peak_calling

Last updated: 2020-03-17

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Knit directory: m6AQTL_reproducibleDocument/

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Peak calling

Count Reads from BAM files

library(MeRIPtools)
samplenames <- c("NA18486","NA18498","NA18499","NA18501","NA18502","NA18504","NA18505","NA18507","NA18508","NA18510","NA18511","NA18516","NA18517","NA18519","NA18522","NA18523","NA18852","NA18855","NA18856","NA18858","NA18861","NA18862","NA18870","NA18907","NA18909","NA18912","NA18913","NA18916","NA19092","NA19093","NA19098","NA19099","NA19101","NA19102","NA19108","NA19114","NA19116","NA19119","NA19127","NA19128","NA19130","NA19131","NA19137","NA19138","NA19140","NA19143","NA19144","NA19147","NA19152","NA19153","NA19159","NA19160","NA19192","NA19193","NA19200","NA19204","NA19209","NA19222","NA19239","NA19257")

LCLs <- countReads(samplenames = samplenames, 
                   gtf = "~/Database/genome/hg19/hg19_UCSC.gtf",
                   fragmentLength = 150,
                   bamFolder = "~/m6AQTL/WASP_OUT_201907/",
                   outputDir = "~/m6AQTL/m6A_QTL_results/",
                   modification = "m6A",
                   binSize = 50,
                   strandToKeep = "opposite",
                   threads = 30
                   )
save(LCLs, file = "~/m6AQTL/m6A_QTL_results/PoissonGamma_analysis/monster.RData" )

Peak calling

LCLs <- callPeakFisher(LCLs,threads = 40)
LCLs <- reportJointPeak(LCLs, joint_threshold = 5, threads = 30)
LCLs <- jointPeakCount(LCLs)

check IP efficiency

.noZero <- function(x){sapply(x,max,1)}
library(MeRIPtools)
#load("~/m6AQTL/peakcalling/MeRIPtools/m6ALCLs_MeRIP.Peak.RData")
LCLs <- readRDS("~/m6AQTL/peakcalling/MeRIPtools/jointPeak_threshold5_MeRIP.RDS")
newLCLs <- filter(LCLs, !apply(extractInput(LCLs), 1, function(x) any(x == 0 )) )
T0 <- colSums(counts(newLCLs)[,1:length(newLCLs@samplenames)] )
T1 <- colSums(counts(newLCLs)[,(length(newLCLs@samplenames)+1) : (2*length(newLCLs@samplenames)) ] )
##filter out peaks with OR < 1
enrichFlag <- apply( t( t(extractIP(newLCLs))/T1 )/ t( t( extractInput(newLCLs) )/T0 ),1,function(x){sum(x>1)> newLCLs@jointPeak_threshold})
newLCLs <-  MeRIPtools::filter(newLCLs, enrichFlag )
OR <- t( apply(extractIP(newLCLs),1,.noZero)/T1 )/ t( t( extractInput(newLCLs) )/T0 )
colnames(OR) <- newLCLs@samplenames

library(reshape2)
OR_melt <- melt(as.data.frame(OR))
ggplot(OR_melt,aes(x = variable, y = log(value) ))+geom_boxplot()+theme_classic()+theme(axis.text.x = element_text(face = "bold",angle = 90))

calculate the offset term for IP efficiency

K_IPe_ij <- apply(logOR[logOR.id,], 2, function(x){
  
  fit <- lm(y~m, data = data.frame(y = x, m=rowMeans(logOR)[logOR.id] ))
  y.est <- predict(fit, newdata =  data.frame(m = rowMeans(logOR)))
  return( y.est - rowMeans(logOR) )
  })

check GC content

## read peaks into GRanges list skipping introns
peaks.gr <- m6Amonster:::.peakToGRangesList(jointPeak(newLCLs))
library(BSgenome.Hsapiens.UCSC.hg19)
library(stringr)

registerDoParallel(30)
peakGC <- foreach( i = 1:length(peaks.gr), .combine = c)%dopar%{
  peakSeq <- paste( getSeq( BSgenome.Hsapiens.UCSC.hg19 , peaks.gr[[i]] ,as.character =T ) , collapse = "")
  sum( str_count(peakSeq, c("G","g","C","c"))  )/nchar(peakSeq)
}

