Last updated: 2024-10-03
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Knit directory: ATAC_learning/
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Unstaged changes:
Modified: analysis/CorMotif_data_n45.Rmd
Modified: analysis/Enrichment_motif.Rmd
Modified: analysis/GO_KEGG_analysis.Rmd
Modified: analysis/Jaspar_motif_ff.Rmd
Modified: analysis/Peak_calling.Rmd
Modified: analysis/Raodah.Rmd
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Modified: analysis/Smaller_set_DAR.Rmd
Modified: analysis/TE_analysis.Rmd
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | e00f11e | reneeisnowhere | 2024-10-03 | adding pdfs |
| html | 9be36fa | reneeisnowhere | 2024-10-02 | Build site. |
| Rmd | 39838be | reneeisnowhere | 2024-10-02 | updates to correlations |
| html | 7b65928 | reneeisnowhere | 2024-09-27 | Build site. |
| Rmd | c0456fb | reneeisnowhere | 2024-09-27 | updates using C and D separately |
| html | 8acb1e8 | reneeisnowhere | 2024-09-23 | Build site. |
| Rmd | 1777f5a | reneeisnowhere | 2024-09-23 | updated group of eight based on 4 individuals |
| Rmd | 82f2027 | reneeisnowhere | 2024-09-16 | updates to plots |
| Rmd | 7778182 | reneeisnowhere | 2024-09-10 | updates |
| Rmd | 437a792 | reneeisnowhere | 2024-09-05 | new updates |
| Rmd | a540a01 | reneeisnowhere | 2024-09-04 | adding in high conf peak set based on final files |
library(tidyverse)
library(kableExtra)
library(broom)
library(RColorBrewer)
library(ChIPseeker)
library("TxDb.Hsapiens.UCSC.hg38.knownGene")
library("org.Hs.eg.db")
library(rtracklayer)
library(edgeR)
library(ggfortify)
library(limma)
library(readr)
library(BiocGenerics)
library(gridExtra)
library(VennDiagram)
library(scales)
library(Cormotif)
library(BiocParallel)
library(ggpubr)
library(devtools)
library(eulerr)
library(genomation)
library(ggsignif)
library(plyranges)
library(ggrepel)
library(ComplexHeatmap)
library(cowplot)
library(smplot2)## Fit limma model using code as it is found in the original cormotif code. It has
## only been modified to add names to the matrix of t values, as well as the
## limma fits
limmafit.default <- function(exprs,groupid,compid) {
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(0,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for(i in 1:compnum) {
selid1 <- which(groupid == compid[i,1])
selid2 <- which(groupid == compid[i,2])
eset <- new("ExpressionSet", exprs=cbind(exprs[,selid1],exprs[,selid2]))
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base=rep(1,(g1num+g2num)), delta=c(rep(0,g1num),rep(1,g2num)))
fit <- lmFit(eset,designmat)
fit <- eBayes(fit)
limmat[,i] <- fit$t[,2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
limmafits[[i]] <- fit
# log odds
# w<-sqrt(1+fit$var.prior[2]/(1/g1num+1/g2num))
# log(0.99)+dt(fit$t[1,2],g1num+g2num-2+fit$df.prior,log=TRUE)-log(0.01)-dt(fit$t[1,2]/w, g1num+g2num-2+fit$df.prior, log=TRUE)+log(w)
}
names(limmafits) <- rownames(compid)
limmacompnum<-nrow(compid)
result<-list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.counts <-
function (exprs, groupid, compid, norm.factor.method = "TMM", voom.normalize.method = "none")
{
limmafits <- list()
compnum <- nrow(compid)
genenum <- nrow(exprs)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- rownames(exprs)
colnames(limmat) <- rownames(compid)
names(limmas2) <- rownames(compid)
names(limmadf) <- rownames(compid)
names(limmav0) <- rownames(compid)
names(limmag1num) <- rownames(compid)
names(limmag2num) <- rownames(compid)
for (i in 1:compnum) {
message(paste("Running limma for comparision",i,"/",compnum))
selid1 <- which(groupid == compid[i, 1])
selid2 <- which(groupid == compid[i, 2])
# make a new count data frame
counts <- cbind(exprs[, selid1], exprs[, selid2])
# remove NAs
not.nas <- which(apply(counts, 1, function(x) !any(is.na(x))) == TRUE)
# runn voom/limma
d <- DGEList(counts[not.nas,])
d <- calcNormFactors(d, method = norm.factor.method)
g1num <- length(selid1)
g2num <- length(selid2)
designmat <- cbind(base = rep(1, (g1num + g2num)), delta = c(rep(0,
g1num), rep(1, g2num)))
y <- voom(d, designmat, normalize.method = voom.normalize.method)
fit <- lmFit(y, designmat)
fit <- eBayes(fit)
limmafits[[i]] <- fit
limmat[not.nas, i] <- fit$t[, 2]
limmas2[i] <- fit$s2.prior
limmadf[i] <- fit$df.prior
limmav0[i] <- fit$var.prior[2]
limmag1num[i] <- g1num
limmag2num[i] <- g2num
}
limmacompnum <- nrow(compid)
names(limmafits) <- rownames(compid)
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
limmafit.list <-
function (fitlist, cmp.idx=2)
{
compnum <- length(fitlist)
genes <- c()
for (i in 1:compnum) genes <- unique(c(genes, rownames(fitlist[[i]])))
genenum <- length(genes)
limmat <- matrix(NA,genenum,compnum)
limmas2 <- rep(0,compnum)
limmadf <- rep(0,compnum)
limmav0 <- rep(0,compnum)
limmag1num <- rep(0,compnum)
limmag2num <- rep(0,compnum)
rownames(limmat) <- genes
colnames(limmat) <- names(fitlist)
names(limmas2) <- names(fitlist)
names(limmadf) <- names(fitlist)
names(limmav0) <- names(fitlist)
names(limmag1num) <- names(fitlist)
names(limmag2num) <- names(fitlist)
for (i in 1:compnum) {
this.t <- fitlist[[i]]$t[,cmp.idx]
limmat[names(this.t),i] <- this.t
limmas2[i] <- fitlist[[i]]$s2.prior
limmadf[i] <- fitlist[[i]]$df.prior
limmav0[i] <- fitlist[[i]]$var.prior[cmp.idx]
limmag1num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==0)
limmag2num[i] <- sum(fitlist[[i]]$design[,cmp.idx]==1)
}
limmacompnum <- compnum
result <- list(t = limmat,
v0 = limmav0,
df0 = limmadf,
s20 = limmas2,
g1num = limmag1num,
g2num = limmag2num,
compnum = limmacompnum,
fits = limmafits)
}
## Rank genes based on statistics
generank<-function(x) {
xcol<-ncol(x)
xrow<-nrow(x)
result<-matrix(0,xrow,xcol)
z<-(1:1:xrow)
for(i in 1:xcol) {
y<-sort(x[,i],decreasing=TRUE,na.last=TRUE)
result[,i]<-match(x[,i],y)
result[,i]<-order(result[,i])
}
result
}
## Log-likelihood for moderated t under H0
modt.f0.loglike<-function(x,df) {
a<-dt(x, df, log=TRUE)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Log-likelihood for moderated t under H1
## param=c(df,g1num,g2num,v0)
modt.f1.loglike<-function(x,param) {
df<-param[1]
g1num<-param[2]
g2num<-param[3]
v0<-param[4]
w<-sqrt(1+v0/(1/g1num+1/g2num))
dt(x/w, df, log=TRUE)-log(w)
a<-dt(x/w, df, log=TRUE)-log(w)
result<-as.vector(a)
flag<-which(is.na(result)==TRUE)
result[flag]<-0
result
}
## Correlation Motif Fit
cmfit.X<-function(x, type, K=1, tol=1e-3, max.iter=100) {
## initialize
xrow <- nrow(x)
xcol <- ncol(x)
loglike0 <- list()
loglike1 <- list()
p <- rep(1, K)/K
q <- matrix(runif(K * xcol), K, xcol)
q[1, ] <- rep(0.01, xcol)
for (i in 1:xcol) {
f0 <- type[[i]][[1]]
f0param <- type[[i]][[2]]
f1 <- type[[i]][[3]]
f1param <- type[[i]][[4]]
loglike0[[i]] <- f0(x[, i], f0param)
loglike1[[i]] <- f1(x[, i], f1param)
}
condlike <- list()
for (i in 1:xcol) {
condlike[[i]] <- matrix(0, xrow, K)
}
loglike.old <- -1e+10
for (i.iter in 1:max.iter) {
if ((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations for K=",
K, sep = ""))
}
err <- tol + 1
clustlike <- matrix(0, xrow, K)
#templike <- matrix(0, xrow, 2)
templike1 <- rep(0, xrow)
templike2 <- rep(0, xrow)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax +
log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
#p.new <- (apply(clustlike, 2, sum) + 1)/(xrow + K)
p.new <- (Rfast::colsums(clustlike) + 1)/(xrow + K)
q.new <- matrix(0, K, xcol)
for (j in 1:K) {
clustpsum <- sum(clustlike[, j])
for (i in 1:xcol) {
q.new[j, i] <- (sum(clustlike[, j] * condlike[[i]][,
j]) + 1)/(clustpsum + 2)
}
}
err.p <- max(abs(p.new - p)/p)
err.q <- max(abs(q.new - q)/q)
err <- max(err.p, err.q)
loglike.new <- (sum(tempmax + log(tempsum)) + sum(log(p.new)) +
sum(log(q.new) + log(1 - q.new)))/xrow
p <- p.new
q <- q.new
loglike.old <- loglike.new
if (err < tol) {
break
}
}
clustlike <- matrix(0, xrow, K)
for (j in 1:K) {
for (i in 1:xcol) {
templike1 <- log(q[j, i]) + loglike1[[i]]
templike2 <- log(1 - q[j, i]) + loglike0[[i]]
tempmax <- Rfast::Pmax(templike1, templike2)
templike1 <- exp(templike1 - tempmax)
templike2 <- exp(templike2 - tempmax)
tempsum <- templike1 + templike2
clustlike[, j] <- clustlike[, j] + tempmax + log(tempsum)
condlike[[i]][, j] <- templike1/tempsum
}
clustlike[, j] <- clustlike[, j] + log(p[j])
}
#tempmax <- apply(clustlike, 1, max)
tempmax <- Rfast::rowMaxs(clustlike, value=TRUE)
for (j in 1:K) {
clustlike[, j] <- exp(clustlike[, j] - tempmax)
}
#tempsum <- apply(clustlike, 1, sum)
tempsum <- Rfast::rowsums(clustlike)
for (j in 1:K) {
clustlike[, j] <- clustlike[, j]/tempsum
}
p.post <- matrix(0, xrow, xcol)
for (j in 1:K) {
for (i in 1:xcol) {
p.post[, i] <- p.post[, i] + clustlike[, j] * condlike[[i]][,
j]
}
}
loglike.old <- loglike.old - (sum(log(p)) + sum(log(q) +
log(1 - q)))/xrow
loglike.old <- loglike.old * xrow
result <- list(p.post = p.post, motif.prior = p, motif.q = q,
loglike = loglike.old, clustlike=clustlike, condlike=condlike)
}
## Fit using (0,0,...,0) and (1,1,...,1)
cmfitall<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01
## compute loglikelihood
L0<-matrix(0,xrow,1)
L1<-matrix(0,xrow,1)
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
L0<-L0+loglike0[[i]]
L1<-L1+loglike1[[i]]
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2])+1)/(xrow+2)
## evaluate convergence
err<-abs(p.new-p)/p
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+log(p.new)+log(1-p.new))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p)+L0
clustlike[,2]<-log(p)+L1
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(i in 1:xcol) {
p.post[,i]<-clustlike[,2]
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-(log(p)+log(1-p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
## Fit each dataset separately
cmfitsep<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
p<-0.01*rep(1,xcol)
loglike.final<-rep(0,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
p.post<-matrix(0,xrow,xcol)
## EM algorithm to get MLE of p
for(coli in 1:xcol) {
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## evaluate whether the log.likelihood increases
loglike.new<-sum(tempmax+log(tempsum))/xrow
## update motif occurrence rate
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(sum(clustlike[,2]))/(xrow)
## evaluate convergence
err<-abs(p.new-p[coli])/p[coli]
loglike.old<-loglike.new
p[coli]<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,2)
clustlike[,1]<-log(1-p[coli])+loglike0[[coli]]
clustlike[,2]<-log(p[coli])+loglike1[[coli]]
tempmax<-apply(clustlike,1,max)
for(j in 1:2) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:2) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post[,coli]<-clustlike[,2]
loglike.final[coli]<-loglike.old
}
## return
loglike.final<-loglike.final*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.final)
}
## Fit the full model
cmfitfull<-function(x, type, tol=1e-3, max.iter=100) {
## initialize
xrow<-nrow(x)
xcol<-ncol(x)
loglike0<-list()
loglike1<-list()
K<-2^xcol
p<-rep(1,K)/K
pattern<-rep(0,xcol)
patid<-matrix(0,K,xcol)
## compute loglikelihood
for(i in 1:xcol) {
f0<-type[[i]][[1]]
f0param<-type[[i]][[2]]
f1<-type[[i]][[3]]
f1param<-type[[i]][[4]]
loglike0[[i]]<-f0(x[,i],f0param)
loglike1[[i]]<-f1(x[,i],f1param)
}
L<-matrix(0,xrow,K)
for(i in 1:K)
{
patid[i,]<-pattern
for(j in 1:xcol) {
if(pattern[j] < 0.5) {
L[,i]<-L[,i]+loglike0[[j]]
} else {
L[,i]<-L[,i]+loglike1[[j]]
}
}
if(i < K) {
pattern[xcol]<-pattern[xcol]+1
j<-xcol
while(pattern[j] > 1) {
pattern[j]<-0
j<-j-1
pattern[j]<-pattern[j]+1
}
}
}
## EM algorithm to get MLE of p and q
loglike.old <- -1e10
for(i.iter in 1:max.iter) {
if((i.iter%%50) == 0) {
print(paste("We have run the first ", i.iter, " iterations",sep=""))
}
err<-tol+1
## compute posterior cluster membership
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
## update motif occurrence rate
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.new<-(apply(clustlike,2,sum)+1)/(xrow+K)
## evaluate convergence
err<-max(abs(p.new-p)/p)
## evaluate whether the log.likelihood increases
loglike.new<-(sum(tempmax+log(tempsum))+sum(log(p.new)))/xrow
loglike.old<-loglike.new
p<-p.new
if(err<tol) {
break;
}
}
## compute posterior p
clustlike<-matrix(0,xrow,K)
for(j in 1:K) {
clustlike[,j]<-log(p[j])+L[,j]
}
tempmax<-apply(clustlike,1,max)
for(j in 1:K) {
clustlike[,j]<-exp(clustlike[,j]-tempmax)
}
tempsum<-apply(clustlike,1,sum)
for(j in 1:K) {
clustlike[,j]<-clustlike[,j]/tempsum
}
p.post<-matrix(0,xrow,xcol)
for(j in 1:K) {
for(i in 1:xcol) {
if(patid[j,i] > 0.5) {
p.post[,i]<-p.post[,i]+clustlike[,j]
}
}
}
## return
#calculate back loglikelihood
loglike.old<-loglike.old-sum(log(p))/xrow
loglike.old<-loglike.old*xrow
result<-list(p.post=p.post, motif.prior=p, loglike=loglike.old)
}
generatetype<-function(limfitted)
{
jtype<-list()
df<-limfitted$g1num+limfitted$g2num-2+limfitted$df0
for(j in 1:limfitted$compnum)
{
jtype[[j]]<-list(f0=modt.f0.loglike, f0.param=df[j], f1=modt.f1.loglike, f1.param=c(df[j],limfitted$g1num[j],limfitted$g2num[j],limfitted$v0[j]))
}
jtype
}
cormotiffit <- function(exprs, groupid=NULL, compid=NULL, K=1, tol=1e-3,
max.iter=100, BIC=TRUE, norm.factor.method="TMM",
voom.normalize.method = "none", runtype=c("logCPM","counts","limmafits"), each=3)
