wrapper"> <p>Report - generated on 2022-04-09, 17:16 + generated on 2022-07-13, 18:20 based on data in: - <code class="mqc_analysis_path">/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/work/8f/71ec4485b2780844d80f43234a84d9</code></p> + <code class="mqc_analysis_path">/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/work/d3/8b18bf740f0ad3c0386242eb736129</code></p> </div> diff --git a/example_of_result/20220120_test_1649703168/reports/nextflow.config b/example_of_result/20220120_test_1657729206/reports/nextflow.config similarity index 97% rename from example_of_result/20220120_test_1649703168/reports/nextflow.config rename to example_of_result/20220120_test_1657729206/reports/nextflow.config index 8e518669f5e7beb06dc75e73fc47bf61c005833b..cd6733931b3ad4fd05ae93c4604d4744b1292439 100644 --- a/example_of_result/20220120_test_1649703168/reports/nextflow.config +++ b/example_of_result/20220120_test_1657729206/reports/nextflow.config @@ -90,6 +90,7 @@ result_folder_name="20220120_test" //// end general variables //// slurm variables +// see https://confluence.pasteur.fr/pages/viewpage.action?pageId=69304504 fastqueue = 'common,dedicated' // fast for -p option of slurm. Example: fastqueue = 'common,dedicated'. Example: fastqueue = 'hubbioit' fastqos= '--qos=fast' // fast for --qos option of slurm. Example: fastqos= '--qos=fast' normalqueue = 'hubbioit' // normal for -p option of slurm. Example: normalqueue = 'bioevo' diff --git a/example_of_result/20220120_test_1657729206/reports/nf_dag.png b/example_of_result/20220120_test_1657729206/reports/nf_dag.png new file mode 100644 index 0000000000000000000000000000000000000000..566f6c6116875a2994b7cc84cf0edf770e4509d7 Binary files /dev/null and b/example_of_result/20220120_test_1657729206/reports/nf_dag.png differ diff --git a/example_of_result/20220120_test_1649703168/reports/nf_report.html b/example_of_result/20220120_test_1657729206/reports/nf_report.html similarity index 93% rename from example_of_result/20220120_test_1649703168/reports/nf_report.html rename to example_of_result/20220120_test_1657729206/reports/nf_report.html index a7bac14a955fe0a1f68458399aa65a724c0675c9..7a81f981c7d1c6f68913f64882ce834b9b5c4a3e 100644 --- a/example_of_result/20220120_test_1649703168/reports/nf_report.html +++ b/example_of_result/20220120_test_1657729206/reports/nf_report.html @@ -18,11 +18,11 @@ <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1, shrink-to-fit=no"> - <meta name="description" content="Nextflow workflow report for run id [determined_hoover]"> + <meta name="description" content="Nextflow workflow report for run id [goofy_pasteur]"> <meta name="author" content="Paolo Di Tommaso, Phil Ewels"> <link rel="icon" type="image/png" href="https://www.nextflow.io/img/favicon.png" /> - <title>[determined_hoover] Nextflow Workflow Report</title> + <title>[goofy_pasteur] Nextflow Workflow Report</title> <style type="text/css"> /*! @@ -137,7 +137,7 @@ table.DTCR_clonedTable.dataTable{position:absolute !important;background-color:r <li class="nav-item"><a class="nav-link" href="#tasks">Tasks</a></li> </ul> <span class="navbar-text"> - [determined_hoover] + [goofy_pasteur] </span> </div> </nav> @@ -146,7 +146,7 @@ table.DTCR_clonedTable.dataTable{position:absolute !important;background-color:r <div class="container"> <h1 class="display-3">Nextflow workflow report</h1> - <h2 class="text-muted mb-4"><samp>[determined_hoover]</samp> <em>(resumed run)</em></h2> + <h2 class="text-muted mb-4"><samp>[goofy_pasteur]</samp> </h2> <div class="alert alert-success mb-4"> @@ -157,26 +157,26 @@ table.DTCR_clonedTable.dataTable{position:absolute !important;background-color:r <dl> <dt>Run times</dt> <dd> - <span id="workflow_start">11-Apr-2022 20:52:48</span> - <span id="workflow_complete">11-Apr-2022 20:57:15</span> - (<span id="completed_fromnow"></span>duration: <strong>4m 26s</strong>) + <span id="workflow_start">13-Jul-2022 18:20:06</span> - <span id="workflow_complete">13-Jul-2022 18:27:03</span> + (<span id="completed_fromnow"></span>duration: <strong>6m 56s</strong>) </dd> <dl> <div class="progress" style="height: 1.6rem; margin: 1.2rem auto; border-radius: 0.20rem;"> - <div style="width: 11.11%" class="progress-bar bg-success" data-toggle="tooltip" data-placement="top" title="6 tasks succeeded"><span class="text-truncate"> 6 succeeded </span></div> - <div style="width: 88.89%" class="progress-bar bg-secondary" data-toggle="tooltip" data-placement="top" title="48 tasks were cached"><span class="text-truncate"> 48 cached </span></div> + <div style="width: 100.0%" class="progress-bar bg-success" data-toggle="tooltip" data-placement="top" title="54 tasks succeeded"><span class="text-truncate"> 54 succeeded </span></div> + <div style="width: 0.0%" class="progress-bar bg-secondary" data-toggle="tooltip" data-placement="top" title="0 tasks were cached"><span class="text-truncate"> 0 cached </span></div> <div style="width: 0.0%" class="progress-bar bg-warning" data-toggle="tooltip" data-placement="top" title="0 tasks reported and error and were ignored"><span class="text-truncate"> 0 ignored </span></div> <div style="width: 0.0%" class="progress-bar bg-danger" data-toggle="tooltip" data-placement="top" title="0 tasks failed"><span class="text-truncate"> 0 failed </span></div> </div> </dl> <dt>Nextflow command</dt> - <dd><pre class="nfcommand"><code>nextflow run main.nf -resume</code></pre></dd> + <dd><pre class="nfcommand"><code>nextflow run main.nf -c nextflow.config</code></pre></dd> </dl> <dl class="row small"> <dt class="col-sm-3">CPU-Hours</dt> - <dd class="col-sm-9"><samp>1.0 (18.9% cached)</samp></dd> + <dd class="col-sm-9"><samp>0.9</samp></dd> <dt class="col-sm-3">Launch directory</dt> <dd class="col-sm-9"><samp>/mnt/c/Users/Gael/Documents/Git_projects/14985_loot</samp></dd> @@ -194,11 +194,11 @@ table.DTCR_clonedTable.dataTable{position:absolute !important;background-color:r <dt class="col-sm-3">Script ID</dt> - <dd class="col-sm-9"><code>029468532b72e162e87fcde8e3abfe95</code></dd> + <dd class="col-sm-9"><code>cd15f64b0c5e1b052f97ce7d30046346</code></dd> <dt class="col-sm-3">Workflow session</dt> - <dd class="col-sm-9"><code>0e3785ac-cc56-4fd7-b82a-301164e5383a</code></dd> + <dd class="col-sm-9"><code>573d1445-dc22-44af-a37c-242aef9f0b99</code></dd> @@ -1029,7 +1029,7 @@ $(function() { // Nextflow report data window.data = { "trace":[ -{"task_id":"2","hash":"25\/7b2ed6","native_id":"281","process":"Nremove","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"Nremove (1)","status":"CACHED","exit":"0","submit":"1649517331366","start":"1649517331460","complete":"1649517336786","duration":"5420","realtime":"1273","%cpu":"37.7","%mem":"0.0","rss":"12468224","vmem":"73990144","peak_rss":"12468224","peak_vmem":"74002432","rchar":"17604799","wchar":"15167118","syscr":"1863","syscw":"1271","read_bytes":"568320","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/25\/7b2ed6336bab428544e9b702e6a042","script":"\n Nremove.sh test.fastq2.gz \"test.fastq2_Nremove.gz\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"853","inv_ctxt":"19"},{"task_id":"3","hash":"f3\/638b7c","native_id":"308","process":"report1","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"report1","status":"CACHED","exit":"0","submit":"1649517331415","start":"1649517331494","complete":"1649517336099","duration":"4684","realtime":"85","%cpu":"6.6","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"106909","wchar":"684","syscr":"189","syscw":"54","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/f3\/638b7c1225ea438db495aefc16a905","script":"\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n### Read coverage\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' > report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"4","hash":"e9\/33684c","native_id":"1241","process":"trim","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-alien_trimmer_v0.4.0-gitlab_v8.1.img","tag":"-","name":"trim (1)","status":"CACHED","exit":"0","submit":"1649517336960","start":"1649517336984","complete":"1649517347469","duration":"10509","realtime":"7348","%cpu":"44.5","%mem":"0.1","rss":"66617344","vmem":"5908262912","peak_rss":"66617344","peak_vmem":"5970448384","rchar":"17145211","wchar":"12629480","syscr":"2381","syscw":"647","read_bytes":"9981952","write_bytes":"32768","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/e9\/33684ce0818a231e05632f82b2dcdb","script":"\n trim.sh test.fastq2_Nremove.gz \"test.fastq2_trim.fq\" 20200520_adapters_TruSeq_B2699_14985_CL.fasta 30 \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3272","inv_ctxt":"9"},{"task_id":"1","hash":"bd\/8b5bfa","native_id":"137","process":"init","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"init","status":"COMPLETED","exit":"0","submit":"1649703170952","start":"1649703171054","complete":"1649703172718","duration":"1766","realtime":"17","%cpu":"5.3","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"106462","wchar":"659","syscr":"190","syscw":"26","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/bd\/8b5bfa3b5577099bccea0d0d626bf7","script":"\n echo \"---\n title: \'Insertion Sites Report\'\n author: \'Gael Millot\'\n date: \'`r Sys.Date()`\'\n output:\n html_document:\n toc: TRUE\n toc_float: TRUE\n ---\n\n \\n\\n<br \/><br \/>\\n\\n\n \" > report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"5","hash":"d5\/d15b46","native_id":"2771","process":"kraken","module":"-","container":"-","tag":"-","name":"kraken (1)","status":"CACHED","exit":"0","submit":"1649517347569","start":"1649517347669","complete":"1649517347880","duration":"311","realtime":"34","%cpu":"81.6","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"154429","wchar":"220","syscr":"228","syscw":"13","read_bytes":"49152","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/d5\/d15b46d768587b15d4a43e3e1416bd","script":"\n echo \"No kraken analysis performed in local running\" > test.fastq2_trim_kraken_std.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"7","hash":"f8\/e48213","native_id":"2908","process":"fivep_filtering","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"fivep_filtering (1)","status":"CACHED","exit":"0","submit":"1649517347765","start":"1649517347828","complete":"1649517355984","duration":"8219","realtime":"6176","%cpu":"27.1","%mem":"0.0","rss":"12136448","vmem":"70533120","peak_rss":"12136448","peak_vmem":"70533120","rchar":"29337231","wchar":"16061720","syscr":"9149","syscw":"5789","read_bytes":"437248","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/f8\/e482130c484e578853920aef7381bf","script":"\n fivep_filtering.sh test.fastq2_trim.fq \"test.fastq2\" \"^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\" 48 3 51 \"CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"6135","inv_ctxt":"2"},{"task_id":"6","hash":"59\/2dbb6e","native_id":"2819","process":"fastqc1","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/evolbioinfo-fastqc-v0.11.8.img","tag":"-","name":"fastqc1 (1)","status":"CACHED","exit":"0","submit":"1649517347681","start":"1649517347770","complete":"1649517366659","duration":"18978","realtime":"17027","%cpu":"52.0","%mem":"0.2","rss":"173170688","vmem":"3289477120","peak_rss":"173256704","peak_vmem":"3342663680","rchar":"14605630","wchar":"1278924","syscr":"7612","syscw":"5170","read_bytes":"19996672","write_bytes":"712704","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/59\/2dbb6e3e89d63b886cbd5e4cc01ec1","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Read QC n\u00B01\\n\\n\" > report.rmd\n echo -e \"Results are published in the [fastQC1](.\/fastQC1) folder\\n\\n\" >> report.rmd\n fastqc test.fastq2_trim.fq | tee tempo.txt\n cat tempo.txt >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"4333","inv_ctxt":"39"},{"task_id":"9","hash":"c8\/fa65a5","native_id":"4175","process":"cutoff","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"cutoff (1)","status":"CACHED","exit":"0","submit":"1649517356282","start":"1649517356311","complete":"1649517359645","duration":"3363","realtime":"988","%cpu":"14.5","%mem":"0.0","rss":"10223616","vmem":"64237568","peak_rss":"10223616","peak_vmem":"64237568","rchar":"7308009","wchar":"4049154","syscr":"2784","syscw":"2034","read_bytes":"384000","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/c8\/fa65a502fe7569089ad77729a27d36","script":"\n cutoff.sh test.fastq2_5pAtccRm.fq 25 \"test.fastq2\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"1167","inv_ctxt":"1"},{"task_id":"8","hash":"b4\/13acb3","native_id":"4147","process":"fastqc2","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/evolbioinfo-fastqc-v0.11.8.img","tag":"-","name":"fastqc2 (1)","status":"CACHED","exit":"0","submit":"1649517356193","start":"1649517356286","complete":"1649517370882","duration":"14689","realtime":"12798","%cpu":"69.2","%mem":"0.2","rss":"184655872","vmem":"3289477120","peak_rss":"184655872","peak_vmem":"3289899008","rchar":"12768081","wchar":"1245410","syscr":"7365","syscw":"5096","read_bytes":"19984384","write_bytes":"688128","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/b4\/13acb3ca013c26eb96eb0cbf07abdd","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Read QC n\u00B02\\n\\n\" > report.rmd\n echo -e \"Results are published in the [fastQC2](.\/fastQC2) folder\\n\\n\" >> report.rmd\n fastqc test.fastq2_5pAtccRm.fq | tee tempo.txt\n cat tempo.txt >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"4333","inv_ctxt":"2"},{"task_id":"11","hash":"96\/3945da","native_id":"5033","process":"bowtie2","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bowtie2_v2.3.4.3_extended_v2.0-gitlab_v8.0.img","tag":"-","name":"bowtie2 (1)","status":"CACHED","exit":"0","submit":"1649517360685","start":"1649517360746","complete":"1649517374142","duration":"13457","realtime":"9333","%cpu":"40.6","%mem":"0.1","rss":"68902912","vmem":"249094144","peak_rss":"120336384","peak_vmem":"251154432","rchar":"36678363","wchar":"17009938","syscr":"3392","syscw":"2516","read_bytes":"7209984","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/96\/3945da1d9d4a1f8976a45bb56ea6e2","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Bowtie2 indexing of the reference sequence\\n\\n\" >> bowtie2_report.txt\n bowtie2-build Ecoli-K12-MG1655_ORI_CENTERED.fasta Ecoli-K12-MG1655_ORI_CENTERED |& tee -a bowtie2_report.txt\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Bowtie2 alignment\\n\\n\" > report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Bowtie2 alignment\\n\\n\" >> bowtie2_report.txt\n bowtie2 --very-sensitive -x Ecoli-K12-MG1655_ORI_CENTERED -U test.fastq2_cutoff.fq -t -S test.fastq2_bowtie2.sam |& tee -a tempo.txt\n # --very-sensitive: no soft clipping allowed and very sensitive seed alignment\n # -t time displayed\n cat tempo.txt >> bowtie2_report.txt\n sed -i -e \':a;N;$!ba;s\/\\n\/\\n<br \\\/>\/g\' tempo.txt\n cat tempo.txt >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### samtools conversion\\n\\n\" >> bowtie2_report.txt\n # samtools faidx Ecoli-K12-MG1655_ORI_CENTERED.fasta\n samtools view -bh -o tempo.bam test.fastq2_bowtie2.sam |& tee -a bowtie2_report.txt\n samtools sort -o test.fastq2_bowtie2.bam tempo.bam |& tee -a bowtie2_report.txt\n samtools index test.fastq2_bowtie2.bam |& tee -a bowtie2_report.txt\n ","scratch":"-","queue":"-","cpus":"12","memory":"17179869184","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"5701","inv_ctxt":"9"},{"task_id":"13","hash":"50\/34788a","native_id":"6971","process":"Q20","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"Q20 (1)","status":"CACHED","exit":"0","submit":"1649517375154","start":"1649517375243","complete":"1649517377849","duration":"2695","realtime":"707","%cpu":"17.8","%mem":"0.0","rss":"4722688","vmem":"44826624","peak_rss":"4722688","peak_vmem":"44834816","rchar":"3392035","wchar":"2260606","syscr":"897","syscw":"567","read_bytes":"1230848","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/50\/34788aeb175a22a1d913b583847a61","script":"\n samtools view -q 20 -b test.fastq2_bowtie2.bam > test.fastq2_q20_dup.bam |& tee q20_report.txt\n samtools index test.fastq2_q20_dup.bam\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Q20 filtering\\n\\n\" > report.rmd\n read_nb_before=$(samtools view test.fastq2_bowtie2.bam | wc -l | cut -f1 -d\' \') # -h to add the header\n read_nb_after=$(samtools view test.fastq2_q20_dup.bam | wc -l | cut -f1 -d\' \') # -h to add the header\n echo -e \"\\n\\nNumber of sequences before Q20 filtering: $(printf \"%\'d\" ${read_nb_before})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of sequences after Q20 filtering: $(printf \"%\'d\" ${read_nb_after})\\n\" >> report.rmd\n echo -e \"Ratio: \" >> report.rmd\n echo -e $(printf \"%.2f\n\" $(echo $\" $read_nb_after \/ $read_nb_before \" | bc -l)) >> report.rmd # the number in \'%.2f\\n\' is the number of decimals\n echo -e \"\\n\\n\" >> report.rmd\n echo $read_nb_before > read_nb_before # because nf cannot output values easily\n echo $read_nb_after > read_nb_after\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"806","inv_ctxt":"0"},{"task_id":"14","hash":"a8\/f98ebd","native_id":"11174","process":"coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"coverage (1)","status":"CACHED","exit":"0","submit":"1649517438784","start":"1649517438890","complete":"1649517443338","duration":"4554","realtime":"2000","%cpu":"18.3","%mem":"0.0","rss":"45502464","vmem":"83861504","peak_rss":"45502464","peak_vmem":"83996672","rchar":"491183","wchar":"93148","syscr":"251","syscw":"3116","read_bytes":"1806336","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/a8\/f98ebde5137cd4a4569ccc04fc89c5","script":"\n # bedtools genomecov -d -ibam ${bam} > ${bam.baseName}.cov |& tee cov_report.txt # coverage per base if ever required but long process\n # to add the chr names | awk \'{h[$NF]++}; END { for(k in h) print k, h[k] }\' | sort -V > ${bam.baseName}.cov\n bedtools genomecov -bga -ibam test.fastq2_bowtie2.bam > test.fastq2_bowtie2_mini.cov |& tee cov_report.txt\n # -g ${ref} not required when inputs are bam files\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3958","inv_ctxt":"1"},{"task_id":"17","hash":"41\/75981b","native_id":"7462","process":"no_soft_clipping","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"no_soft_clipping (1)","status":"CACHED","exit":"0","submit":"1649517378863","start":"1649517379002","complete":"1649517383173","duration":"4310","realtime":"1228","%cpu":"14.5","%mem":"0.0","rss":"5394432","vmem":"60452864","peak_rss":"5394432","peak_vmem":"60452864","rchar":"2188565","wchar":"1583809","syscr":"718","syscw":"417","read_bytes":"1153024","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/41\/75981b0207214e9680f067f4eccfbd","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Control that no more soft clipping in reads\\n\\n\" > report.rmd\n echo -e \"nb of reads with soft clipping (S) in CIGAR: $(printf \"%\'d\" $(samtools view test.fastq2_q20_dup.bam | awk \'$6 ~ \/.*[S].*\/{print $0}\' | wc -l | cut -f1 -d\' \'))\" >> report.rmd\n echo -e \"\\n\\ntotal nb of reads: $(printf \"%\'d\" $(samtools view test.fastq2_q20_dup.bam | wc -l | cut -f1 -d\' \'))\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"28","inv_ctxt":"1"},{"task_id":"15","hash":"8f\/71ec44","native_id":"6997","process":"multiQC","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/ewels-multiqc-1.10.1.img","tag":"-","name":"multiQC","status":"CACHED","exit":"0","submit":"1649517375192","start":"1649517375268","complete":"1649517398413","duration":"23221","realtime":"23000","%cpu":"36.5","%mem":"0.1","rss":"74149888","vmem":"85012480","peak_rss":"74149888","peak_vmem":"85012480","rchar":"29716377","wchar":"2404869","syscr":"9278","syscw":"295","read_bytes":"22820864","write_bytes":"1253376","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/8f\/71ec4485b2780844d80f43234a84d9","script":"\n multiqc . -n multiqc_report.html\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### MultiQC\\n\\n\" > report.rmd\n if [[ local == \"local\" ]] ; then\n echo -e \"\\n\\nWarning: no Kraken performed when using local run\\n\" >> report.rmd\n fi\n echo -e \"\\n\\nResults are published in the [Report](.