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Update Wednesday/MFA/data/ENCODE_metadata_bigWig_foldChangeOverControl.released.txt, Wednesday/MFA/data/ENCODE_mergedPeaks_H3K4me2.ov, Wednesday/MFA/EPITOME_CS_integration.Rmd, Wednesday/MFA/EPITOME_CS_integration.html
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---
title: "Epigenomic memory of infection: Data integration"
subtitle : "H3K4me2, transcriptome time courses and chromatin state"
author: "Claudia Chica"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
keep_md: yes
number_sections: yes
smart: no
toc: yes
toc_float: yes
editor_options:
chunk_output_type: console
---
```{r setup, message = FALSE, warning = FALSE, echo = FALSE}
knitr::opts_chunk$set(message=FALSE,warning=FALSE,echo=FALSE)#, cache=TRUE, cache.lazy = FALSE)
knitr::opts_knit$set(progress = FALSE, verbose = FALSE)
library(ggplot2)
library(plyranges)
library(RColorBrewer)
library(knitr)
library(pheatmap)
library(patchwork)
library(FactoMineR)
library(org.Hs.eg.db)
library(msigdbr)
WDIR="./"
setwd(WDIR)
```
# Methylation profile
```{r loadEPIGresults}
# Load epigenomic counts
mark="H3K4me2"
fileName=paste0("./RData/countNormLength_",mark,".RData")
load(fileName)
# Get list of DM peaks
fileName=paste0("./RData/DM_results_",mark,".RData")
load(fileName)
# Get DM profiles
fileName=paste0("./RData/DMpeaks_",mark,".RData")
load(fileName)
save(DMs,file=fileName)
kable(table(DMs$Kind,DMs$Sign),caption = paste0(mark,": peak profiles"))
```
# Transcriptional profile
```{r loadTOMEresults}
# Expression data normalised and batch corrected
fileName=paste0("./RData/TOME_RMAandNoBatch.RData")
load(fileName)
# DA results, same object used for the functional analysis
fileName=paste0("./RData/DE_results_Annot.RData")
load(fileName)
# DEGs padjTh=.05 ; lfcTh=0
fileName=paste0("./RData/DEGs.RData")
load(fileName)
kable(table(DEGs$DEG_kind,DEGs$DEG_sign),caption = "Transcriptome: expression profiles")
```
```{r load_peaks2Gene}
# Load Tgene peak - gene links
fileName=("./data/peaks2GeneTgene_H3K4me2_links.tsv")
load(fileName)
```
# Chromatin states
```{r ENCODE_heatmap, fig.height=10, fig.width=8}
ENCODE_heatmap <- function(matSignal,AnnotationRows,AnnotationCols,colsAnnot) {
breaks=seq(-4,4,.1) ; cols=colorRampPalette(c("purple","black","yellow"))(length(breaks)+1)
pheatmap(matSignal,
scale = "row", cluster_rows = T, cluster_cols = F,
clustering_method = "ward.D2",
show_rownames = F, show_colnames = F,
annotation_col = AnnotationCols,
annotation_row = AnnotationRows,
annotation_colors = colsAnnot,
color = cols, border = NA,
fontsize = 12)
}
```
```{r ENCODE_chromatinStates}
# Upload matrix with ENCODE intensities
fileSignal=paste0("./data/ENCODE_mergedPeaks_",mark,".ov")
matSignal <- read.table(file=fileSignal,header=TRUE,row.names = 1,sep="\t")
# Get profiles = chromatin states -> clustering of ENCODE information
# If with other datasets you get an error on the clustering,
# because some distances are 0 -> delete before all 0 rows
# allZero=which(apply(matSignal,1,sum)==0)
# if (length(allZero)>0) matSignal=matSignal[-allZero,]
# This step may take some time (order of minutes)
fileName=paste0("./RData/",mark,"_allPeaks_CS.RData")
if (file.exists(fileName)) {load(fileName)} else {
mtx=t(matSignal)
d=dist(t(scale(mtx)))
hc=hclust(d,method = "ward.D2")
nClust=8
clust=cutree(hc,k=nClust)
save(clust,file=fileName)
}
```
```{r annotation}
# Get column annotation
fileMetadata="./data/ENCODE_metadata_bigWig_foldChangeOverControl.released.txt"
Encode_metadata <- read.table(file=fileMetadata,header=TRUE,sep="\t")
marks=c("H3K4me3", "H3K4me2", "H3K27ac", "H3K4me1", "H3K79me2", "H3K27me3")
AnnotationCols <- data.frame(
Mark=factor(sub("-human","",Encode_metadata$Experiment.target),levels = marks),
row.names = Encode_metadata$File.accession)
# Upload row annotation -> annotation per region
fileName=paste0("./RData/AnnotationRows.RData")
load(fileName)