{
# first I want to do some typechecking. Input can be either a normalized
# matrix, a count matrix, or a list of limma fits. Dispatch the correct
# limmafit accordingly.
# todo: add some typechecking here
limfitted <- list()
if (runtype=="counts") {
limfitted <- limmafit.counts(exprs,groupid,compid, norm.factor.method, voom.normalize.method)
} else if (runtype=="logCPM") {
limfitted <- limmafit.default(exprs,groupid,compid)
} else if (runtype=="limmafits") {
limfitted <- limmafit.list(exprs)
} else {
stop("runtype must be one of 'logCPM', 'counts', or 'limmafits'")
}
jtype<-generatetype(limfitted)
fitresult<-list()
ks <- rep(K, each = each)
fitresult <- bplapply(1:length(ks), function(i, x, type, ks, tol, max.iter) {
cmfit.X(x, type, K = ks[i], tol = tol, max.iter = max.iter)
}, x=limfitted$t, type=jtype, ks=ks, tol=tol, max.iter=max.iter)
best.fitresults <- list()
for (i in 1:length(K)) {
w.k <- which(ks==K[i])
this.bic <- c()
for (j in w.k) this.bic[j] <- -2 * fitresult[[j]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
w.min <- which(this.bic == min(this.bic, na.rm = TRUE))[1]
best.fitresults[[i]] <- fitresult[[w.min]]
}
fitresult <- best.fitresults
bic <- rep(0, length(K))
aic <- rep(0, length(K))
loglike <- rep(0, length(K))
for (i in 1:length(K)) loglike[i] <- fitresult[[i]]$loglike
for (i in 1:length(K)) bic[i] <- -2 * fitresult[[i]]$loglike + (K[i] - 1 + K[i] * limfitted$compnum) * log(dim(limfitted$t)[1])
for (i in 1:length(K)) aic[i] <- -2 * fitresult[[i]]$loglike + 2 * (K[i] - 1 + K[i] * limfitted$compnum)
if(BIC==TRUE) {
bestflag=which(bic==min(bic))
}
else {
bestflag=which(aic==min(aic))
}
result<-list(bestmotif=fitresult[[bestflag]],bic=cbind(K,bic),
aic=cbind(K,aic),loglike=cbind(K,loglike), allmotifs=fitresult)
}
cormotiffitall<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitall(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitsep<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitsep(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
cormotiffitfull<-function(exprs,groupid,compid, tol=1e-3, max.iter=100)
{
limfitted<-limmafit(exprs,groupid,compid)
jtype<-generatetype(limfitted)
fitresult<-cmfitfull(limfitted$t,type=jtype,tol=1e-3,max.iter=max.iter)
}
plotIC<-function(fitted_cormotif)
{
oldpar<-par(mfrow=c(1,2))
plot(fitted_cormotif$bic[,1], fitted_cormotif$bic[,2], type="b",xlab="Motif Number", ylab="BIC", main="BIC")
plot(fitted_cormotif$aic[,1], fitted_cormotif$aic[,2], type="b",xlab="Motif Number", ylab="AIC", main="AIC")
}
plotMotif<-function(fitted_cormotif,title="")
{
layout(matrix(1:2,ncol=2))
u<-1:dim(fitted_cormotif$bestmotif$motif.q)[2]
v<-1:dim(fitted_cormotif$bestmotif$motif.q)[1]
image(u,v,t(fitted_cormotif$bestmotif$motif.q),
col=gray(seq(from=1,to=0,by=-0.1)),xlab="Study",yaxt = "n",
ylab="Corr. Motifs",main=paste(title,"pattern",sep=" "))
axis(2,at=1:length(v))
for(i in 1:(length(u)+1))
{
abline(v=(i-0.5))
}
for(i in 1:(length(v)+1))
{
abline(h=(i-0.5))
}
Ng=10000
if(is.null(fitted_cormotif$bestmotif$p.post)!=TRUE)
Ng=nrow(fitted_cormotif$bestmotif$p.post)
genecount=floor(fitted_cormotif$bestmotif$motif.p*Ng)
NK=nrow(fitted_cormotif$bestmotif$motif.q)
plot(0,0.7,pch=".",xlim=c(0,1.2),ylim=c(0.75,NK+0.25),
frame.plot=FALSE,axes=FALSE,xlab="No. of genes",ylab="", main=paste(title,"frequency",sep=" "))
segments(0,0.7,fitted_cormotif$bestmotif$motif.p[1],0.7)
rect(0,1:NK-0.3,fitted_cormotif$bestmotif$motif.p,1:NK+0.3,
col="dark grey")
mtext(1:NK,at=1:NK,side=2,cex=0.8)
text(fitted_cormotif$bestmotif$motif.p+0.15,1:NK,
labels=floor(fitted_cormotif$bestmotif$motif.p*Ng))
}drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
pca_plot <-
function(df,
col_var = NULL,
shape_var = NULL,
title = "") {
ggplot(df) + geom_point(aes_string(
x = "PC1",
y = "PC2",
color = col_var,
shape = shape_var
),
size = 5) +
labs(title = title, x = "PC 1", y = "PC 2") +
scale_color_manual(values = c(
"#8B006D",
"#DF707E",
"#F1B72B",
"#3386DD",
"#707031",
"#41B333"
))
}
pca_var_plot <- function(pca) {
# x: class == prcomp
pca.var <- pca$sdev ^ 2
pca.prop <- pca.var / sum(pca.var)
var.plot <-
qplot(PC, prop, data = data.frame(PC = 1:length(pca.prop),
prop = pca.prop)) +
labs(title = 'Variance contributed by each PC',
x = 'PC', y = 'Proportion of variance')
plot(var.plot)
}
calc_pca <- function(x) {
# Performs principal components analysis with prcomp
# x: a sample-by-gene numeric matrix
prcomp(x, scale. = TRUE, retx = TRUE)
}
get_regr_pval <- function(mod) {
# Returns the p-value for the Fstatistic of a linear model
# mod: class lm
stopifnot(class(mod) == "lm")
fstat <- summary(mod)$fstatistic
pval <- 1 - pf(fstat[1], fstat[2], fstat[3])
return(pval)
}
plot_versus_pc <- function(df, pc_num, fac) {
# df: data.frame
# pc_num: numeric, specific PC for plotting
# fac: column name of df for plotting against PC
pc_char <- paste0("PC", pc_num)
# Calculate F-statistic p-value for linear model
pval <- get_regr_pval(lm(df[, pc_char] ~ df[, fac]))
if (is.numeric(df[, f])) {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_point() +
geom_smooth(method = "lm") + labs(title = sprintf("p-val: %.2f", pval))
} else {
ggplot(df, aes_string(x = f, y = pc_char)) + geom_boxplot() +
labs(title = sprintf("p-val: %.2f", pval))
}
}
x_axis_labels = function(labels, every_nth = 1, ...) {
axis(side = 1,
at = seq_along(labels),
labels = F)
text(
x = (seq_along(labels))[seq_len(every_nth) == 1],
y = par("usr")[3] - 0.075 * (par("usr")[4] - par("usr")[3]),
labels = labels[seq_len(every_nth) == 1],
xpd = TRUE,
...
)
}Adding analysis of first peak counts into one large file
drug_pal <- c("#8B006D","#DF707E","#F1B72B", "#3386DD","#707031","#41B333")
Ind1_summary <- read.csv("data/Ind1_summary.txt", row.names = 1) %>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('75DX','DOX',trt),
trt= gsub('75E','EPI', trt),
trt=gsub('75DA','DNR',trt),
trt=gsub('75M','MTX',trt),
trt=gsub('75T','TRZ',trt),
trt=gsub('75V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind2_summary <- read.csv("data/Ind2_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('87DX','DOX',trt),
trt= gsub('87E','EPI', trt),
trt=gsub('87DA','DNR',trt),
trt=gsub('87M','MTX',trt),
trt=gsub('87T','TRZ',trt),
trt=gsub('87V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind3_summary <- read.csv("data/Ind3_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('77DX','DOX',trt),
trt= gsub('77E','EPI', trt),
trt=gsub('77DA','DNR',trt),
trt=gsub('77M','MTX',trt),
trt=gsub('77T','TRZ',trt),
trt=gsub('77V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Ind6_summary <- read.csv("data/Ind6_summary.txt", row.names = 1)%>%
dplyr::rename("sample"=X1,"reads"=X2,"mapped"=X3)%>%
mutate(sample=gsub(pattern = "flagstat_first/trimmed_","total_",sample)) %>%
mutate(sample= gsub(pattern= "flagstat_noM/trimmed_","nuclear_",sample)) %>%
mutate(sample=gsub(pattern = "filt_files/trimmed_","other_",sample)) %>%
mutate(sample = gsub("24h","_24h",sample),
sample = gsub("3h","_3h",sample)) %>%
separate(sample, into=c("read_type","indv","trt","time",NA,"neat")) %>%
mutate(read_type=if_else(read_type=="other",neat,read_type)) %>%
mutate(trt= gsub('71DX','DOX',trt),
trt= gsub('71E','EPI', trt),
trt=gsub('71DA','DNR',trt),
trt=gsub('71M','MTX',trt),
trt=gsub('71T','TRZ',trt),
trt=gsub('71V','VEH',trt)) %>%
separate(reads,into=c("reads",NA),sep= " ") %>%
mutate(reads=as.numeric(reads)) %>%
separate(mapped, into= c("mapped_reads", NA,NA, "percent_mapped_reads",NA)) %>%
mutate(mapped_reads=as.numeric(mapped_reads)) %>%
tidyr::unite("sample",indv:time,sep = "_",remove = FALSE) %>%
dplyr::select(sample, indv:time, read_type,reads, mapped_reads)
Final_four_counts <- Ind1_summary %>% rbind(Ind2_summary) %>% rbind(Ind3_summary) %>% rbind(Ind6_summary)
FF_bound <- Final_four_counts %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
pivot_longer(cols = reads:mapped_reads, names_to="count_type", values_to = "read_num") %>%
mutate(read_type=case_when(read_type=="fin"~"unique",.default = read_type)) %>%
tidyr::unite("Total_reads",read_type:count_type,sep = "_", remove = TRUE) %>%
tidyr::unite("Total_reads_time",Total_reads:time,sep = "_", remove = FALSE) %>%
mutate(Total_reads=factor(Total_reads, levels = c("total_reads", "total_mapped_reads","nuclear_reads","nuclear_mapped_reads","unique_reads", "unique_mapped_reads", "nodup_reads", "nodup_mapped_reads"))) %>%
dplyr::filter(Total_reads != "nuclear_reads") %>%
dplyr::filter(Total_reads != "nodup_reads") %>%
dplyr::filter(Total_reads != "unique_reads") %>%
dplyr::mutate(trt=factor(trt, levels = c("DOX", "EPI","DNR", "MTX","TRZ","VEH"))) #%>%
FF_bound %>%
ggplot(., aes(x=Total_reads, y=read_num, group=interaction(indv,time)))+
geom_col(aes(fill=trt), position="dodge")+
theme_minimal()+
scale_fill_manual(values=drug_pal)+
facet_wrap(~time+trt+indv,nrow = 6, ncol = 12 )+
theme(strip.text = element_text(face = "bold", hjust = 0, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'),
axis.text.x=element_text(angle = 90, vjust = 0.5, hjust=1))FF_bound %>%
ggplot(., aes(x=Total_reads, y=read_num, col=indv, group=interaction(time,Total_reads_time)))+
geom_boxplot(aes(fill=trt)) +
geom_point(aes(col=indv))+
theme_bw()+
facet_wrap(~trt+time,nrow = 3, ncol = 6 )+
scale_fill_manual(values=drug_pal)+
theme(strip.text = element_text(face = "bold", hjust = 0, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'),
axis.text.x=element_text(angle = 90, vjust = 0.5, hjust=1))Final_four_counts %>%
mutate(time=factor(time, levels =c("3h","24h"))) %>%
pivot_longer(cols = reads:mapped_reads, names_to="count_type", values_to = "read_num") %>%
dplyr::filter(read_type=="nodup"& count_type=="mapped_reads") %>%
mutate(trt= factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes(x=trt,y=read_num,group=(interaction(time,trt))))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
# geom_hline(yintercept = 20)+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Reads across treatment and time")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))Purity_table <- cbind("indv"= c("75-1","87-1","77-1","71-1"),
"assign"=c("D","A","B","C"),
"num"= c(1,2,3,6),
"cm_purity"=c(98.5,98.3,99.5,86.75))
Purity_table %>%
kable(.,caption = "cTNT2 percent positive table") %>%
kable_paper("striped", full_width = TRUE) %>%
kable_styling(full_width = FALSE, font_size = 16) %>%
scroll_box(height = "800px")| indv | assign | num | cm_purity |
|---|---|---|---|
| 75-1 | D | 1 | 98.5 |
| 87-1 | A | 2 | 98.3 |
| 77-1 | B | 3 | 99.5 |
| 71-1 | C | 6 | 86.75 |
allTSSE <- readRDS( "data/all_TSSE_scores.RDS")
allTSSE %>% as.data.frame() %>%
rownames_to_column("sample") %>%
separate(sample, into = c("indv","trt","time"), sep= "_") %>%
