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==Mouse MHB== * Latest MHB bed file: '''/media/Home_Raid1/shg047/work/Alice/mouse/mhb''' 2016-9-7: Download 4 differentiation SRR files(SRR2011294/SRR2011297/SRR2011296/SRR2011295) *remove all single-cell methylome *remove SRX1126148,SRX1091395,SRX1091396 since mapping ratio<5% *[[36 + 4 samples= 40 sample(SRX080191 to SRX701302)]] cd /home/shg047/oasis/mouse/fastq perl ~/bin/trimGolrePBS.pl FastqMatchConfig.txt nonsubmit qsub SRR2011294_1.fastq.gz.job qsub SRR2011295_1.fastq.gz.job qsub SRR2011296_1.fastq.gz.job qsub SRR2011297_1.fastq.gz.job perl ~/bin/bismark2bamPBS.pl FastqMatchConfig.txt nosubmit qsub SRR2011294_1.fastq.gz.job qsub SRR2011295_1.fastq.gz.job qsub SRR2011296_1.fastq.gz.job qsub SRR2011297_1.fastq.gz.job * RD90UP80(First Version) and Sliding Windows() [[2016-Mouse-MHB]] * Our own data haplotypes: '''/media/Home_Raid1/shg047/work/Alice/mouse/hapinfo/kun''' * Public 40 data haplotypes: '''/media/Home_Raid1/shg047/work/Alice/mouse/hapinfo/public''' * here, i update the idea. since usually our mhb calling input data is quite large and usually we merge large number WGBS data and therefore the coverage and depth is quite high. therefore, I tried fix the genomic intervals (10K as the window) and then call the haplotype for each sample. then I reassemble the code so that we can add the new samples to our current haplotype database, then we can update the MHB easily each time. * I also update the bam2hapinfo.pl and now hapinfo2bedgraph.pl is also available so that our methylation haplotype pipeline could be connected with all the existed methyaltion analysis pipelne. * bam2hapinfo perl /home/shg047/bin/bam2hapInfo2PBS.pl bam2hapinfo.config submit bismark /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt cd /home/shg047/oasis/mouse/sortBam perl ~/bin/bam2hapInfo2PBS.pl SamConfig.txt submit bismark /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt cd /home/shg047/oasis/mouse/alice/bam perl ~/bin/bam2hapInfo2PBS.pl SamConfig.txt submit bisreadMapper /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt cd ../mergeBam/ perl ../saminfoPre4bam2hapinfo.pl > SamConfig.txt perl /home/shg047/bin/bam2hapInfo2PBS.pl bam2hapinfo.config submit bismark /home/shg047/oasis/db/mm9/mm9.chrom.sizes /home/shg047/oasis/db/mm9/MM9.CpG.positions.txt qsub SRX1019864.job qsub SRX1019865.job qsub SRX1019866.job qsub SRX1019867.job * merge haploinfo (cat) and Unique (hapinfoMerge.pl) cat ************ | sort > Adult.sort.hapinfo.txt cat ************ | sort > mESC.sort.hapinfo.txt perl /home/shg047/oasis/mouse/mergeHapinfo/hapinfoMerge.pl Adult.sort.hapinfo.txt perl /home/shg047/oasis/mouse/mergeHapinfo/hapinfoMerge.pl mESC.sort.hapinfo.txt * hapinfo to MHB perl ~/bin/hapinfo2mhb.pl SRX202086.hapInfo.txt 0.1 * Hapinfo2LD-R2(here: MHB regions,hapinfo2LDR2ByBed.pl) cd /home/shg047/oasis/mouse/mergeHapinfo perl ~/bin/hapinfo2BlocAvgR2.pl SRX1019864.hapInfo.txt > SRX1019864.R2.txt cd /home/shg047/oasis/mouse/mergeHapinfo/mf perl /home/shg047/bin/hapinfo2mf.pl /home/shg047/oasis/mouse/mergeHapinfo/mf > output.mf [[File:4FD2.tm.png|400px]] *hapinfo to R2 by chr10:126500749-126500761 perl ~/bin/hapinfo2LDR2.pl rlt chr10:126500749-126500761 < SRX202086.hapInfo.txt * hapinfo2mf (5me) cd /home/shg047/oasis/mouse/mergeHapinfo/mf perl ~/bin/hapinfo2mf.pl grep chr10:100004267-100004288 output.mf [[File:DD51.tm.png]] * Alice's own data cd /home/shg047/oasis/mouse/alice/raw ls *.txt.gz | awk -F. '{print $1}' | awk -F_ '{print $4}' | sort -u * Identify differential MHBs among ESC, Somatic tissue and IPS-SCNT groups for i in {1..100} do perl ~/bin/randomSampleFromHaploInfo.pl tissue.hapinfo.txt > tissue.hapinfo.txt.