\/reports\/multiqc_report.html) folder\\n\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"34782","inv_ctxt":"285"},{"task_id":"18","hash":"da\/6b7a6c","native_id":"11388","process":"coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"coverage (2)","status":"CACHED","exit":"0","submit":"1649517443376","start":"1649517443438","complete":"1649517447638","duration":"4262","realtime":"1911","%cpu":"19.1","%mem":"0.0","rss":"45707264","vmem":"83861504","peak_rss":"45707264","peak_vmem":"83996672","rchar":"343061","wchar":"84347","syscr":"239","syscw":"2824","read_bytes":"1806336","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/da\/6b7a6c60ccfee9229a0531b342010e","script":"\n # bedtools genomecov -d -ibam ${bam} > ${bam.baseName}.cov |& tee cov_report.txt # coverage per base if ever required but long process\n # to add the chr names | awk \'{h[$NF]++}; END { for(k in h) print k, h[k] }\' | sort -V > ${bam.baseName}.cov\n bedtools genomecov -bga -ibam test.fastq2_q20_dup.bam > test.fastq2_q20_dup_mini.cov |& tee cov_report.txt\n # -g ${ref} not required when inputs are bam files\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3888","inv_ctxt":"0"},{"task_id":"16","hash":"0a\/40301f","native_id":"7482","process":"duplicate_removal","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"duplicate_removal (1)","status":"CACHED","exit":"0","submit":"1649517378891","start":"1649517379023","complete":"1649517388551","duration":"9660","realtime":"6625","%cpu":"24.6","%mem":"0.0","rss":"13029376","vmem":"89198592","peak_rss":"13029376","peak_vmem":"89198592","rchar":"13491677","wchar":"6912516","syscr":"7202","syscw":"5709","read_bytes":"1376256","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/0a\/40301fd02966cf17f1eabcfffba711","script":"\n duplicate_removal.sh test.fastq2_q20_dup.bam Ecoli-K12-MG1655_ORI_CENTERED.fasta \"test.fastq2_q20_nodup.bam\" \"dup_report.txt\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"2449","inv_ctxt":"4"},{"task_id":"23","hash":"91\/2fd95b","native_id":"9315","process":"insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"insertion (1)","status":"CACHED","exit":"0","submit":"1649517389566","start":"1649517389651","complete":"1649517393899","duration":"4333","realtime":"2260","%cpu":"19.6","%mem":"0.0","rss":"8163328","vmem":"68767744","peak_rss":"8163328","peak_vmem":"68775936","rchar":"2614607","wchar":"1832311","syscr":"1537","syscw":"1173","read_bytes":"1236992","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/91\/2fd95b201a10093515dd0da96fa3c8","script":"\n if [[ test.fastq2_q20_nodup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Insertion positions\\n\\n\" > report.rmd\n echo -e \"\\n\\nOne of the step is to recover positions of reverse reads (16), that use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0).\\nIt consist in the redefinition of POS according to FLAG in the bam file. See the [insertion_report.txt](.\/reports\/insertion_report.txt) file in the reports folders for details\\n\\n\" >> report.rmd\n fi\n\n # extraction of bam column 2, 4 and 10, i.e., FLAG, POS and SEQ\n samtools view test.fastq2_q20_nodup.bam | awk \'BEGIN{FS=\"\\t\" ; OFS=\"\" ; ORS=\"\"}{print \">\"$2\"\\t\"$4\"\\n\"$10\"\\n\" }\' > tempo\n # Of note, samtools fasta $DIR\/$SAMPLE_NAME > ${OUTPUT}.fasta # convert bam into fasta\n echo -e \"\\n\\n######## test.fastq2_q20_nodup.bam file\\n\\n\" > insertion_report.txt\n cat tempo | head -60 | tail -20 >> insertion_report.txt\n echo -e \"\\n\\nExtraction of the FLAG (containing the read orientation) the POS and the SEQ of the bams\\nHeader is the 1) sens of insersion (0 or 16) and 2) insertion site position\\n\\n\" >> insertion_report.txt\n # redefinition of POS according to FLAG\n awk \'BEGIN{FS=\"\t\" ; OFS=\"\" ; ORS=\"\"}{lineKind=(NR-1)%2}lineKind==0{orient=($1~\">16\") ; if(orient){var1 = $1 ; var2 = $2}else{print $0\"\\n\"}}lineKind==1{if(orient){var3 = length($0) ; var4 = var2 + var3 - 1 ; print var1\"\\t\"var4\"\\n\"$0\"\\n\"}else{print $0\"\\n\"}}\' tempo > test.fastq2_reorient.fasta\n echo -e \"\\n\\nFinal fasta file\\n\\nPositions of reverse reads (16) use the 3\\\' end of the read as insertion site and not the 5\\\' part as with forward reads (0)\\n\\n\" >> insertion_report.txt\n cat test.fastq2_reorient.fasta | head -60 | tail -20 >> insertion_report.txt\n awk \'{lineKind=(NR-1)%2}lineKind==0{gsub(\/>\/, \"\", $1) ; print $0}\' test.fastq2_reorient.fasta > test.fastq2_q20_nodup.pos\n echo -e \"\\n\\nFinal pos file\\n\\n\" >> insertion_report.txt\n cat test.fastq2_q20_nodup.pos | head -60 | tail -20 >> insertion_report.txt\n\n read_nb_before=$(samtools view test.fastq2_q20_nodup.bam | wc -l | cut -f1 -d\' \') # -h to add the header. Thus do not put here\n read_nb_after=$(wc -l test.fastq2_q20_nodup.pos | cut -f1 -d\' \')\n if [[ test.fastq2_q20_nodup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\nNumber of reads used for insertion computation: $(printf \"%\'d\" ${read_nb_before})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of insertions: $(printf \"%\'d\" ${read_nb_after})\\n\" >> report.rmd\n echo -e \"Ratio: \" >> report.rmd\n echo -e $(printf \"%.2f\n\" $(echo $\" $read_nb_after \/ $read_nb_before \" | bc -l)) >> report.rmd # the number in \'%.2f\\n\' is the number of decimals\n echo -e \"\\n\\n\" >> report.rmd\n else\n echo -e \"\\n\\n\" >> report.rmd\n fi\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"819","inv_ctxt":"1"},{"task_id":"24","hash":"22\/3c6c1a","native_id":"9335","process":"insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"insertion (2)","status":"CACHED","exit":"0","submit":"1649517389595","start":"1649517389671","complete":"1649517394064","duration":"4469","realtime":"2475","%cpu":"20.4","%mem":"0.0","rss":"9629696","vmem":"68907008","peak_rss":"9629696","peak_vmem":"68907008","rchar":"3142153","wchar":"2312414","syscr":"1877","syscw":"1514","read_bytes":"1267712","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/22\/3c6c1a1ef3ab1682b6e551be1cb552","script":"\n if [[ test.fastq2_q20_dup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Insertion positions\\n\\n\" > report.rmd\n echo -e \"\\n\\nOne of the step is to recover positions of reverse reads (16), that use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0).\\nIt consist in the redefinition of POS according to FLAG in the bam file. See the [insertion_report.txt](.\/reports\/insertion_report.txt) file in the reports folders for details\\n\\n\" >> report.rmd\n fi\n\n # extraction of bam column 2, 4 and 10, i.e., FLAG, POS and SEQ\n samtools view test.fastq2_q20_dup.bam | awk \'BEGIN{FS=\"\\t\" ; OFS=\"\" ; ORS=\"\"}{print \">\"$2\"\\t\"$4\"\\n\"$10\"\\n\" }\' > tempo\n # Of note, samtools fasta $DIR\/$SAMPLE_NAME > ${OUTPUT}.fasta # convert bam into fasta\n echo -e \"\\n\\n######## test.fastq2_q20_dup.bam file\\n\\n\" > insertion_report.txt\n cat tempo | head -60 | tail -20 >> insertion_report.txt\n echo -e \"\\n\\nExtraction of the FLAG (containing the read orientation) the POS and the SEQ of the bams\\nHeader is the 1) sens of insersion (0 or 16) and 2) insertion site position\\n\\n\" >> insertion_report.txt\n # redefinition of POS according to FLAG\n awk \'BEGIN{FS=\"\t\" ; OFS=\"\" ; ORS=\"\"}{lineKind=(NR-1)%2}lineKind==0{orient=($1~\">16\") ; if(orient){var1 = $1 ; var2 = $2}else{print $0\"\\n\"}}lineKind==1{if(orient){var3 = length($0) ; var4 = var2 + var3 - 1 ; print var1\"\\t\"var4\"\\n\"$0\"\\n\"}else{print $0\"\\n\"}}\' tempo > test.fastq2_reorient.fasta\n echo -e \"\\n\\nFinal fasta file\\n\\nPositions of reverse reads (16) use the 3\\\' end of the read as insertion site and not the 5\\\' part as with forward reads (0)\\n\\n\" >> insertion_report.txt\n cat test.fastq2_reorient.fasta | head -60 | tail -20 >> insertion_report.txt\n awk \'{lineKind=(NR-1)%2}lineKind==0{gsub(\/>\/, \"\", $1) ; print $0}\' test.fastq2_reorient.fasta > test.fastq2_q20_dup.pos\n echo -e \"\\n\\nFinal pos file\\n\\n\" >> insertion_report.txt\n cat test.fastq2_q20_dup.pos | head -60 | tail -20 >> insertion_report.txt\n\n read_nb_before=$(samtools view test.fastq2_q20_dup.bam | wc -l | cut -f1 -d\' \') # -h to add the header. Thus do not put here\n read_nb_after=$(wc -l test.fastq2_q20_dup.pos | cut -f1 -d\' \')\n if [[ test.fastq2_q20_dup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\nNumber of reads used for insertion computation: $(printf \"%\'d\" ${read_nb_before})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of insertions: $(printf \"%\'d\" ${read_nb_after})\\n\" >> report.rmd\n echo -e \"Ratio: \" >> report.rmd\n echo -e $(printf \"%.2f\n\" $(echo $\" $read_nb_after \/ $read_nb_before \" | bc -l)) >> report.rmd # the number in \'%.2f\\n\' is the number of decimals\n echo -e \"\\n\\n\" >> report.rmd\n else\n echo -e \"\\n\\n\" >> report.rmd\n fi\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"1018","inv_ctxt":"2"},{"task_id":"22","hash":"d5\/03b2f8","native_id":"11599","process":"coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"coverage (3)","status":"CACHED","exit":"0","submit":"1649517447665","start":"1649517447738","complete":"1649517451839","duration":"4174","realtime":"1845","%cpu":"19.8","%mem":"0.0","rss":"45600768","vmem":"83861504","peak_rss":"45600768","peak_vmem":"83996672","rchar":"317679","wchar":"84031","syscr":"235","syscw":"2820","read_bytes":"1806336","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/d5\/03b2f893a259a7c22397def25c8629","script":"\n # bedtools genomecov -d -ibam ${bam} > ${bam.baseName}.cov |& tee cov_report.txt # coverage per base if ever required but long process\n # to add the chr names | awk \'{h[$NF]++}; END { for(k in h) print k, h[k] }\' | sort -V > ${bam.baseName}.cov\n bedtools genomecov -bga -ibam test.fastq2_q20_nodup.bam > test.fastq2_q20_nodup_mini.cov |& tee cov_report.txt\n # -g ${ref} not required when inputs are bam files\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3928","inv_ctxt":"0"},{"task_id":"25","hash":"86\/7a76f6","native_id":"226","process":"backup","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"backup","status":"COMPLETED","exit":"0","submit":"1649703171343","start":"1649703171363","complete":"1649703173137","duration":"1794","realtime":"11","%cpu":"6.3","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"106668","wchar":"494","syscr":"189","syscw":"23","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/86\/7a76f639c6c9b7424ecdea86f085d9","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Backup\\n\\n\" > report.rmd\n echo -e \"See the [reports](.\/reports) folder for all the details of the analysis, including the parameters used in the .config file\" >> report.rmd\n echo -e \"\\n\\nFull .nextflow.log is in: \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot<br \/>The one in the [reports](.\/reports) folder is not complete (miss the end)\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"26","hash":"ec\/876577","native_id":"262","process":"workflowVersion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"workflowVersion","status":"COMPLETED","exit":"0","submit":"1649703171403","start":"1649703171460","complete":"1649703173707","duration":"2304","realtime":"744","%cpu":"14.3","%mem":"0.0","rss":"5267456","vmem":"40312832","peak_rss":"5267456","peak_vmem":"40312832","rchar":"136704","wchar":"2126","syscr":"315","syscw":"67","read_bytes":"1660928","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/ec\/876577f13b2f140f1624c6a3cddd3f","script":"\n modules= # this is just to deal with variable interpretation during the creation of the .command.sh file by nextflow. See also $modules below\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Workflow Version\\n\\n\" > report.rmd\n echo -e \"\\n\\n#### General\\n\\n\n| Variable | Value |\n| :-- | :-- |\n| Project<br \/>(empty means no .git folder where the main.nf file is present) | $(git -C \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot remote -v | head -n 1) | # works only if the main script run is located in a directory that has a .git folder, i.e., that is connected to a distant repo\n| Git info<br \/>(empty means no .git folder where the main.nf file is present) | $(git -C \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot describe --abbrev=10 --dirty --always --tags) | # idem. Provide the small commit number of the script and nextflow.config used in the execution\n| Cmd line | nextflow run main.nf -resume |\n| execution mode | local |\" >> report.rmd \n\n if [[ ! -z $modules ]] ; then\n echo \"| loaded modules (according to specification by the user thanks to the --modules argument of main.nf) | |\" >> report.rmd\n fi\n \n echo \"| Manifest\'s pipeline version | null |\n| result path | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168 |\n| nextflow version | 21.04.2 |\n \" >> report.rmd\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Implicit variables\\n\\n\n| Name | Description | Value | \n| :-- | :-- | :-- |\n| launchDir | Directory where the workflow is run | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot |\n| nprojectDir | Directory where the main.nf script is located | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot |\n| workDir | Directory where tasks temporary files are created | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work |\n \" >> report.rmd\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### User variables\\n\\n\n| Name | Description | Value | \n| :-- | :-- | :-- |\n| out_path | output folder path | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168 |\n| in_path | input folder path | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset |\n \" >> report.rmd\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Workflow diagram\\n\\nSee the [nf_dag.png](.\/reports\/nf_dag.png) file\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"28","inv_ctxt":"0"},{"task_id":"21","hash":"88\/636f95","native_id":"14201","process":"plot_coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_coverage 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","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"42843","inv_ctxt":"11"},{"task_id":"29","hash":"37\/f53840","native_id":"15016","process":"plot_coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_coverage (3)","status":"CACHED","exit":"0","submit":"1649517535680","start":"1649517535748","complete":"1649517561551","duration":"25871","realtime":"23860","%cpu":"49.5","%mem":"0.2","rss":"226594816","vmem":"371625984","peak_rss":"226594816","peak_vmem":"371658752","rchar":"19149294","wchar":"445351","syscr":"3786","syscw":"293","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/37\/f53840827946ca3b72549721599e38","script":"\n plot_coverage.R \"test.fastq2_q20_nodup_mini\" \"dup_read_nb\" \"2320711 2320942\" \"4627368 4627400\" \"5\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"cute_little_R_functions.R\" \"plot_coverage_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"44003","inv_ctxt":"11"},{"task_id":"19","hash":"9d\/64d05b","native_id":"13378","process":"plot_coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_coverage (1)","status":"CACHED","exit":"0","submit":"1649517482485","start":"1649517482559","complete":"1649517509228","duration":"26743","realtime":"24483","%cpu":"49.2","%mem":"0.2","rss":"220262400","vmem":"363646976","peak_rss":"220262400","peak_vmem":"363679744","rchar":"19158411","wchar":"450691","syscr":"3788","syscw":"294","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/9d\/64d05bc1b1f46cbdd0dc53ec9bc0e0","script":"\n plot_coverage.R \"test.fastq2_bowtie2_mini\" \"read_nb_before\" \"2320711 2320942\" \"4627368 4627400\" \"5\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"cute_little_R_functions.R\" \"plot_coverage_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"42359","inv_ctxt":"13"},{"task_id":"27","hash":"13\/37b94c","native_id":"11809","process":"final_insertion_files","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"final_insertion_files (1)","status":"CACHED","exit":"0","submit":"1649517451877","start":"1649517451940","complete":"1649517466971","duration":"15094","realtime":"12958","%cpu":"39.8","%mem":"0.1","rss":"127500288","vmem":"258732032","peak_rss":"127500288","peak_vmem":"258764800","rchar":"18003744","wchar":"275694","syscr":"2302","syscw":"549","read_bytes":"28325888","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/13\/37b94c10e8ea0dd3e2a107a360e17d","script":"\n final_insertion_files.R \"test.fastq2_q20_nodup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"6\" \"test.fastq2_q20_nodup\" \"cute_little_R_functions.R\" \"final_insertion_files_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27284","inv_ctxt":"11"},{"task_id":"28","hash":"23\/48d601","native_id":"12520","process":"final_insertion_files","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"final_insertion_files (2)","status":"CACHED","exit":"0","submit":"1649517466998","start":"1649517467072","complete":"1649517482460","duration":"15462","realtime":"13021","%cpu":"39.8","%mem":"0.1","rss":"127520768","vmem":"258723840","peak_rss":"127520768","peak_vmem":"258756608","rchar":"18009229","wchar":"285506","syscr":"2303","syscw":"563","read_bytes":"28325888","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/23\/48d601bd9aa70901c4f07c6555b90c","script":"\n final_insertion_files.R \"test.fastq2_q20_dup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"6\" \"test.fastq2_q20_dup\" \"cute_little_R_functions.R\" \"final_insertion_files_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27255","inv_ctxt":"16"},{"task_id":"31","hash":"08\/101dfa","native_id":"23264","process":"report3","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"report3 (1)","status":"CACHED","exit":"0","submit":"1649631364071","start":"1649631364094","complete":"1649631365539","duration":"1468","realtime":"65","%cpu":"9.5","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"305829","wchar":"44602","syscr":"275","syscw":"49","read_bytes":"405504","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/08\/101dfacb4777d5793a816180256474","script":"\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Final insertion site files\\n\\n\" > report.rmd\n echo -e \"\\n\\nSee the [test.fastq2_q20_nodup_annot.pos](.\/files\/test.fastq2_q20_nodup_annot.pos) and [test.fastq2_q20_nodup_annot.freq](.