mutate(trt= factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
mutate(time = factor(time, levels = c("3","24"),labels = c("3 hours","24 hours"))) %>%
dplyr::filter(indv !=4 &indv !=5) %>%
ggplot(., aes(x= time, y= V1, group = indv))+
# geom_col(position= "dodge",aes(fill=trt)) %>%
geom_jitter(aes(col = trt, size = 1.5, alpha = 0.5) , position=position_jitter(0.25))+
geom_hline(yintercept=5, linetype = 3)+
geom_hline(yintercept=7, col = "blue")+
facet_wrap(~indv)+
theme_bw()+
ggtitle("TSS enrichment scores")+
scale_color_manual(values=drug_pal)+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1))Ind1_TSS_peaks_plot <- readRDS("data/Ind1_TSS_peaks.RDS")
Ind2_TSS_peaks_plot <- readRDS("data/Ind2_TSS_peaks.RDS")
Ind3_TSS_peaks_plot <- readRDS("data/Ind3_TSS_peaks.RDS")
Ind6_TSS_peaks_plot <- readRDS("data/Ind6_TSS_peaks.RDS")
a1<- plotAvgProf(Ind1_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 1" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:46:42 AM b1 <- plotAvgProf(Ind2_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 2" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:46:55 AM c1 <- plotAvgProf(Ind3_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 3" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:47:05 AM d1 <- plotAvgProf(Ind6_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 6" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:47:09 AM a2 <- plotAvgProf(Ind1_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 1" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:47:10 AM b2 <- plotAvgProf(Ind2_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 2" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:47:22 AM c2 <- plotAvgProf(Ind3_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 3" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:47:38 AM d2 <- plotAvgProf(Ind6_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 6" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-10-03 10:47:45 AM plot_grid(a1,a2, b1,b2,c1,c2,d1,d2, axis="l",align = "hv",nrow=4, ncol=2)# a2+coord_cartesian(xlim=c(-2000,2000))Analysis using the four selected individuals and the peaks generated from just that set:
# Peaks_bed_file <- rtracklayer::BEDFile("~/ATAC_downloads/finalfour_bl_filt_peaks.bed")
# import(Peaks_bed_file) %>% as.data.frame()
# write_delim(Snyder_41peaks, "data/other_papers/ENCFF966JZT_bed_Snyder_41peaks.bed",delim = "\t")
all_four_filt_counts <- read.delim("data/all_four_filtered_counts.txt",header=TRUE) %>%
GRanges() %>%
keepStandardChromosomes(pruning.mode = "coarse") %>%
as.data.frame()# all_four_filt_counts %>%
names(all_four_filt_counts) = gsub(pattern = "_S.*", replacement = "", x = names(all_four_filt_counts))
names(all_four_filt_counts) = gsub(pattern = "ind1.trimmed.filt_files.trimmed_|ind2.trimmed.filt_files.trimmed_|ind3.trimmed.filt_files.trimmed_|ind6.trimmed.filt_files.trimmed_", replacement = "", x = names(all_four_filt_counts))
names(all_four_filt_counts) = gsub(pattern = "ind1.trimmed.filt_files.trimmed_|ind2.trimmed.filt_files.trimmed_|ind3.trimmed.filt_files.trimmed_|ind6.trimmed.filt_files.trimmed_", replacement = "", x = names(all_four_filt_counts))summary of peaks by sample
##check commandline file for how I pulled the wc -l on all files.
# peakcount_new4 <- read_delim("~/ATAC_downloads/master_peaks/4_indonly/peakcount_new4.txt",
# + delim = "\t", escape_double = FALSE,
# # + col_names = FALSE, trim_ws = TRUE)
# peakcount_new4 %>%
# mutate(X1=gsub("75","1_", X1),
# X1=gsub("87","2_",X1),
# X1=gsub("77","3_",X1),
# X1=gsub("71","6_",X1),
# X1=gsub("DA","DNR_",X1),
# X1=gsub("DX","DOX_",X1),
# X1=gsub("E","EPI_",X1),
# X1=gsub("T","TRZ_",X1),
# X1=gsub("M","MTX_",X1),
# X1=gsub("V","VEH_",X1)) %>%
# separate(X1, into=c("indv","trt","time"), sep= "_", remove = TRUE) %>%
# dplyr::rename(peak_number=X2) %>% write_delim(., file="data/Final_four_data/Peak_count_ff.txt", delim = "\t")
peakcount_ff <- read_delim("data/Final_four_data/Peak_count_ff.txt",delim= "\t")
frip_newpeaks <- c(38.8,36.3,46.0,38.9,49.6,40.0,39.2,30.2,52.1,39.8,51.1,28.0,
42.3,40.3,39.7,38.7,37.9,36.6,36.0,48.7,50.4,44.2,52.0,31.9,
40.5,34.1,41.2,33.7,43.5,28.6,34.7,42.8,38.1,40.3,44.6,26.4,
46.5,23.9,46.9,25.8,46.7,23.8,21.8,39.2,33.2,22.8,36.8,34.8)
peakcount_ff$frip_newpeaks <- frip_newpeaks
peakcount_ff %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=factor(indv)) %>%
ggplot(., aes(x=trt,y=peak_number))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Peak number across treatment and time")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))peakcount_ff %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=factor(indv)) %>%
ggplot(., aes(x=trt,y=frip_newpeaks))+
geom_boxplot(aes(fill=trt))+
geom_point(aes(col=indv, size =3))+
geom_hline(aes(yintercept = 20), linetype=2, color="red")+
facet_wrap(time~.)+
scale_fill_manual(values=drug_pal)+
scale_color_brewer(palette = "Dark2")+
ggtitle("Fraction of reads in peaks, percentage")+
theme_bw()+
theme(strip.text = element_text(face = "bold", hjust = .5, size = 8),
strip.background = element_rect(fill = "white", linetype = "solid",
color = "black", linewidth = 1),
panel.spacing = unit(1, 'points'))+
coord_cartesian(ylim = c(0,100))peakcount_ff %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH"))) %>%
mutate(indv=factor(indv)) %>%
group_by(time) %>%
summarize(medal=median(peak_number))# A tibble: 2 × 2
time medal
<fct> <dbl>
1 3 hours 73241
2 24 hours 88738.PCA4mat <- all_four_filt_counts%>%
dplyr::select(Geneid,Ind1_75DA24h:Ind6_71V3h) %>%
column_to_rownames("Geneid") %>%
as.matrix()
annotation_mat <- data.frame(timeset=colnames(PCA4mat)) %>%
mutate(sample = timeset) %>%
mutate(timeset=gsub("Ind1_75","1_",timeset)) %>%
mutate(timeset=gsub("Ind2_87","2_",timeset)) %>%
mutate(timeset=gsub("Ind3_77","3_",timeset)) %>%
mutate(timeset=gsub("Ind6_71","4_",timeset)) %>%
mutate(timeset = gsub("24h","_24h",timeset),
timeset = gsub("3h","_3h",timeset)) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(trt= case_match(trt, 'DX' ~'DOX', 'E'~'EPI', 'DA'~'DNR', 'M'~'MTX', 'T'~'TRZ', 'V'~'VEH',.default = trt)) %>%
# mutate(indv = factor(indv, levels = c("1", "2", "3", "4", "5", "6"))) %>%
mutate(time = factor(time, levels = c("3h", "24h"), labels= c("3 hours","24 hours"))) %>%
mutate(trt = factor(trt, levels = c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")))
PCA4_info <- (prcomp(t(PCA4mat), scale. = TRUE))
PCA4_info_anno <- PCA4_info$x %>% cbind(.,annotation_mat)
# autoplot(PCA_info)
summary(PCA4_info)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 271.5384 161.7523 112.03795 84.95590 77.83204 62.46278
Proportion of Variance 0.4275 0.1517 0.07278 0.04185 0.03512 0.02262
Cumulative Proportion 0.4275 0.5792 0.65195 0.69380 0.72892 0.75154
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 55.50414 54.53991 50.63479 45.33378 42.9600 39.54174
Proportion of Variance 0.01786 0.01725 0.01486 0.01192 0.0107 0.00907
Cumulative Proportion 0.76940 0.78665 0.80151 0.81343 0.8241 0.83319
PC13 PC14 PC15 PC16 PC17 PC18
Standard deviation 38.68789 36.80855 36.40024 34.44726 33.89483 33.36466
Proportion of Variance 0.00868 0.00786 0.00768 0.00688 0.00666 0.00645
Cumulative Proportion 0.84187 0.84973 0.85741 0.86429 0.87095 0.87740
PC19 PC20 PC21 PC22 PC23 PC24
Standard deviation 33.17758 32.55288 31.31915 30.91018 30.44559 30.19378
Proportion of Variance 0.00638 0.00614 0.00569 0.00554 0.00537 0.00529
Cumulative Proportion 0.88378 0.88993 0.89561 0.90115 0.90653 0.91181
PC25 PC26 PC27 PC28 PC29 PC30
Standard deviation 29.89402 29.6621 29.11618 28.74140 28.45611 28.05219
Proportion of Variance 0.00518 0.0051 0.00492 0.00479 0.00469 0.00456
Cumulative Proportion 0.91699 0.9221 0.92701 0.93180 0.93649 0.94106
PC31 PC32 PC33 PC34 PC35 PC36
Standard deviation 27.94847 27.87613 27.64626 27.04068 25.9458 25.73918
Proportion of Variance 0.00453 0.00451 0.00443 0.00424 0.0039 0.00384
Cumulative Proportion 0.94559 0.95009 0.95452 0.95876 0.9627 0.96651
PC37 PC38 PC39 PC40 PC41 PC42
Standard deviation 25.52298 25.2772 24.66403 24.2316 23.83300 23.52718
Proportion of Variance 0.00378 0.0037 0.00353 0.0034 0.00329 0.00321
Cumulative Proportion 0.97028 0.9740 0.97751 0.9809 0.98421 0.98742
PC43 PC44 PC45 PC46 PC47 PC48
Standard deviation 22.30846 21.65858 21.00631 20.3327 18.66373 5.022e-13
Proportion of Variance 0.00289 0.00272 0.00256 0.0024 0.00202 0.000e+00
Cumulative Proportion 0.99031 0.99303 0.99558 0.9980 1.00000 1.000e+00# %>% as.tibble() %>%
# kable(., caption = "Summary of PCA variables") %>%
# kable_paper("striped", full_width = TRUE) %>%
# kable_styling(full_width = FALSE, font_size = 16) %>%
# scroll_box(height = "500px")
# cpm(PCAmat, log=TRUE)
pca_plot(PCA4_info_anno, col_var='trt', shape_var = 'time')+ggtitle("PCA of raw counts by time and treatment")pca_plot(PCA4_info_anno, col_var='trt', shape_var = 'indv')+ggtitle("PCA of raw counts by treatment and individual")FCmatrix_full <- PCA4mat %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.)) %>%
as.matrix() %>%
cpm(., log = TRUE) %>%
cor()
filmat_groupmat_col <- data.frame(timeset = colnames(FCmatrix_full))
counts_corr_mat <-filmat_groupmat_col %>%
# mutate(sample = timeset) %>%
separate(timeset, into = c("indv","trt","time"), sep= "_") %>%
mutate(class = if_else(trt == "DNR", "AC",
if_else(trt == "DOX", "AC",
if_else(trt == "EPI", "AC", "nAC")))) %>%
mutate(TOP2i = if_else(trt == "DNR", "yes",
if_else(trt == "DOX", "yes",
if_else(trt == "EPI", "yes",
if_else(trt == "MTX", "yes", "no")))))
mat_colors <- list(
trt= c("#F1B72B","#8B006D","#DF707E","#3386DD","#707031","#41B333"),
indv=c("#1B9E77", "#D95F02" ,"#7570B3", "#E6AB02"),
time=c("pink", "chocolate4"),
class=c("yellow1","darkorange1"),
TOP2i =c("darkgreen","lightgreen"))
names(mat_colors$trt) <- unique(counts_corr_mat$trt)
names(mat_colors$indv) <- unique(counts_corr_mat$indv)
names(mat_colors$time) <- unique(counts_corr_mat$time)
names(mat_colors$class) <- unique(counts_corr_mat$class)
names(mat_colors$TOP2i) <- unique(counts_corr_mat$TOP2i)
# mat_col_ff <-
# data.frame(
# time = c(rep("3 hours", 5), rep("24 hours", 5)),
# class = (c(
# "AC", "AC", "AC", "nAC","nAC", "AC", "AC", "AC", "nAC","nAC"
# )))
# rownames(mat_col_ff) <- colnames(FCmatrix_ff)
#
# mat_colors_ff <-
# list(
# time = c("pink", "chocolate4"),
# class = c("yellow1", "lightgreen"))
# names(mat_colors_ff$time) <- unique(mat_col_ff$time)
# names(mat_colors_ff$class) <- unique(mat_col_ff$class)
# names(mat_colors_FC$TOP2i) <- unique(mat_col_FC$TOP2i)
# corrFC_ff <- cor(FCmatrix_ff)
htanno_full <- ComplexHeatmap::HeatmapAnnotation(df = counts_corr_mat, col = mat_colors)