# Add chromatin state information from clustering
AnnotationRows$CS=factor(clust[row.names(AnnotationRows)],
levels=c("1","2","3","4","5","6","7","8"))
# Define annotation colors
colsG=brewer.pal(n = 3, name = "Greys")
colsM=brewer.pal(n = 4, name = "Greens")
colsS=brewer.pal(n = 3, name = "Blues")[2:3]
nClust=8 ; colsC=brewer.pal(n = nClust, name = "Set2")
colsAnnot <- list(Mark=c("H3K4me3"=colsS[1], "H3K4me2"=colsS[2], "H3K27ac"=colsM[1], "H3K4me1"=colsM[2], "H3K79me2"=colsM[3], "H3K27me3"=colsM[4]),
DM_sign=c("Gain"="#FF0000", "Loss"="#FF000020"),
DM_kind=c("DM"="#000000", "notDM"="transparent"),
Localization=c("TSS"=colsG[1], "intraG"=colsG[2], "interG"=colsG[3]),
CS=c("1"=colsC[1],"2"=colsC[2],"3"=colsC[3],"4"=colsC[4],"5"=colsC[5],"6"=colsC[6],"7"=colsC[7],"8"=colsC[8]))
```
## Plot peaks annotation
```{r ENCODE_counts, fig.height=10, fig.width=8, cache=TRUE}
# Tables
t=table(AnnotationRows$CS)
kable(ceiling(t/sum(t)*100),col.names = c("CS","Freq"))
t1=table(AnnotationRows$Localization)
tt=table(AnnotationRows$CS,AnnotationRows$Localization)
kable(ceiling(t1/sum(t1)*100),col.names = c("Localization","Freq"))
kable(ceiling(tt/as.numeric(t)*100),row.names = T)
# Produce heatmap only on a subset of peaks, to make it faster .... does it look fine?
# Explain
ranIdx=sample(1:dim(matSignal)[1],3e3)
ranPeaks=row.names(matSignal)[ranIdx]
ENCODE_heatmap(matSignal[ranPeaks,],AnnotationRows[ranPeaks,],AnnotationCols,colsAnnot)
#ENCODE_heatmap(matSignal,AnnotationRows,AnnotationCols,colsAnnot)
```
# Multi factorial analysis
Multi factorial analysis (MFA) (Escofier & Pages, Computational Statistics & Data Analysis, 1994) studies several groups of variables (numerical and/or categorical) defined on the same set of individuals. MFA is a factor analysis applied to the array including all groups of variables. Roughly, the behavior of the method is equivalent to PCA (concerning quantitative variables) or to MCA (concerning qualitative variables).
```{r MFA_functions}
#http://juliejosse.com/wp-content/uploads/2019/03/MFA_staf2015.pdf
MFAstats <- function(mfa.res,xlim,ylim) {
# How many components are necessary to describe the total inertia of the dataset
print(kable(mfa.res[["eig"]][1:10,],
caption = "Eigen values of global analysis"))
# Correlation coeffs
# How much of the stt defined by component C depends on groups(s) G : G -> C
# Fundamental to see if there are structures COMMON to groups -> useful to integrate
print(kable(mfa.res[["group"]][["correlation"]],caption = "Group correlation with factor"))
# Inertia or group contribution
# How much of the inertia of group G is explained by component C : C -> G
print(kable(mfa.res[["group"]][["cos2"]],caption = "Group inertia per factor"))
# Group contribution
plotGrouprepresentation(mfa.res)
# Variable contribution
plotVarrepresentation(mfa.res)
# Individual contribution
plotIndrepresentation(mfa.res,xlim,ylim)
}
plotGrouprepresentation <- function(mfa.res) {
p1 <- plot.MFA(mfa.res,choix = "group",axes = c(1,2), title = "")
p2 <- plot.MFA(mfa.res,choix = "group",axes = c(1,3), title = "")
p3 <- plot.MFA(mfa.res,choix = "group",axes = c(1,4), title = "")
p4 <- plot.MFA(mfa.res,choix = "group",axes = c(1,5), title = "")
print((p1 | p2) / (p3 | p4) + plot_annotation(title = 'Group representation'))
}
plotVarrepresentation <- function(mfa.res) {
title=""
p1 <- plot.MFA(mfa.res,choix="var", axes=c(1,2),title=title,cex=.7)
p2 <- plot.MFA(mfa.res,choix="var", axes=c(1,3),title=title,cex=.7)
p3 <- plot.MFA(mfa.res,choix="var", axes=c(1,4),title=title,cex=.7)
p4 <- plot.MFA(mfa.res,choix="var", axes=c(1,5),title=title,cex=.7)
print((p1 | p2) / (p3 | p4) + plot_layout(guides = "collect") + plot_annotation(title = 'Variable representation'))
}
plotIndrepresentation <- function(mfa.res,xlim,ylim) {
if (ylim[1]!="" & xlim[1]!="") {
p1 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,2),
title = "",ylim=ylim,xlim=xlim)