\/files\/test.fastq2_q20_nodup_annot.freq) files\\n\\n\" >> report.rmd\n pos_nb=$(( $(wc -l test.fastq2_q20_nodup_annot.pos | cut -f1 -d\' \') - 1)) # -1 because first line is the header\n pos_uniq_nb=$(( $(sort -u test.fastq2_q20_nodup_annot.pos | wc -l | cut -f1 -d\' \') - 1)) # -1 because first line is the header\n echo -e \"\\n\\nNumber of total positions without duplicates: $(printf \"%\'d\" ${pos_nb})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of different positions without duplicates: $(printf \"%\'d\" ${pos_uniq_nb})\\n\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649631360\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"20","hash":"c6\/bef5aa","native_id":"560","process":"motif","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"motif","status":"CACHED","exit":"0","submit":"1649517332488","start":"1649517332563","complete":"1649517378942","duration":"46454","realtime":"42507","%cpu":"42.6","%mem":"0.2","rss":"210448384","vmem":"341352448","peak_rss":"210448384","peak_vmem":"341385216","rchar":"49872281","wchar":"41616533","syscr":"5761","syscw":"34229","read_bytes":"28351488","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/c6\/bef5aab063b9c049c73b56dc16a5d0","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Motif selected for the random insertions\\n\\n\" > report.rmd\n echo -e \"\\n\\nThe forward motif is: G[AT]T\\n\\n\" >> report.rmd\n echo -e \"\\n\\nThe reverse motif is: A[AT]C\\n\\n\" >> report.rmd\n if [[ G[AT]T != \"NULL\" ]] ; then\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'G[AT]T\' > motif_fw.pos\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'A[AT]C\' > motif_rev.pos\n else\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'[ACGT]\' > motif_fw.pos\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'[ACGT]\' > motif_rev.pos\n fi\n echo -e \"\nINDICATED POSITIONS IN FILES START AT ZERO AND CORRESPOND TO THE FIRST LEFT BASE OF THE MOTIF\n\"\n motif.R \"motif_fw.pos\" \"motif_rev.pos\" \"2320711 2320942\" \"4627368 4627400\" \"4641652\" \"G[AT]T\" \"A[AT]C\" \"cute_little_R_functions.R\" \"motif_report.txt\" \"report.rmd\"\n\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"56263","inv_ctxt":"515"},{"task_id":"12","hash":"2f\/6c72d5","native_id":"10356","process":"plot_read_length","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_read_length (1)","status":"CACHED","exit":"0","submit":"1649517410093","start":"1649517410161","complete":"1649517438749","duration":"28656","realtime":"26476","%cpu":"53.9","%mem":"0.2","rss":"208457728","vmem":"353968128","peak_rss":"208457728","peak_vmem":"354000896","rchar":"19621130","wchar":"708057","syscr":"4089","syscw":"430","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/2f\/6c72d54a358a5ba24052af31872307","script":"\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n### Length of initial reads\\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n### Length of reads after selection of attC in 5 prime \\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n### Length of reads after trimming \\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n### Read length after cut-off\\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' > report.rmd\n plot_read_length.R \"test.fastq2_ini.length\" \"test.fastq2_5pAttc.length\" \"test.fastq2_5pAtccRm.stat\" \"test.fastq2_cutoff.length\" \"cute_little_R_functions.R\" \"plot_read_length_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"44866","inv_ctxt":"18"},{"task_id":"10","hash":"51\/09f085","native_id":"7550","process":"plot_fivep_filtering_stat","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_fivep_filtering_stat (1)","status":"CACHED","exit":"0","submit":"1649517378958","start":"1649517379042","complete":"1649517410062","duration":"31104","realtime":"28457","%cpu":"42.9","%mem":"0.2","rss":"220442624","vmem":"363499520","peak_rss":"220442624","peak_vmem":"363532288","rchar":"19122541","wchar":"811482","syscr":"3774","syscw":"392","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/51\/09f08545d1cb5212f837380cb66f1d","script":"\n echo -e \"\n\\n\\n<br \/><br \/>\\n\\n### Base frequencies at the 5\' extremity of reads\\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \" > report.rmd\n plot_fivep_filtering_stat.R \"test.fastq2_5pAttc_1-51.stat\" \"CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\" \"cute_little_R_functions.R\" \"plot_fivep_filtering_stat_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"45486","inv_ctxt":"163"},{"task_id":"32","hash":"61\/3abc05","native_id":"17353","process":"seq_around_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"seq_around_insertion (2)","status":"CACHED","exit":"0","submit":"1649517601464","start":"1649517601588","complete":"1649517616248","duration":"14784","realtime":"12663","%cpu":"40.7","%mem":"0.1","rss":"127565824","vmem":"258732032","peak_rss":"127565824","peak_vmem":"258764800","rchar":"17988623","wchar":"146404","syscr":"2295","syscw":"273","read_bytes":"28199936","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/61\/3abc0565b8de06c8d45f9b4688fe97","script":"\n seq_around_insertion.R \"test.fastq2_q20_dup_selected_if_dup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"20\" \"test.fastq2_q20_dup_selected_if_dup\" \"cute_little_R_functions.R\" \"seq_around_insertion_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27263","inv_ctxt":"7"},{"task_id":"30","hash":"16\/ef3227","native_id":"15833","process":"seq_around_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"seq_around_insertion (1)","status":"CACHED","exit":"0","submit":"1649517561578","start":"1649517561650","complete":"1649517576718","duration":"15140","realtime":"12931","%cpu":"40.7","%mem":"0.1","rss":"127676416","vmem":"258727936","peak_rss":"127676416","peak_vmem":"258760704","rchar":"18001648","wchar":"213511","syscr":"2298","syscw":"375","read_bytes":"28199936","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/16\/ef32272c34739f656bad122d66b667","script":"\n seq_around_insertion.R \"test.fastq2_q20_nodup_selected_if_dup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"20\" \"test.fastq2_q20_nodup_selected_if_dup\" \"cute_little_R_functions.R\" \"seq_around_insertion_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27272","inv_ctxt":"4"},{"task_id":"36","hash":"73\/f0b62e","native_id":"18913","process":"extract_seq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"extract_seq (1)","status":"CACHED","exit":"0","submit":"1649517644033","start":"1649517644135","complete":"1649517649189","duration":"5156","realtime":"2804","%cpu":"19.6","%mem":"0.0","rss":"10805248","vmem":"57536512","peak_rss":"18477056","peak_vmem":"65474560","rchar":"9503313","wchar":"4763907","syscr":"866","syscw":"3349","read_bytes":"6359040","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/73\/f0b62ecf2ff3b5cf5a16212d9e5cfe","script":"\n echo -e \"\n\n######## test.fastq2_q20_nodup_selected_if_dup_around_insertion.bed file\n\n\" > extract_seq_report.txt\n # make a bed file from the reference genome\n echo \">ref\" > tempo.ref.fasta\n awk \'{lineKind=(NR-1)%2}lineKind==1{print $0}\' Ecoli-K12-MG1655_ORI_CENTERED.fasta >> tempo.ref.fasta |& tee extract_seq_report.txt\n bedtools getfasta -fi tempo.ref.fasta -bed test.fastq2_q20_nodup_selected_if_dup_around_insertion.bed -fo \"test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta\" -name |& tee extract_seq_report.txt\n rm tempo.ref.fasta\n rm tempo.ref.fasta.fai\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3829","inv_ctxt":"1"},{"task_id":"35","hash":"52\/691666","native_id":"27108","process":"extract_seq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"extract_seq (2)","status":"CACHED","exit":"0","submit":"1649517996849","start":"1649517997120","complete":"1649518000598","duration":"3749","realtime":"1457","%cpu":"17.8","%mem":"0.0","rss":"6295552","vmem":"52846592","peak_rss":"6295552","peak_vmem":"52846592","rchar":"9452267","wchar":"4672192","syscr":"860","syscw":"847","read_bytes":"2721792","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/52\/69166677daa4c07a65c8abc0ce22da","script":"\n echo -e \"\n\n######## test.fastq2_q20_dup_selected_if_dup_around_insertion.bed file\n\n\" > extract_seq_report.txt\n # make a bed file from the reference genome\n echo \">ref\" > tempo.ref.fasta\n awk \'{lineKind=(NR-1)%2}lineKind==1{print $0}\' Ecoli-K12-MG1655_ORI_CENTERED.fasta >> tempo.ref.fasta |& tee extract_seq_report.txt\n bedtools getfasta -fi tempo.ref.fasta -bed test.fastq2_q20_dup_selected_if_dup_around_insertion.bed -fo \"test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta\" -name |& tee extract_seq_report.txt\n rm tempo.ref.fasta\n rm tempo.ref.fasta.fai\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"649","inv_ctxt":"0"},{"task_id":"33","hash":"8b\/74bcbf","native_id":"403","process":"dup_insertion_and_logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"dup_insertion_and_logo (1)","status":"COMPLETED","exit":"0","submit":"1649703172046","start":"1649703172061","complete":"1649703184167","duration":"12121","realtime":"10997","%cpu":"64.5","%mem":"0.2","rss":"247107584","vmem":"425803776","peak_rss":"283983872","peak_vmem":"426561536","rchar":"17562590","wchar":"600302","syscr":"3936","syscw":"456","read_bytes":"44530688","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/8b\/74bcbf845d8c1180cbc16b8f011cf9","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Analysis with duplicates\\n\\n\" > report.rmd\n dup_insertion_and_logo.R \"test.fastq2_q20_dup_annot.freq\" \"test.fastq2_q20_dup_annot_selected.freq\" \"2320711 2320942\" \"4627368 4627400\" \"4641652\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"dup\" \"20\" \"cute_little_R_functions.R\" \"dup_insertion_and_logo_report.txt\" # logo\n\n echo -e \"\\n\\nSee the [test.fastq2_q20_dup_selected_if_dup.pos](.\/files\/test.fastq2_q20_dup_selected_if_dup.pos) and [test.fastq2_q20_dup_annot_selected.freq](.\/files\/test.fastq2_q20_dup_annot_selected.freq) files\\n\\n\" >> report.rmd\n echo -e \"\\n\\nWarning: more than the 6 most frequent used sites can be present in the case of frequency equality\\n\\n\" >> report.rmd\n\n pos_nb=$(( $(wc -l test.fastq2_q20_dup_annot.pos | cut -f1 -d\' \') - 1 )) # -1 because first line is the header\n echo -e \"\\n\\nNumber of total positions using duplicated reads: $(printf \"%\'d\" ${pos_nb})\\n\" >> report.rmd\n\n freq_nb=$(( $(wc -l test.fastq2_q20_dup_annot.freq | cut -f1 -d\' \') - 1 )) # -1 because first line is the header\n echo -e \"\\n\\nNumber of different positions using duplicated reads: $(printf \"%\'d\" ${freq_nb})\\n\" >> report.rmd\n\n pos_selected_nb=$(wc -l test.fastq2_q20_dup_selected_if_dup.pos | cut -f1 -d\' \')\n echo -e \"\\n\\nNumber of total positions after selection of the 6 highest used sites: $(printf \"%\'d\" ${pos_selected_nb})\\n\" >> report.rmd\n\n freq_selected_nb=$(( $(wc -l test.fastq2_q20_dup_annot_selected.freq | cut -f1 -d\' \') - 1 )) # -1 because first line is the header\n echo -e \"\\n\\nNumber of different positions after selection of the 6 highest used sites: $(printf \"%\'d\" ${freq_selected_nb})\\n\" >> report.rmd\n\n TEMPO=(50000 200000 500000)\n FIG_NB_BEFORE=$(($(echo ${#TEMPO[@]}) * 2)) # nb of elements in the window size * nb of figure plotted\n if [[ \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/TSS_compatible_essential.txt != \"NULL\" ]] ; then\n FIG_NB=$(( 34 + $FIG_NB_BEFORE + 1 + 1)) # 2 * because two figures\n else\n FIG_NB=$(( 24 + $FIG_NB_BEFORE + 1 + 1))\n fi\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\nSelected sites with frequencies:\\n\\n\" >> report.rmd\n echo \"\n\\`\\`\\`{r, echo = FALSE}\ntempo <- read.table(\'.\/files\/test.fastq2_q20_dup_annot_selected.freq\', header = TRUE, colClasses = \'character\', sep = \'\\t\', check.names = FALSE) ; \nkableExtra::kable_styling(knitr::kable(tempo, row.names = TRUE, digits = 0, caption = NULL, format=\'html\'), c(\'striped\', \'bordered\', \'responsive\', \'condensed\'), font_size=10, full_width = FALSE, position = \'left\')\n\\`\\`\\`\n \n\n\n \" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n to extend)](.\/figures\/alignment.html){width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\nWarning: the frequency of each position is taken into account in the logo plot\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"43393","inv_ctxt":"8"},{"task_id":"41","hash":"49\/575b96","native_id":"27342","process":"goalign","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/evolbioinfo-goalign-v0.3.5.img","tag":"-","name":"goalign (1)","status":"CACHED","exit":"0","submit":"1649518000735","start":"1649518000798","complete":"1649518007039","duration":"6304","realtime":"2873","%cpu":"13.2","%mem":"0.0","rss":"14262272","vmem":"734232576","peak_rss":"14262272","peak_vmem":"734240768","rchar":"131211","wchar":"356117","syscr":"276","syscw":"108","read_bytes":"3862528","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/49\/575b963f7bb8fe6e513f1f993e68d2","script":"\n # Remove duplicated data in a fasta file according to duplicated header\n awk \'\n \/^>\/{f=!d[$1];d[$1]=1}f\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > tempo\n\n # split the fasta file according to forward or reverse sequences\n PATTERN=\'LEADING_16|LAGGING_16\'\n awk -v var1=$PATTERN \'\n BEGIN{ORS=\"\\n\"}\n {lineKind=(NR-1)%2}\n lineKind==0{record=$0 ; next}\n lineKind==1{\n toGet=(record ~ var1)\n if(toGet){\n print record > \"reverse.fasta\"\n print $0 > \"reverse.fasta\"\n }else{\n print record > \"forward.fasta\"\n print $0 > \"forward.fasta\"\n }\n next\n }\n \' tempo\n\n # Goalign\n if [ -s reverse.fasta ] ; then\n goalign revcomp --unaligned -i reverse.fasta -o tempo2 # rev-comp the 16 sequences\n cat forward.fasta tempo2 > final.fasta\n else # we cannot have neither reverse nor forward\n cp forward.fasta final.fasta\n fi\n # add a hyphen before or after the sequence, to have correct alignment\n awk -v var1=$PATTERN \'\n BEGIN{ORS=\"\\n\"}\n {lineKind=(NR-1)%2}\n lineKind==0{record=$0 ; print $0 ; next}\n lineKind==1{\n toGet=(record ~ var1)\n if(toGet){\n print \"-\"$0 ; next\n }else{\n print $0\"-\" ; next\n }\n }\n \' final.fasta > tempo3\n goalign draw biojs --auto-detect -i tempo3 -o alignment.html |& tee -a goalign_report.txt\n ","scratch":"-","queue":"-","cpus":"12","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3223","inv_ctxt":"2"},{"task_id":"37","hash":"35\/ec05ea","native_id":"19358","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (1)","status":"CACHED","exit":"0","submit":"1649517649698","start":"1649517649791","complete":"1649517654517","duration":"4819","realtime":"396","%cpu":"10.0","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"286518","wchar":"17181","syscr":"526","syscw":"46","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/35\/ec05ea9f6b312a191057dce3bb593d","script":"\n # file splitting into seq\n awk -v var1=LEADING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"45","hash":"b4\/c8c939","native_id":"27489","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (8)","status":"CACHED","exit":"0","submit":"1649518001036","start":"1649518001118","complete":"1649518005804","duration":"4768","realtime":"201","%cpu":"9.6","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"184866","wchar":"7118","syscr":"328","syscw":"27","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/b4\/c8c93938121f441e49b361efe8f001","script":"\n # file splitting into seq\n awk -v var1=LAGGING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"38","hash":"ac\/24a1a8","native_id":"19211","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (2)","status":"CACHED","exit":"0","submit":"1649517649332","start":"1649517649389","complete":"1649517654136","duration":"4804","realtime":"399","%cpu":"11.1","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"288657","wchar":"19344","syscr":"530","syscw":"50","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/ac\/24a1a84c8dccc7d267164f15a6a0dd","script":"\n # file splitting into seq\n awk -v var1=LEADING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"42","hash":"e7\/c17b46","native_id":"27448","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (5)","status":"CACHED","exit":"0","submit":"1649518001008","start":"1649518001099","complete":"1649518005653","duration":"4645","realtime":"197","%cpu":"9.7","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"186870","wchar":"9131","syscr":"332","syscw":"31","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/e7\/c17b4628d9b5cfa9de18551ae5efa8","script":"\n # file splitting into seq\n awk -v var1=LEADING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"40","hash":"f0\/df4b99","native_id":"19278","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (4)","status":"CACHED","exit":"0","submit":"1649517649523","start":"1649517649590","complete":"1649517654353","duration":"4830","realtime":"432","%cpu":"11.3","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"288822","wchar":"19500","syscr":"531","syscw":"51","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/f0\/df4b990558220e25f113d0cfeac3ea","script":"\n # file splitting into seq\n awk -v var1=LAGGING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"44","hash":"24\/882b89","native_id":"27418","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (7)","status":"CACHED","exit":"0","submit":"1649518000971","start":"1649518000999","complete":"1649518005740","duration":"4769","realtime":"172","%cpu":"9.8","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"180638","wchar":"2832","syscr":"319","syscw":"18","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/24\/882b89ee5e23a0484fa80121a46044","script":"\n # file splitting into seq\n awk -v var1=LAGGING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"39","hash":"3c\/c2beee","native_id":"19315","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (3)","status":"CACHED","exit":"0","submit":"1649517649606","start":"1649517649691","complete":"1649517654451","duration":"4845","realtime":"426","%cpu":"11.9","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"289470","wchar":"20168","syscr":"532","syscw":"52","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/3c\/c2beee63bd2a590f3d93228009f675","script":"\n # file splitting into seq\n awk -v var1=LAGGING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"43","hash":"63\/faaafa","native_id":"28010","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (6)","status":"CACHED","exit":"0","submit":"1649518005691","start":"1649518005866","complete":"1649518008672","duration":"2981","realtime":"109","%cpu":"11.6","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"183021","wchar":"5250","syscr":"324","syscw":"23","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/63\/faaafa410111d32d4fa1a81f842bfa","script":"\n # file splitting into seq\n awk -v var1=LEADING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"50","hash":"71\/fcd5ac","native_id":"28454","process":"report2","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"report2","status":"CACHED","exit":"0","submit":"1649518009697","start":"1649518009773","complete":"1649518012408","duration":"2711","realtime":"45","%cpu":"5.6","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"135292","wchar":"2465","syscr":"248","syscw":"103","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/71\/fcd5ac705daf0d2c90e84a7d3a87f3","script":"\n echo -e \"\n\\n\\n<br \/><br \/>\\n\\n### Logos\\n\\n\n\\n\\nIn each sequence of length $((20 * 2)) <br \/>position $((20 + 1)) corresponds to the first nucleotide of the reference genome part of the read\n\" > report.rmd\n count=0 # always goes to 4 because 4 figures, one for each forward\/reverse leading\/lagging\n for i in $(echo [test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16, test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16, test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0, test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0] | sed \'s\/^\\[\/\/\' | sed \'s\/\\]$\/\/\' | sed \'s\/,\/\/g\') ; do\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n count=$((count + 1))\n done\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"34","hash":"34\/a909b8","native_id":"18724","process":"random_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"random_insertion (1)","status":"CACHED","exit":"0","submit":"1649699456918","start":"1649699456985","complete":"1649699466417","duration":"9499","realtime":"8510","%cpu":"57.3","%mem":"0.3","rss":"368607232","vmem":"511221760","peak_rss":"368607232","peak_vmem":"511254528","rchar":"31432716","wchar":"1296711","syscr":"5927","syscw":"1485","read_bytes":"35685376","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/34\/a909b82f7c0fa1fec6a1b178946ae2","script":"\n random_insertion.R \"test.fastq2_q20_nodup_annot.pos\" \"motif_sites.pos\" \"2320711 2320942\" \"4627368 4627400\" \"G[AT]T\" \"4641652\" \"test.fastq2\" \"cute_little_R_functions.R\" \"random_insertion_report.txt\"\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Random insertion sites\\n\\n\" > report.rmd\n echo -e \"\\n\\n#### Insertion site counts\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the [random_insertion_report.txt](.