README.md

In tengxufei/m6Ascan: scanning for m6A sites

m6Ascan

scanning for m6A modifications based on MeRIP-Seq data

Install and require m6Ascan from Github

install.packages("devtools") devtools::install_github("tengxufei/m6Ascan") library(m6Ascan)

Load gtf annotation file and bam file

gtf <- "your/workdir/example.gtf" f1= "your/workdir/rep1-Input.bam" f2= "your/workdir/rep1-IP.bam" f3= "your/workdir/rep2-Input.bam" f4= "your/workdir/rep2-IP.bam" f5= "your/workdir/rep3-Input.bam" f6= "your/workdir/rep3-IP.bam"

MeRIP-Seq experiment IP efficiency test

effiTest(ip_bam=c(f2,f4,f6), input_bam=c(f1,f3,f5), gtf=gtf)

Split transcripts into 100bp bins with 10bp step then count reads

win.gtf <- sub.win.gtf(gtf=gtf, binSize=100, step=10) win.counts <- get.win.counts(IP.bam=c(f2,f4,f6), Input.bam=c(f1,f3,f5), gtf=win.gtf, strandSpecific=0, isPairedEnd=F)

Normalize the sequencing library and calculate the enrichment score

input.site <- c(3:ncol(win.counts))[c(3:ncol(win.counts))%% 2 != 0] IP.site <- c(3:ncol(win.counts))[c(3:ncol(win.counts))%% 2 == 0] norm.F <- calc.NormFactors(counts=win.counts, input.site=input.site, IP.site=IP.site)) win.counts[,3:ncol(win.counts)] <- t(t(win.counts[,3:ncol(win.counts)])/norm.F ES.counts <- calcES(input.site=input.site, counts=win.counts)

Test the significance of a window based on glm.nb test then report the results in bed format

win.sigMethyl <- sig.test(counts= ES.counts, condition="WT", cutoff=100, rep=3) win.peaks <- reportResults(gtf=win.gtf, catMethyl= win.sigMethyl, threads=4)

Plot the distribution of m6A peaks and visualization of MeRIP-Seq data in a certain region

plot.peaks(peak=win.peaks, gtf=gtf, frac = c(20,100,80), strand = "all") plot.single.peak(site, gtf=gtf)

Motif Finding and visualization

motifFind(peak=win.peaks, gtf=gtf, frac = c(20,100,80), strand = "all") plot.motif(peak, gtf=gtf)

tengxufei/m6Ascan documentation built on May 18, 2019, 1:29 p.m.