SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The statistical analysis performed by SHAMAN is based on DESeq2 R package [[Anders and Huber 2010](http://www.ncbi.nlm.nih.gov/pubmed/20979621)] which robustly identifies the differential abundant features as suggested in [[McMurdie and Holmes 2014](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/)] and [[Jonsson2016](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/)].
SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [[Love 2014](http://www.ncbi.nlm.nih.gov/pubmed/25516281)].
SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats. Hereafter is the global workflow of the SHAMAN application:
SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).
The bioinformatics treatment is based on Vsearch [[Rognes 2016](http://www.ncbi.nlm.nih.gov/pubmed/27781170)] which showed to be both accurate and fast [[Wescott 2015](http://www.ncbi.nlm.nih.gov/pubmed/26664811)].The statistical analysis is based on DESeq2 R package [[Anders and Huber 2010](http://www.ncbi.nlm.nih.gov/pubmed/20979621)] which robustly identifies the differential abundant features as suggested in [[McMurdie and Holmes 2014](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/)] and [[Jonsson2016](http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/)].
SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2 [[Love 2014](http://www.ncbi.nlm.nih.gov/pubmed/25516281)].
SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats.
A presentation about SHAMAN is available [here](www/shaman_presentation.pdf) and a poster [here](www/shaman_poster.pdf).
Hereafter is the global workflow of the SHAMAN application:
p("SHAMAN is a SHiny application for Metagenomic ANalysis including the normalization,
the differential analysis and mutiple visualization.",style="font-family: 'times'; font-si16pt"),
p("SHAMAN is based on DESeq2 R package",a("[Anders and Huber 2010]",href="http://www.ncbi.nlm.nih.gov/pubmed/20979621"),"for the analysis of metagenomic data, as suggested in",a("[McMurdie and Holmes 2014,",href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/"),a("Jonsson2016]",href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/"),
". SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2",a("[Love 2014]",href="http://www.ncbi.nlm.nih.gov/pubmed/25516281"),".
Resulting p-values are adjusted according to the Benjamini and Hochberg procedure [Benjamini and Hochberg 1995].
The PCOA is performed with the",a("ade4 R package",href="http://pbil.univ-lyon1.fr/ade4/"),"and plots are generated with",a("ggplot2",href="http://ggplot2.org/"),"or",a("D3.js packages",href="http://d3js.org/"),".
A presentation about SHAMAN is available",a("here",target="_blank",href="shaman_presentation.pdf")," and a poster",a("here.",target="_blank",href="shaman_poster.pdf"),br(),
"SHAMAN is compatible with standard formats for metagenomic analysis. We also provide a complete pipeline for OTU picking and annotation named",a("MASQUE",href="https://github.com/aghozlane/masque"),"used in production at Institut Pasteur.",style="font-family: 'times'; font-si16pt"),
p("SHAMAN is a shiny application for differential analysis of metagenomic data (16S, 18S, 23S, 28S, ITS and WGS) including bioinformatics treatment of raw reads for targeted metagenomics, statistical analysis and results visualization with a large variety of plots (barplot, boxplot, heatmap, …).",style="font-family: 'times'; font-si16pt"),
p("The bioinformatics treatment is based on Vsearch",a("[Rognes 2016]",href="http://www.ncbi.nlm.nih.gov/pubmed/27781170"),"which showed to be both accurate and fast",a("[Wescott 2015]",href="http://www.ncbi.nlm.nih.gov/pubmed/26664811"),". The statistical analysis is based on DESeq2 R package",a("[Anders and Huber 2010]",href="http://www.ncbi.nlm.nih.gov/pubmed/20979621"),
"which robustly identifies the differential abundant features as suggested in",a("[McMurdie and Holmes 2014,",href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/"),a("Jonsson2016]",href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4727335/"),
". SHAMAN robustly identifies the differential abundant genera with the Generalized Linear Model implemented in DESeq2",a("[Love 2014]",href="http://www.ncbi.nlm.nih.gov/pubmed/25516281"),".",
"SHAMAN is compatible with standard formats for metagenomic analysis (.csv, .tsv, .biom) and figures can be downloaded in several formats.",
"A presentation about SHAMAN is available",a("here",target="_blank",href="shaman_presentation.pdf")," and a poster",a("here.",target="_blank",href="shaman_poster.pdf"),style="font-family: 'times'; font-si16pt"),br(),
p("Hereafter is the global workflow of the SHAMAN application:"),