\/reports\/random_insertion_report.txt) file for details, notably the number of random sites (which should be the same as the number of observed sites)\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Insertion site proportions\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=500}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=500}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=500}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649699453\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"39640","inv_ctxt":"20"},{"task_id":"46","hash":"12\/8d4218","native_id":"30389","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (4)","status":"CACHED","exit":"0","submit":"1649518055457","start":"1649518055529","complete":"1649518072378","duration":"16921","realtime":"14779","%cpu":"44.2","%mem":"0.1","rss":"125968384","vmem":"307650560","peak_rss":"125968384","peak_vmem":"307683328","rchar":"14488278","wchar":"1026153","syscr":"2474","syscw":"334","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/12\/8d42187c499a07e1b2854cb1783557","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"26386","inv_ctxt":"6"},{"task_id":"47","hash":"49\/98147b","native_id":"27326","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (1)","status":"CACHED","exit":"0","submit":"1649518000631","start":"1649518000698","complete":"1649518021750","duration":"21119","realtime":"17706","%cpu":"38.5","%mem":"0.1","rss":"142135296","vmem":"285728768","peak_rss":"176672768","peak_vmem":"321765376","rchar":"14488308","wchar":"873213","syscr":"2474","syscw":"314","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/49\/98147bec76c1caa3b8cf85164cb6b1","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"30254","inv_ctxt":"601"},{"task_id":"48","hash":"20\/686354","native_id":"28980","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (2)","status":"CACHED","exit":"0","submit":"1649518021775","start":"1649518021849","complete":"1649518038231","duration":"16456","realtime":"14490","%cpu":"44.4","%mem":"0.1","rss":"129908736","vmem":"311144448","peak_rss":"129908736","peak_vmem":"311177216","rchar":"14488301","wchar":"904086","syscr":"2474","syscw":"316","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/20\/6863545e9b2fd0bd51b448b5219110","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"26525","inv_ctxt":"6"},{"task_id":"49","hash":"d7\/09132f","native_id":"29683","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (3)","status":"CACHED","exit":"0","submit":"1649518038258","start":"1649518038331","complete":"1649518055429","duration":"17171","realtime":"15098","%cpu":"43.2","%mem":"0.1","rss":"123715584","vmem":"271429632","peak_rss":"123715584","peak_vmem":"271462400","rchar":"14488313","wchar":"894424","syscr":"2474","syscw":"314","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/d7\/09132fb6c80831584aaec35d37e3d9","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"25754","inv_ctxt":"3"},{"task_id":"51","hash":"6d\/f8b929","native_id":"31807","process":"global_logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"global_logo (1)","status":"CACHED","exit":"0","submit":"1649518090164","start":"1649518090253","complete":"1649518107759","duration":"17595","realtime":"15576","%cpu":"46.3","%mem":"0.1","rss":"122327040","vmem":"266919936","peak_rss":"122327040","peak_vmem":"266952704","rchar":"14497087","wchar":"867376","syscr":"2488","syscw":"311","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/6d\/f8b92904090b1f68582fc5640c92e5","script":"\n global_logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat\" \"test.fastq2\" \"20\" \"cute_little_R_functions.R\" \"global_logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"24904","inv_ctxt":"5"},{"task_id":"52","hash":"e9\/9bbb1e","native_id":"31094","process":"global_logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"global_logo (2)","status":"CACHED","exit":"0","submit":"1649518072403","start":"1649518072478","complete":"1649518090138","duration":"17735","realtime":"15591","%cpu":"46.4","%mem":"0.1","rss":"122126336","vmem":"267714560","peak_rss":"122126336","peak_vmem":"267747328","rchar":"14496507","wchar":"990822","syscr":"2488","syscw":"331","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/e9\/9bbb1ef3d79cf349635852b4413b30","script":"\n global_logo.R \"test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.stat test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.stat\" \"test.fastq2\" \"20\" \"cute_little_R_functions.R\" \"global_logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649517328\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"25009","inv_ctxt":"4"},{"task_id":"53","hash":"f8\/d685f3","native_id":"1420","process":"plot_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_insertion (1)","status":"COMPLETED","exit":"0","submit":"1649703184174","start":"1649703184268","complete":"1649703424377","duration":"240203","realtime":"239503","%cpu":"35.7","%mem":"0.4","rss":"413442048","vmem":"572653568","peak_rss":"414097408","peak_vmem":"572731392","rchar":"46946944","wchar":"23740340","syscr":"34549","syscw":"18913","read_bytes":"53133312","write_bytes":"450560","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/f8\/d685f3eec85cf489de186325f95b3e","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Insertion plots\\n\\n\" > report.rmd\n plot_insertion.R \"obs_rd_insertions.pos\" \"obs_rd_insertions.freq\" \"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/TSS_compatible_essential.txt\" \"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/Essential_genes_MG1655.tsv\" \"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/cds_ORI_CENTERED.txt\" \"2320711 2320942\" \"4627368 4627400\" \"Ecoli Genome (bp)\" \"4641652\" \"0.88\" \"0.08\" \"50000 200000 500000\" \"100\" \"test.fastq2\" \"12\" \"cute_little_R_functions.R\" \"plot_insertion_report.txt\"\n echo -e \"\\n\\n#### Histograms\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Raw frequencies\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the CL Labbook section 24.7.3 to explain the limitation around 100 bp\\n\" >> report.rmd\n echo -e \'\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Binned frequencies\\n\\n\" >> report.rmd\n count=1\n for i in 50000 200000 500000 ; do\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n count=$((count + 1))\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n count=$((count + 1))\n done\n if [[ \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/TSS_compatible_essential.txt != \"NULL\" ]] ; then\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Transcription start site (TSS) plots\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the CL Labbook section 48.3 to to get the theoretical proportion of the codant\/non codant essential\/non essential genome\\n\\nSee the [plot_insertion_report.txt](.\/reports\/plot_insertion_report.txt) file for details about the plotted values.\" >> report.rmd\n echo -e \'\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \\n\\n<br \/><br \/>\\n\\nThe number of insertions sites are indicated above graphs.\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \\n\\n<br \/><br \/>\\n\\n### Coding sequences (CDS) plots\\n\\nThe number of insertions sites inside CDS are indicated above graphs.\\n\\nSee the [plot_insertion_report.txt](.\/reports\/plot_insertion_report.txt) file for details about the plotted values.\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \\n\\nWarning: the number of observed and random insertions indicated above graphs can be greater than those indicated above Figure \'$((28 + $count))\', since a position that overlaps two genes is counted twice (see the [plot_insertion_report.txt](.\/reports\/plot_insertion_report.txt) file for details).\\n\\n\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \' >> report.rmd\n fi\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1649703168\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"99826","inv_ctxt":"165"},{"task_id":"54","hash":"ac\/8ffc76","native_id":"7745","process":"print_report","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"print_report (1)","status":"COMPLETED","exit":"0","submit":"1649703425043","start":"1649703425078","complete":"1649703435217","duration":"10174","realtime":"9314","%cpu":"45.4","%mem":"0.2","rss":"279404544","vmem":"1100166922240","peak_rss":"279404544","peak_vmem":"1100167069696","rchar":"54377295","wchar":"27580275","syscr":"7160","syscw":"2194","read_bytes":"48487424","write_bytes":"4096","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/ac\/8ffc760018645c1fbeb583ce7f83d2","script":"\n cp report.rmd report_file.rmd # this is to get hard files, not symlinks\n mkdir figures\n mkdir files\n mkdir reports\n cat stat_tempo > .\/files\/test.fastq2_5pAttc_1-51.stat # this is to get hard files, not symlinks\n cp head.fw.txt head.rv.txt table1.txt table2.txt table3.txt table4.txt table8.txt test.fastq2_q20_dup_annot_selected.freq .\/files\/ # this is to get hard files, not symlinks\n cp plot_fivep_filtering_stat.png plot_read_length_cutoff.png plot_read_length_fivep_filtering.png plot_read_length_fivep_filtering_cut.png plot_read_length_ini.png plot_test.fastq2_q20_dup_mini.png plot_test.fastq2_q20_nodup_mini.png plot_test.fastq2_bowtie2_mini.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.png global_logo_nodup_test.fastq2.png global_logo_dup_test.fastq2.png plot_motif_insertion_per_fork.png plot_motif_insertion_per_fork_and_strand.png plot_motif_insertion_per_fork_and_strand_prop.png plot_motif_insertion_per_fork_prop.png plot_motif_insertion_per_strand.png plot_motif_insertion_per_strand_prop.png barplot_test.fastq2_all.png barplot_test.fastq2_all_relative.png barplot_test.fastq2_ess_uness.png barplot_test.fastq2_inside_outside.png boxplot_test.fastq2_cds.png 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500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"863","inv_ctxt":"10"},{"task_id":"3","hash":"32\/0ca22b","native_id":"1393","process":"report1","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"report1","status":"COMPLETED","exit":"0","submit":"1657729208710","start":"1657729208798","complete":"1657729210119","duration":"1409","realtime":"15","%cpu":"9.4","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"106896","wchar":"682","syscr":"189","syscw":"54","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/32\/0ca22b4abe25ac54d847fd10e04df0","script":"\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n### Read coverage\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' > report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"5","hash":"32\/daee1c","native_id":"1481","process":"backup","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"backup","status":"COMPLETED","exit":"0","submit":"1657729208882","start":"1657729208898","complete":"1657729210318","duration":"1436","realtime":"49","%cpu":"8.9","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"106737","wchar":"499","syscr":"189","syscw":"23","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/32\/daee1ce79d442290bfa03589f40bf7","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Backup\\n\\n\" > report.rmd\n echo -e \"See the [reports](.\/reports) folder for all the details of the analysis, including the parameters used in the .config file\" >> report.rmd\n echo -e \"\\n\\nFull .nextflow.log is in: \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot<br \/>The one in the [reports](.\/reports) folder is not complete (miss the end)\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"6","hash":"e4\/9b44e5","native_id":"1505","process":"workflowVersion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"workflowVersion","status":"COMPLETED","exit":"0","submit":"1657729208910","start":"1657729208998","complete":"1657729211158","duration":"2248","realtime":"1022","%cpu":"12.8","%mem":"0.0","rss":"5214208","vmem":"40312832","peak_rss":"5214208","peak_vmem":"40312832","rchar":"193482","wchar":"26577","syscr":"321","syscw":"70","read_bytes":"3288064","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/e4\/9b44e5dc872aa0d91709e25c2a6ac1","script":"\n modules= # this is just to deal with variable interpretation during the creation of the .command.sh file by nextflow. See also $modules below\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Workflow Version\\n\\n\" > report.rmd\n echo -e \"\\n\\n#### General\\n\\n\n| Variable | Value |\n| :-- | :-- |\n| Project<br \/>(empty means no .git folder where the main.nf file is present) | $(git -C \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot remote -v | head -n 1) | # works only if the main script run is located in a directory that has a .git folder, i.e., that is connected to a distant repo\n| Git info<br \/>(empty means no .git folder where the main.nf file is present) | $(git -C \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot describe --abbrev=10 --dirty --always --tags) | # idem. Provide the small commit number of the script and nextflow.config used in the execution\n| Cmd line | nextflow run main.nf -c nextflow.config |\n| execution mode | local |\" >> report.rmd \n\n if [[ ! -z $modules ]] ; then\n echo \"| loaded modules (according to specification by the user thanks to the --modules argument of main.nf) | |\" >> report.rmd\n fi\n \n echo \"| Manifest\'s pipeline version | null |\n| result path | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206 |\n| nextflow version | 21.04.2 |\n \" >> report.rmd\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Implicit variables\\n\\n\n| Name | Description | Value | \n| :-- | :-- | :-- |\n| launchDir | Directory where the workflow is run | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot |\n| nprojectDir | Directory where the main.nf script is located | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot |\n| workDir | Directory where tasks temporary files are created | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work |\n \" >> report.rmd\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### User variables\\n\\n\n| Name | Description | Value | \n| :-- | :-- | :-- |\n| out_path | output folder path | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206 |\n| in_path | input folder path | \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset |\n \" >> report.rmd\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Workflow diagram\\n\\nSee the [nf_dag.png](.\/reports\/nf_dag.png) file\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"31","inv_ctxt":"1"},{"task_id":"4","hash":"4f\/cb0182","native_id":"2144","process":"motif","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"motif","status":"COMPLETED","exit":"0","submit":"1657729210407","start":"1657729210419","complete":"1657729225908","duration":"15501","realtime":"14623","%cpu":"50.6","%mem":"0.2","rss":"196534272","vmem":"327684096","peak_rss":"201019392","peak_vmem":"333217792","rchar":"49872565","wchar":"41616686","syscr":"5761","syscw":"34231","read_bytes":"28351488","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/4f\/cb018272b048edd56f7f0814423c9a","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Motif selected for the random insertions\\n\\n\" > report.rmd\n echo -e \"\\n\\nThe forward motif is: G[AT]T\\n\\n\" >> report.rmd\n echo -e \"\\n\\nThe reverse motif is: A[AT]C\\n\\n\" >> report.rmd\n if [[ G[AT]T != \"NULL\" ]] ; then\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'G[AT]T\' > motif_fw.pos\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'A[AT]C\' > motif_rev.pos\n else\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'[ACGT]\' > motif_fw.pos\n cat Ecoli-K12-MG1655_ORI_CENTERED.fasta | sed \'1d\' | grep -Ebo \'[ACGT]\' > motif_rev.pos\n fi\n echo -e \"\\n\\nWarning: the position indicated is the first nucleotide of the genomic part of the read (the W of the 5\'GWT3\' consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position.\\n\\n\" >> report.rmd\n motif.R \"motif_fw.pos\" \"motif_rev.pos\" \"2320711 2320942\" \"4627368 4627400\" \"4641652\" \"G[AT]T\" \"A[AT]C\" \"cute_little_R_functions.R\" \"motif_report.txt\" \"report.rmd\"\n\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"46916","inv_ctxt":"203"},{"task_id":"7","hash":"d3\/55e04f","native_id":"2198","process":"trim","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-alien_trimmer_v0.4.0-gitlab_v8.1.img","tag":"-","name":"trim (1)","status":"COMPLETED","exit":"0","submit":"1657729210558","start":"1657729210620","complete":"1657729213748","duration":"3190","realtime":"2120","%cpu":"42.5","%mem":"0.0","rss":"41426944","vmem":"5903519744","peak_rss":"41426944","peak_vmem":"5903519744","rchar":"17145203","wchar":"12629480","syscr":"2382","syscw":"647","read_bytes":"9977856","write_bytes":"32768","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/d3\/55e04fe36e9144a44907944519a25d","script":"\n trim.sh test.fastq2_Nremove.gz \"test.fastq2_trim.fq\" 20200520_adapters_TruSeq_B2699_14985_CL.fasta 30 \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"3276","inv_ctxt":"0"},{"task_id":"10","hash":"03\/a38aa6","native_id":"2810","process":"kraken","module":"-","container":"-","tag":"-","name":"kraken (1)","status":"COMPLETED","exit":"0","submit":"1657729213778","start":"1657729213848","complete":"1657729213864","duration":"86","realtime":"8","%cpu":"57.1","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"154412","wchar":"220","syscr":"228","syscw":"13","read_bytes":"159744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/03\/a38aa607f3411d81ba8734b6de2d34","script":"\n echo \"No kraken analysis performed in local running\" > test.fastq2_trim_kraken_std.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"8","hash":"29\/0de1d1","native_id":"2832","process":"fastqc1","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/evolbioinfo-fastqc-v0.11.8.img","tag":"-","name":"fastqc1 (1)","status":"COMPLETED","exit":"0","submit":"1657729213804","start":"1657729213872","complete":"1657729220868","duration":"7064","realtime":"6113","%cpu":"55.0","%mem":"0.2","rss":"169955328","vmem":"3289477120","peak_rss":"170041344","peak_vmem":"3289899008","rchar":"14603822","wchar":"1278924","syscr":"7605","syscw":"5193","read_bytes":"20000768","write_bytes":"712704","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/29\/0de1d1d5d22097b97ec9ecc4b2e7d3","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Read QC n\u00B01\\n\\n\" > report.rmd\n echo -e \"Results are published in the [fastQC1](.\/fastQC1) folder\\n\\n\" >> report.rmd\n fastqc test.fastq2_trim.fq | tee tempo.txt\n cat tempo.txt >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"4334","inv_ctxt":"1"},{"task_id":"9","hash":"ad\/589edd","native_id":"2897","process":"fivep_filtering","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"fivep_filtering (1)","status":"COMPLETED","exit":"0","submit":"1657729213828","start":"1657729213886","complete":"1657729216448","duration":"2620","realtime":"1644","%cpu":"27.0","%mem":"0.0","rss":"10145792","vmem":"64376832","peak_rss":"10145792","peak_vmem":"64376832","rchar":"29337212","wchar":"16061719","syscr":"9149","syscw":"5789","read_bytes":"437248","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/ad\/589eddb789f253e7adf6a166829fa7","script":"\n fivep_filtering.sh test.fastq2_trim.fq \"test.fastq2\" \"^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\" 48 3 51 \"CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"1158","inv_ctxt":"2"},{"task_id":"12","hash":"9a\/bb6bb5","native_id":"3641","process":"fastqc2","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/evolbioinfo-fastqc-v0.11.8.img","tag":"-","name":"fastqc2 (1)","status":"COMPLETED","exit":"0","submit":"1657729216503","start":"1657729216548","complete":"1657729223138","duration":"6635","realtime":"5753","%cpu":"88.7","%mem":"0.2","rss":"204603392","vmem":"3289477120","peak_rss":"204603392","peak_vmem":"3294511104","rchar":"12766784","wchar":"1245410","syscr":"7360","syscw":"5096","read_bytes":"19984384","write_bytes":"688128","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/9a\/bb6bb57571a91ffcbcbc9e6a2dbb70","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Read QC n\u00B02\\n\\n\" > report.rmd\n echo -e \"Results are published in the [fastQC2](.