Calculate the GC content offset term

peakGC_l <- round(peakGC,digits = 2)
peakGC_l[which(peakGC_l<0.2)] <-median(peakGC_l[which(peakGC_l<0.2)] ) # combine some bins at low GC due to low number of peaks
peakGC_l[which(peakGC_l>0.84)] <-median(peakGC_l[which(peakGC_l>0.84)] )# combine some bins at high GC due to low number of peaks
l <- sort(unique(peakGC_l))
y <- log( OR ) - K_IPe_ij
colnames(y) <- newLCLs@samplenames
b.l <- tapply(rowMeans( y ), peakGC_l ,  median) 
bil <- apply( y, 2, tapply, peakGC_l, median )
bi. <- apply(  y , 2, median )
b.. <- median(  y )
Fil <- as.data.frame( as.matrix(bil) - as.vector(b.l) ) - ( bi. - b.. )
Fil$GC <- l

Fij <- foreach( ii = 1:length(newLCLs@samplenames), .combine = cbind)%dopar%{
      GC_fit <- lm(Fil[,ii] ~ poly(l,4) )
      predict(GC_fit, newdata = data.frame(l = peakGC) )
}
colnames(Fij) <- newLCLs@samplenames

The final Log-OR corrected for IP efficency and GC bias is obtained by

Log.OR <- log(OR)- K_IPe_ij - Fij

Analysis of m6A peaks

Load m6AQTL and m6Apeaks

library(MeRIPtools)
library(BSgenome.Hsapiens.UCSC.hg19)

LCLs <- readRDS( "~/m6AQTL/peakcalling/MeRIPtools/jointPeak_threshold5_MeRIP.RDS")

m6AQTL <-  readRDS("~/m6AQTL/m6A_QTL_results/linear_model/m6AQTL.m6APeak_logOR_GC.IP.adjusted_qqnorm.15PCs.fastQTL.nominals.rds")
lead_m6AQTL <- readRDS( file = "~/m6AQTL/m6A_QTL_results/linear_model/lead.m6AQTL.m6APeak_logOR_GC.IP.adjusted_qqnorm.15PCs.fastQTL.nominals.rds")

permutation_m6AQTL <-  readRDS("~/m6AQTL/m6A_QTL_results/linear_model/m6AQTL.m6APeak_logOR_GC.IP.adjusted_qqnorm.15PCs.fastQTL.permutations.rds")

sig.m6AQTL <- lead_QTL <- lead_m6AQTL[lead_m6AQTL$PEAK %in% permutation_m6AQTL[permutation_m6AQTL$qvalue<0.1,"PEAK"], ]



m6Apeaks <- jointPeak(LCLs)

LCLs_tested <- filter(LCLs, paste0(m6Apeaks$chr,":",m6Apeaks$start,"-",m6Apeaks$end,"_",m6Apeaks$name,"_",m6Apeaks$strand ) %in% unique(m6AQTL$PEAK) ) 

LCLs_lead <- filter(LCLs, paste0(m6Apeaks$chr,":",m6Apeaks$start,"-",m6Apeaks$end,"_",m6Apeaks$name,"_",m6Apeaks$strand ) %in% unique(lead_QTL$PEAK) )

m6Apeaks <- m6Apeaks[which( paste0(m6Apeaks$chr,":",m6Apeaks$start,"-",m6Apeaks$end,"_",m6Apeaks$name,"_",m6Apeaks$strand ) %in% unique(m6AQTL$PEAK)),]
sig.QTL.peaks <- m6Apeaks[match(unique(lead_QTL$PEAK), paste0(m6Apeaks$chr,":",m6Apeaks$start,"-",m6Apeaks$end,"_",m6Apeaks$name,"_",m6Apeaks$strand) ),1:12]

Plot distribution of variant associated peaks on metagene.

plotMetaGeneMulti(list("All joint peaks" = m6Apeaks,"Variant associated peaks"=sig.QTL.peaks),"~/Database/genome/hg19/hg19_UCSC.gtf")

All peaks

peakDistribution( LCLs_tested )

variant associated peaks

peakDistribution( LCLs_lead )