Heatmap(FCmatrix_full, top_annotation = htanno_full)# saveRDS(x4_filtered,"data/Final_four_data/ATAC_filtered_raw_counts_allsamples.RDS")
l4cpm <- cpm(PCA4mat, log=TRUE) ### for determining the basic cutoffs
dim(l4cpm)[1] 172481 48row_means <- rowMeans(l4cpm)
x4_filtered <- PCA4mat[row_means > 0,]
dim(x4_filtered)[1] 155560 48filt4_matrix_lcpm <- cpm(x4_filtered, log=TRUE)
PCA4_info_filter <- (prcomp(t(filt4_matrix_lcpm), scale. = TRUE))
summary(PCA4_info_filter)Importance of components:
PC1 PC2 PC3 PC4 PC5 PC6
Standard deviation 186.3046 145.1035 112.75334 92.12518 74.32713 65.16030
Proportion of Variance 0.2231 0.1353 0.08173 0.05456 0.03551 0.02729
Cumulative Proportion 0.2231 0.3585 0.44020 0.49476 0.53027 0.55757
PC7 PC8 PC9 PC10 PC11 PC12
Standard deviation 62.14680 60.93584 55.20555 51.92794 50.29842 49.63894
Proportion of Variance 0.02483 0.02387 0.01959 0.01733 0.01626 0.01584
Cumulative Proportion 0.58240 0.60627 0.62586 0.64319 0.65945 0.67529
PC13 PC14 PC15 PC16 PC17 PC18
Standard deviation 48.89413 46.56074 46.04427 45.27015 44.82248 44.19051
Proportion of Variance 0.01537 0.01394 0.01363 0.01317 0.01291 0.01255
Cumulative Proportion 0.69066 0.70460 0.71823 0.73140 0.74432 0.75687
PC19 PC20 PC21 PC22 PC23 PC24
Standard deviation 43.76294 43.54048 42.37991 41.72939 41.5544 40.40543
Proportion of Variance 0.01231 0.01219 0.01155 0.01119 0.0111 0.01049
Cumulative Proportion 0.76918 0.78137 0.79291 0.80411 0.8152 0.82570
PC25 PC26 PC27 PC28 PC29 PC30
Standard deviation 40.23834 39.2449 38.71749 38.53516 37.86304 37.48148
Proportion of Variance 0.01041 0.0099 0.00964 0.00955 0.00922 0.00903
Cumulative Proportion 0.83611 0.8460 0.85565 0.86519 0.87441 0.88344
PC31 PC32 PC33 PC34 PC35 PC36
Standard deviation 36.76445 36.31594 36.08503 35.40489 35.25544 34.3866
Proportion of Variance 0.00869 0.00848 0.00837 0.00806 0.00799 0.0076
Cumulative Proportion 0.89213 0.90061 0.90898 0.91704 0.92503 0.9326
PC37 PC38 PC39 PC40 PC41 PC42
Standard deviation 33.9352 33.63268 33.55568 32.46461 31.17213 31.02734
Proportion of Variance 0.0074 0.00727 0.00724 0.00678 0.00625 0.00619
Cumulative Proportion 0.9400 0.94730 0.95454 0.96132 0.96756 0.97375
PC43 PC44 PC45 PC46 PC47 PC48
Standard deviation 30.58641 30.12543 28.78207 27.44490 25.66405 7.625e-13
Proportion of Variance 0.00601 0.00583 0.00533 0.00484 0.00423 0.000e+00
Cumulative Proportion 0.97976 0.98560 0.99092 0.99577 1.00000 1.000e+00pca_var_plot(PCA4_info_filter)pca_final_four <- calc_pca(t(filt4_matrix_lcpm))
pca_final_four_anno <- data.frame(annotation_mat, pca_final_four$x)
pca_final_four_anno %>%
ggplot(.,aes(x = PC1, y = PC2, col=trt, shape=time, group=indv))+
geom_point(size= 5)+
scale_color_manual(values=drug_pal)+
ggrepel::geom_text_repel(aes(label = indv))+
ggtitle(expression("PCA of log"[2]*"(cpm) new filtered peak set"))+
theme_bw()+
guides(col="none", size =4)+
labs(y = "PC 2 (13.5%)", x ="PC 1 (22.3%)")+
theme(plot.title=element_text(size= 14,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"))pca_final_four_anno %>%
ggplot(.,aes(x = PC3, y = PC4, col=trt, shape=time, group=indv))+
geom_point(size= 5)+
scale_color_manual(values=drug_pal)+
ggrepel::geom_text_repel(aes(label = indv))+
ggtitle(expression("PCA of log"[2]*"(cpm) new filtered peak set"))+
theme_bw()+
guides(col="none", size =4)+
labs(y = "PC 4 (5.5%)", x ="PC 3 (8.2%)")+
theme(plot.title=element_text(size= 14,hjust = 0.5),
axis.title = element_text(size = 12, color = "black"))We go from 172481, 48 peaks to155560, 48 peaks using rowMeans (log2cpm) > 0 across all samples. This is my new high confidence peak set.
pending code
facs <- c("indv", "trt", "time")
names(facs) <- c("Individual", "Treatment", "Time")
drug1 <- c("DOX","EPI", "DNR", "MTX", "TRZ", "VEH")##for changing shapes and colors
time <- rep(c("24h", "3h"),24) %>% factor(., levels = c("3h","24h"))
##gglistmaking
for (f in facs) {
# PC1 v PC2
pca_plot(pca_final_four_anno, col_var = f, shape_var = time,
title = names(facs)[which(facs == f)])
print(last_plot())
# Plot f versus PC1 and PC2
f_v_pc1 <- arrangeGrob(plot_versus_pc(pca_final_four_anno, 1, f))
f_v_pc2 <- arrangeGrob(plot_versus_pc(pca_final_four_anno, 2, f))
grid.arrange(f_v_pc1, f_v_pc2, ncol = 2, top = names(facs)[which(facs == f)])
# summary(plot_versus_pc(PCA_info_anno_all, 1, f))
# summary(plot_versus_pc(PCA_info_anno_all, 2, f))
}# write_delim((filt4_matrix_lcpm %>% as.data.frame(.) %>% rownames_to_column("Peakid")), "data/Final_four_data/LCPM_matrix_ff.txt", delim = "/t")
# filt4_matrix_lcpm <- read_delim("data/Final_four_data/LCPM_matrix_ff.txt",delim = "/t")
new_peaks_list <- row.names(filt4_matrix_lcpm) %>% as.data.frame() %>% separate_wider_delim(., cols =.,names=c("chr","start","end"), delim = ".", cols_remove = FALSE) %>% dplyr::rename("peakid"=4)
new_peaks_gr <- new_peaks_list %>%
dplyr::filter(chr!="chrY") %>%
GRanges() %>%
keepStandardChromosomes(., pruning.mode = "coarse")
stdChr <- standardChromosomes(new_peaks_gr)
peakAnnoList_n45_motif <- readRDS("data/peakAnnoList_n45_motif.RDS")
old_peaks_gr <- peakAnnoList_n45_motif$background_gr %>% as.GRanges()
old_peaks_gr$old_width <- width(old_peaks_gr)
a <- join_overlap_intersect(old_peaks_gr,new_peaks_gr,suffix = c("old", "new")) %>%
as.data.frame()
a %>%
dplyr::select(id,peakid,width,old_width) %>%
mutate(check =if_else(id ==peakid, "TRUE","FALSE")) %>%
dplyr::filter(check=="FALSE") %>%
mutate(width_ol=if_else(width<old_width,"smaller","larger")) %>%
dplyr::rename("old_peak_id"="id","new_peak_id"="peakid") %>%
mutate(diff= old_width - width) %>%
kable(., caption = "summary of newpeaks that are smaller than old peaks, but are basically the same") %>%
kable_paper("striped", full_width = FALSE) %>%
kable_styling(
full_width = FALSE,
position = "left",
bootstrap_options = c("striped", "hover")
) %>%
scroll_box(width = "100%", height = "400px")| old_peak_id | new_peak_id | width | old_width | check | width_ol | diff |
|---|---|---|---|---|---|---|
| chr1.821114.821933 | chr1.821114.821757 | 644 | 821 | FALSE | smaller | 177 |
| chr1.920872.921848 | chr1.920872.921733 | 862 | 978 | FALSE | smaller | 116 |
| chr1.960487.960762 | chr1.960492.960762 | 271 | 277 | FALSE | smaller | 6 |
| chr1.961274.961788 | chr1.961274.961703 | 430 | 516 | FALSE | smaller | 86 |
| chr1.979643.980042 | chr1.979686.980042 | 357 | 401 | FALSE | smaller | 44 |
| chr1.1024609.1025381 | chr1.1024609.1025344 | 736 | 774 | FALSE | smaller | 38 |
| chr1.1041893.1042721 | chr1.1042221.1042721 | 501 | 830 | FALSE | smaller | 329 |
| chr1.1043580.1044925 | chr1.1044510.1044915 | 406 | 1347 | FALSE | smaller | 941 |
| chr1.1050736.1051096 | chr1.1050794.1051096 | 303 | 362 | FALSE | smaller | 59 |
| chr1.1069144.1069685 | chr1.1069144.1069625 | 482 | 543 | FALSE | smaller | 61 |
| chr1.1122855.1123919 | chr1.1122855.1123828 | 974 | 1066 | FALSE | smaller | 92 |
| chr1.1217896.1218634 | chr1.1217914.1218541 | 628 | 740 | FALSE | smaller | 112 |
| chr1.1231222.1233459 | chr1.1231222.1232501 | 1280 | 2239 | FALSE | smaller | 959 |
| chr1.1307178.1309120 | chr1.1307243.1309120 | 1878 | 1944 | FALSE | smaller | 66 |
| chr1.1340102.1340799 | chr1.1340206.1340601 | 396 | 699 | FALSE | smaller | 303 |
| chr1.1371553.1372351 | chr1.1371659.1372320 | 662 | 800 | FALSE | smaller | 138 |
| chr1.1381391.1382163 | chr1.1381510.1382163 | 654 | 774 | FALSE | smaller | 120 |
| chr1.1406544.1408158 | chr1.1406544.1408077 | 1534 | 1616 | FALSE | smaller | 82 |
| chr1.1419289.1421201 | chr1.1419325.1419926 | 602 | 1914 | FALSE | smaller | 1312 |
| chr1.1435372.1435871 | chr1.1435372.1435855 | 484 | 501 | FALSE | smaller | 17 |
| chr1.1479180.1480142 | chr1.1479180.1480138 | 959 | 964 | FALSE | smaller | 5 |
| chr1.1535413.1536277 | chr1.1535548.1536277 | 730 | 866 | FALSE | smaller | 136 |
| chr1.1537637.1538609 | chr1.1537912.1538606 | 695 | 974 | FALSE | smaller | 279 |
| chr1.1550814.1551382 | chr1.1550842.1551382 | 541 | 570 | FALSE | smaller | 29 |
| chr1.1554541.1555602 | chr1.1554541.1555504 | 964 | 1063 | FALSE | smaller | 99 |
| chr1.1628373.1629316 | chr1.1628477.1629294 | 818 | 945 | FALSE | smaller | 127 |
| chr1.1678292.1679123 | chr1.1678465.1679123 | 659 | 833 | FALSE | smaller | 174 |
| chr1.1760355.1760840 | chr1.1760576.1760840 | 265 | 487 | FALSE | smaller | 222 |
| chr1.1796812.1797274 | chr1.1796843.1797274 | 432 | 464 | FALSE | smaller | 32 |
| chr1.1826457.1827708 | chr1.1827153.1827708 | 556 | 1253 | FALSE | smaller | 697 |
| chr1.1873128.1873487 | chr1.1873139.1873487 | 349 | 361 | FALSE | smaller | 12 |
| chr1.1890818.1892543 | chr1.1890818.1892336 | 1519 | 1727 | FALSE | smaller | 208 |
| chr1.1980529.1981576 | chr1.1980529.1981342 | 814 | 1049 | FALSE | smaller | 235 |
| chr1.2046528.2046745 | chr1.2046542.2046745 | 204 | 219 | FALSE | smaller | 15 |
| chr1.2099928.2100159 | chr1.2099966.2100135 | 170 | 233 | FALSE | smaller | 63 |
| chr1.2108390.2108851 | chr1.2108429.2108851 | 423 | 463 | FALSE | smaller | 40 |
| chr1.2121076.2121580 | chr1.2121078.2121580 | 503 | 506 | FALSE | smaller | 3 |
| chr1.2179778.2180788 | chr1.2179979.2180774 | 796 | 1012 | FALSE | smaller | 216 |
| chr1.2190449.2190881 | chr1.2190487.2190881 | 395 | 434 | FALSE | smaller | 39 |
| chr1.2204323.2204898 | chr1.2204373.2204898 | 526 | 577 | FALSE | smaller | 51 |
| chr1.2214027.2214452 | chr1.2214131.2214452 | 322 | 427 | FALSE | smaller | 105 |
| chr1.2228943.2229752 | chr1.2228949.2229752 | 804 | 811 | FALSE | smaller | 7 |
| chr1.2240101.2240543 | chr1.2240101.2240542 | 442 | 444 | FALSE | smaller | 2 |
| chr1.2279018.2279871 | chr1.2279093.2279871 | 779 | 855 | FALSE | smaller | 76 |
| chr1.2446428.2446977 | chr1.2446428.2446954 | 527 | 551 | FALSE | smaller | 24 |
| chr1.2925986.2926908 | chr1.2925986.2926863 | 878 | 924 | FALSE | smaller | 46 |
| chr1.3066258.3066748 | chr1.3066258.3066708 | 451 | 492 | FALSE | smaller | 41 |
| chr1.3114717.3115158 | chr1.3114790.3115158 | 369 | 443 | FALSE | smaller | 74 |
| chr1.3146492.3147615 | chr1.3146983.3147615 | 633 | 1125 | FALSE | smaller | 492 |
| chr1.3164665.3165177 | chr1.3164665.3165159 | 495 | 514 | FALSE | smaller | 19 |
| chr1.3378423.3378961 | chr1.3378423.3378869 | 447 | 540 | FALSE | smaller | 93 |
| chr1.3410241.3411141 | chr1.3410241.3410926 | 686 | 902 | FALSE | smaller | 216 |
| chr1.3424432.3425134 | chr1.3424432.3425112 | 681 | 704 | FALSE | smaller | 23 |
| chr1.3471185.3472241 | chr1.3471265.3472241 | 977 | 1058 | FALSE | smaller | 81 |
| chr1.3491182.3492583 | chr1.3491182.3492272 | 1091 | 1403 | FALSE | smaller | 312 |
| chr1.3491182.3492583 | chr1.3492412.3492583 | 172 | 1403 | FALSE | smaller | 1231 |