\/fastQC2) folder\\n\\n\" >> report.rmd\n fastqc test.fastq2_5pAtccRm.fq | tee tempo.txt\n cat tempo.txt >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"4335","inv_ctxt":"2"},{"task_id":"11","hash":"ef\/88d2e7","native_id":"3685","process":"cutoff","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"cutoff (1)","status":"COMPLETED","exit":"0","submit":"1657729216532","start":"1657729216563","complete":"1657729218228","duration":"1696","realtime":"652","%cpu":"19.9","%mem":"0.0","rss":"9936896","vmem":"64237568","peak_rss":"9936896","peak_vmem":"64237568","rchar":"7307990","wchar":"4049154","syscr":"2784","syscw":"2034","read_bytes":"384000","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/ef\/88d2e7555ad9d8b1f861fbaa9957fd","script":"\n cutoff.sh test.fastq2_5pAtccRm.fq 25 \"test.fastq2\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"1146","inv_ctxt":"2"},{"task_id":"15","hash":"7a\/473ae2","native_id":"4475","process":"bowtie2","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bowtie2_v2.3.4.3_extended_v2.0-gitlab_v8.0.img","tag":"-","name":"bowtie2 (1)","status":"COMPLETED","exit":"0","submit":"1657729219237","start":"1657729219330","complete":"1657729224048","duration":"4811","realtime":"3721","%cpu":"54.8","%mem":"0.0","rss":"65777664","vmem":"249090048","peak_rss":"120528896","peak_vmem":"251150336","rchar":"36678343","wchar":"17009936","syscr":"3391","syscw":"2516","read_bytes":"7209984","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/7a\/473ae2a25f71ea4dead130ab064dbe","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Bowtie2 indexing of the reference sequence\\n\\n\" >> bowtie2_report.txt\n bowtie2-build Ecoli-K12-MG1655_ORI_CENTERED.fasta Ecoli-K12-MG1655_ORI_CENTERED |& tee -a bowtie2_report.txt\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Bowtie2 alignment\\n\\n\" > report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Bowtie2 alignment\\n\\n\" >> bowtie2_report.txt\n bowtie2 --very-sensitive -x Ecoli-K12-MG1655_ORI_CENTERED -U test.fastq2_cutoff.fq -t -S test.fastq2_bowtie2.sam |& tee -a tempo.txt\n # --very-sensitive: no soft clipping allowed and very sensitive seed alignment\n # -t time displayed\n cat tempo.txt >> bowtie2_report.txt\n sed -i -e \':a;N;$!ba;s\/\\n\/\\n<br \\\/>\/g\' tempo.txt\n cat tempo.txt >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### samtools conversion\\n\\n\" >> bowtie2_report.txt\n # samtools faidx Ecoli-K12-MG1655_ORI_CENTERED.fasta\n samtools view -bh -o tempo.bam test.fastq2_bowtie2.sam |& tee -a bowtie2_report.txt\n samtools sort -o test.fastq2_bowtie2.bam tempo.bam |& tee -a bowtie2_report.txt\n samtools index test.fastq2_bowtie2.bam |& tee -a bowtie2_report.txt\n ","scratch":"-","queue":"-","cpus":"12","memory":"17179869184","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"5227","inv_ctxt":"49"},{"task_id":"17","hash":"4b\/0af112","native_id":"5571","process":"Q20","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"Q20 (1)","status":"COMPLETED","exit":"0","submit":"1657729225055","start":"1657729225150","complete":"1657729226603","duration":"1548","realtime":"390","%cpu":"16.2","%mem":"0.0","rss":"6692864","vmem":"45260800","peak_rss":"6762496","peak_vmem":"45391872","rchar":"3392016","wchar":"2260604","syscr":"879","syscw":"567","read_bytes":"1301504","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/4b\/0af112ef31583a36f3c231cd4847fc","script":"\n samtools view -q 20 -b test.fastq2_bowtie2.bam > test.fastq2_q20_dup.bam |& tee q20_report.txt\n samtools index test.fastq2_q20_dup.bam\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Q20 filtering\\n\\n\" > report.rmd\n read_nb_before=$(samtools view test.fastq2_bowtie2.bam | wc -l | cut -f1 -d\' \') # -h to add the header\n read_nb_after=$(samtools view test.fastq2_q20_dup.bam | wc -l | cut -f1 -d\' \') # -h to add the header\n echo -e \"\\n\\nNumber of sequences before Q20 filtering: $(printf \"%\'d\" ${read_nb_before})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of sequences after Q20 filtering: $(printf \"%\'d\" ${read_nb_after})\\n\" >> report.rmd\n echo -e \"Ratio: \" >> report.rmd\n echo -e $(printf \"%.2f\n\" $(echo $\" $read_nb_after \/ $read_nb_before \" | bc -l)) >> report.rmd # the number in \'%.2f\\n\' is the number of decimals\n echo -e \"\\n\\n\" >> report.rmd\n echo $read_nb_before > read_nb_before # because nf cannot output values easily\n echo $read_nb_after > read_nb_after\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"917","inv_ctxt":"1"},{"task_id":"18","hash":"d3\/8b18bf","native_id":"5597","process":"multiQC","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/ewels-multiqc-1.10.1.img","tag":"-","name":"multiQC","status":"COMPLETED","exit":"0","submit":"1657729225075","start":"1657729225162","complete":"1657729233339","duration":"8264","realtime":"8000","%cpu":"42.0","%mem":"0.1","rss":"76664832","vmem":"87621632","peak_rss":"76664832","peak_vmem":"87621632","rchar":"29716354","wchar":"2404868","syscr":"9278","syscw":"294","read_bytes":"22820864","write_bytes":"1253376","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/d3\/8b18bf740f0ad3c0386242eb736129","script":"\n multiqc . -n multiqc_report.html\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### MultiQC\\n\\n\" > report.rmd\n if [[ local == \"local\" ]] ; then\n echo -e \"\\n\\nWarning: no Kraken performed when using local run\\n\" >> report.rmd\n fi\n echo -e \"\\n\\nResults are published in the [Report](.\/reports\/multiqc_report.html) folder\\n\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"-","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"35008","inv_ctxt":"356"},{"task_id":"13","hash":"20\/8b2e3e","native_id":"5791","process":"plot_fivep_filtering_stat","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_fivep_filtering_stat (1)","status":"COMPLETED","exit":"0","submit":"1657729225918","start":"1657729226008","complete":"1657729238318","duration":"12400","realtime":"11577","%cpu":"56.3","%mem":"0.2","rss":"217604096","vmem":"362532864","peak_rss":"217604096","peak_vmem":"362565632","rchar":"19122509","wchar":"811478","syscr":"3774","syscw":"392","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/20\/8b2e3e8410a5a3280500b845882762","script":"\n echo -e \"\n\\n\\n<br \/><br \/>\\n\\n### Base frequencies at the 5\' extremity of reads\\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \" > report.rmd\n plot_fivep_filtering_stat.R \"test.fastq2_5pAttc_1-51.stat\" \"CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\" \"cute_little_R_functions.R\" \"plot_fivep_filtering_stat_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"44358","inv_ctxt":"87"},{"task_id":"19","hash":"10\/a73c63","native_id":"6094","process":"no_soft_clipping","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"no_soft_clipping (1)","status":"COMPLETED","exit":"0","submit":"1657729227612","start":"1657729227704","complete":"1657729228978","duration":"1366","realtime":"257","%cpu":"13.9","%mem":"0.0","rss":"3563520","vmem":"40300544","peak_rss":"3563520","peak_vmem":"40300544","rchar":"2188546","wchar":"1583807","syscr":"703","syscw":"417","read_bytes":"1153024","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/10\/a73c636ddb5eeca2a8681734e8fd51","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Control that no more soft clipping in reads\\n\\n\" > report.rmd\n echo -e \"nb of reads with soft clipping (S) in CIGAR: $(printf \"%\'d\" $(samtools view test.fastq2_q20_dup.bam | awk \'$6 ~ \/.*[S].*\/{print $0}\' | wc -l | cut -f1 -d\' \'))\" >> report.rmd\n echo -e \"\\n\\ntotal nb of reads: $(printf \"%\'d\" $(samtools view test.fastq2_q20_dup.bam | wc -l | cut -f1 -d\' \'))\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"31","inv_ctxt":"0"},{"task_id":"21","hash":"6d\/e18f26","native_id":"6119","process":"duplicate_removal","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"duplicate_removal (1)","status":"COMPLETED","exit":"0","submit":"1657729227632","start":"1657729227716","complete":"1657729230549","duration":"2917","realtime":"1792","%cpu":"26.4","%mem":"0.0","rss":"6619136","vmem":"40308736","peak_rss":"6619136","peak_vmem":"40308736","rchar":"13491656","wchar":"6920705","syscr":"7195","syscw":"5709","read_bytes":"1376256","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/6d\/e18f261ac36d6440fb9593e7d0fd49","script":"\n duplicate_removal.sh test.fastq2_q20_dup.bam Ecoli-K12-MG1655_ORI_CENTERED.fasta \"test.fastq2_q20_nodup.bam\" \"dup_report.txt\" \"report.rmd\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"236","inv_ctxt":"3"},{"task_id":"23","hash":"53\/120110","native_id":"7181","process":"insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"insertion (1)","status":"COMPLETED","exit":"0","submit":"1657729231557","start":"1657729231650","complete":"1657729233047","duration":"1490","realtime":"470","%cpu":"20.2","%mem":"0.0","rss":"9687040","vmem":"68902912","peak_rss":"9687040","peak_vmem":"68902912","rchar":"2614576","wchar":"1832308","syscr":"1551","syscw":"1173","read_bytes":"1236992","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/53\/120110294259912e7020cc8d59993f","script":"\n if [[ test.fastq2_q20_nodup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Insertion positions\\n\\n\" > report.rmd\n echo -e \"\\n\\nOne of the step is to recover positions of reverse reads (16), that use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0).\\nIt consist in the redefinition of POS according to FLAG in the bam file. See the [insertion_report.txt](.\/reports\/insertion_report.txt) file in the reports folders for details\\n\\n\" >> report.rmd\n fi\n\n # extraction of bam column 2, 4 and 10, i.e., FLAG, POS and SEQ\n samtools view test.fastq2_q20_nodup.bam | awk \'BEGIN{FS=\"\\t\" ; OFS=\"\" ; ORS=\"\"}{print \">\"$2\"\\t\"$4\"\\n\"$10\"\\n\" }\' > tempo\n # Of note, samtools fasta $DIR\/$SAMPLE_NAME > ${OUTPUT}.fasta # convert bam into fasta\n echo -e \"\\n\\n######## test.fastq2_q20_nodup.bam file\\n\\n\" > insertion_report.txt\n cat tempo | head -60 | tail -20 >> insertion_report.txt\n echo -e \"\\n\\nExtraction of the FLAG (containing the read orientation) the POS and the SEQ of the bams\\nHeader is the 1) sens of insersion (0 or 16) and 2) insertion site position\\n\\n\" >> insertion_report.txt\n # redefinition of POS according to FLAG\n awk \'BEGIN{FS=\"\t\" ; OFS=\"\" ; ORS=\"\"}{lineKind=(NR-1)%2}lineKind==0{orient=($1~\">16\") ; if(orient){var1 = $1 ; var2 = $2}else{print $0\"\\n\"}}lineKind==1{if(orient){var3 = length($0) ; var4 = var2 + var3 - 1 ; print var1\"\\t\"var4\"\\n\"$0\"\\n\"}else{print $0\"\\n\"}}\' tempo > test.fastq2_reorient.fasta\n echo -e \"\\n\\nFinal fasta file\\n\\nPositions of reverse reads (16) use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0)\\n\\n\" >> insertion_report.txt\n cat test.fastq2_reorient.fasta | head -60 | tail -20 >> insertion_report.txt\n awk \'{lineKind=(NR-1)%2}lineKind==0{gsub(\/>\/, \"\", $1) ; print $0}\' test.fastq2_reorient.fasta > test.fastq2_q20_nodup.pos\n echo -e \"\\n\\nFinal pos file\\n\\n\" >> insertion_report.txt\n cat test.fastq2_q20_nodup.pos | head -60 | tail -20 >> insertion_report.txt\n\n read_nb_before=$(samtools view test.fastq2_q20_nodup.bam | wc -l | cut -f1 -d\' \') # -h to add the header. Thus do not put here\n read_nb_after=$(wc -l test.fastq2_q20_nodup.pos | cut -f1 -d\' \')\n if [[ test.fastq2_q20_nodup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\nNumber of reads used for insertion computation: $(printf \"%\'d\" ${read_nb_before})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of insertions: $(printf \"%\'d\" ${read_nb_after})\\n\" >> report.rmd\n echo -e \"Ratio: \" >> report.rmd\n echo -e $(printf \"%.2f\n\" $(echo $\" $read_nb_after \/ $read_nb_before \" | bc -l)) >> report.rmd # the number in \'%.2f\\n\' is the number of decimals\n echo -e \"\\n\\n\" >> report.rmd\n else\n echo -e \"\\n\\n\" >> report.rmd\n fi\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"1030","inv_ctxt":"2"},{"task_id":"24","hash":"99\/5749d0","native_id":"7202","process":"insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-samtools_v1.14-gitlab_v8.0.img","tag":"-","name":"insertion (2)","status":"COMPLETED","exit":"0","submit":"1657729231578","start":"1657729231661","complete":"1657729233108","duration":"1530","realtime":"512","%cpu":"21.7","%mem":"0.0","rss":"10031104","vmem":"68907008","peak_rss":"10031104","peak_vmem":"68907008","rchar":"3142123","wchar":"2312411","syscr":"1885","syscw":"1514","read_bytes":"1267712","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/99\/5749d0145dc1dd36381be500c91070","script":"\n if [[ test.fastq2_q20_dup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Insertion positions\\n\\n\" > report.rmd\n echo -e \"\\n\\nOne of the step is to recover positions of reverse reads (16), that use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0).\\nIt consist in the redefinition of POS according to FLAG in the bam file. See the [insertion_report.txt](.\/reports\/insertion_report.txt) file in the reports folders for details\\n\\n\" >> report.rmd\n fi\n\n # extraction of bam column 2, 4 and 10, i.e., FLAG, POS and SEQ\n samtools view test.fastq2_q20_dup.bam | awk \'BEGIN{FS=\"\\t\" ; OFS=\"\" ; ORS=\"\"}{print \">\"$2\"\\t\"$4\"\\n\"$10\"\\n\" }\' > tempo\n # Of note, samtools fasta $DIR\/$SAMPLE_NAME > ${OUTPUT}.fasta # convert bam into fasta\n echo -e \"\\n\\n######## test.fastq2_q20_dup.bam file\\n\\n\" > insertion_report.txt\n cat tempo | head -60 | tail -20 >> insertion_report.txt\n echo -e \"\\n\\nExtraction of the FLAG (containing the read orientation) the POS and the SEQ of the bams\\nHeader is the 1) sens of insersion (0 or 16) and 2) insertion site position\\n\\n\" >> insertion_report.txt\n # redefinition of POS according to FLAG\n awk \'BEGIN{FS=\"\t\" ; OFS=\"\" ; ORS=\"\"}{lineKind=(NR-1)%2}lineKind==0{orient=($1~\">16\") ; if(orient){var1 = $1 ; var2 = $2}else{print $0\"\\n\"}}lineKind==1{if(orient){var3 = length($0) ; var4 = var2 + var3 - 1 ; print var1\"\\t\"var4\"\\n\"$0\"\\n\"}else{print $0\"\\n\"}}\' tempo > test.fastq2_reorient.fasta\n echo -e \"\\n\\nFinal fasta file\\n\\nPositions of reverse reads (16) use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0)\\n\\n\" >> insertion_report.txt\n cat test.fastq2_reorient.fasta | head -60 | tail -20 >> insertion_report.txt\n awk \'{lineKind=(NR-1)%2}lineKind==0{gsub(\/>\/, \"\", $1) ; print $0}\' test.fastq2_reorient.fasta > test.fastq2_q20_dup.pos\n echo -e \"\\n\\nFinal pos file\\n\\n\" >> insertion_report.txt\n cat test.fastq2_q20_dup.pos | head -60 | tail -20 >> insertion_report.txt\n\n read_nb_before=$(samtools view test.fastq2_q20_dup.bam | wc -l | cut -f1 -d\' \') # -h to add the header. Thus do not put here\n read_nb_after=$(wc -l test.fastq2_q20_dup.pos | cut -f1 -d\' \')\n if [[ test.fastq2_q20_dup.bam == \"test.fastq2_q20_nodup.bam\" ]] ; then\n echo -e \"\\n\\nNumber of reads used for insertion computation: $(printf \"%\'d\" ${read_nb_before})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of insertions: $(printf \"%\'d\" ${read_nb_after})\\n\" >> report.rmd\n echo -e \"Ratio: \" >> report.rmd\n echo -e $(printf \"%.2f\n\" $(echo $\" $read_nb_after \/ $read_nb_before \" | bc -l)) >> report.rmd # the number in \'%.2f\\n\' is the number of decimals\n echo -e \"\\n\\n\" >> report.rmd\n else\n echo -e \"\\n\\n\" >> report.rmd\n fi\n ","scratch":"-","queue":"-","cpus":"1","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"1049","inv_ctxt":"2"},{"task_id":"14","hash":"16\/40e161","native_id":"8047","process":"plot_read_length","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_read_length (1)","status":"COMPLETED","exit":"0","submit":"1657729238326","start":"1657729238418","complete":"1657729250599","duration":"12273","realtime":"11553","%cpu":"65.4","%mem":"0.2","rss":"264704000","vmem":"407347200","peak_rss":"290496512","peak_vmem":"433303552","rchar":"19621096","wchar":"708052","syscr":"4089","syscw":"430","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/16\/40e161c30dd9886aac9aa3b844314c","script":"\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n### Length of initial reads\\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n### Length of reads after selection of attC in 5 prime \\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n### Length of reads after trimming \\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n### Read length after cut-off\\n\\n\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' > report.rmd\n plot_read_length.R \"test.fastq2_ini.length\" \"test.fastq2_5pAttc.length\" \"test.fastq2_5pAtccRm.stat\" \"test.fastq2_cutoff.length\" \"cute_little_R_functions.R\" \"plot_read_length_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"45593","inv_ctxt":"10"},{"task_id":"16","hash":"1e\/df6e9e","native_id":"8831","process":"coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"coverage (1)","status":"COMPLETED","exit":"0","submit":"1657729250605","start":"1657729250698","complete":"1657729252228","duration":"1623","realtime":"619","%cpu":"17.8","%mem":"0.0","rss":"5353472","vmem":"46727168","peak_rss":"5353472","peak_vmem":"46727168","rchar":"491151","wchar":"93145","syscr":"251","syscw":"3116","read_bytes":"1806336","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/1e\/df6e9edbbf9c8694caf7614ef8cca4","script":"\n # bedtools genomecov -d -ibam ${bam} > ${bam.baseName}.cov |& tee cov_report.txt # coverage per base if ever required but long process\n # to add the chr names | awk \'{h[$NF]++}; END { for(k in h) print k, h[k] }\' | sort -V > ${bam.baseName}.cov\n bedtools genomecov -bga -ibam test.fastq2_bowtie2.bam > test.fastq2_bowtie2_mini.cov |& tee cov_report.txt\n # -g ${ref} not required when inputs are bam files\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"731","inv_ctxt":"0"},{"task_id":"20","hash":"59\/eb24cd","native_id":"8991","process":"coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"coverage (2)","status":"COMPLETED","exit":"0","submit":"1657729252234","start":"1657729252328","complete":"1657729253618","duration":"1384","realtime":"566","%cpu":"18.8","%mem":"0.0","rss":"5320704","vmem":"46727168","peak_rss":"5320704","peak_vmem":"46727168","rchar":"343030","wchar":"84344","syscr":"239","syscw":"2824","read_bytes":"1806336","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/59\/eb24cdb108b3cde6c30f3bb86203a9","script":"\n # bedtools genomecov -d -ibam ${bam} > ${bam.baseName}.cov |& tee cov_report.txt # coverage per base if ever required but long process\n # to add the chr names | awk \'{h[$NF]++}; END { for(k in h) print k, h[k] }\' | sort -V > ${bam.baseName}.cov\n bedtools genomecov -bga -ibam test.fastq2_q20_dup.bam > test.fastq2_q20_dup_mini.cov |& tee cov_report.txt\n # -g ${ref} not required when inputs are bam files\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"808","inv_ctxt":"0"},{"task_id":"22","hash":"12\/80c100","native_id":"9147","process":"coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"coverage (3)","status":"COMPLETED","exit":"0","submit":"1657729253624","start":"1657729253718","complete":"1657729254968","duration":"1344","realtime":"547","%cpu":"19.4","%mem":"0.0","rss":"5378048","vmem":"46727168","peak_rss":"5378048","peak_vmem":"46727168","rchar":"317647","wchar":"84028","syscr":"235","syscw":"2820","read_bytes":"1806336","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/12\/80c100625ea5a6b60dfac61f693cb2","script":"\n # bedtools genomecov -d -ibam ${bam} > ${bam.baseName}.cov |& tee cov_report.txt # coverage per base if ever required but long process\n # to add the chr names | awk \'{h[$NF]++}; END { for(k in h) print k, h[k] }\' | sort -V > ${bam.baseName}.cov\n bedtools genomecov -bga -ibam test.fastq2_q20_nodup.bam > test.fastq2_q20_nodup_mini.cov |& tee cov_report.txt\n # -g ${ref} not required when inputs are bam files\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"806","inv_ctxt":"0"},{"task_id":"25","hash":"3c\/9192fc","native_id":"9303","process":"final_insertion_files","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"final_insertion_files (1)","status":"COMPLETED","exit":"0","submit":"1657729254975","start":"1657729255068","complete":"1657729260268","duration":"5293","realtime":"4602","%cpu":"46.9","%mem":"0.1","rss":"127737856","vmem":"259059712","peak_rss":"127737856","peak_vmem":"259092480","rchar":"18003711","wchar":"275691","syscr":"2302","syscw":"549","read_bytes":"28325888","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/3c\/9192fc3a8e028bdc7634f456bb774c","script":"\n final_insertion_files.R \"test.fastq2_q20_nodup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"6\" \"test.fastq2_q20_nodup\" \"cute_little_R_functions.R\" \"final_insertion_files_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27640","inv_ctxt":"5"},{"task_id":"26","hash":"38\/7955e0","native_id":"9692","process":"final_insertion_files","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"final_insertion_files (2)","status":"COMPLETED","exit":"0","submit":"1657729260275","start":"1657729260368","complete":"1657729265728","duration":"5453","realtime":"4618","%cpu":"45.4","%mem":"0.1","rss":"127963136","vmem":"259473408","peak_rss":"127963136","peak_vmem":"259506176","rchar":"18009195","wchar":"285503","syscr":"2303","syscw":"563","read_bytes":"28325888","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/38\/7955e058f1b99376846947887fdb69","script":"\n final_insertion_files.R \"test.fastq2_q20_dup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"6\" \"test.fastq2_q20_dup\" \"cute_little_R_functions.R\" \"final_insertion_files_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27666","inv_ctxt":"6"},{"task_id":"31","hash":"3a\/7d7d11","native_id":"9703","process":"report3","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"report3 (1)","status":"COMPLETED","exit":"0","submit":"1657729260297","start":"1657729260380","complete":"1657729261368","duration":"1071","realtime":"54","%cpu":"11.7","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"306027","wchar":"44869","syscr":"275","syscw":"54","read_bytes":"405504","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/3a\/7d7d1119297bad1cc36ab27c8cf405","script":"\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Final insertion site files\\n\\n\" > report.rmd\n echo -e \"\\n\\nWarning: in these files, the position indicated is the first nucleotide of the genomic part of the read (the W of the 5\'GWT3\' consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position.\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the [test.fastq2_q20_nodup_annot.pos](.\/files\/test.fastq2_q20_nodup_annot.pos) and [test.fastq2_q20_nodup_annot.freq](.