p2 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,3),
title = "",ylim=ylim,xlim=xlim)
p3 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,4),
title = "",ylim=ylim,xlim=xlim)
p4 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,5),
title = "",ylim=ylim,xlim=xlim)
} else
{
p1 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,2), title = "")
p2 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,3), title = "")
p3 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,4), title = "")
p4 <- plot.MFA(mfa.res,choix = "ind", invisible = "ind", axes = c(1,5), title = "")
}
print((p1 | p2) / (p3 | p4) + plot_annotation(title = 'Individual representation'))
}
clusterStats <- function(hcpc.res) {
### Cluster description
## By variables:
# Test if variable (OMIC samples) values distribution per cluster is random or not
# For quantitative variables
print(kable(hcpc.res[["desc.var"]]$quanti,caption = "Description by quantitative variables"))
# For qualitative variables
print(kable(hcpc.res[["desc.var"]][["category"]],caption = "Description by qualitative variables"))
## By components
# Test if individuals coordinates distribution per cluster is random or not
# For quantitative variables
print(kable(hcpc.res[["desc.axes"]][["quanti"]],
caption = "Description by factors (quantitative variables)"))
# For qualitative variables
print(kable(hcpc.res[["desc.var"]][["test.chi2"]],
caption = "Description by factors (qualitative variables)"))
## By individuals (genomic regions)
# Nearest individuals to the cluster's centroid
print("Description by individuals (nearest):")
print(hcpc.res[["desc.ind"]]$para)
# Furthest individuals to the cluster's centroid
print("Description by individuals (furthest):")
print(hcpc.res[["desc.ind"]]$dist)
}
```
## Prepare MFA input matrix
```{r MFA_prepareMtx}
## Qualitative data
DEGs2peaks=merge(DEGs,peaks2GeneTgene_H3K4me2,by.x=1,by.y=2)
QualitativeData=merge(DEGs2peaks[,c(5,1,2,3,4,6,7,8)],AnnotationRows[,c(4,2,3)],by.x=1,by.y=0)
QualitativeData$Distance=log(abs(QualitativeData$Distance)+1,10)
## Prepare methylation: scale normalised counts,
## add DM inigial and final time points: D7vsUI
METHOME=merge(t(scale(t(countNormLength_H3K4me2))),
DM_results$D7vsUI[,1:2],by=0)
METHOME$baseMean=log(METHOME$baseMean,2)
colnames(METHOME)[8:9]=c("H3K4me2_log2baseMean","H3K4me2_D7vsUI_log2FC")
## Prepare transcriptome, log2
TOME=log(TOME+1,2)
## Prepare ENCODE data: get mean intensity per mark and scale
samples=AnnotationCols[colnames(matSignal),]
names(samples)=colnames(matSignal)
ENCODEmean=t(scale(t(
data.frame(
H3K4me3=apply(matSignal[,names(samples[samples=="H3K4me2"])],1,function(x) mean((x))),
H3K4me2=apply(matSignal[,names(samples[samples=="H3K4me2"])],1,function(x) mean((x))),
H3K4me1=apply(matSignal[,names(samples[samples=="H3K4me1"])],1,function(x) mean((x))),
H3K27ac=apply(matSignal[,names(samples[samples=="H3K27ac"])],1,function(x) mean((x))),
H3K79me2=apply(matSignal[,names(samples[samples=="H3K79me2"])],1,function(x) mean((x))),
H3K27me3=apply(matSignal[,names(samples[samples=="H3K27me3"])],1,function(x) mean((x))),
row.names = row.names(matSignal))
)))
## Merge qualitative data/annotations, methylation, ENCODE and transcriptome signal
EPITOME=merge(QualitativeData,METHOME,by=1)
EPITOME=merge(EPITOME,ENCODEmean,by.x=1,by.y=0)
EPITOME=merge(EPITOME,TOME,by.x=2,by.y=0)
#row.names(EPITOME)=paste0(EPITOME$peakID,"_",EPITOME$ENTREZID)
# Reorder columns .... just for order :-)
EPITOME=EPITOME[,c(2,1,3,6:11,4,5,12:36)]
## Select peak-gene association that contain a DM or a DEG
mfa.EPITOME=subset(EPITOME,EPITOME$DM_kind!="notDM" | EPITOME$DEG_kind!="notSpecific")
#fileName=paste0("./RData/mfa.EPITOME.RData")
#save(EPITOME,file=fileName)
#load(fileName)
```
MFA is performed on the `r dim(mfa.EPITOME)[1]` peak-gene associations that include a DMR or a DEG. This is the distribution of quantitative and qualitative group variables.