| chr1.3501475.3502639 | chr1.3501476.3501969 | 494 | 1166 | FALSE | smaller | 672 |
| chr1.3501475.3502639 | chr1.3502068.3502639 | 572 | 1166 | FALSE | smaller | 594 |
| chr1.3529400.3529760 | chr1.3529405.3529760 | 356 | 362 | FALSE | smaller | 6 |
| chr1.3565863.3566425 | chr1.3565983.3566425 | 443 | 564 | FALSE | smaller | 121 |
| chr1.3567806.3568284 | chr1.3567877.3568284 | 408 | 480 | FALSE | smaller | 72 |
| chr1.3585881.3586600 | chr1.3586032.3586600 | 569 | 721 | FALSE | smaller | 152 |
| chr1.3611029.3612045 | chr1.3611051.3612045 | 995 | 1018 | FALSE | smaller | 23 |
| chr1.3613317.3614136 | chr1.3613317.3614126 | 810 | 821 | FALSE | smaller | 11 |
| chr1.3625399.3625896 | chr1.3625399.3625845 | 447 | 499 | FALSE | smaller | 52 |
| chr1.3643071.3643436 | chr1.3643071.3643418 | 348 | 367 | FALSE | smaller | 19 |
| chr1.3649839.3650242 | chr1.3649859.3650242 | 384 | 405 | FALSE | smaller | 21 |
| chr1.3713083.3713447 | chr1.3713084.3713447 | 364 | 366 | FALSE | smaller | 2 |
| chr1.4650520.4650770 | chr1.4650520.4650755 | 236 | 252 | FALSE | smaller | 16 |
| chr1.4654752.4656772 | chr1.4655205.4656772 | 1568 | 2022 | FALSE | smaller | 454 |
| chr1.4710778.4711657 | chr1.4710778.4711524 | 747 | 881 | FALSE | smaller | 134 |
| chr1.4749935.4750589 | chr1.4750133.4750589 | 457 | 656 | FALSE | smaller | 199 |
| chr1.5423150.5423597 | chr1.5423163.5423597 | 435 | 449 | FALSE | smaller | 14 |
| chr1.5551482.5552010 | chr1.5551482.5551970 | 489 | 530 | FALSE | smaller | 41 |
| chr1.5652075.5652782 | chr1.5652105.5652782 | 678 | 709 | FALSE | smaller | 31 |
| chr1.5757923.5758663 | chr1.5757962.5758663 | 702 | 742 | FALSE | smaller | 40 |
| chr1.6016163.6016620 | chr1.6016177.6016620 | 444 | 459 | FALSE | smaller | 15 |
| chr1.6206990.6208123 | chr1.6206990.6208117 | 1128 | 1135 | FALSE | smaller | 7 |
| chr1.6235407.6236232 | chr1.6235707.6236232 | 526 | 827 | FALSE | smaller | 301 |
| chr1.6238710.6239018 | chr1.6238721.6239018 | 298 | 310 | FALSE | smaller | 12 |
| chr1.6290089.6290453 | chr1.6290089.6290438 | 350 | 366 | FALSE | smaller | 16 |
| chr1.6453884.6455160 | chr1.6453904.6454980 | 1077 | 1278 | FALSE | smaller | 201 |
| chr1.6455309.6455871 | chr1.6455500.6455871 | 372 | 564 | FALSE | smaller | 192 |
| chr1.6489915.6491040 | chr1.6489915.6491017 | 1103 | 1127 | FALSE | smaller | 24 |
| chr1.6519972.6520623 | chr1.6520072.6520623 | 552 | 653 | FALSE | smaller | 101 |
| chr1.6612747.6614536 | chr1.6612747.6614525 | 1779 | 1791 | FALSE | smaller | 12 |
| chr1.6666804.6667424 | chr1.6666804.6667023 | 220 | 622 | FALSE | smaller | 402 |
| chr1.6770647.6771281 | chr1.6770655.6771281 | 627 | 636 | FALSE | smaller | 9 |
| chr1.6904594.6905061 | chr1.6904594.6905029 | 436 | 469 | FALSE | smaller | 33 |
| chr1.6925805.6926224 | chr1.6925805.6926158 | 354 | 421 | FALSE | smaller | 67 |
| chr1.6962028.6963541 | chr1.6962149.6963541 | 1393 | 1515 | FALSE | smaller | 122 |
| chr1.7075459.7075727 | chr1.7075463.7075727 | 265 | 270 | FALSE | smaller | 5 |
| chr1.7207782.7208486 | chr1.7207782.7208352 | 571 | 706 | FALSE | smaller | 135 |
| chr1.7438711.7439094 | chr1.7438777.7439094 | 318 | 385 | FALSE | smaller | 67 |
| chr1.7500578.7501069 | chr1.7500713.7501069 | 357 | 493 | FALSE | smaller | 136 |
| chr1.7526392.7526950 | chr1.7526394.7526950 | 557 | 560 | FALSE | smaller | 3 |
| chr1.7539588.7540154 | chr1.7539588.7540093 | 506 | 568 | FALSE | smaller | 62 |
| chr1.7614248.7615016 | chr1.7614248.7614910 | 663 | 770 | FALSE | smaller | 107 |
| chr1.7704462.7704924 | chr1.7704512.7704924 | 413 | 464 | FALSE | smaller | 51 |
| chr1.7719629.7719973 | chr1.7719660.7719967 | 308 | 346 | FALSE | smaller | 38 |
| chr1.7990049.7990678 | chr1.7990106.7990678 | 573 | 631 | FALSE | smaller | 58 |
| chr1.8025629.8027358 | chr1.8025629.8027230 | 1602 | 1731 | FALSE | smaller | 129 |
| chr1.8062015.8062412 | chr1.8062015.8062372 | 358 | 399 | FALSE | smaller | 41 |
| chr1.8202264.8202849 | chr1.8202264.8202839 | 576 | 587 | FALSE | smaller | 11 |
| chr1.8276278.8276855 | chr1.8276278.8276811 | 534 | 579 | FALSE | smaller | 45 |
| chr1.8317889.8318454 | chr1.8317983.8318454 | 472 | 567 | FALSE | smaller | 95 |
| chr1.8357465.8358203 | chr1.8357465.8358089 | 625 | 740 | FALSE | smaller | 115 |
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| chr1.32869723.32871728 | chr1.32869723.32871714 | 1992 | 2007 | FALSE | smaller | 15 |
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| chr2.28587941.28588294 | chr2.28587941.28588242 | 302 | 355 | FALSE | smaller | 53 |
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| chr2.60139898.60140629 | chr2.60139992.60140629 | 638 | 733 | FALSE | smaller | 95 |
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| chr2.60653239.60653776 | chr2.60653275.60653776 | 502 | 539 | FALSE | smaller | 37 |
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| chr2.238948478.238948720 | chr2.238948478.238948715 | 238 | 244 | FALSE | smaller | 6 |
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| chr2.239101121.239101458 | chr2.239101162.239101458 | 297 | 339 | FALSE | smaller | 42 |
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| chr2.240877984.240878330 | chr2.240878012.240878330 | 319 | 348 | FALSE | smaller | 29 |
| chr2.240896144.240896392 | chr2.240896144.240896333 | 190 | 250 | FALSE | smaller | 60 |
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| chr2.241149082.241150956 | chr2.241149097.241150956 | 1860 | 1876 | FALSE | smaller | 16 |
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| chr2.241216073.241216505 | chr2.241216144.241216488 | 345 | 434 | FALSE | smaller | 89 |
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| chr2.241413338.241413648 | chr2.241413338.241413604 | 267 | 312 | FALSE | smaller | 45 |
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| chr2.241655875.241656684 | chr2.241655925.241656684 | 760 | 811 | FALSE | smaller | 51 |
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| chr3.536010.536516 | chr3.536068.536516 | 449 | 508 | FALSE | smaller | 59 |
| chr3.2098332.2100443 | chr3.2098381.2099640 | 1260 | 2113 | FALSE | smaller | 853 |
| chr3.2098332.2100443 | chr3.2099808.2100443 | 636 | 2113 | FALSE | smaller | 1477 |
| chr3.2484210.2484940 | chr3.2484210.2484929 | 720 | 732 | FALSE | smaller | 12 |
| chr3.2654694.2655720 | chr3.2654694.2655662 | 969 | 1028 | FALSE | smaller | 59 |
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| chr3.3110726.3111123 | chr3.3110726.3111028 | 303 | 399 | FALSE | smaller | 96 |
| chr3.4303088.4304131 | chr3.4303088.4304108 | 1021 | 1045 | FALSE | smaller | 24 |
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| chr3.4571868.4572278 | chr3.4571888.4572278 | 391 | 412 | FALSE | smaller | 21 |
| chr3.4606875.4607290 | chr3.4606887.4607245 | 359 | 417 | FALSE | smaller | 58 |
| chr3.4670407.4670607 | chr3.4670439.4670607 | 169 | 202 | FALSE | smaller | 33 |
| chr3.4720474.4721055 | chr3.4720552.4721055 | 504 | 583 | FALSE | smaller | 79 |
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| chr3.4746604.4746843 | chr3.4746631.4746833 | 203 | 241 | FALSE | smaller | 38 |
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| chr3.5002361.5002746 | chr3.5002399.5002746 | 348 | 387 | FALSE | smaller | 39 |
| chr3.5238482.5239634 | chr3.5238482.5239624 | 1143 | 1154 | FALSE | smaller | 11 |
| chr3.5363677.5363943 | chr3.5363677.5363941 | 265 | 268 | FALSE | smaller | 3 |
| chr3.5426241.5426801 | chr3.5426241.5426791 | 551 | 562 | FALSE | smaller | 11 |
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| chr3.5787783.5788277 | chr3.5787813.5788277 | 465 | 496 | FALSE | smaller | 31 |
| chr3.7270863.7271439 | chr3.7270863.7271114 | 252 | 578 | FALSE | smaller | 326 |
| chr3.7656573.7656757 | chr3.7656586.7656741 | 156 | 186 | FALSE | smaller | 30 |
| chr3.7897386.7898119 | chr3.7897467.7898119 | 653 | 735 | FALSE | smaller | 82 |
| chr3.8001714.8002027 | chr3.8001724.8002027 | 304 | 315 | FALSE | smaller | 11 |
| chr3.8136859.8137685 | chr3.8136886.8137685 | 800 | 828 | FALSE | smaller | 28 |
| chr3.8441900.8442564 | chr3.8442056.8442447 | 392 | 666 | FALSE | smaller | 274 |
| chr3.8483209.8483634 | chr3.8483248.8483634 | 387 | 427 | FALSE | smaller | 40 |
| chr3.8497690.8497902 | chr3.8497690.8497893 | 204 | 214 | FALSE | smaller | 10 |
| chr3.8540510.8541094 | chr3.8540566.8541094 | 529 | 586 | FALSE | smaller | 57 |
| chr3.8629513.8629993 | chr3.8629523.8629993 | 471 | 482 | FALSE | smaller | 11 |
| chr3.8794370.8794935 | chr3.8794370.8794857 | 488 | 567 | FALSE | smaller | 79 |
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| chr3.8996852.8997364 | chr3.8996864.8997364 | 501 | 514 | FALSE | smaller | 13 |
| chr3.9110782.9111273 | chr3.9110790.9111273 | 484 | 493 | FALSE | smaller | 9 |
| chr3.9135789.9136610 | chr3.9135858.9136610 | 753 | 823 | FALSE | smaller | 70 |
| chr3.9198936.9199347 | chr3.9199009.9199347 | 339 | 413 | FALSE | smaller | 74 |
| chr3.9302188.9302882 | chr3.9302240.9302882 | 643 | 696 | FALSE | smaller | 53 |
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| chr3.9494628.9495145 | chr3.9494630.9495145 | 516 | 519 | FALSE | smaller | 3 |
| chr3.9691106.9691621 | chr3.9691106.9691473 | 368 | 517 | FALSE | smaller | 149 |
| chr3.9746448.9746650 | chr3.9746453.9746650 | 198 | 204 | FALSE | smaller | 6 |
| chr3.9749412.9750471 | chr3.9749412.9750447 | 1036 | 1061 | FALSE | smaller | 25 |
| chr3.9769487.9770099 | chr3.9769490.9770099 | 610 | 614 | FALSE | smaller | 4 |
| chr3.9932633.9934328 | chr3.9932738.9934328 | 1591 | 1697 | FALSE | smaller | 106 |
| chr3.10199056.10199524 | chr3.10199113.10199524 | 412 | 470 | FALSE | smaller | 58 |
| chr3.10459928.10460585 | chr3.10460002.10460585 | 584 | 659 | FALSE | smaller | 75 |
| chr3.10489509.10489900 | chr3.10489556.10489900 | 345 | 393 | FALSE | smaller | 48 |
| chr3.10494914.10495574 | chr3.10494914.10495370 | 457 | 662 | FALSE | smaller | 205 |
| chr3.10596859.10597473 | chr3.10596859.10597410 | 552 | 616 | FALSE | smaller | 64 |
| chr3.10680325.10680602 | chr3.10680338.10680602 | 265 | 279 | FALSE | smaller | 14 |
| chr3.10707676.10708808 | chr3.10707682.10708808 | 1127 | 1134 | FALSE | smaller | 7 |
| chr3.10758469.10758936 | chr3.10758469.10758913 | 445 | 469 | FALSE | smaller | 24 |
| chr3.10873481.10874018 | chr3.10873481.10874008 | 528 | 539 | FALSE | smaller | 11 |
| chr3.10944270.10944524 | chr3.10944270.10944474 | 205 | 256 | FALSE | smaller | 51 |
| chr3.10956923.10957421 | chr3.10956930.10957421 | 492 | 500 | FALSE | smaller | 8 |
| chr3.11245913.11246358 | chr3.11245929.11246358 | 430 | 447 | FALSE | smaller | 17 |