\/files\/test.fastq2_q20_nodup_annot.freq) files\\n\\n\" >> report.rmd\n pos_nb=$(( $(wc -l test.fastq2_q20_nodup_annot.pos | cut -f1 -d\' \') - 1)) # -1 because first line is the header\n pos_uniq_nb=$(( $(sort -u test.fastq2_q20_nodup_annot.pos | wc -l | cut -f1 -d\' \') - 1)) # -1 because first line is the header\n echo -e \"\\n\\nNumber of total positions without duplicates: $(printf \"%\'d\" ${pos_nb})\\n\" >> report.rmd\n echo -e \"\\n\\nNumber of different positions without duplicates: $(printf \"%\'d\" ${pos_uniq_nb})\\n\" >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"27","hash":"c1\/4ec175","native_id":"10214","process":"plot_coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_coverage (1)","status":"COMPLETED","exit":"0","submit":"1657729265735","start":"1657729265828","complete":"1657729276448","duration":"10713","realtime":"9987","%cpu":"61.1","%mem":"0.2","rss":"229625856","vmem":"372568064","peak_rss":"232247296","peak_vmem":"376274944","rchar":"19158378","wchar":"450684","syscr":"3788","syscw":"294","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/c1\/4ec175f671d03bb1a6f531fb3f09e7","script":"\n plot_coverage.R \"test.fastq2_bowtie2_mini\" \"read_nb_before\" \"2320711 2320942\" \"4627368 4627400\" \"5\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"cute_little_R_functions.R\" \"plot_coverage_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"45347","inv_ctxt":"8"},{"task_id":"28","hash":"fa\/28ed19","native_id":"10872","process":"plot_coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_coverage (2)","status":"COMPLETED","exit":"0","submit":"1657729276456","start":"1657729276548","complete":"1657729287448","duration":"10992","realtime":"10323","%cpu":"61.1","%mem":"0.2","rss":"227549184","vmem":"370569216","peak_rss":"227549184","peak_vmem":"370601984","rchar":"19149581","wchar":"444679","syscr":"3786","syscw":"292","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/fa\/28ed194022fd1493bac8e24d08b321","script":"\n plot_coverage.R \"test.fastq2_q20_dup_mini\" \"read_nb_after\" \"2320711 2320942\" \"4627368 4627400\" \"5\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"cute_little_R_functions.R\" \"plot_coverage_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"45353","inv_ctxt":"8"},{"task_id":"29","hash":"9d\/bb1716","native_id":"11525","process":"plot_coverage","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_coverage (3)","status":"COMPLETED","exit":"0","submit":"1657729287455","start":"1657729287549","complete":"1657729298308","duration":"10853","realtime":"10083","%cpu":"61.2","%mem":"0.2","rss":"229519360","vmem":"372596736","peak_rss":"229519360","peak_vmem":"372629504","rchar":"19149270","wchar":"445347","syscr":"3786","syscw":"293","read_bytes":"46611456","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/9d\/bb17161b6b6bb4f3630ba5383f80d8","script":"\n plot_coverage.R \"test.fastq2_q20_nodup_mini\" \"dup_read_nb\" \"2320711 2320942\" \"4627368 4627400\" \"5\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"cute_little_R_functions.R\" \"plot_coverage_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"45359","inv_ctxt":"9"},{"task_id":"30","hash":"9c\/909d2f","native_id":"12178","process":"seq_around_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"seq_around_insertion (1)","status":"COMPLETED","exit":"0","submit":"1657729298315","start":"1657729298408","complete":"1657729303498","duration":"5183","realtime":"4483","%cpu":"46.2","%mem":"0.1","rss":"128569344","vmem":"259690496","peak_rss":"128569344","peak_vmem":"259723264","rchar":"18001623","wchar":"213509","syscr":"2298","syscw":"375","read_bytes":"28199936","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/9c\/909d2f0266c849905245df29a765f4","script":"\n seq_around_insertion.R \"test.fastq2_q20_nodup_selected_if_dup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"20\" \"test.fastq2_q20_nodup_selected_if_dup\" \"cute_little_R_functions.R\" \"seq_around_insertion_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27636","inv_ctxt":"32"},{"task_id":"32","hash":"0a\/d92a3c","native_id":"12557","process":"random_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"random_insertion (1)","status":"COMPLETED","exit":"0","submit":"1657729303506","start":"1657729303597","complete":"1657729312388","duration":"8882","realtime":"8185","%cpu":"57.6","%mem":"0.3","rss":"333017088","vmem":"475217920","peak_rss":"368943104","peak_vmem":"511254528","rchar":"31432684","wchar":"1296709","syscr":"5927","syscw":"1485","read_bytes":"35685376","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/0a\/d92a3cf8eb6ecd7e24ac1da66f94ca","script":"\n random_insertion.R \"test.fastq2_q20_nodup_annot.pos\" \"motif_sites.pos\" \"2320711 2320942\" \"4627368 4627400\" \"G[AT]T\" \"4641652\" \"test.fastq2\" \"cute_little_R_functions.R\" \"random_insertion_report.txt\"\n\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Random insertion sites\\n\\n\" > report.rmd\n echo -e \"\\n\\n#### Insertion site counts\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the [random_insertion_report.txt](.\/reports\/random_insertion_report.txt) file for details, notably the number of random sites (which should be the same as the number of observed sites)\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Insertion site proportions\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=500}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=500}\n\\n\\n<\/center>\\n\\n\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=500}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"38985","inv_ctxt":"16"},{"task_id":"33","hash":"3b\/051e3e","native_id":"13098","process":"seq_around_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"seq_around_insertion (2)","status":"COMPLETED","exit":"0","submit":"1657729312395","start":"1657729312489","complete":"1657729317587","duration":"5192","realtime":"4429","%cpu":"45.2","%mem":"0.1","rss":"128364544","vmem":"259506176","peak_rss":"128364544","peak_vmem":"259538944","rchar":"17988600","wchar":"146402","syscr":"2295","syscw":"273","read_bytes":"28199936","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/3b\/051e3e2a0f2fb956fd80d5137f7d5f","script":"\n seq_around_insertion.R \"test.fastq2_q20_dup_selected_if_dup.pos\" \"2320711 2320942\" \"4627368 4627400\" \"20\" \"test.fastq2_q20_dup_selected_if_dup\" \"cute_little_R_functions.R\" \"seq_around_insertion_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27647","inv_ctxt":"6"},{"task_id":"34","hash":"68\/10705c","native_id":"13490","process":"dup_insertion_and_logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"dup_insertion_and_logo (1)","status":"COMPLETED","exit":"0","submit":"1657729317596","start":"1657729317687","complete":"1657729329178","duration":"11582","realtime":"10856","%cpu":"65.8","%mem":"0.2","rss":"247386112","vmem":"426381312","peak_rss":"283508736","peak_vmem":"426565632","rchar":"17562712","wchar":"600308","syscr":"3936","syscw":"456","read_bytes":"44530688","write_bytes":"20480","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/68\/10705cb81383c5c72b44dcc72551b3","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Analysis with duplicates\\n\\n\" > report.rmd\n dup_insertion_and_logo.R \"test.fastq2_q20_dup_annot.freq\" \"test.fastq2_q20_dup_annot_selected.freq\" \"2320711 2320942\" \"4627368 4627400\" \"4641652\" \"Ecoli Genome (bp)\" \"test.fastq2\" \"dup\" \"20\" \"cute_little_R_functions.R\" \"dup_insertion_and_logo_report.txt\" # logo\n\n echo -e \"\\n\\nSee the [test.fastq2_q20_dup_selected_if_dup.pos](.\/files\/test.fastq2_q20_dup_selected_if_dup.pos) and [test.fastq2_q20_dup_annot_selected.freq](.\/files\/test.fastq2_q20_dup_annot_selected.freq) files\\n\\n\" >> report.rmd\n echo -e \"\\n\\nWarning: more than the 6 most frequent used sites can be present in the case of frequency equality\\n\\n\" >> report.rmd\n\n pos_nb=$(( $(wc -l test.fastq2_q20_dup_annot.pos | cut -f1 -d\' \') - 1 )) # -1 because first line is the header\n echo -e \"\\n\\nNumber of total positions using duplicated reads: $(printf \"%\'d\" ${pos_nb})\\n\" >> report.rmd\n\n freq_nb=$(( $(wc -l test.fastq2_q20_dup_annot.freq | cut -f1 -d\' \') - 1 )) # -1 because first line is the header\n echo -e \"\\n\\nNumber of different positions using duplicated reads: $(printf \"%\'d\" ${freq_nb})\\n\" >> report.rmd\n\n pos_selected_nb=$(wc -l test.fastq2_q20_dup_selected_if_dup.pos | cut -f1 -d\' \')\n echo -e \"\\n\\nNumber of total positions after selection of the 6 highest used sites: $(printf \"%\'d\" ${pos_selected_nb})\\n\" >> report.rmd\n\n freq_selected_nb=$(( $(wc -l test.fastq2_q20_dup_annot_selected.freq | cut -f1 -d\' \') - 1 )) # -1 because first line is the header\n echo -e \"\\n\\nNumber of different positions after selection of the 6 highest used sites: $(printf \"%\'d\" ${freq_selected_nb})\\n\" >> report.rmd\n\n TEMPO=(50000 200000 500000)\n FIG_NB_BEFORE=$(($(echo ${#TEMPO[@]}) * 2)) # nb of elements in the window size * nb of figure plotted\n if [[ \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/TSS_compatible_essential.txt != \"NULL\" ]] ; then\n FIG_NB=$(( 34 + $FIG_NB_BEFORE + 1 + 1)) # 2 * because two figures\n else\n FIG_NB=$(( 24 + $FIG_NB_BEFORE + 1 + 1))\n fi\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\nSelected sites with frequencies:\\n\\n\" >> report.rmd\n echo \"\n\\`\\`\\`{r, echo = FALSE}\ntempo <- read.table(\'.\/files\/test.fastq2_q20_dup_annot_selected.freq\', header = TRUE, colClasses = \'character\', sep = \'\\t\', check.names = FALSE) ; \nkableExtra::kable_styling(knitr::kable(tempo, row.names = TRUE, digits = 0, caption = NULL, format=\'html\'), c(\'striped\', \'bordered\', \'responsive\', \'condensed\'), font_size=10, full_width = FALSE, position = \'left\')\n\\`\\`\\`\n \n\n\n \" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n to extend)](.\/figures\/alignment.html){width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\nWarning: the frequency of each position is taken into account in the logo plot\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"43405","inv_ctxt":"8"},{"task_id":"35","hash":"55\/ebbde4","native_id":"14219","process":"extract_seq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"extract_seq (1)","status":"COMPLETED","exit":"0","submit":"1657729329185","start":"1657729329278","complete":"1657729330838","duration":"1653","realtime":"781","%cpu":"18.7","%mem":"0.0","rss":"6156288","vmem":"52846592","peak_rss":"6156288","peak_vmem":"52846592","rchar":"9503291","wchar":"4763905","syscr":"866","syscw":"3349","read_bytes":"6359040","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/55\/ebbde485ba925626f74c3c728783f6","script":"\n echo -e \"\n\n######## test.fastq2_q20_nodup_selected_if_dup_around_insertion.bed file\n\n\" > extract_seq_report.txt\n # make a bed file from the reference genome\n echo \">ref\" > tempo.ref.fasta\n awk \'{lineKind=(NR-1)%2}lineKind==1{print $0}\' Ecoli-K12-MG1655_ORI_CENTERED.fasta >> tempo.ref.fasta |& tee extract_seq_report.txt\n bedtools getfasta -fi tempo.ref.fasta -bed test.fastq2_q20_nodup_selected_if_dup_around_insertion.bed -fo \"test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta\" -name |& tee extract_seq_report.txt\n rm tempo.ref.fasta\n rm tempo.ref.fasta.fai\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"653","inv_ctxt":"0"},{"task_id":"36","hash":"54\/bcf3c8","native_id":"14414","process":"plot_insertion","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"plot_insertion (1)","status":"COMPLETED","exit":"0","submit":"1657729330847","start":"1657729330938","complete":"1657729572408","duration":"241561","realtime":"240577","%cpu":"36.1","%mem":"0.4","rss":"413851648","vmem":"573362176","peak_rss":"414507008","peak_vmem":"573440000","rchar":"46947001","wchar":"23740350","syscr":"34549","syscw":"18913","read_bytes":"53133312","write_bytes":"450560","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/54\/bcf3c803346b96d6a6a8f7aaea212b","script":"\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Insertion plots\\n\\n\" > report.rmd\n plot_insertion.R \"obs_rd_insertions.pos\" \"obs_rd_insertions.freq\" \"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/TSS_compatible_essential.txt\" \"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/Essential_genes_MG1655.tsv\" \"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/cds_ORI_CENTERED.txt\" \"2320711 2320942\" \"4627368 4627400\" \"Ecoli Genome (bp)\" \"4641652\" \"0.88\" \"0.08\" \"50000 200000 500000\" \"100\" \"test.fastq2\" \"12\" \"cute_little_R_functions.R\" \"plot_insertion_report.txt\"\n echo -e \"\\n\\n#### Histograms\\n\\n\" >> report.rmd\n echo -e \'\n\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Raw frequencies\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the CL Labbook section 24.7.3 to explain the limitation around 100 bp\\n\" >> report.rmd\n echo -e \'\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \' >> report.rmd\n echo -e \"\\n\\n<br \/><br \/>\\n\\n#### Binned frequencies\\n\\n\" >> report.rmd\n count=1\n for i in 50000 200000 500000 ; do\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n count=$((count + 1))\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n count=$((count + 1))\n done\n if [[ \/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\/TSS_compatible_essential.txt != \"NULL\" ]] ; then\n echo -e \"\\n\\n<br \/><br \/>\\n\\n### Transcription start site (TSS) plots\\n\\n\" >> report.rmd\n echo -e \"\\n\\nSee the CL Labbook section 48.3 to to get the theoretical proportion of the codant\/non codant essential\/non essential genome\\n\\nSee the [plot_insertion_report.txt](.\/reports\/plot_insertion_report.txt) file for details about the plotted values.\" >> report.rmd\n echo -e \'\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=400}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \\n\\n<br \/><br \/>\\n\\nThe number of insertions sites are indicated above graphs.\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \\n\\n<br \/><br \/>\\n\\n### Coding sequences (CDS) plots\\n\\nThe number of insertions sites inside CDS are indicated above graphs.\\n\\nSee the [plot_insertion_report.txt](.\/reports\/plot_insertion_report.txt) file for details about the plotted values.\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \\n\\nWarning: the number of observed and random insertions indicated above graphs can be greater than those indicated above Figure \'$((28 + $count))\', since a position that overlaps two genes is counted twice (see the [plot_insertion_report.txt](.\/reports\/plot_insertion_report.txt) file for details).\\n\\n\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n<br \/><br \/>\\n\\n\n \' >> report.rmd\n fi\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"99746","inv_ctxt":"131"},{"task_id":"39","hash":"8b\/4e5ce1","native_id":"14438","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (2)","status":"COMPLETED","exit":"0","submit":"1657729330870","start":"1657729330951","complete":"1657729332348","duration":"1478","realtime":"87","%cpu":"12.4","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"288630","wchar":"19342","syscr":"530","syscw":"50","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/8b\/4e5ce1ea937cca280dc5c764001561","script":"\n # file splitting into seq\n awk -v var1=LEADING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"38","hash":"29\/443a5b","native_id":"14493","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (1)","status":"COMPLETED","exit":"0","submit":"1657729330914","start":"1657729330963","complete":"1657729332428","duration":"1514","realtime":"92","%cpu":"9.4","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"286492","wchar":"17177","syscr":"526","syscw":"46","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/29\/443a5b84f9b6b8093bfee45316822f","script":"\n # file splitting into seq\n awk -v var1=LEADING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"41","hash":"7c\/b40174","native_id":"14544","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (4)","status":"COMPLETED","exit":"0","submit":"1657729330934","start":"1657729330976","complete":"1657729332627","duration":"1693","realtime":"96","%cpu":"11.5","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"288797","wchar":"19498","syscr":"531","syscw":"51","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/7c\/b401748402f640f6b278fbb36c6ea5","script":"\n # file splitting into seq\n awk -v var1=LAGGING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"40","hash":"cf\/abb905","native_id":"14579","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (3)","status":"COMPLETED","exit":"0","submit":"1657729330953","start":"1657729331038","complete":"1657729332638","duration":"1685","realtime":"98","%cpu":"11.5","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"289446","wchar":"20166","syscr":"532","syscw":"52","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/cf\/abb905bcd5a9839463ac27990024f2","script":"\n # file splitting into seq\n awk -v var1=LAGGING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_nodup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.seq > test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"37","hash":"2d\/a978bc","native_id":"21252","process":"extract_seq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bedtools_v2.30.0-gitlab_v8.0.img","tag":"-","name":"extract_seq (2)","status":"COMPLETED","exit":"0","submit":"1657729572416","start":"1657729572508","complete":"1657729573748","duration":"1332","realtime":"452","%cpu":"20.6","%mem":"0.0","rss":"6074368","vmem":"52641792","peak_rss":"6074368","peak_vmem":"52658176","rchar":"9452241","wchar":"4672190","syscr":"859","syscw":"845","read_bytes":"2721792","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/2d\/a978bca367df6269ab4da133fdfd1f","script":"\n echo -e \"\n\n######## test.fastq2_q20_dup_selected_if_dup_around_insertion.bed file\n\n\" > extract_seq_report.txt\n # make a bed file from the reference genome\n echo \">ref\" > tempo.ref.fasta\n awk \'{lineKind=(NR-1)%2}lineKind==1{print $0}\' Ecoli-K12-MG1655_ORI_CENTERED.fasta >> tempo.ref.fasta |& tee extract_seq_report.txt\n bedtools getfasta -fi tempo.ref.fasta -bed test.fastq2_q20_dup_selected_if_dup_around_insertion.bed -fo \"test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta\" -name |& tee extract_seq_report.txt\n rm tempo.ref.fasta\n rm tempo.ref.fasta.fai\n ","scratch":"-","queue":"-","cpus":"12","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"594","inv_ctxt":"0"},{"task_id":"43","hash":"f4\/73666f","native_id":"21470","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (2)","status":"COMPLETED","exit":"0","submit":"1657729573756","start":"1657729573848","complete":"1657729580668","duration":"6912","realtime":"5847","%cpu":"46.6","%mem":"0.2","rss":"179806208","vmem":"325083136","peak_rss":"179806208","peak_vmem":"325115904","rchar":"14488463","wchar":"1018960","syscr":"2474","syscw":"333","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/f4\/73666f7766ccafad5741a17d990016","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"29908","inv_ctxt":"1558"},{"task_id":"46","hash":"f8\/483905","native_id":"21480","process":"goalign","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/evolbioinfo-goalign-v0.3.5.img","tag":"-","name":"goalign (1)","status":"COMPLETED","exit":"0","submit":"1657729573778","start":"1657729573867","complete":"1657729575688","duration":"1910","realtime":"767","%cpu":"15.1","%mem":"0.0","rss":"5242880","vmem":"731348992","peak_rss":"5242880","peak_vmem":"731357184","rchar":"131186","wchar":"356115","syscr":"276","syscw":"108","read_bytes":"3785728","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/f8\/4839052ccd72ad1ab14969dabf08c7","script":"\n # Remove duplicated data in a fasta file according to duplicated header\n awk \'\n \/^>\/{f=!d[$1];d[$1]=1}f\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > tempo\n\n # split the fasta file according to forward or reverse sequences\n PATTERN=\'LEADING_16|LAGGING_16\'\n awk -v var1=$PATTERN \'\n BEGIN{ORS=\"\\n\"}\n {lineKind=(NR-1)%2}\n lineKind==0{record=$0 ; next}\n lineKind==1{\n toGet=(record ~ var1)\n if(toGet){\n print record > \"reverse.fasta\"\n print $0 > \"reverse.fasta\"\n }else{\n print record > \"forward.fasta\"\n print $0 > \"forward.fasta\"\n }\n next\n }\n \' tempo\n\n # Goalign\n if [ -s reverse.fasta ] ; then\n goalign revcomp --unaligned -i reverse.fasta -o tempo2 # rev-comp the 16 sequences\n cat forward.fasta tempo2 > final.fasta\n else # we cannot have neither reverse nor forward\n cp forward.fasta final.fasta\n fi\n # add a hyphen before or after the sequence, to have correct alignment\n awk -v var1=$PATTERN \'\n BEGIN{ORS=\"\\n\"}\n {lineKind=(NR-1)%2}\n lineKind==0{record=$0 ; print $0 ; next}\n lineKind==1{\n toGet=(record ~ var1)\n if(toGet){\n print \"-\"$0 ; next\n }else{\n print $0\"-\" ; next\n }\n }\n \' final.fasta > tempo3\n goalign draw biojs --auto-detect -i tempo3 -o alignment.html |& tee -a goalign_report.txt\n ","scratch":"-","queue":"-","cpus":"12","memory":"1073741824","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"931","inv_ctxt":"0"},{"task_id":"48","hash":"40\/f4cbe7","native_id":"21560","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (6)","status":"COMPLETED","exit":"0","submit":"1657729573836","start":"1657729573879","complete":"1657729575368","duration":"1532","realtime":"36","%cpu":"8.4","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"180609","wchar":"2829","syscr":"319","syscw":"18","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/40\/f4cbe733142a100d9347cf8e542690","script":"\n # file splitting into seq\n awk -v var1=LAGGING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"50","hash":"bd\/ea2f89","native_id":"21598","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (8)","status":"COMPLETED","exit":"0","submit":"1657729573862","start":"1657729573948","complete":"1657729575519","duration":"1657","realtime":"47","%cpu":"10.8","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"184838","wchar":"7116","syscr":"328","syscw":"27","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/bd\/ea2f89c6d5c09beff7086171b29776","script":"\n # file splitting into seq\n awk -v var1=LAGGING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LAGGING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"49","hash":"3d\/46265a","native_id":"21632","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (7)","status":"COMPLETED","exit":"0","submit":"1657729573881","start":"1657729573959","complete":"1657729575530","duration":"1649","realtime":"46","%cpu":"10.9","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"182993","wchar":"5247","syscr":"324","syscw":"23","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/3d\/46265a1b1686adb97b6cc7350f5c0a","script":"\n # file splitting into seq\n awk -v var1=LEADING_16 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_16.