```{r MFA_distribution}
## Check MFA matrix distribution
boxplot(mfa.EPITOME[,c(26:36,18:19,4,5,20:25)],las=2,pch=19,cex=.5,
ylab="Scaled values by group",cex.axis=.5,
col=c(rep(palette()[8],11),rep(palette()[6],2),
rep(palette()[3],1),rep(palette()[4],1),rep(palette()[5],6)),
main="Distribution of quantitative variables for MFA")
tmp=unique(mfa.EPITOME[,c(3,10,11)])
kable(table(tmp[,2:3]),caption = "DEGs for MFA")
tmp=unique(mfa.EPITOME[,c(2,8,9)])
kable(table(tmp[,2:3]),caption = "DMRs for MFA")
```
## Perfom MFA and components description
```{r MFA, fig.height=10, fig.width=10, cache=TRUE}
# For the sake of speed select only DM peaks plus 500 random peaks
idx=unique(c(which(mfa.EPITOME$DM_kind=="DM"),sample(1:dim(mfa.EPITOME)[1],500)))
# Try different MFA settings
colInds=c(26:36,18:19,4,5,20:25,7,10:11,8:9)
fileOut=paste0(WDIR,"./RData/mfa.res_TOMEMETHDISTCORREPIGDEGDMR_CS_filtered.RData")
if (file.exists(fileOut)) {load(fileOut)} else {
mfa.res_TOMEMETHDISTCORREPIGDEGDMR_CS <- MFA(mfa.EPITOME[idx,colInds], ncp=5,
group=c(11,2,1,1,6,1,2,2), type=c(rep("c",5),rep("n",3)),
name.group=c("TOME","METH","DIST","CORR","EPIG","CS","DEG","DMR"),num.group.sup=c(6),
graph = FALSE)
save(mfa.res_TOMEMETHDISTCORREPIGDEGDMR_CS,file=fileOut)
}
colInds=c(26:36,18:19,4,5,20:25,10:11,8:9)
fileOut=paste0(WDIR,"./RData/mfa.res_TOMEMETHDISTCORREPIGDEGDMR.RData")
if (file.exists(fileOut)) {load(fileOut)} else {
mfa.res_TOMEMETHDISTCORREPIGDEGDMR <- MFA(mfa.EPITOME[idx,colInds], ncp=5,
group=c(11,2,1,1,6,2,2), type=c(rep("c",5),rep("n",2)),
name.group=c("TOME","METH","DIST","CORR","EPIG","DEG","DMR"),graph = FALSE)
save(mfa.res_TOMEMETHDISTCORREPIGDEGDMR,file=fileOut)
}
#### MFA stats
mfa.res=mfa.res_TOMEMETHDISTCORREPIGDEGDMR
xlim=c(-2,2) ; ylim=c(-2,2)
MFAstats(mfa.res,xlim,ylim)
```
# Hierarchical clustering on MFA components
Clustering is performed on the first 5 components obtained with the MFA.