| chr3.11555301.11555621 | chr3.11555324.11555621 | 298 | 322 | FALSE | smaller | 24 |
| chr3.11568505.11569144 | chr3.11568505.11569069 | 565 | 641 | FALSE | smaller | 76 |
| chr3.11738963.11739625 | chr3.11738993.11739625 | 633 | 664 | FALSE | smaller | 31 |
| chr3.11803921.11804367 | chr3.11803938.11804367 | 430 | 448 | FALSE | smaller | 18 |
| chr3.12513771.12514095 | chr3.12513771.12514001 | 231 | 326 | FALSE | smaller | 95 |
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| chr3.12741004.12741566 | chr3.12741024.12741566 | 543 | 564 | FALSE | smaller | 21 |
| chr3.12798345.12798905 | chr3.12798388.12798905 | 518 | 562 | FALSE | smaller | 44 |
| chr3.12875854.12876981 | chr3.12875854.12876702 | 849 | 1129 | FALSE | smaller | 280 |
| chr3.12905030.12905893 | chr3.12905030.12905802 | 773 | 865 | FALSE | smaller | 92 |
| chr3.12922164.12922712 | chr3.12922260.12922712 | 453 | 550 | FALSE | smaller | 97 |
| chr3.12935421.12935735 | chr3.12935421.12935697 | 277 | 316 | FALSE | smaller | 39 |
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| chr3.12969458.12969775 | chr3.12969459.12969775 | 317 | 319 | FALSE | smaller | 2 |
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| chr3.13008146.13008415 | chr3.13008146.13008400 | 255 | 271 | FALSE | smaller | 16 |
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| chr3.13192783.13193156 | chr3.13192783.13193150 | 368 | 375 | FALSE | smaller | 7 |
| chr3.13199296.13200216 | chr3.13199792.13200216 | 425 | 922 | FALSE | smaller | 497 |
| chr3.13232241.13232587 | chr3.13232246.13232587 | 342 | 348 | FALSE | smaller | 6 |
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| chr3.13364943.13365688 | chr3.13364943.13365532 | 590 | 747 | FALSE | smaller | 157 |
| chr3.13377961.13378620 | chr3.13377961.13378615 | 655 | 661 | FALSE | smaller | 6 |
| chr3.13484995.13486057 | chr3.13484995.13485766 | 772 | 1064 | FALSE | smaller | 292 |
| chr3.13500235.13500633 | chr3.13500235.13500617 | 383 | 400 | FALSE | smaller | 17 |
| chr3.13548544.13549329 | chr3.13548652.13549329 | 678 | 787 | FALSE | smaller | 109 |
| chr3.13645987.13646320 | chr3.13646006.13646320 | 315 | 335 | FALSE | smaller | 20 |
| chr3.13753872.13754257 | chr3.13753948.13754241 | 294 | 387 | FALSE | smaller | 93 |
| chr3.13850055.13850856 | chr3.13850122.13850762 | 641 | 803 | FALSE | smaller | 162 |
| chr3.13890568.13890915 | chr3.13890568.13890845 | 278 | 349 | FALSE | smaller | 71 |
| chr3.14273922.14274399 | chr3.14273922.14274293 | 372 | 479 | FALSE | smaller | 107 |
| chr3.14440382.14440793 | chr3.14440383.14440793 | 411 | 413 | FALSE | smaller | 2 |
| chr3.14480252.14480993 | chr3.14480252.14480946 | 695 | 743 | FALSE | smaller | 48 |
| chr3.14555469.14555997 | chr3.14555469.14555987 | 519 | 530 | FALSE | smaller | 11 |
| chr3.14587938.14588364 | chr3.14587938.14588352 | 415 | 428 | FALSE | smaller | 13 |
| chr3.14643698.14644201 | chr3.14643733.14644201 | 469 | 505 | FALSE | smaller | 36 |
| chr3.14767704.14768131 | chr3.14767704.14768088 | 385 | 429 | FALSE | smaller | 44 |
| chr3.14810147.14811564 | chr3.14810147.14810575 | 429 | 1419 | FALSE | smaller | 990 |
| chr3.14810147.14811564 | chr3.14810680.14811564 | 885 | 1419 | FALSE | smaller | 534 |
| chr3.15174913.15175233 | chr3.15174913.15175228 | 316 | 322 | FALSE | smaller | 6 |
| chr3.15241487.15241597 | chr3.15241487.15241596 | 110 | 112 | FALSE | smaller | 2 |
| chr3.15310110.15311155 | chr3.15310110.15311123 | 1014 | 1047 | FALSE | smaller | 33 |
| chr3.15324517.15324726 | chr3.15324518.15324726 | 209 | 211 | FALSE | smaller | 2 |
| chr3.15523933.15524316 | chr3.15523938.15524316 | 379 | 385 | FALSE | smaller | 6 |
| chr3.15601037.15602322 | chr3.15601185.15602321 | 1137 | 1287 | FALSE | smaller | 150 |
| chr3.16074318.16074665 | chr3.16074376.16074665 | 290 | 349 | FALSE | smaller | 59 |
| chr3.16151131.16151442 | chr3.16151146.16151432 | 287 | 313 | FALSE | smaller | 26 |
| chr3.16467379.16469159 | chr3.16467383.16469159 | 1777 | 1782 | FALSE | smaller | 5 |
| chr3.16473610.16474796 | chr3.16473634.16474796 | 1163 | 1188 | FALSE | smaller | 25 |
| chr3.16604882.16606246 | chr3.16604882.16605473 | 592 | 1366 | FALSE | smaller | 774 |
| chr3.17094586.17095127 | chr3.17094606.17095127 | 522 | 543 | FALSE | smaller | 21 |
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| chr3.17179580.17180101 | chr3.17179621.17180101 | 481 | 523 | FALSE | smaller | 42 |
| chr3.17190366.17191052 | chr3.17190530.17191052 | 523 | 688 | FALSE | smaller | 165 |
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| chr3.17552723.17553435 | chr3.17552723.17553431 | 709 | 714 | FALSE | smaller | 5 |
| chr3.17662409.17663078 | chr3.17662409.17663063 | 655 | 671 | FALSE | smaller | 16 |
| chr3.18124498.18125024 | chr3.18124502.18125024 | 523 | 528 | FALSE | smaller | 5 |
| chr3.19147948.19148372 | chr3.19148013.19148372 | 360 | 426 | FALSE | smaller | 66 |
| chr3.19757662.19758175 | chr3.19757804.19758175 | 372 | 515 | FALSE | smaller | 143 |
| chr3.19934148.19934339 | chr3.19934176.19934339 | 164 | 193 | FALSE | smaller | 29 |
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| chr3.20511863.20512510 | chr3.20511863.20512379 | 517 | 649 | FALSE | smaller | 132 |
| chr3.20962514.20963082 | chr3.20962519.20963082 | 564 | 570 | FALSE | smaller | 6 |
| chr3.21017134.21017828 | chr3.21017134.21017813 | 680 | 696 | FALSE | smaller | 16 |
| chr3.21484167.21484837 | chr3.21484179.21484837 | 659 | 672 | FALSE | smaller | 13 |
| chr3.22979968.22980746 | chr3.22980036.22980731 | 696 | 780 | FALSE | smaller | 84 |
| chr3.23052494.23053079 | chr3.23052658.23053079 | 422 | 587 | FALSE | smaller | 165 |
| chr3.23096086.23096454 | chr3.23096086.23096417 | 332 | 370 | FALSE | smaller | 38 |
| chr3.23159667.23160513 | chr3.23159667.23160397 | 731 | 848 | FALSE | smaller | 117 |
| chr3.23485962.23486818 | chr3.23486087.23486818 | 732 | 858 | FALSE | smaller | 126 |
| chr3.23620060.23621083 | chr3.23620094.23621083 | 990 | 1025 | FALSE | smaller | 35 |
| chr3.23882445.23883033 | chr3.23882445.23882957 | 513 | 590 | FALSE | smaller | 77 |
| chr3.23944908.23946663 | chr3.23944953.23946663 | 1711 | 1757 | FALSE | smaller | 46 |
| chr3.24373313.24373710 | chr3.24373332.24373710 | 379 | 399 | FALSE | smaller | 20 |
| chr3.24487589.24488344 | chr3.24487591.24488344 | 754 | 757 | FALSE | smaller | 3 |
| chr3.24521135.24522469 | chr3.24521171.24522438 | 1268 | 1336 | FALSE | smaller | 68 |
| chr3.24524319.24524640 | chr3.24524340.24524640 | 301 | 323 | FALSE | smaller | 22 |
| chr3.25365765.25366121 | chr3.25365765.25366045 | 281 | 358 | FALSE | smaller | 77 |
| chr3.25433639.25434376 | chr3.25433654.25434376 | 723 | 739 | FALSE | smaller | 16 |
| chr3.25455477.25456007 | chr3.25455491.25456007 | 517 | 532 | FALSE | smaller | 15 |
| chr3.25789780.25790530 | chr3.25789780.25790524 | 745 | 752 | FALSE | smaller | 7 |
| chr3.26570213.26570576 | chr3.26570226.26570576 | 351 | 365 | FALSE | smaller | 14 |
| chr3.26622442.26624000 | chr3.26622477.26624000 | 1524 | 1560 | FALSE | smaller | 36 |
| chr3.27680851.27681568 | chr3.27680851.27681425 | 575 | 719 | FALSE | smaller | 144 |
| chr3.27729930.27730457 | chr3.27729930.27730435 | 506 | 529 | FALSE | smaller | 23 |
| chr3.28672351.28672665 | chr3.28672351.28672657 | 307 | 316 | FALSE | smaller | 9 |
| chr3.28745502.28746448 | chr3.28745550.28746448 | 899 | 948 | FALSE | smaller | 49 |
| chr3.28799110.28799390 | chr3.28799112.28799390 | 279 | 282 | FALSE | smaller | 3 |
| chr3.29311880.29312085 | chr3.29311882.29312085 | 204 | 207 | FALSE | smaller | 3 |
| chr3.29330128.29330627 | chr3.29330198.29330627 | 430 | 501 | FALSE | smaller | 71 |
| chr3.29639654.29640261 | chr3.29639654.29640223 | 570 | 609 | FALSE | smaller | 39 |
| chr3.29701362.29702054 | chr3.29701408.29702054 | 647 | 694 | FALSE | smaller | 47 |
| chr3.29718584.29719018 | chr3.29718589.29719015 | 427 | 436 | FALSE | smaller | 9 |
| chr3.29763954.29764875 | chr3.29763954.29764871 | 918 | 923 | FALSE | smaller | 5 |
| chr3.30606203.30607465 | chr3.30606203.30607442 | 1240 | 1264 | FALSE | smaller | 24 |
| chr3.31021597.31021822 | chr3.31021597.31021804 | 208 | 227 | FALSE | smaller | 19 |
| chr3.31196951.31197297 | chr3.31196953.31197297 | 345 | 348 | FALSE | smaller | 3 |
| chr3.31356980.31357202 | chr3.31357013.31357202 | 190 | 224 | FALSE | smaller | 34 |
| chr3.31361424.31362248 | chr3.31361424.31362232 | 809 | 826 | FALSE | smaller | 17 |
| chr3.31531803.31534314 | chr3.31531846.31534314 | 2469 | 2513 | FALSE | smaller | 44 |
| chr3.31546243.31546508 | chr3.31546244.31546508 | 265 | 267 | FALSE | smaller | 2 |
| chr3.31732488.31733302 | chr3.31732094.31732487 | 1 | 816 | FALSE | smaller | 815 |
| chr3.31771083.31771490 | chr3.31771083.31771370 | 288 | 409 | FALSE | smaller | 121 |
| chr3.31980366.31982043 | chr3.31980366.31982017 | 1652 | 1679 | FALSE | smaller | 27 |
| chr3.32143868.32144738 | chr3.32143902.32144738 | 837 | 872 | FALSE | smaller | 35 |
| chr3.32177179.32178310 | chr3.32177179.32178304 | 1126 | 1133 | FALSE | smaller | 7 |
| chr3.32864559.32864872 | chr3.32864559.32864829 | 271 | 315 | FALSE | smaller | 44 |
| chr3.33170482.33170898 | chr3.33170499.33170898 | 400 | 418 | FALSE | smaller | 18 |
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| chr3.33377846.33378400 | chr3.33377913.33378400 | 488 | 556 | FALSE | smaller | 68 |
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| chr3.34178352.34178880 | chr3.34178352.34178871 | 520 | 530 | FALSE | smaller | 10 |
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| chr3.35664360.35665172 | chr3.35664473.35665172 | 700 | 814 | FALSE | smaller | 114 |
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| chr3.36923465.36924009 | chr3.36923525.36924009 | 485 | 546 | FALSE | smaller | 61 |
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| chr3.37055179.37055399 | chr3.37055205.37055399 | 195 | 222 | FALSE | smaller | 27 |
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| chr3.38397308.38397854 | chr3.38397367.38397854 | 488 | 548 | FALSE | smaller | 60 |
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| chr3.41998067.41998508 | chr3.41998067.41998487 | 421 | 443 | FALSE | smaller | 22 |
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| chr3.42489880.42490915 | chr3.42490007.42490915 | 909 | 1037 | FALSE | smaller | 128 |
| chr3.42515386.42516025 | chr3.42515452.42516025 | 574 | 641 | FALSE | smaller | 67 |
| chr3.42520925.42521224 | chr3.42520950.42521224 | 275 | 301 | FALSE | smaller | 26 |
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| chr3.43769367.43771282 | chr3.43769372.43771282 | 1911 | 1917 | FALSE | smaller | 6 |