$i perl ~/bin/hapinfo2BlocAvgR2.pl tissue.hapinfo.txt.$i > R2.tissue.hapinfo.txt.$i perl ~/bin/randomSampleFromHaploInfo.pl ipsNT.hapinfo.txt > ipsNT.hapinfo.txt.$i perl ~/bin/hapinfo2BlocAvgR2.pl ipsNT.hapinfo.txt.$i > R2.ipsNT.hapinfo.txt.$i perl ~/bin/randomSampleFromHaploInfo.pl ESC.hapinfo.txt > ESC.hapinfo.txt.$i perl ~/bin/hapinfo2BlocAvgR2.pl ESC.hapinfo.txt.$i > R2.ESC.hapinfo.txt.$i done * Differential Linkage MHBs Among IPS, STEM, TISSUE data<-read.table("MatrixR2.txt",head=T) x1<-grep("ESC",colnames(data)) x2<-grep("ips",colnames(data)) x3<-grep("tissue",colnames(data)) newdata<-data.frame(Y=c(rep("ESC",length(x1)),rep("IPS",length(x2)),rep("TISSUE",length(x3))),t(data),check.names=F) Pvalue<-c() for(i in 2:ncol(newdata)){ print(i) Newdata<-na.omit(newdata[,c(1,i)]) if(length(unique(Newdata$Y))>1){ fit <- try(aov(Newdata[,2]~ Y, data=Newdata)) p<-summary(fit)[[1]][1,5] }else{ p<-NA } Pvalue<-c(Pvalue,p) } rlt<-data.frame(MHB=colnames(newdata)[2:ncol(newdata)],Pvalue) write.table(rlt,file="MHB.Pvalue.Bootstrap.txt",sep="\t",col.names=F,row.names=F,quote=F) data1<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.Pvalue.Bootstrap.txt",head=F) data2<-read.table("/home/shg047/oasis/mouse/hapinfo/tmp/MHB.R2.txt",head=T) newdata<-data.frame(data2,Pvalue=data1[match(rownames(data2),data1[,1]),2]) write.table(newdata,file="MHB.R2.Pvalue.Bootstrap.txt",sep="\t",col.names=NA,row.names=T,quote=F) data3<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.R2.Pvalue.Bootstrap.txt",head=F) Subset1<-subset(data3,R2.ESC.hapinfo.txt<R2.ipsNT.hapinfo.txt & R2.tissue.hapinfo.txt<R2.ipsNT.hapinfo.txt) dim(Subset1) Subset2<-subset(data3,R2.ESC.hapinfo.txt<0.1 & R2.tissue.hapinfo.txt<0.1 & R2.ipsNT.hapinfo.txt>0.2) dim(Subset2) * Two group wilcox test data<-read.table("MatrixR2.txt",head=T) x1<-grep("ESC",colnames(data)) x2<-grep("ips",colnames(data)) data<-data[,c(x1,x2)] newdata<-data.frame(Y=c(rep("ESC",length(x1)),rep("IPS",length(x2))),t(data),check.names=F) Pvalue<-c() for(i in 2:ncol(newdata)){ print(i) Newdata<-na.omit(newdata[,c(1,i)]) if(length(unique(Newdata$Y))>1){ fit <- try(wilcox.test(Newdata[,2]~ Y, data=Newdata)) p<-fit$p.value }else{ p<-NA } Pvalue<-c(Pvalue,p) } rlt<-data.frame(MHB=colnames(newdata)[2:ncol(newdata)],Pvalue) write.table(rlt,file="MHB.Pvalue.Bootstrap.txt",sep="\t",col.names=F,row.names=F,quote=F) data1<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.Pvalue.Bootstrap.txt",head=F) data2<-read.table("/home/shg047/oasis/mouse/hapinfo/tmp/MHB.R2.txt",head=T) newdata<-data.frame(data2,Pvalue=data1[match(rownames(data2),data1[,1]),2]) write.table(newdata,file="MHB.R2.Pvalue.Bootstrap.txt",sep="\t",col.names=NA,row.names=T,quote=F) data3<-read.table("/oasis/tscc/scratch/shg047/mouse/hapinfo/group/MHB.R2.Pvalue.Bootstrap.txt",head=F) Subset1<-subset(data3,R2.ESC.hapinfo.txt<R2.ipsNT.hapinfo.txt & R2.tissue.hapinfo.txt<R2.ipsNT.hapinfo.txt) dim(Subset1) Subset2<-subset(data3,R2.ESC.hapinfo.txt<0.1 & R2.tissue.hapinfo.txt<0.1 & R2.ipsNT.hapinfo.txt>0.2) dim(Subset2)
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