```{r HCPCclustering, fig.height=10, fig.width=10, cache=TRUE}
#### Clustering
nClust=10 #; maxClustSize=1400
hcpc.res <- HCPC(mfa.res, nb.clust=nClust, conso=0, min=3, max=10,
metric = "euclidean", method="ward",graph = TRUE)#,nb.par = maxClustSize)
# Cluster description in term of qualitative variables
lapply(hcpc.res[["desc.var"]][["category"]],function(x) {kable(subset(x,x[,"v.test"]>5))})
lapply(hcpc.res[["desc.var"]][["category"]],function(x) {kable(subset(x,x[,"v.test"]>5))})
```
# References
```{r}
sessionInfo()
```
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File accession File format File type File format type Output type File assembly Experiment accession Assay Biosample term id Biosample term name Biosample type Biosample organism Biosample treatments Biosample treatments amount Biosample treatments duration Biosample genetic modifications methods Biosample genetic modifications categories Biosample genetic modifications targets Biosample genetic modifications gene targets Biosample genetic modifications site coordinates Biosample genetic modifications zygosity Experiment target Library made from Library depleted in Library extraction method Library lysis method Library crosslinking method Library strand specific Experiment date released Project RBNS protein concentration Library fragmentation method Library size range Biological replicate(s) Technical replicate(s) Read length Mapped read length Run type Paired end Paired with Derived from Size Lab md5sum dbxrefs File download URL Genome annotation Platform Controlled by File Status s3_uri Audit WARNING Audit INTERNAL_ACTION Audit NOT_COMPLIANT Audit ERROR
ENCFF421ZNH bigWig bigWig fold change over control hg19 ENCSR000AST ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens dexamethasone 100 nM 1 hour H3K4me3-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF289PMY, ENCFF698OND, ENCFF843TBR 474155322 ENCODE Processing Pipeline 0b673bce293cb3e83333e45abad30f54 https://www.encodeproject.org/files/ENCFF421ZNH/@@download/ENCFF421ZNH.bigWig released s3://encode-public/2017/10/27/c1795411-c15d-481e-aefb-49d83ff32592/ENCFF421ZNH.bigWig moderate library complexity, mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample severe bottlenecking
ENCFF250OYQ bigWig bigWig fold change over control hg19 ENCSR000AST ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens dexamethasone 100 nM 1 hour H3K4me3-human DNA 2012-08-06 ENCODE 2 2_1 ENCFF103BSF, ENCFF289PMY, ENCFF698OND 348604941 ENCODE Processing Pipeline 9adf8b43e65c18efa9a2517e6bf5296d https://www.encodeproject.org/files/ENCFF250OYQ/@@download/ENCFF250OYQ.bigWig released s3://encode-public/2017/10/27/c4db1231-c80d-4197-b6d3-a3cb317ee6f3/ENCFF250OYQ.bigWig moderate library complexity, mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample severe bottlenecking
ENCFF189JCB bigWig bigWig fold change over control hg19 ENCSR000AST ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens dexamethasone 100 nM 1 hour H3K4me3-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_1 ENCFF103BSF, ENCFF289PMY, ENCFF698OND, ENCFF843TBR 667767681 ENCODE Processing Pipeline dd6dde042d9714702f06755ad0cbfe77 https://www.encodeproject.org/files/ENCFF189JCB/@@download/ENCFF189JCB.bigWig released s3://encode-public/2017/10/27/c2e752c7-10cf-4772-b166-f95341c2dcb1/ENCFF189JCB.bigWig moderate library complexity, mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample severe bottlenecking
ENCFF562LEK bigWig bigWig fold change over control hg19 ENCSR000AVF ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens dexamethasone 100 nM 1 hour H3K27ac-human DNA 2012-08-06 ENCODE 2 2_1 ENCFF289PMY, ENCFF529MDW, ENCFF698OND 513488442 ENCODE Processing Pipeline dab45e8b65769292daedaaf31c678261 https://www.encodeproject.org/files/ENCFF562LEK/@@download/ENCFF562LEK.bigWig released s3://encode-public/2017/04/25/565dd6fe-f7c6-4b6a-a8d2-3a7993ccd189/ENCFF562LEK.bigWig moderate library complexity, mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample
ENCFF597SRK bigWig bigWig fold change over control hg19 ENCSR000AVF ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens dexamethasone 100 nM 1 hour H3K27ac-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_1 ENCFF289PMY, ENCFF352HEC, ENCFF529MDW, ENCFF698OND 827163378 ENCODE Processing Pipeline 8c924d3b088add13850aa336c5c88c5b https://www.encodeproject.org/files/ENCFF597SRK/@@download/ENCFF597SRK.bigWig released s3://encode-public/2017/04/25/67a55a60-4ca9-4c8c-b2a5-f21913c26c74/ENCFF597SRK.bigWig moderate library complexity, mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample
ENCFF216GUZ bigWig bigWig fold change over control hg19 ENCSR000AVF ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens dexamethasone 100 nM 1 hour H3K27ac-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF289PMY, ENCFF352HEC, ENCFF698OND 544874008 ENCODE Processing Pipeline e1ae2f0f7953b74860b9fe7e95e10533 https://www.encodeproject.org/files/ENCFF216GUZ/@@download/ENCFF216GUZ.bigWig released s3://encode-public/2017/04/25/8bcc9600-f0a9-4e04-83c9-869bcc1c5cc8/ENCFF216GUZ.bigWig moderate library complexity, mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample
ENCFF137KNW bigWig bigWig fold change over control hg19 ENCSR000AUI ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K27ac-human DNA 2012-08-06 ENCODE 2 2_1 ENCFF265SHJ, ENCFF408NTN, ENCFF502CGT 718775491 ENCODE Processing Pipeline 81a1c02f638f43fe2c365a107d7176e6 https://www.encodeproject.org/files/ENCFF137KNW/@@download/ENCFF137KNW.bigWig released s3://encode-public/2017/04/13/2593d803-8ffe-445a-8354-26bd82ad9b67/ENCFF137KNW.bigWig mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample
ENCFF103BLQ bigWig bigWig fold change over control hg19 ENCSR000AUI ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K27ac-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_1 ENCFF122XDP, ENCFF265SHJ, ENCFF408NTN, ENCFF502CGT 1287453814 ENCODE Processing Pipeline 065782a8b37db5d0d76f58d350f09340 https://www.encodeproject.org/files/ENCFF103BLQ/@@download/ENCFF103BLQ.bigWig released s3://encode-public/2017/04/13/62d59abb-5249-4370-9b91-8956dd909fed/ENCFF103BLQ.bigWig mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample
ENCFF663ILW bigWig bigWig fold change over control hg19 ENCSR000AUI ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K27ac-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF122XDP, ENCFF408NTN, ENCFF502CGT 1023319248 ENCODE Processing Pipeline a4b6c88e19947b6f3f718b2813630ea4 https://www.encodeproject.org/files/ENCFF663ILW/@@download/ENCFF663ILW.bigWig released s3://encode-public/2017/04/13/c6fa4b34-14e7-4e4f-af38-1c05a01f8f15/ENCFF663ILW.bigWig mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample
ENCFF165ZPD bigWig bigWig fold change over control hg19 ENCSR000AUM ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me1-human DNA 2012-08-06 ENCODE 2 2_2 ENCFF408NTN, ENCFF502CGT, ENCFF921GLJ 807936071 ENCODE Processing Pipeline dc1cc7a94be3ebb59b64c2469c995e55 https://www.encodeproject.org/files/ENCFF165ZPD/@@download/ENCFF165ZPD.bigWig released s3://encode-public/2017/02/11/4de07159-494c-4fb2-a1b9-46f917db706e/ENCFF165ZPD.bigWig mild to moderate bottlenecking, low read length out of date analysis, biological replicates with identical biosample insufficient read depth
ENCFF613NHX bigWig bigWig fold change over control hg19 ENCSR000AUM ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me1-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_2 ENCFF336KSW, ENCFF408NTN, ENCFF502CGT, ENCFF921GLJ 1918249392 ENCODE Processing Pipeline 93163dbd6fab65e875b5917e34cd937b https://www.encodeproject.org/files/ENCFF613NHX/@@download/ENCFF613NHX.bigWig released s3://encode-public/2017/02/11/50082973-51fd-482e-8c54-5bfb725ba787/ENCFF613NHX.bigWig mild to moderate bottlenecking, low read length out of date analysis, biological replicates with identical biosample insufficient read depth
ENCFF569ZJQ bigWig bigWig fold change over control hg19 ENCSR000AUM ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me1-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF336KSW, ENCFF408NTN, ENCFF502CGT 1792248047 ENCODE Processing Pipeline 796aac45d1413c74e1caf549f3597df7 https://www.encodeproject.org/files/ENCFF569ZJQ/@@download/ENCFF569ZJQ.bigWig released s3://encode-public/2017/02/11/d692f934-9c78-435e-92d2-037669202706/ENCFF569ZJQ.bigWig mild to moderate bottlenecking, low read length out of date analysis, biological replicates with identical biosample insufficient read depth