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| chr3.45036993.45038173 | chr3.45037077.45038173 | 1097 | 1182 | FALSE | smaller | 85 |
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| chr3.51669161.51669633 | chr3.51669161.51669575 | 415 | 474 | FALSE | smaller | 59 |
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| chr3.52253275.52254117 | chr3.52253311.52254117 | 807 | 844 | FALSE | smaller | 37 |
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| chr3.52299898.52301072 | chr3.52299898.52301020 | 1123 | 1176 | FALSE | smaller | 53 |
| chr3.52444952.52445468 | chr3.52444986.52445468 | 483 | 518 | FALSE | smaller | 35 |
| chr3.52449732.52450646 | chr3.52449734.52450519 | 786 | 916 | FALSE | smaller | 130 |
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| chr3.52519557.52519854 | chr3.52519557.52519797 | 241 | 299 | FALSE | smaller | 58 |
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| chr3.52536280.52537421 | chr3.52536298.52537421 | 1124 | 1143 | FALSE | smaller | 19 |
| chr3.52643077.52643693 | chr3.52643077.52643577 | 501 | 618 | FALSE | smaller | 117 |
| chr3.52714379.52715421 | chr3.52714389.52715421 | 1033 | 1044 | FALSE | smaller | 11 |
| chr3.52801837.52802525 | chr3.52801837.52802408 | 572 | 690 | FALSE | smaller | 118 |
| chr3.52998979.52999737 | chr3.52998979.52999564 | 586 | 760 | FALSE | smaller | 174 |
| chr3.53113814.53114567 | chr3.53113814.53114396 | 583 | 755 | FALSE | smaller | 172 |
| chr3.53162397.53163379 | chr3.53162397.53163348 | 952 | 984 | FALSE | smaller | 32 |
| chr3.53235959.53236688 | chr3.53235959.53236574 | 616 | 731 | FALSE | smaller | 115 |
| chr3.53242091.53242665 | chr3.53242203.53242665 | 463 | 576 | FALSE | smaller | 113 |
| chr3.53328008.53328320 | chr3.53328034.53328293 | 260 | 314 | FALSE | smaller | 54 |
| chr3.53501122.53501597 | chr3.53501122.53501463 | 342 | 477 | FALSE | smaller | 135 |
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| chr3.53780263.53780498 | chr3.53780272.53780498 | 227 | 237 | FALSE | smaller | 10 |
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| chr3.137761309.137761938 | chr3.137761309.137761909 | 601 | 631 | FALSE | smaller | 30 |
| chr3.137770616.137771180 | chr3.137770686.137771180 | 495 | 566 | FALSE | smaller | 71 |
| chr3.138012514.138012870 | chr3.138012633.138012847 | 215 | 358 | FALSE | smaller | 143 |
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| chr5.2011599.2012103 | chr5.2011599.2012048 | 450 | 506 | FALSE | smaller | 56 |
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| chr5.14250098.14250520 | chr5.14250098.14250489 | 392 | 424 | FALSE | smaller | 32 |
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| chr5.14487011.14487471 | chr5.14487011.14487385 | 375 | 462 | FALSE | smaller | 87 |
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| chr5.16700928.16701583 | chr5.16700976.16701583 | 608 | 657 | FALSE | smaller | 49 |
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| chr5.41509236.41510936 | chr5.41509786.41510936 | 1151 | 1702 | FALSE | smaller | 551 |
| chr5.41708220.41708802 | chr5.41708220.41708722 | 503 | 584 | FALSE | smaller | 81 |
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| chr5.42691413.42691935 | chr5.42691413.42691911 | 499 | 524 | FALSE | smaller | 25 |
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| chr5.43007448.43008838 | chr5.43007496.43008838 | 1343 | 1392 | FALSE | smaller | 49 |
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| chr5.43555882.43557743 | chr5.43555882.43557554 | 1673 | 1863 | FALSE | smaller | 190 |
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| chr5.44789260.44789748 | chr5.44789331.44789748 | 418 | 490 | FALSE | smaller | 72 |
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| chr5.49659478.49659943 | chr5.49659573.49659938 | 366 | 467 | FALSE | smaller | 101 |
| chr5.50684749.50685216 | chr5.50684773.50685216 | 444 | 469 | FALSE | smaller | 25 |
| chr5.50962879.50963847 | chr5.50962879.50963812 | 934 | 970 | FALSE | smaller | 36 |
| chr5.51382725.51384123 | chr5.51382735.51384123 | 1389 | 1400 | FALSE | smaller | 11 |
| chr5.51388782.51390200 | chr5.51388782.51390160 | 1379 | 1420 | FALSE | smaller | 41 |
| chr5.51398552.51399894 | chr5.51398552.51399889 | 1338 | 1344 | FALSE | smaller | 6 |
| chr5.51653008.51653305 | chr5.51653008.51653302 | 295 | 299 | FALSE | smaller | 4 |
| chr5.52690060.52691077 | chr5.52690185.52691077 | 893 | 1019 | FALSE | smaller | 126 |
| chr5.52725270.52725697 | chr5.52725270.52725689 | 420 | 429 | FALSE | smaller | 9 |
| chr5.53944880.53945258 | chr5.53944880.53945248 | 369 | 380 | FALSE | smaller | 11 |
| chr5.53975844.53976448 | chr5.53975844.53976443 | 600 | 606 | FALSE | smaller | 6 |
| chr5.54116116.54116530 | chr5.54116116.54116406 | 291 | 416 | FALSE | smaller | 125 |
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| chr5.54637647.54637991 | chr5.54637647.54637984 | 338 | 346 | FALSE | smaller | 8 |
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| chr5.57780232.57780756 | chr5.57780327.57780756 | 430 | 526 | FALSE | smaller | 96 |
| chr5.58239781.58240026 | chr5.58239790.58240026 | 237 | 247 | FALSE | smaller | 10 |
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| chr5.58640082.58640604 | chr5.58640149.58640604 | 456 | 524 | FALSE | smaller | 68 |
| chr5.58886502.58887310 | chr5.58886546.58887310 | 765 | 810 | FALSE | smaller | 45 |
| chr5.59025781.59026027 | chr5.59025823.59026009 | 187 | 248 | FALSE | smaller | 61 |
| chr5.59116466.59117044 | chr5.59116577.59117044 | 468 | 580 | FALSE | smaller | 112 |
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| chr5.59800000.59800768 | chr5.59800000.59800697 | 698 | 770 | FALSE | smaller | 72 |
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| chr5.61333499.61333931 | chr5.61333499.61333906 | 408 | 434 | FALSE | smaller | 26 |
| chr5.61449768.61451267 | chr5.61449857.61451267 | 1411 | 1501 | FALSE | smaller | 90 |
| chr5.61610324.61610990 | chr5.61610324.61610846 | 523 | 668 | FALSE | smaller | 145 |
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| chr5.62432161.62432480 | chr5.62432161.62432457 | 297 | 321 | FALSE | smaller | 24 |
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| chr5.63131587.63131961 | chr5.63131587.63131935 | 349 | 376 | FALSE | smaller | 27 |
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| chr5.64299806.64300101 | chr5.64299813.64300101 | 289 | 297 | FALSE | smaller | 8 |
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| chr5.64939515.64940358 | chr5.64939515.64940209 | 695 | 845 | FALSE | smaller | 150 |
| chr5.65128251.65128649 | chr5.65128328.65128649 | 322 | 400 | FALSE | smaller | 78 |
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| chr5.65488351.65488807 | chr5.65488371.65488807 | 437 | 458 | FALSE | smaller | 21 |
| chr5.65623764.65625421 | chr5.65623933.65625421 | 1489 | 1659 | FALSE | smaller | 170 |
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| chr5.65775198.65775789 | chr5.65775198.65775698 | 501 | 593 | FALSE | smaller | 92 |
| chr5.65811055.65811373 | chr5.65811055.65811368 | 314 | 320 | FALSE | smaller | 6 |
| chr5.66518657.66519159 | chr5.66518659.66519159 | 501 | 504 | FALSE | smaller | 3 |
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| chr5.66653310.66653743 | chr5.66653374.66653743 | 370 | 435 | FALSE | smaller | 65 |
| chr5.66661101.66661468 | chr5.66661148.66661468 | 321 | 369 | FALSE | smaller | 48 |
| chr5.66737886.66738583 | chr5.66737886.66738488 | 603 | 699 | FALSE | smaller | 96 |
| chr5.66967166.66967585 | chr5.66967166.66967511 | 346 | 421 | FALSE | smaller | 75 |
| chr5.66970036.66970318 | chr5.66970036.66970295 | 260 | 284 | FALSE | smaller | 24 |
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| chr5.67123901.67124451 | chr5.67123947.67124451 | 505 | 552 | FALSE | smaller | 47 |
| chr5.67164293.67165032 | chr5.67164293.67164924 | 632 | 741 | FALSE | smaller | 109 |
| chr5.67182384.67182810 | chr5.67182384.67182797 | 414 | 428 | FALSE | smaller | 14 |
| chr5.67301230.67301806 | chr5.67301333.67301806 | 474 | 578 | FALSE | smaller | 104 |
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| chr5.68898886.68899509 | chr5.68898893.68899509 | 617 | 625 | FALSE | smaller | 8 |
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| chr5.90093929.90094386 | chr5.90093929.90094378 | 450 | 459 | FALSE | smaller | 9 |
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| chr5.91312720.91313375 | chr5.91312744.91313375 | 632 | 657 | FALSE | smaller | 25 |
| chr5.93273192.93273791 | chr5.93273193.93273784 | 592 | 601 | FALSE | smaller | 9 |
| chr5.93570363.93571465 | chr5.93570363.93571326 | 964 | 1104 | FALSE | smaller | 140 |
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| chr5.93583967.93584533 | chr5.93583967.93584491 | 525 | 568 | FALSE | smaller | 43 |
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| chr5.93596338.93597455 | chr5.93596338.93597360 | 1023 | 1119 | FALSE | smaller | 96 |
| chr5.94094937.94095363 | chr5.94094937.94095289 | 353 | 428 | FALSE | smaller | 75 |
| chr5.95486016.95486836 | chr5.95486016.95486828 | 813 | 822 | FALSE | smaller | 9 |
| chr5.95598527.95599425 | chr5.95598549.95599425 | 877 | 900 | FALSE | smaller | 23 |
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| chr5.95723456.95724013 | chr5.95723466.95724013 | 548 | 559 | FALSE | smaller | 11 |
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| chr6.4371246.4372001 | chr6.4371250.4372001 | 752 | 757 | FALSE | smaller | 5 |
| chr6.4567138.4567463 | chr6.4567155.4567463 | 309 | 327 | FALSE | smaller | 18 |
| chr6.4580390.4580695 | chr6.4580390.4580530 | 141 | 307 | FALSE | smaller | 166 |
| chr6.4589297.4589892 | chr6.4589297.4589737 | 441 | 597 | FALSE | smaller | 156 |
| chr6.4931536.4932179 | chr6.4931550.4932179 | 630 | 645 | FALSE | smaller | 15 |
| chr6.4964792.4965238 | chr6.4964792.4965192 | 401 | 448 | FALSE | smaller | 47 |
| chr6.5030684.5030972 | chr6.5030684.5030970 | 287 | 290 | FALSE | smaller | 3 |
| chr6.5036331.5036924 | chr6.5036331.5036829 | 499 | 595 | FALSE | smaller | 96 |
| chr6.5084384.5086535 | chr6.5084384.5086432 | 2049 | 2153 | FALSE | smaller | 104 |
| chr6.5108094.5108660 | chr6.5108104.5108660 | 557 | 568 | FALSE | smaller | 11 |
| chr6.5213733.5215483 | chr6.5213814.5215483 | 1670 | 1752 | FALSE | smaller | 82 |
| chr6.5469085.5469626 | chr6.5469099.5469626 | 528 | 543 | FALSE | smaller | 15 |
| chr6.5672160.5672978 | chr6.5672266.5672978 | 713 | 820 | FALSE | smaller | 107 |
| chr6.6008555.6009630 | chr6.6008596.6009630 | 1035 | 1077 | FALSE | smaller | 42 |
| chr6.6218685.6219264 | chr6.6218685.6219112 | 428 | 581 | FALSE | smaller | 153 |