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"47","hash":"e8\/a25744","native_id":"22144","process":"base_freq","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"base_freq (5)","status":"COMPLETED","exit":"0","submit":"1657729575376","start":"1657729575468","complete":"1657729576478","duration":"1102","realtime":"29","%cpu":"13.5","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"186842","wchar":"9129","syscr":"332","syscw":"31","read_bytes":"279552","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/e8\/a257442d93dfaee7d6d8b4c0d05183","script":"\n # file splitting into seq\n awk -v var1=LEADING_0 \'\n {lineKind=(NR-1)%2}\n lineKind==0{toGet=($0 ~ \">\" var1) ; next}\n lineKind==1{if(toGet){print $0}}\n \' test.fastq2_q20_dup_selected_if_dup_around_insertion.fasta > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.seq |& tee base_freq_report.txt\n # ATGC contingency\n gawk \'{\n L=length($0);\n for(i=1;i<=L;i++) {\n B=substr($0,i,1);\n T[i][B]++;\n }\n }\n END{\n for(BI=0;BI<5;BI++) {\n B=(BI==0?\"A\":(BI==1?\"C\":(BI==2?\"G\":(BI==3?\"T\":\"Other\"))));\n printf(\"%s\",B); \n for(i in T) {\n tot=0.0;\n for(B2 in T[i]){\n tot+=T[i][B2];\n }\n printf(\"\t%0.5f\",(T[i][B])); # T[i][B]\/tot for proportion\n } \n printf(\"\\n\");\n }\n }\' test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.seq > test.fastq2_q20_dup_selected_if_dup_around_insertion_LEADING_0.stat |& tee base_freq_report.txt\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"51","hash":"46\/95072b","native_id":"22422","process":"report2","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-bash-extended_v4.0-gitlab_v8.0.img","tag":"-","name":"report2","status":"COMPLETED","exit":"0","submit":"1657729577485","start":"1657729577580","complete":"1657729578408","duration":"923","realtime":"23","%cpu":"12.9","%mem":"0.0","rss":"0","vmem":"0","peak_rss":"0","peak_vmem":"0","rchar":"135265","wchar":"2463","syscr":"248","syscw":"103","read_bytes":"242688","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/46\/95072b73c1bcdeb338ec644d26f20f","script":"\n echo -e \"\n\\n\\n<br \/><br \/>\\n\\n### Logos\\n\\n\n\\n\\nIn each sequence of length $((20 * 2)) <br \/>position $((20 + 1)) corresponds to the first nucleotide of the reference genome part of the read\n\" > report.rmd\n count=0 # always goes to 4 because 4 figures, one for each forward\/reverse leading\/lagging\n for i in $(echo [test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16, test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0, test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16, test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0] | sed \'s\/^\\[\/\/\' | sed \'s\/\\]$\/\/\' | sed \'s\/,\/\/g\') ; do\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n count=$((count + 1))\n done\n echo -e \'\n\\n\\n<br \/><br \/>\\n\\n<\/center>\\n\\n\n{width=600}\n\\n\\n<\/center>\\n\\n\n \' >> report.rmd\n ","scratch":"-","queue":"-","cpus":"1","memory":"3221225472","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"0","inv_ctxt":"0"},{"task_id":"42","hash":"1f\/497ee9","native_id":"22727","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (1)","status":"COMPLETED","exit":"0","submit":"1657729580674","start":"1657729580768","complete":"1657729586698","duration":"6024","realtime":"5301","%cpu":"50.5","%mem":"0.2","rss":"173649920","vmem":"319934464","peak_rss":"173649920","peak_vmem":"319967232","rchar":"14488496","wchar":"865984","syscr":"2474","syscw":"312","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/1f\/497ee93d79fe46ee17ad35f9c50025","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"27156","inv_ctxt":"1"},{"task_id":"44","hash":"6f\/524be5","native_id":"23161","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (3)","status":"COMPLETED","exit":"0","submit":"1657729586705","start":"1657729586798","complete":"1657729592758","duration":"6053","realtime":"5400","%cpu":"51.0","%mem":"0.2","rss":"172462080","vmem":"317677568","peak_rss":"177479680","peak_vmem":"322445312","rchar":"14488489","wchar":"896195","syscr":"2474","syscw":"314","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/6f\/524be5fe599a7447720b4e9dc9012e","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"30317","inv_ctxt":"4"},{"task_id":"45","hash":"6b\/1f0539","native_id":"23593","process":"logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"logo (4)","status":"COMPLETED","exit":"0","submit":"1657729592765","start":"1657729592858","complete":"1657729598838","duration":"6073","realtime":"5346","%cpu":"50.8","%mem":"0.2","rss":"174174208","vmem":"320438272","peak_rss":"177438720","peak_vmem":"322330624","rchar":"14488501","wchar":"886578","syscr":"2474","syscw":"312","read_bytes":"30879744","write_bytes":"0","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/6b\/1f0539459ac05b646a37a12cd4f007","script":"\n logo.R \"test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.stat\" 20 \"cute_little_R_functions.R\" \"logo_report.txt\"\n ","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 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2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"30090","inv_ctxt":"7"},{"task_id":"53","hash":"8b\/8edeb9","native_id":"24470","process":"global_logo","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"global_logo 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","scratch":"-","queue":"-","cpus":"1","memory":"68719476736","disk":"-","time":"-","env":"git_path=https:\/\/gitlab.pasteur.fr\/gmillot\/14985_loot\/\nin_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\nfastq_file=test.fastq2.gz\nkraken_db=\/pasteur\/zeus\/services\/p01\/banques-prod\/prod\/rel\/kraken_standard\/current\/kraken\/2.1.1\/standard\/\nprimer_fasta=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/20200520_adapters_TruSeq_B2699_14985_CL.fasta\nalientrimmer_l_param=30\nattc_seq=CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG\nfivep_seq_filtering=^CAATTCATTCAAGCCGACGCCGCTTCGCGGCGCGGCTTAATTCAAGCG.+$\nfivep_seq_nb=48\nadded_nb=3\ncutoff_nb=25\nref_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/dataset\/coli_K12_MG1655_NC_000913.3_ORI_CENTERED\/\nref_file=Ecoli-K12-MG1655_ORI_CENTERED.fasta\ntss_file=TSS_compatible_essential.txt\ness_file=Essential_genes_MG1655.tsv\ncds_file=cds_ORI_CENTERED.txt\nori_coord=2320711 2320942\nter_coord=4627368 4627400\ncolor_coverage=5\nxlab=Ecoli Genome (bp)\ngenome_size=4641652\nprop_coding_genome=0.88\nprop_ess_coding_genome=0.08\ninsertion_dist=20\nmotif_fw=G[AT]T\nmotif_rev=A[AT]C\nwindow_size=50000 200000 500000\nstep=100\nnb_max_insertion_sites=6\ncute_path=https:\/\/gitlab.pasteur.fr\/gmillot\/cute_little_R_functions\/-\/raw\/v11.4.0\/cute_little_R_functions.R\nsystem_exec=local\nout_path=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/results\/20220120_test_1657729206\nPATH=\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/bin:$PATH\n","error_action":"-","vol_ctxt":"30079","inv_ctxt":"5"},{"task_id":"54","hash":"b2\/9012a5","native_id":"24917","process":"print_report","module":"-","container":"\/mnt\/c\/Users\/Gael\/Documents\/singularity\/gmillot-r_v4.1.2_extended_v2.0-gitlab_v8.2.img","tag":"-","name":"print_report (1)","status":"COMPLETED","exit":"0","submit":"1657729612365","start":"1657729612459","complete":"1657729623068","duration":"10703","realtime":"9929","%cpu":"43.6","%mem":"0.3","rss":"315035648","vmem":"1100166938624","peak_rss":"315035648","peak_vmem":"1100167086080","rchar":"54280457","wchar":"27511769","syscr":"7189","syscw":"2189","read_bytes":"48487424","write_bytes":"4096","attempt":"1","workdir":"\/mnt\/c\/Users\/Gael\/Documents\/Git_projects\/14985_loot\/work\/b2\/9012a5247c813391e8b16456cc2c56","script":"\n cp report.rmd report_file.rmd # this is to get hard files, not symlinks\n mkdir figures\n mkdir files\n mkdir reports\n cat stat_tempo > .\/files\/test.fastq2_5pAttc_1-51.stat # this is to get hard files, not symlinks\n cp head.fw.txt head.rv.txt table1.txt table2.txt table3.txt table4.txt table8.txt test.fastq2_q20_dup_annot_selected.freq .\/files\/ # this is to get hard files, not symlinks\n cp plot_fivep_filtering_stat.png plot_read_length_cutoff.png plot_read_length_fivep_filtering.png plot_read_length_fivep_filtering_cut.png plot_read_length_ini.png plot_test.fastq2_bowtie2_mini.png plot_test.fastq2_q20_dup_mini.png plot_test.fastq2_q20_nodup_mini.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_0.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LEADING_16.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_16.png logo_test.fastq2_q20_nodup_selected_if_dup_around_insertion_LAGGING_0.png global_logo_nodup_test.fastq2.png global_logo_dup_test.fastq2.png plot_motif_insertion_per_fork.png plot_motif_insertion_per_fork_and_strand.png plot_motif_insertion_per_fork_and_strand_prop.png plot_motif_insertion_per_fork_prop.png plot_motif_insertion_per_strand.png plot_motif_insertion_per_strand_prop.png barplot_test.fastq2_all.png barplot_test.fastq2_all_relative.png barplot_test.fastq2_ess_uness.png barplot_test.fastq2_inside_outside.png boxplot_test.fastq2_cds.png boxplot_test.fastq2_cds_wo_unknown.png boxplot_test.fastq2_tss.png boxplot_test.fastq2_tss_wo_unknown.png hist_test.fastq2_tss_distance_freq.png hist_test.fastq2_tss_distance_freq_Nlaw.png plot_test.fastq2_insertion_bin_200000.png plot_test.fastq2_insertion_bin_50000.png plot_test.fastq2_insertion_bin_500000.png plot_test.fastq2_insertion_hist_forward.png plot_test.fastq2_insertion_hist_reverse.png plot_test.fastq2_insertion_hist_tot.png plot_test.fastq2_insertion_hist_tot_zoom.png plot_test.fastq2_insertion_raw.png plot_test.fastq2_lead_lag_insertion_bin_200000.png plot_test.fastq2_lead_lag_insertion_bin_50000.png plot_test.fastq2_lead_lag_insertion_bin_500000.png plot_test.fastq2_promoter_per_genes.png alignment.html plot_test.fastq2_insertion_dup_raw.png plot_test.fastq2_insertion_dup_selected.png plot_test.fastq2_insertion_hist_tot_selected.png .\/figures\/ # Warning several files\n cp plot_fivep_filtering_stat.png .\/reports\/nf_dag.png # trick to delude the knitting during the print report\n cp multiqc_report.html .\/reports\/ # this is to get hard files from html from multiqc_ch, not symlinks\n print_report.R \"cute_little_R_functions.R\" \"report_file.rmd\" \"print_report.txt\"\n 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a/example_of_result/20220120_test_1649703168/reports/nf_timeline.html +++ b/example_of_result/20220120_test_1657729206/reports/nf_timeline.html @@ -205,64 +205,64 @@ $(function() { // Nextflow report data window.data = { - "elapsed": "4m 27s", - "beginningMillis": 1649517331366, - "endingMillis": 1649703436276, + "elapsed": "6m 57s", + "beginningMillis": 1657729206806, + "endingMillis": 1657729624097, "processes": [ - {"label": "Nremove (1)", "cached": true, "index": 0, "times": [{"starting_time": 1649517331366, "ending_time": 1649517331460}, {"starting_time": 1649517331460, "ending_time": 1649517332733, "label": "5.4s \/ 11.9 MB \/ CACHED"}, {"starting_time": 1649517332733, "ending_time": 1649517336786}]}, - {"label": "report1", "cached": true, "index": 1, "times": [{"starting_time": 1649517331415, "ending_time": 1649517331494}, {"starting_time": 1649517331494, "ending_time": 1649517331579, "label": "4.7s \/ 0 \/ CACHED"}, {"starting_time": 1649517331579, "ending_time": 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"ending_time": 1657729611758}]}, + {"label": "print_report (1)", "cached": false, "index": 34, "times": [{"starting_time": 1657729612365, "ending_time": 1657729612459}, {"starting_time": 1657729612459, "ending_time": 1657729622388, "label": "10.7s \/ 300.4 MB"}, {"starting_time": 1657729622388, "ending_time": 1657729623068}]} ] } ; diff --git a/example_of_result/20220120_test_1657729206/reports/nf_trace.txt b/example_of_result/20220120_test_1657729206/reports/nf_trace.txt new file mode 100644 index 0000000000000000000000000000000000000000..b172f578ab31af0daad44f4805d243f8f91f9303 --- /dev/null +++ b/example_of_result/20220120_test_1657729206/reports/nf_trace.txt @@ -0,0 +1,55 @@ +task_id hash native_id name status exit submit duration realtime %cpu peak_rss peak_vmem rchar wchar +1 07/33d925 1329 init COMPLETED 0 2022-07-13 18:20:08.633 1.3s 10ms 6.7% 0 0 104 KB 664 B +3 32/0ca22b 1393 report1 COMPLETED 0 2022-07-13 18:20:08.710 1.4s 15ms 9.4% 0 0 104.4 KB 682 B +5 32/daee1c 1481 backup COMPLETED 0 2022-07-13 18:20:08.882 1.4s 49ms 8.9% 0 0 104.2 KB 499 B +2 d8/bcda22 1356 Nremove (1) COMPLETED 0 2022-07-13 18:20:08.673 1.8s 430ms 50.3% 11.8 MB 70.6 MB 16.8 MB 14.5 MB +6 e4/9b44e5 1505 workflowVersion COMPLETED 0 2022-07-13 18:20:08.910 2.2s 1s 12.8% 5 MB 38.4 MB 188.9 KB 26 KB +7 d3/55e04f 2198 trim (1) COMPLETED 0 2022-07-13 18:20:10.558 3.2s 2.1s 42.5% 39.5 MB 5.5 GB 16.4 MB 12 MB +10 03/a38aa6 2810 kraken (1) COMPLETED 0 2022-07-13 18:20:13.778 86ms 8ms 57.1% 0 0 150.8 KB 220 B +9 ad/589edd 2897 fivep_filtering (1) COMPLETED 0 2022-07-13 18:20:13.828 2.6s 1.6s 27.0% 9.7 MB 61.4 MB 28 MB 15.3 MB +11 ef/88d2e7 3685 cutoff (1) COMPLETED 0 2022-07-13 18:20:16.532 1.7s 652ms 19.9% 9.5 MB 61.3 MB 7 MB 3.9 MB +8 29/0de1d1 2832 fastqc1 (1) COMPLETED 0 2022-07-13 18:20:13.804 7.1s 6.1s 55.0% 162.2 MB 3.1 GB 13.9 MB 1.2 MB +12 9a/bb6bb5 3641 fastqc2 (1) COMPLETED 0 2022-07-13 18:20:16.503 6.6s 5.8s 88.7% 195.1 MB 3.1 GB 12.2 MB 1.2 MB +15 7a/473ae2 4475 bowtie2 (1) COMPLETED 0 2022-07-13 18:20:19.237 4.8s 3.7s 54.8% 114.9 MB 239.5 MB 35 MB 16.2 MB +4 4f/cb0182 2144 motif COMPLETED 0 2022-07-13 18:20:10.407 15.5s 14.6s 50.6% 191.7 MB 317.8 MB 47.6 MB 39.7 MB +17 4b/0af112 5571 Q20 (1) COMPLETED 0 2022-07-13 18:20:25.055 1.5s 390ms 16.2% 6.4 MB 43.3 MB 3.2 MB 2.2 MB +19 10/a73c63 6094 no_soft_clipping (1) COMPLETED 0 2022-07-13 18:20:27.612 1.4s 257ms 13.9% 3.4 MB 38.4 MB 2.1 MB 1.5 MB +21 6d/e18f26 6119 duplicate_removal (1) COMPLETED 0 2022-07-13 18:20:27.632 2.9s 1.8s 26.4% 6.3 MB 38.4 MB 12.9 MB 6.6 MB +23 53/120110 7181 insertion (1) COMPLETED 0 2022-07-13 18:20:31.557 1.5s 470ms 20.2% 9.2 MB 65.7 MB 2.5 MB 1.7 MB +24 99/5749d0 7202 insertion (2) COMPLETED 0 2022-07-13 18:20:31.578 1.5s 512ms 21.7% 9.6 MB 65.7 MB 3 MB 2.2 MB +18 d3/8b18bf 5597 multiQC COMPLETED 0 2022-07-13 18:20:25.075 8.3s 8s 42.0% 73.1 MB 83.6 MB 28.3 MB 2.3 MB +13 20/8b2e3e 5791 plot_fivep_filtering_stat (1) COMPLETED 0 2022-07-13 18:20:25.918 12.4s 11.6s 56.3% 207.5 MB 345.8 MB 18.2 MB 792.5 KB +14 16/40e161 8047 plot_read_length (1) COMPLETED 0 2022-07-13 18:20:38.326 12.3s 11.6s 65.4% 277 MB 413.2 MB 18.7 MB 691.5 KB +16 1e/df6e9e 8831 coverage (1) COMPLETED 0 2022-07-13 18:20:50.605 1.6s 619ms 17.8% 5.1 MB 44.6 MB 479.6 KB 91 KB +20 59/eb24cd 8991 coverage (2) COMPLETED 0 2022-07-13 18:20:52.234 1.4s 566ms 18.8% 5.1 MB 44.6 MB 335 KB 82.4 KB +22 12/80c100 9147 coverage (3) COMPLETED 0 2022-07-13 18:20:53.624 1.3s 547ms 19.4% 5.1 MB 44.6 MB 310.2 KB 82.1 KB +25 3c/9192fc 9303 final_insertion_files (1) COMPLETED 0 2022-07-13 18:20:54.975 5.3s 4.6s 46.9% 121.8 MB 247.1 MB 17.2 MB 269.2 KB +31 3a/7d7d11 9703 report3 (1) COMPLETED 0 2022-07-13 18:21:00.297 1.1s 54ms 11.7% 0 0 298.9 KB 43.8 KB +26 38/7955e0 9692 final_insertion_files (2) COMPLETED 0 2022-07-13 18:21:00.275 5.5s 4.6s 45.4% 122 MB 247.5 MB 17.2 MB 278.8 KB +27 c1/4ec175 10214 plot_coverage (1) COMPLETED 0 2022-07-13 18:21:05.735 10.7s 10s 61.1% 221.5 MB 358.8 MB 18.3 MB 440.1 KB +28 fa/28ed19 10872 plot_coverage (2) COMPLETED 0 2022-07-13 18:21:16.456 11s 10.3s 61.1% 217 MB 353.4 MB 18.3 MB 434.3 KB +29 9d/bb1716 11525 plot_coverage (3) COMPLETED 0 2022-07-13 18:21:27.455 10.9s 10.1s 61.2% 218.9 MB 355.4 MB 18.3 MB 434.9 KB +30 9c/909d2f 12178 seq_around_insertion (1) COMPLETED 0 2022-07-13 18:21:38.315 5.2s 4.5s 46.2% 122.6 MB 247.7 MB 17.2 MB 208.5 KB +32 0a/d92a3c 12557 random_insertion (1) COMPLETED 0 2022-07-13 18:21:43.506 8.9s 8.2s 57.6% 351.9 MB 487.6 MB 30 MB 1.2 MB +33 3b/051e3e 13098 seq_around_insertion (2) COMPLETED 0 2022-07-13 18:21:52.395 5.2s 4.4s 45.2% 122.4 MB 247.5 MB 17.2 MB 143 KB +34 68/10705c 13490 dup_insertion_and_logo (1) COMPLETED 0 2022-07-13 18:21:57.596 11.6s 10.9s 65.8% 270.4 MB 406.8 MB 16.7 MB 586.2 KB +35 55/ebbde4 14219 extract_seq (1) COMPLETED 0 2022-07-13 18:22:09.185 1.7s 781ms 18.7% 5.9 MB 50.4 MB 9.1 MB 4.5 MB +39 8b/4e5ce1 14438 base_freq (2) COMPLETED 0 2022-07-13 18:22:10.870 1.5s 87ms 12.4% 0 0 281.9 KB 18.9 KB +38 29/443a5b 14493 base_freq (1) COMPLETED 0 2022-07-13 18:22:10.914 1.5s 92ms 9.4% 0 0 279.8 KB 16.8 KB +41 7c/b40174 14544 base_freq (4) COMPLETED 0 2022-07-13 18:22:10.934 1.7s 96ms 11.5% 0 0 282 KB 19 KB +40 cf/abb905 14579 base_freq (3) COMPLETED 0 2022-07-13 18:22:10.953 1.7s 98ms 11.5% 0 0 282.7 KB 19.7 KB +36 54/bcf3c8 14414 plot_insertion (1) COMPLETED 0 2022-07-13 18:22:10.847 4m 2s 4m 1s 36.1% 395.3 MB 546.9 MB 44.8 MB 22.6 MB +37 2d/a978bc 21252 extract_seq (2) COMPLETED 0 2022-07-13 18:26:12.416 1.3s 452ms 20.6% 5.8 MB 50.2 MB 9 MB 4.5 MB +48 40/f4cbe7 21560 base_freq (6) COMPLETED 0 2022-07-13 18:26:13.836 1.5s 36ms 8.4% 0 0 176.4 KB 2.8 KB +50 bd/ea2f89 21598 base_freq (8) COMPLETED 0 2022-07-13 18:26:13.862 1.7s 47ms 10.8% 0 0 180.5 KB 6.9 KB +49 3d/46265a 21632 base_freq (7) COMPLETED 0 2022-07-13 18:26:13.881 1.6s 46ms 10.9% 0 0 178.7 KB 5.1 KB +46 f8/483905 21480 goalign (1) COMPLETED 0 2022-07-13 18:26:13.778 1.9s 767ms 15.1% 5 MB 697.5 MB 128.1 KB 347.8 KB +47 e8/a25744 22144 base_freq (5) COMPLETED 0 2022-07-13 18:26:15.376 1.1s 29ms 13.5% 0 0 182.5 KB 8.9 KB +51 46/95072b 22422 report2 COMPLETED 0 2022-07-13 18:26:17.485 923ms 23ms 12.9% 0 0 132.1 KB 2.4 KB +43 f4/73666f 21470 logo (2) COMPLETED 0 2022-07-13 18:26:13.756 6.9s 5.8s 46.6% 171.5 MB 310.1 MB 13.8 MB 995.1 KB +42 1f/497ee9 22727 logo (1) COMPLETED 0 2022-07-13 18:26:20.674 6s 5.3s 50.5% 165.6 MB 305.1 MB 13.8 MB 845.7 KB +44 6f/524be5 23161 logo (3) COMPLETED 0 2022-07-13 18:26:26.705 6.1s 5.4s 51.0% 169.3 MB 307.5 MB 13.8 MB 875.2 KB +45 6b/1f0539 23593 logo (4) COMPLETED 0 2022-07-13 18:26:32.765 6.1s 5.3s 50.8% 169.2 MB 307.4 MB 13.8 MB 865.8 KB +52 53/48195c 24028 global_logo (1) COMPLETED 0 2022-07-13 18:26:38.845 6.4s 5.7s 53.5% 170 MB 307.2 MB 13.8 MB 847 KB +53 8b/8edeb9 24470 global_logo (2) COMPLETED 0 2022-07-13 18:26:45.275 6.5s 5.8s 53.2% 171.8 MB 308.8 MB 13.8 MB 967.6 KB +54 b2/9012a5 24917 print_report (1) COMPLETED 0 2022-07-13 18:26:52.365 10.7s 9.9s 43.6% 300.4 MB 1 TB 51.8 MB 26.2 MB diff --git a/example_of_result/20220120_test_1649703168/reports/plot_cov_report.txt b/example_of_result/20220120_test_1657729206/reports/plot_cov_report.txt similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/plot_cov_report.txt rename to example_of_result/20220120_test_1657729206/reports/plot_cov_report.txt index b645fd8ce4883759c1355cdf8fecb3493e1345b5..e619b361a764cadee0d27a9c6192cf9035613faf 100644 --- a/example_of_result/20220120_test_1649703168/reports/plot_cov_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/plot_cov_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:18:43 +2022-07-13 16:21:32 @@ -31,12 +31,12 @@ -END TIME: 