ENCFF931LYX bigWig bigWig fold change over control hg19 ENCSR000ATP ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K79me2-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF408NTN, ENCFF502CGT, ENCFF692XBS 1344675316 ENCODE Processing Pipeline 16e91254481d2662396d22a57911a0a7 https://www.encodeproject.org/files/ENCFF931LYX/@@download/ENCFF931LYX.bigWig released s3://encode-public/2017/02/11/d12fcbc6-72ce-420d-8a1b-20d8c8877689/ENCFF931LYX.bigWig mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample severe bottlenecking, poor library complexity, insufficient read depth
ENCFF154HXA bigWig bigWig fold change over control hg19 ENCSR000ATP ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K79me2-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_2 ENCFF408NTN, ENCFF502CGT, ENCFF692XBS, ENCFF913MDC 1418818572 ENCODE Processing Pipeline 4e3ec0a9ef14aa5364d8141534bbe641 https://www.encodeproject.org/files/ENCFF154HXA/@@download/ENCFF154HXA.bigWig released s3://encode-public/2017/02/11/0b59489f-67c6-46a4-b779-5477d8a45ab7/ENCFF154HXA.bigWig mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample severe bottlenecking, poor library complexity, insufficient read depth
ENCFF892LYD bigWig bigWig fold change over control hg19 ENCSR000ATP ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K79me2-human DNA 2012-08-06 ENCODE 2 2_2 ENCFF408NTN, ENCFF502CGT, ENCFF913MDC 327590030 ENCODE Processing Pipeline de4a4d6dd78e0e688efc347d1402f460 https://www.encodeproject.org/files/ENCFF892LYD/@@download/ENCFF892LYD.bigWig released s3://encode-public/2017/02/11/b1a58fac-a2b5-426a-9ab4-e3e92c0867fd/ENCFF892LYD.bigWig mild to moderate bottlenecking, low read length, low read depth biological replicates with identical biosample severe bottlenecking, poor library complexity, insufficient read depth
ENCFF313GRK bigWig bigWig fold change over control hg19 ENCSR000ASH ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me3-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF079TOP, ENCFF408NTN, ENCFF502CGT 1725679919 ENCODE Processing Pipeline 45b794af2ba7d2881a033568062169ec https://www.encodeproject.org/files/ENCFF313GRK/@@download/ENCFF313GRK.bigWig released s3://encode-public/2016/11/16/4bc67423-4fd9-40cb-914f-8da51af977e1/ENCFF313GRK.bigWig mild to moderate bottlenecking, low read length biological replicates with identical biosample severe bottlenecking, poor library complexity, insufficient read depth
ENCFF635VEW bigWig bigWig fold change over control hg19 ENCSR000ASH ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me3-human DNA 2012-08-06 ENCODE 2 2_1 ENCFF408NTN, ENCFF502CGT, ENCFF761OKC 341380076 ENCODE Processing Pipeline 657dd7ccde33920bd6cba357d683a1de https://www.encodeproject.org/files/ENCFF635VEW/@@download/ENCFF635VEW.bigWig released s3://encode-public/2016/11/16/9a6a0ba0-5228-4e8a-922f-7ec892beda3d/ENCFF635VEW.bigWig mild to moderate bottlenecking, low read length biological replicates with identical biosample severe bottlenecking, poor library complexity, insufficient read depth
ENCFF709KWY bigWig bigWig fold change over control hg19 ENCSR000ASH ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me3-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_1 ENCFF079TOP, ENCFF408NTN, ENCFF502CGT, ENCFF761OKC 1807796364 ENCODE Processing Pipeline d0fd761c22d63cde912e91848f0b48b6 https://www.encodeproject.org/files/ENCFF709KWY/@@download/ENCFF709KWY.bigWig released s3://encode-public/2016/11/16/7dd36438-ed3f-4942-a63f-046bafbbd9dc/ENCFF709KWY.bigWig mild to moderate bottlenecking, low read length biological replicates with identical biosample severe bottlenecking, poor library complexity, insufficient read depth
ENCFF419LFZ bigWig bigWig fold change over control hg19 ENCSR000AVI ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me2-human DNA 2012-08-06 ENCODE 2 2_2 ENCFF408NTN, ENCFF502CGT, ENCFF700NZJ 1061349525 ENCODE Processing Pipeline 4acf3fced58e85c2604f643826f7f6a1 https://www.encodeproject.org/files/ENCFF419LFZ/@@download/ENCFF419LFZ.bigWig released s3://encode-public/2017/02/11/28ce6333-d9f1-4491-a4f0-de342dac4f1b/ENCFF419LFZ.bigWig low read length biological replicates with identical biosample