De novo motif analysis by Homer2 for all Joint peaks and variant associated peaks

write.table(sig.QTL.peaks,
              file = paste0("~/m6AQTL/m6A_QTL_results/linear_model/variant_associated_peaks.bed"),sep = "\t",row.names = F,col.names = F,quote = F)
system2(command = "bedtools", args = paste0("getfasta -fi ~/Database/genome/hg19/hg19_UCSC.fa -bed ~/m6AQTL/m6A_QTL_results/linear_model/variant_associated_peaks.bed -s -split > ~/m6AQTL/m6A_QTL_results/linear_model/variant_associated_peaks.fa ") )

system2(command = "findMotifs.pl", args = paste0("~/m6AQTL/m6A_QTL_results/linear_model/variant_associated_peaks.fa fasta  ~/m6AQTL/m6A_QTL_results/linear_model/Homer2 -fasta ~/Database/transcriptome/backgroud_peaks/hg38_200bp_randomPeak.fa -len 5,6 -rna -p 20 -S 5 -noknown"),wait = F )

write.table(m6Apeaks,
              file = paste0("~/m6AQTL/m6A_QTL_results/linear_model/joint_peaks.bed"),sep = "\t",row.names = F,col.names = F,quote = F)
system2(command = "bedtools", args = paste0("getfasta -fi ~/Database/genome/hg19/hg19_UCSC.fa -bed ~/m6AQTL/m6A_QTL_results/linear_model/joint_peaks.bed -s -split > ~/m6AQTL/m6A_QTL_results/linear_model/joint_peaks_peaks.fa ") )
system2(command = "findMotifs.pl", args = paste0("~/m6AQTL/m6A_QTL_results/linear_model/joint_peaks_peaks.fa fasta  ~/m6AQTL/m6A_QTL_results/linear_model/JointPeaks/Homer2 -fasta ~/Database/transcriptome/backgroud_peaks/hg38_200bp_randomPeak.fa -len 5,6,7 -rna -p 20 -S 5 -noknown"),wait = F )

Plot motif

library(Logolas)
color_motif <- c( "orange", "blue", "red","green")
for(i in 1:3){
  pwm.m <- t( read.table(paste0("~/m6AQTL/m6A_QTL_results/linear_model/JointPeaks/Homer2/homerResults/motif",i,".motif"), header = F, comment.char = ">",col.names = c("A","C","G","U")) )
  motif_p <- sapply(1:3, function(x){
   strsplit( as.character( read.table(paste0("~/m6AQTL/m6A_QTL_results/linear_model/JointPeaks/Homer2/homerResults/motif",x,".motif"),comment.char = "", nrows = 1)[,12]  ), split= ":")[[1]][4] 
})
  colnames(pwm.m) <- 1:ncol(pwm.m)

  try(logomaker(pwm.m ,type = "EDLogo",colors = color_motif,color_type = "per_row" ,logo_control =  list(pop_name = paste0("m6A motif",i,"  p-value:",motif_p[i])))
  )

}

for(i in 1:3){
  pwm.m <- t( read.table(paste0("~/m6AQTL/m6A_QTL_results/linear_model/Homer2/homerResults/motif",i,".motif"), header = F, comment.char = ">",col.names = c("A","C","G","U")) )
  motif_p <- sapply(1:3, function(x){
   strsplit( as.character( read.table(paste0("~/m6AQTL/m6A_QTL_results/linear_model/Homer2/homerResults/motif",x,".motif"),comment.char = "", nrows = 1)[,12]  ), split= ":")[[1]][4] 
})
  colnames(pwm.m) <- 1:ncol(pwm.m)

  logomaker(pwm.m ,type = "EDLogo",colors = color_motif,color_type = "per_row" ,logo_control =  list(pop_name = paste0("m6A motif",i,"  p-value:",motif_p[i])))
}

sessionInfo()

R version 3.5.3 (2019-03-11)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 17.10

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/openblas/libblas.so.3
LAPACK: /usr/lib/x86_64-linux-gnu/libopenblasp-r0.2.20.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.3.0 Rcpp_1.0.1      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.4 git2r_0.25.2    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.4.3   fs_1.3.0        whisker_0.3-2   rmarkdown_1.12 
[13] tools_3.5.3     stringr_1.4.0   glue_1.3.1      xfun_0.6       
[17] yaml_2.2.0      compiler_3.5.3  htmltools_0.3.6 knitr_1.22

peak_calling

scottzijiezhang

2020-03-17

Peak calling

Count Reads from BAM files

Peak calling

check IP efficiency

check GC content

Analysis of m6A peaks

Load m6AQTL and m6Apeaks

Plot distribution of variant associated peaks on metagene.

De novo motif analysis by Homer2 for all Joint peaks and variant associated peaks