2022-04-09 15:18:54 +END TIME: 2022-07-13 16:21:37 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 6S @@ -64,9 +64,9 @@ erase.objects TRUE erase.graphs TRUE script plot_coverage run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/plot_coverage.R,--args,test.fastq2_q20_dup_mini,read_nb_after,2320711 2320942,4627368 4627400,5,Ecoli Genome (bp),test.fastq2,cute_little_R_functions.R,plot_coverage_report.txt -cov test.fastq2_q20_dup_mini -read_nb read_nb_after +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/plot_coverage.R,--args,test.fastq2_q20_nodup_mini,dup_read_nb,2320711 2320942,4627368 4627400,5,Ecoli Genome (bp),test.fastq2,cute_little_R_functions.R,plot_coverage_report.txt +cov test.fastq2_q20_nodup_mini +read_nb dup_read_nb ori_coord 2320711 2320942 ter_coord 4627368 4627400 color_coverage 5 @@ -121,9 +121,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:18:54 +TIME: 2022-07-13 16:21:37 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 6S @@ -141,7 +141,7 @@ TOTAL TIME LAPSE: 12S -2022-04-09 15:18:16 +2022-07-13 16:21:21 @@ -160,12 +160,12 @@ TOTAL TIME LAPSE: 12S -END TIME: 2022-04-09 15:18:28 +END TIME: 2022-07-13 16:21:27 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 6S @@ -193,9 +193,9 @@ erase.objects TRUE erase.graphs TRUE script plot_coverage run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/plot_coverage.R,--args,test.fastq2_bowtie2_mini,read_nb_before,2320711 2320942,4627368 4627400,5,Ecoli Genome (bp),test.fastq2,cute_little_R_functions.R,plot_coverage_report.txt -cov test.fastq2_bowtie2_mini -read_nb read_nb_before +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/plot_coverage.R,--args,test.fastq2_q20_dup_mini,read_nb_after,2320711 2320942,4627368 4627400,5,Ecoli Genome (bp),test.fastq2,cute_little_R_functions.R,plot_coverage_report.txt +cov test.fastq2_q20_dup_mini +read_nb read_nb_after ori_coord 2320711 2320942 ter_coord 4627368 4627400 color_coverage 5 @@ -250,9 +250,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:18:28 +TIME: 2022-07-13 16:21:27 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 6S @@ -270,7 +270,7 @@ TOTAL TIME LAPSE: 12S -2022-04-09 15:19:08 +2022-07-13 16:21:10 @@ -289,12 +289,12 @@ TOTAL TIME LAPSE: 12S -END TIME: 2022-04-09 15:19:20 +END TIME: 2022-07-13 16:21:16 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 6S @@ -322,9 +322,9 @@ erase.objects TRUE erase.graphs TRUE script plot_coverage run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/plot_coverage.R,--args,test.fastq2_q20_nodup_mini,dup_read_nb,2320711 2320942,4627368 4627400,5,Ecoli Genome (bp),test.fastq2,cute_little_R_functions.R,plot_coverage_report.txt -cov test.fastq2_q20_nodup_mini -read_nb dup_read_nb +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/plot_coverage.R,--args,test.fastq2_bowtie2_mini,read_nb_before,2320711 2320942,4627368 4627400,5,Ecoli Genome (bp),test.fastq2,cute_little_R_functions.R,plot_coverage_report.txt +cov test.fastq2_bowtie2_mini +read_nb read_nb_before ori_coord 2320711 2320942 ter_coord 4627368 4627400 color_coverage 5 @@ -379,9 +379,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:19:20 +TIME: 2022-07-13 16:21:16 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 6S diff --git a/example_of_result/20220120_test_1649703168/reports/plot_fivep_filtering_stat_report.txt b/example_of_result/20220120_test_1657729206/reports/plot_fivep_filtering_stat_report.txt similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/plot_fivep_filtering_stat_report.txt rename to example_of_result/20220120_test_1657729206/reports/plot_fivep_filtering_stat_report.txt index a5b3b87b4eefeb40d28da2b7088e427d1d6a91e9..3c3cb1b8ad781ed6010e0a4f61d935e9da27bf26 100644 --- a/example_of_result/20220120_test_1649703168/reports/plot_fivep_filtering_stat_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/plot_fivep_filtering_stat_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:16:37 +2022-07-13 16:20:31 @@ -31,12 +31,12 @@ -END TIME: 2022-04-09 15:16:49 +END TIME: 2022-07-13 16:20:38 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 7S @@ -116,9 +116,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:16:49 +TIME: 2022-07-13 16:20:38 -TOTAL TIME LAPSE: 12S +TOTAL TIME LAPSE: 7S diff --git a/example_of_result/20220120_test_1649703168/reports/plot_insertion_report.txt b/example_of_result/20220120_test_1657729206/reports/plot_insertion_report.txt similarity index 99% rename from example_of_result/20220120_test_1649703168/reports/plot_insertion_report.txt rename to example_of_result/20220120_test_1657729206/reports/plot_insertion_report.txt index 482a02780195c52d09d4a190fff35c506abeaa0c..81e1c72663afd52ab00ec0aba55673e8f50547dc 100644 --- a/example_of_result/20220120_test_1649703168/reports/plot_insertion_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/plot_insertion_report.txt @@ -12,7 +12,7 @@ -2022-04-11 18:53:09 +2022-07-13 16:22:16 @@ -512,7 +512,7 @@ LINES REPLACED IN THE GENE OBJECT: -PARALLELIZATION INITIATED AT: 2022-04-11 18:56:05 +PARALLELIZATION INITIATED AT: 2022-07-13 16:25:11 @@ -1668,12 +1668,12 @@ NUMBER OF INSERTIONS IN REANNOTATED res3 FILE (NORMALIZATION TO MAX 1): -END TIME: 2022-04-11 18:57:03 +END TIME: 2022-07-13 16:26:11 -TOTAL TIME LAPSE: 59S +TOTAL TIME LAPSE: 1M 0S @@ -1767,9 +1767,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-11 18:57:03 +TIME: 2022-07-13 16:26:11 -TOTAL TIME LAPSE: 59S +TOTAL TIME LAPSE: 1M 0S diff --git a/example_of_result/20220120_test_1649703168/reports/plot_read_length_report.txt b/example_of_result/20220120_test_1657729206/reports/plot_read_length_report.txt similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/plot_read_length_report.txt rename to example_of_result/20220120_test_1657729206/reports/plot_read_length_report.txt index 9c56e3bef0525584139b95c127af4e404c830833..e545a7b5cb2d24d42825b1daa29a3019f856658d 100644 --- a/example_of_result/20220120_test_1649703168/reports/plot_read_length_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/plot_read_length_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:17:03 +2022-07-13 16:20:43 @@ -31,12 +31,12 @@ -END TIME: 2022-04-09 15:17:18 +END TIME: 2022-07-13 16:20:50 -TOTAL TIME LAPSE: 14S +TOTAL TIME LAPSE: 7S @@ -118,9 +118,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:17:18 +TIME: 2022-07-13 16:20:50 -TOTAL TIME LAPSE: 14S +TOTAL TIME LAPSE: 7S diff --git a/example_of_result/20220120_test_1649703168/reports/print_report.txt b/example_of_result/20220120_test_1657729206/reports/print_report.txt similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/print_report.txt rename to example_of_result/20220120_test_1657729206/reports/print_report.txt index d7ba680ccc1ca3e4493bfede49b71df2c37f46b5..730769375579dc83f45357b103399f0cc174e234 100644 --- a/example_of_result/20220120_test_1649703168/reports/print_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/print_report.txt @@ -12,7 +12,7 @@ -2022-04-11 18:57:09 +2022-07-13 16:26:56 @@ -31,12 +31,12 @@ -END TIME: 2022-04-11 18:57:14 +END TIME: 2022-07-13 16:27:02 -TOTAL TIME LAPSE: 5S +TOTAL TIME LAPSE: 6S @@ -114,9 +114,9 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-11 18:57:14 +TIME: 2022-07-13 16:27:02 -TOTAL TIME LAPSE: 5S +TOTAL TIME LAPSE: 6S diff --git a/example_of_result/20220120_test_1649703168/reports/q20_report.txt b/example_of_result/20220120_test_1657729206/reports/q20_report.txt similarity index 100% rename from example_of_result/20220120_test_1649703168/reports/q20_report.txt rename to example_of_result/20220120_test_1657729206/reports/q20_report.txt diff --git a/example_of_result/20220120_test_1649703168/reports/random_insertion_report.txt b/example_of_result/20220120_test_1657729206/reports/random_insertion_report.txt similarity index 98% rename from example_of_result/20220120_test_1649703168/reports/random_insertion_report.txt rename to example_of_result/20220120_test_1657729206/reports/random_insertion_report.txt index 7150bfae31d6e1a7a681160b5a9d1a9a5d95bd7c..6e13623e0cbecbda8f4ca1790bff87c97abe0767 100644 --- a/example_of_result/20220120_test_1649703168/reports/random_insertion_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/random_insertion_report.txt @@ -12,7 +12,7 @@ -2022-04-11 17:51:02 +2022-07-13 16:21:48 @@ -131,7 +131,7 @@ random: -END TIME: 2022-04-11 17:51:06 +END TIME: 2022-07-13 16:21:52 @@ -221,7 +221,7 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-11 17:51:06 +TIME: 2022-07-13 16:21:52 TOTAL TIME LAPSE: 4S diff --git a/example_of_result/20220120_test_1649703168/reports/report.rmd b/example_of_result/20220120_test_1657729206/reports/report.rmd similarity index 95% rename from example_of_result/20220120_test_1649703168/reports/report.rmd rename to example_of_result/20220120_test_1657729206/reports/report.rmd index 3d54215a177d7d534c88c09364008a9d982e8b0b..fe7d0bcc2c37b177a860b958e044d5513207c391 100644 --- a/example_of_result/20220120_test_1649703168/reports/report.rmd +++ b/example_of_result/20220120_test_1657729206/reports/report.rmd @@ -32,7 +32,7 @@ Ratio: AlienTrimmer main options: -k 10 -l 30 -m 5 -q 20 -p 0 (Phred+33) / 26 alien sequence(s) / 810 k-mers (k=10) -<br />[00:02] 8,932 reads processed: 4,767 trimmed 223 removed +<br />[00:00] 8,932 reads processed: 4,767 trimmed 223 removed <br /><br />AlienTrimmer also removes reads according to quality criteria Number of sequence before trimming: 8,932 @@ -226,15 +226,15 @@ Analysis complete for test.fastq2_5pAtccRm.fq Time loading reference: 00:00:00 <br />Time loading forward index: 00:00:00 <br />Time loading mirror index: 00:00:00 -<br />Multiseed full-index search: 00:00:01 +<br />Multiseed full-index search: 00:00:00 <br />3742 reads; of these: <br /> 3742 (100.00%) were unpaired; of these: <br /> 1240 (33.14%) aligned 0 times <br /> 2308 (61.68%) aligned exactly 1 time <br /> 194 (5.18%) aligned >1 times <br />66.86% overall alignment rate -<br />Time searching: 00:00:01 -<br />Overall time: 00:00:01 +<br />Time searching: 00:00:00 +<br />Overall time: 00:00:00 <br /><br /> @@ -403,7 +403,7 @@ In each sequence of length 40 <br />position 21 corresponds to the first nucleot </center> -{width=600} +{width=600} </center> @@ -418,7 +418,7 @@ In each sequence of length 40 <br />position 21 corresponds to the first nucleot </center> -{width=600} +{width=600} </center> @@ -433,7 +433,7 @@ In each sequence of length 40 <br />position 21 corresponds to the first nucleot </center> -{width=600} +{width=600} </center> @@ -464,6 +464,11 @@ In each sequence of length 40 <br />position 21 corresponds to the first nucleot +Warning: in these files, the position indicated is the first nucleotide of the genomic part of the read (the W of the 5'GWT3' consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position. + + + + See the [test.fastq2_q20_nodup_annot.pos](./files/test.fastq2_q20_nodup_annot.pos) and [test.fastq2_q20_nodup_annot.freq](./files/test.fastq2_q20_nodup_annot.freq) files @@ -494,6 +499,11 @@ The reverse motif is: A[AT]C +Warning: the position indicated is the first nucleotide of the genomic part of the read (the W of the 5'GWT3' consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position. + + + + Beginning of the motif positions in the forward strand: @@ -1209,11 +1219,11 @@ Full .nextflow.log is in: /mnt/c/Users/Gael/Documents/Git_projects/14985_loot<br | Variable | Value | | :-- | :-- | | Project<br />(empty means no .git folder where the main.nf file is present) | loot https://gitlab.pasteur.fr/gmillot/14985_loot (fetch) | # works only if the main script run is located in a directory that has a .git folder, i.e., that is connected to a distant repo -| Git info<br />(empty means no .git folder where the main.nf file is present) | v8.5.0-dirty | # idem. Provide the small commit number of the script and nextflow.config used in the execution -| Cmd line | nextflow run main.nf -resume | +| Git info<br />(empty means no .git folder where the main.nf file is present) | v8.6.0-dirty | # idem. Provide the small commit number of the script and nextflow.config used in the execution +| Cmd line | nextflow run main.nf -c nextflow.config | | execution mode | local | | Manifest's pipeline version | null | -| result path | /mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1649703168 | +| result path | /mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1657729206 | | nextflow version | 21.04.2 | @@ -1238,7 +1248,7 @@ Full .nextflow.log is in: /mnt/c/Users/Gael/Documents/Git_projects/14985_loot<br | Name | Description | Value | | :-- | :-- | :-- | -| out_path | output folder path | /mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1649703168 | +| out_path | output folder path | /mnt/c/Users/Gael/Documents/Git_projects/14985_loot/results/20220120_test_1657729206 | | in_path | input folder path | /mnt/c/Users/Gael/Documents/Git_projects/14985_loot/dataset | diff --git a/example_of_result/20220120_test_1649703168/reports/seq_around_insertion_report.txt b/example_of_result/20220120_test_1657729206/reports/seq_around_insertion_report.txt similarity index 97% rename from example_of_result/20220120_test_1649703168/reports/seq_around_insertion_report.txt rename to example_of_result/20220120_test_1657729206/reports/seq_around_insertion_report.txt index 1828cf73e3c3cf190f06ae8b9a601a62bd5210a7..c7c2207da2eaa2a89bd3b2e3c1d000cc4b4b548e 100644 --- a/example_of_result/20220120_test_1649703168/reports/seq_around_insertion_report.txt +++ b/example_of_result/20220120_test_1657729206/reports/seq_around_insertion_report.txt @@ -12,7 +12,7 @@ -2022-04-09 15:19:35 +2022-07-13 16:21:57 @@ -31,7 +31,7 @@ -END TIME: 2022-04-09 15:19:35 +END TIME: 2022-07-13 16:21:57 @@ -64,12 +64,12 @@ erase.objects TRUE erase.graphs TRUE script seq_around_insertion run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/seq_around_insertion.R,--args,test.fastq2_q20_nodup_selected_if_dup.pos,2320711 2320942,4627368 4627400,20,test.fastq2_q20_nodup_selected_if_dup,cute_little_R_functions.R,seq_around_insertion_report.txt -pos test.fastq2_q20_nodup_selected_if_dup.pos +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/seq_around_insertion.R,--args,test.fastq2_q20_dup_selected_if_dup.pos,2320711 2320942,4627368 4627400,20,test.fastq2_q20_dup_selected_if_dup,cute_little_R_functions.R,seq_around_insertion_report.txt +pos test.fastq2_q20_dup_selected_if_dup.pos ori_coord 2320711 2320942 ter_coord 4627368 4627400 insertion_dist 20 -file_name test.fastq2_q20_nodup_selected_if_dup +file_name test.fastq2_q20_dup_selected_if_dup cute cute_little_R_functions.R log seq_around_insertion_report.txt @@ -118,7 +118,7 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:19:35 +TIME: 2022-07-13 16:21:57 TOTAL TIME LAPSE: 0S @@ -138,7 +138,7 @@ TOTAL TIME LAPSE: 0S -2022-04-09 15:20:15 +2022-07-13 16:21:43 @@ -157,7 +157,7 @@ TOTAL TIME LAPSE: 0S -END TIME: 2022-04-09 15:20:15 +END TIME: 2022-07-13 16:21:43 @@ -190,12 +190,12 @@ erase.objects TRUE erase.graphs TRUE script seq_around_insertion run.way SCRIPT -command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/seq_around_insertion.R,--args,test.fastq2_q20_dup_selected_if_dup.pos,2320711 2320942,4627368 4627400,20,test.fastq2_q20_dup_selected_if_dup,cute_little_R_functions.R,seq_around_insertion_report.txt -pos test.fastq2_q20_dup_selected_if_dup.pos +command /usr/lib/R/bin/exec/R,--no-echo,--no-restore,--file=/mnt/c/Users/Gael/Documents/Git_projects/14985_loot/bin/seq_around_insertion.R,--args,test.fastq2_q20_nodup_selected_if_dup.pos,2320711 2320942,4627368 4627400,20,test.fastq2_q20_nodup_selected_if_dup,cute_little_R_functions.R,seq_around_insertion_report.txt +pos test.fastq2_q20_nodup_selected_if_dup.pos ori_coord 2320711 2320942 ter_coord 4627368 4627400 insertion_dist 20 -file_name test.fastq2_q20_dup_selected_if_dup +file_name test.fastq2_q20_nodup_selected_if_dup cute cute_little_R_functions.R log seq_around_insertion_report.txt @@ -244,7 +244,7 @@ loaded via a namespace (and not attached): ################################ JOB END -TIME: 2022-04-09 15:20:15 +TIME: 2022-07-13 16:21:43 TOTAL TIME LAPSE: 0S diff --git a/main.nf b/main.nf index 5a62214a72c79ba7678aa80e92ee92c0e764e554..f89a89c584201b27e7be17002f101bb0b5a889bd 100755 --- a/main.nf +++ b/main.nf @@ -638,7 +638,7 @@ process insertion { // section 24.7 of the labbook 20200707 """ if [[ ${bam} == "${file_name}_q20_nodup.bam" ]] ; then echo -e "\\n\\n<br /><br />\\n\\n### Insertion positions\\n\\n" > report.rmd - echo -e "\\n\\nOne of the step is to recover positions of reverse reads (16), that use the 3\' end of the read as insertion site and not the 5\' part as with forward reads (0).\\nIt consist in the redefinition of POS according to FLAG in the bam file. See the [insertion_report.txt](./reports/insertion_report.txt) file in the reports folders for details\\n\\n" >> report.rmd + echo -e "\\n\\nOne of the step is to recover positions of reverse reads (16), that use the 3' end of the read as insertion site and not the 5' part as with forward reads (0).\\nIt consist in the redefinition of POS according to FLAG in the bam file. See the [insertion_report.txt](./reports/insertion_report.txt) file in the reports folders for details\\n\\n" >> report.rmd fi # extraction of bam column 2, 4 and 10, i.e., FLAG, POS and SEQ @@ -649,7 +649,7 @@ process insertion { // section 24.7 of the labbook 20200707 echo -e "\\n\\nExtraction of the FLAG (containing the read orientation) the POS and the SEQ of the bams\\nHeader is the 1) sens of insersion (0 or 16) and 2) insertion site position\\n\\n" >> insertion_report.txt # redefinition of POS according to FLAG awk 'BEGIN{FS="\t" ; OFS="" ; ORS=""}{lineKind=(NR-1)%2}lineKind==0{orient=(\$1~">16") ; if(orient){var1 = \$1 ; var2 = \$2}else{print \$0"\\n"}}lineKind==1{if(orient){var3 = length(\$0) ; var4 = var2 + var3 - 1 ; print var1"\\t"var4"\\n"\$0"\\n"}else{print \$0"\\n"}}' tempo > ${file_name}_reorient.fasta - echo -e "\\n\\nFinal fasta file\\n\\nPositions of reverse reads (16) use the 3\\' end of the read as insertion site and not the 5\\' part as with forward reads (0)\\n\\n" >> insertion_report.txt + echo -e "\\n\\nFinal fasta file\\n\\nPositions of reverse reads (16) use the 3' end of the read as insertion site and not the 5' part as with forward reads (0)\\n\\n" >> insertion_report.txt cat ${file_name}_reorient.fasta | head -60 | tail -20 >> insertion_report.txt awk '{lineKind=(NR-1)%2}lineKind==0{gsub(/>/, "", \$1) ; print \$0}' ${file_name}_reorient.fasta > ${bam.baseName}.pos echo -e "\\n\\nFinal pos file\\n\\n" >> insertion_report.txt @@ -952,6 +952,7 @@ process report3 { """ echo -e "\\n\\n<br /><br />\\n\\n### Final insertion site files\\n\\n" > report.rmd + echo -e "\\n\\nWarning: in these files, the position indicated is the first nucleotide of the genomic part of the read (the W of the 5'GWT3' consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position.\\n\\n" >> report.rmd echo -e "\\n\\nSee the [${pos.baseName}.pos](./files/${pos.baseName}.pos) and [${pos.baseName}.freq](./files/${pos.baseName}.freq) files\\n\\n" >> report.rmd pos_nb=\$(( \$(wc -l ${pos} | cut -f1 -d' ') - 1)) # -1 because first line is the header pos_uniq_nb=\$(( \$(sort -u ${pos} | wc -l | cut -f1 -d' ') - 1)) # -1 because first line is the header @@ -997,7 +998,7 @@ process motif { // 43 of the labbook 20201209 cat ${ref} | sed '1d' | grep -Ebo '[ACGT]' > motif_fw.pos cat ${ref} | sed '1d' | grep -Ebo '[ACGT]' > motif_rev.pos fi - echo -e "\nINDICATED POSITIONS IN FILES START AT ZERO AND CORRESPOND TO THE FIRST LEFT BASE OF THE MOTIF\n" + echo -e "\\n\\nWarning: the position indicated is the first nucleotide of the genomic part of the read (the W of the 5'GWT3' consensus site). This means that in FORWARD, the cutting site is before the position. But in REVERSE, the cutting site is after the position.\\n\\n" >> report.rmd motif.R "motif_fw.pos" "motif_rev.pos" "${ori_coord}" "${ter_coord}" "${genome_size}" "${motif_fw}" "${motif_rev}" "${cute_file}" "motif_report.txt" "report.rmd" """ diff --git a/nextflow.config b/nextflow.config index 8e518669f5e7beb06dc75e73fc47bf61c005833b..cd6733931b3ad4fd05ae93c4604d4744b1292439 100755 --- a/nextflow.config +++ b/nextflow.config @@ -90,6 +90,7 @@ result_folder_name="20220120_test" //// end general variables //// slurm variables +// see https://confluence.pasteur.fr/pages/viewpage.action?pageId=69304504 fastqueue = 'common,dedicated' // fast for -p option of slurm. Example: fastqueue = 'common,dedicated'. Example: fastqueue = 'hubbioit' fastqos= '--qos=fast' // fast for --qos option of slurm. Example: fastqos= '--qos=fast' normalqueue = 'hubbioit' // normal for -p option of slurm. Example: normalqueue = 'bioevo'