ENCFF975YWP bigWig bigWig fold change over control hg19 ENCSR000AVI ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me2-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF408NTN, ENCFF471RCX, ENCFF502CGT 1319593402 ENCODE Processing Pipeline 07c85511f0fb2773725d52253d6f204a https://www.encodeproject.org/files/ENCFF975YWP/@@download/ENCFF975YWP.bigWig released s3://encode-public/2017/02/11/0b94336a-d7cf-4a0f-ae3a-51f23c35f210/ENCFF975YWP.bigWig low read length biological replicates with identical biosample
ENCFF876DTJ bigWig bigWig fold change over control hg19 ENCSR000AVI ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K4me2-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_2 ENCFF408NTN, ENCFF471RCX, ENCFF502CGT, ENCFF700NZJ 1733174237 ENCODE Processing Pipeline f0e3f50fc187fdcc64a1fb65d38c4e82 https://www.encodeproject.org/files/ENCFF876DTJ/@@download/ENCFF876DTJ.bigWig released s3://encode-public/2017/02/11/0f7af076-6e2b-4a4d-a73c-725264ce88bd/ENCFF876DTJ.bigWig low read length biological replicates with identical biosample
ENCFF134YLO bigWig bigWig fold change over control hg19 ENCSR000AUK ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K27me3-human DNA 2012-08-06 ENCODE 1 1_1 ENCFF359QIW, ENCFF408NTN, ENCFF502CGT 2087548055 ENCODE Processing Pipeline ef443d8b5e76d2d650d453f8c8201a52 https://www.encodeproject.org/files/ENCFF134YLO/@@download/ENCFF134YLO.bigWig released s3://encode-public/2017/02/11/4a8b4233-d27b-4736-bb61-dbcbd5bee42b/ENCFF134YLO.bigWig moderate library complexity, mild to moderate bottlenecking, low read length biological replicates with identical biosample insufficient read depth
ENCFF819QSK bigWig bigWig fold change over control hg19 ENCSR000AUK ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K27me3-human DNA 2012-08-06 ENCODE 2 2_2 ENCFF060OKK, ENCFF408NTN, ENCFF502CGT 850692405 ENCODE Processing Pipeline 934f4c352b0b2a7bc1121fc4b58b0f7d https://www.encodeproject.org/files/ENCFF819QSK/@@download/ENCFF819QSK.bigWig released s3://encode-public/2017/02/11/2c2dbb12-6222-4651-b391-41065919c68f/ENCFF819QSK.bigWig moderate library complexity, mild to moderate bottlenecking, low read length biological replicates with identical biosample insufficient read depth
ENCFF336AWS bigWig bigWig fold change over control hg19 ENCSR000AUK ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens ethanol 0.02 percent 1 hour H3K27me3-human DNA 2012-08-06 ENCODE 1, 2 1_1, 2_2 ENCFF060OKK, ENCFF359QIW, ENCFF408NTN, ENCFF502CGT 2189199766 ENCODE Processing Pipeline 8eb6be80fbbc2f0ecc886be01c16b179 https://www.encodeproject.org/files/ENCFF336AWS/@@download/ENCFF336AWS.bigWig released s3://encode-public/2017/02/11/e511c01b-0240-4080-88eb-20f858e0f760/ENCFF336AWS.bigWig moderate library complexity, mild to moderate bottlenecking, low read length biological replicates with identical biosample insufficient read depth
ENCFF510LTC bigWig bigWig fold change over control hg19 ENCSR000DPD ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens H3K4me3-human DNA 2011-11-17 ENCODE 2 2_1 ENCFF251TYG, ENCFF810JVM 167996043 ENCODE Processing Pipeline 9e645668728df8454f3803bed99ee385 https://www.encodeproject.org/files/ENCFF510LTC/@@download/ENCFF510LTC.bigWig released s3://encode-public/2017/10/27/25e0b2d6-b06c-40c2-a876-1f5e181d3bdf/ENCFF510LTC.bigWig control low read depth, mild to moderate bottlenecking, low read length, low read depth
ENCFF556OVF bigWig bigWig fold change over control hg19 ENCSR000DPD ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens H3K4me3-human DNA 2011-11-17 ENCODE 1, 2 1_1, 2_1 ENCFF251TYG, ENCFF661XHM, ENCFF810JVM 268706172 ENCODE Processing Pipeline 51f894db4499b2ea3b54c343c1e890ad https://www.encodeproject.org/files/ENCFF556OVF/@@download/ENCFF556OVF.bigWig released s3://encode-public/2017/10/27/2e5a2ddc-fef6-4b97-bb5a-489b83de90d3/ENCFF556OVF.bigWig control low read depth, mild to moderate bottlenecking, low read length, low read depth
ENCFF628ANV bigWig bigWig fold change over control hg19 ENCSR000DPD ChIP-seq ChIP-seq EFO:0001086 A549 cell line Homo sapiens H3K4me3-human DNA 2011-11-17 ENCODE 1 1_1 ENCFF251TYG, ENCFF661XHM 150084073 ENCODE Processing Pipeline cffdf90b046aacfe896aa3c5d3afae70 https://www.encodeproject.org/files/ENCFF628ANV/@@download/ENCFF628ANV.bigWig released s3://encode-public/2017/10/27/a0ea706c-3076-4cf1-973a-165c3c938fc5/ENCFF628ANV.bigWig control low read depth, mild to moderate bottlenecking, low read length, low read depth
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