| chr6.6494729.6495080 | chr6.6494729.6495079 | 351 | 353 | FALSE | smaller | 2 |
| chr6.6546199.6547715 | chr6.6546199.6547682 | 1484 | 1518 | FALSE | smaller | 34 |
| chr6.6676736.6677202 | chr6.6676736.6677200 | 465 | 468 | FALSE | smaller | 3 |
| chr6.6699765.6700364 | chr6.6699765.6700363 | 599 | 601 | FALSE | smaller | 2 |
| chr6.6890347.6890858 | chr6.6890347.6890853 | 507 | 513 | FALSE | smaller | 6 |
| chr6.6892977.6893609 | chr6.6892977.6893607 | 631 | 634 | FALSE | smaller | 3 |
| chr6.7052513.7052769 | chr6.7052532.7052769 | 238 | 258 | FALSE | smaller | 20 |
| chr6.7172752.7173196 | chr6.7172752.7173128 | 377 | 446 | FALSE | smaller | 69 |
| chr6.7216864.7217545 | chr6.7216864.7217541 | 678 | 683 | FALSE | smaller | 5 |
| chr6.7246342.7246751 | chr6.7246342.7246745 | 404 | 411 | FALSE | smaller | 7 |
| chr6.7275514.7276327 | chr6.7275563.7276327 | 765 | 815 | FALSE | smaller | 50 |
| chr6.7339088.7339362 | chr6.7339088.7339360 | 273 | 276 | FALSE | smaller | 3 |
| chr6.7414652.7415228 | chr6.7414652.7415224 | 573 | 578 | FALSE | smaller | 5 |
| chr6.7568032.7568407 | chr6.7568032.7568354 | 323 | 377 | FALSE | smaller | 54 |
| chr6.7615160.7615889 | chr6.7615285.7615889 | 605 | 731 | FALSE | smaller | 126 |
| chr6.7640759.7641535 | chr6.7640802.7641535 | 734 | 778 | FALSE | smaller | 44 |
| chr6.7672830.7673859 | chr6.7672870.7673703 | 834 | 1031 | FALSE | smaller | 197 |
| chr6.7699371.7699892 | chr6.7699371.7699875 | 505 | 523 | FALSE | smaller | 18 |
| chr6.7727149.7728817 | chr6.7727431.7728817 | 1387 | 1670 | FALSE | smaller | 283 |
| chr6.7805553.7806372 | chr6.7805553.7806246 | 694 | 821 | FALSE | smaller | 127 |
| chr6.8406009.8406328 | chr6.8406014.8406328 | 315 | 321 | FALSE | smaller | 6 |
| chr6.8562117.8562455 | chr6.8562192.8562439 | 248 | 340 | FALSE | smaller | 92 |
| chr6.8579482.8579938 | chr6.8579482.8579902 | 421 | 458 | FALSE | smaller | 37 |
| chr6.9481631.9482603 | chr6.9481631.9482599 | 969 | 974 | FALSE | smaller | 5 |
| chr6.9851905.9852322 | chr6.9851905.9852270 | 366 | 419 | FALSE | smaller | 53 |
| chr6.10383298.10383864 | chr6.10383337.10383864 | 528 | 568 | FALSE | smaller | 40 |
| chr6.10412592.10413153 | chr6.10412594.10413153 | 560 | 563 | FALSE | smaller | 3 |
| chr6.10487683.10488554 | chr6.10487688.10488550 | 863 | 873 | FALSE | smaller | 10 |
| chr6.10587620.10589575 | chr6.10587620.10589574 | 1955 | 1957 | FALSE | smaller | 2 |
| chr6.10621213.10621825 | chr6.10621213.10621806 | 594 | 614 | FALSE | smaller | 20 |
| chr6.11224340.11225004 | chr6.11224423.11225004 | 582 | 666 | FALSE | smaller | 84 |
| chr6.11270111.11270879 | chr6.11270111.11270785 | 675 | 770 | FALSE | smaller | 95 |
| chr6.11316186.11316469 | chr6.11316186.11316425 | 240 | 285 | FALSE | smaller | 45 |
| chr6.11427103.11427809 | chr6.11427262.11427809 | 548 | 708 | FALSE | smaller | 160 |
| chr6.11434801.11435180 | chr6.11434802.11435180 | 379 | 381 | FALSE | smaller | 2 |
| chr6.11494713.11495124 | chr6.11494788.11495124 | 337 | 413 | FALSE | smaller | 76 |
| chr6.11687453.11688197 | chr6.11687453.11688187 | 735 | 746 | FALSE | smaller | 11 |
| chr6.11702275.11702777 | chr6.11702307.11702732 | 426 | 504 | FALSE | smaller | 78 |
| chr6.11847705.11848377 | chr6.11847705.11848341 | 637 | 674 | FALSE | smaller | 37 |
| chr6.11920931.11921375 | chr6.11920931.11921369 | 439 | 446 | FALSE | smaller | 7 |
| chr6.12010758.12011829 | chr6.12010764.12011829 | 1066 | 1073 | FALSE | smaller | 7 |
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| chr6.12031649.12031988 | chr6.12031738.12031988 | 251 | 341 | FALSE | smaller | 90 |
| chr6.12039606.12040104 | chr6.12039612.12040104 | 493 | 500 | FALSE | smaller | 7 |
| chr6.12065349.12066254 | chr6.12065407.12066254 | 848 | 907 | FALSE | smaller | 59 |
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| chr6.12275045.12275449 | chr6.12275098.12275449 | 352 | 406 | FALSE | smaller | 54 |
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| chr6.12577031.12577776 | chr6.12577077.12577732 | 656 | 747 | FALSE | smaller | 91 |
| chr6.12585108.12586143 | chr6.12585108.12586020 | 913 | 1037 | FALSE | smaller | 124 |
| chr6.12749909.12750356 | chr6.12749909.12750323 | 415 | 449 | FALSE | smaller | 34 |
| chr6.13013355.13015207 | chr6.13013359.13015207 | 1849 | 1854 | FALSE | smaller | 5 |
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| chr6.13079144.13079814 | chr6.13079180.13079814 | 635 | 672 | FALSE | smaller | 37 |
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| chr6.13334745.13335324 | chr6.13334745.13335317 | 573 | 581 | FALSE | smaller | 8 |
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| chr6.13385012.13385484 | chr6.13385017.13385484 | 468 | 474 | FALSE | smaller | 6 |
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| chr6.13412498.13412774 | chr6.13412498.13412742 | 245 | 278 | FALSE | smaller | 33 |
| chr6.13422708.13423089 | chr6.13422723.13423089 | 367 | 383 | FALSE | smaller | 16 |
| chr6.13437702.13437900 | chr6.13437702.13437893 | 192 | 200 | FALSE | smaller | 8 |
| chr6.13468911.13469198 | chr6.13468916.13469198 | 283 | 289 | FALSE | smaller | 6 |
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| chr6.13493754.13494587 | chr6.13494278.13494587 | 310 | 835 | FALSE | smaller | 525 |
| chr6.13693429.13694016 | chr6.13693458.13694016 | 559 | 589 | FALSE | smaller | 30 |
| chr6.13859883.13860880 | chr6.13859883.13860297 | 415 | 999 | FALSE | smaller | 584 |
| chr6.13859883.13860880 | chr6.13860328.13860880 | 553 | 999 | FALSE | smaller | 446 |
| chr6.14182958.14183171 | chr6.14182965.14183171 | 207 | 215 | FALSE | smaller | 8 |
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| chr6.14547641.14548023 | chr6.14547653.14548023 | 371 | 384 | FALSE | smaller | 13 |
| chr6.14732601.14733489 | chr6.14732601.14733315 | 715 | 890 | FALSE | smaller | 175 |
| chr6.14855259.14855643 | chr6.14855260.14855643 | 384 | 386 | FALSE | smaller | 2 |
| chr6.14932376.14933488 | chr6.14932452.14933488 | 1037 | 1114 | FALSE | smaller | 77 |
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| chr6.15247905.15248338 | chr6.15247915.15248338 | 424 | 435 | FALSE | smaller | 11 |
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| chr6.15293780.15294234 | chr6.15293780.15294089 | 310 | 456 | FALSE | smaller | 146 |
| chr6.15323033.15323424 | chr6.15323239.15323424 | 186 | 393 | FALSE | smaller | 207 |
| chr6.15358905.15359820 | chr6.15358910.15359820 | 911 | 917 | FALSE | smaller | 6 |
| chr6.15400017.15400363 | chr6.15400030.15400363 | 334 | 348 | FALSE | smaller | 14 |
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| chr6.15512113.15512431 | chr6.15512120.15512431 | 312 | 320 | FALSE | smaller | 8 |
| chr6.15754171.15754573 | chr6.15754296.15754573 | 278 | 404 | FALSE | smaller | 126 |
| chr6.15793090.15793639 | chr6.15793090.15793615 | 526 | 551 | FALSE | smaller | 25 |
| chr6.15962247.15962782 | chr6.15962412.15962782 | 371 | 537 | FALSE | smaller | 166 |
| chr6.15977011.15977448 | chr6.15977023.15977448 | 426 | 439 | FALSE | smaller | 13 |
| chr6.15997685.15998307 | chr6.15997685.15998258 | 574 | 624 | FALSE | smaller | 50 |
| chr6.16215350.16215920 | chr6.16215350.16215919 | 570 | 572 | FALSE | smaller | 2 |
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| chr6.16333641.16334072 | chr6.16333641.16334050 | 410 | 433 | FALSE | smaller | 23 |
| chr6.16416693.16416908 | chr6.16416695.16416907 | 213 | 217 | FALSE | smaller | 4 |
| chr6.16477795.16478082 | chr6.16477810.16478082 | 273 | 289 | FALSE | smaller | 16 |
| chr6.16494802.16495172 | chr6.16494858.16495172 | 315 | 372 | FALSE | smaller | 57 |
| chr6.16497143.16497851 | chr6.16497143.16497849 | 707 | 710 | FALSE | smaller | 3 |
| chr6.16564016.16564757 | chr6.16564139.16564757 | 619 | 743 | FALSE | smaller | 124 |
| chr6.16646102.16646773 | chr6.16646102.16646715 | 614 | 673 | FALSE | smaller | 59 |
| chr6.16803276.16804024 | chr6.16803330.16804021 | 692 | 750 | FALSE | smaller | 58 |
| chr6.17045378.17046200 | chr6.17045378.17046197 | 820 | 824 | FALSE | smaller | 4 |
| chr6.17115688.17116376 | chr6.17115696.17116376 | 681 | 690 | FALSE | smaller | 9 |
| chr6.17278745.17279190 | chr6.17278771.17279190 | 420 | 447 | FALSE | smaller | 27 |
| chr6.17385361.17386063 | chr6.17385405.17386063 | 659 | 704 | FALSE | smaller | 45 |
| chr6.17692089.17692509 | chr6.17692089.17692497 | 409 | 422 | FALSE | smaller | 13 |
| chr6.17815063.17815795 | chr6.17815067.17815795 | 729 | 734 | FALSE | smaller | 5 |
| chr6.18154654.18156402 | chr6.18154654.18156286 | 1633 | 1750 | FALSE | smaller | 117 |
| chr6.18209595.18210025 | chr6.18209665.18210014 | 350 | 432 | FALSE | smaller | 82 |
| chr6.18280181.18280461 | chr6.18280181.18280460 | 280 | 282 | FALSE | smaller | 2 |
| chr6.18609737.18610090 | chr6.18609737.18610037 | 301 | 355 | FALSE | smaller | 54 |
| chr6.19013017.19013679 | chr6.19013119.19013679 | 561 | 664 | FALSE | smaller | 103 |
| chr6.19180671.19181104 | chr6.19180716.19181104 | 389 | 435 | FALSE | smaller | 46 |
| chr6.19205057.19205667 | chr6.19205057.19205566 | 510 | 612 | FALSE | smaller | 102 |
| chr6.19368537.19368995 | chr6.19368589.19368995 | 407 | 460 | FALSE | smaller | 53 |
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| chr6.19836783.19839260 | chr6.19836789.19839260 | 2472 | 2479 | FALSE | smaller | 7 |
| chr6.19935868.19936515 | chr6.19935879.19936464 | 586 | 649 | FALSE | smaller | 63 |
| chr6.20030060.20030557 | chr6.20030063.20030557 | 495 | 499 | FALSE | smaller | 4 |
| chr6.20238704.20239247 | chr6.20238704.20239089 | 386 | 545 | FALSE | smaller | 159 |
| chr6.20328319.20328822 | chr6.20328353.20328822 | 470 | 505 | FALSE | smaller | 35 |
| chr6.20401140.20402026 | chr6.20401140.20401939 | 800 | 888 | FALSE | smaller | 88 |
| chr6.20464098.20464702 | chr6.20464098.20464697 | 600 | 606 | FALSE | smaller | 6 |
| chr6.20528274.20528639 | chr6.20528286.20528639 | 354 | 367 | FALSE | smaller | 13 |
| chr6.21200193.21200819 | chr6.21200193.21200801 | 609 | 628 | FALSE | smaller | 19 |
| chr6.21357348.21357952 | chr6.21357389.21357952 | 564 | 606 | FALSE | smaller | 42 |
| chr6.21360249.21360870 | chr6.21360249.21360814 | 566 | 623 | FALSE | smaller | 57 |
| chr6.21593450.21596124 | chr6.21593450.21595624 | 2175 | 2676 | FALSE | smaller | 501 |
| chr6.21593450.21596124 | chr6.21595646.21596124 | 479 | 2676 | FALSE | smaller | 2197 |
| chr6.21753320.21754079 | chr6.21753479.21754079 | 601 | 761 | FALSE | smaller | 160 |
| chr6.21826033.21826495 | chr6.21826033.21826348 | 316 | 464 | FALSE | smaller | 148 |
| chr6.21846707.21848579 | chr6.21846707.21848505 | 1799 | 1874 | FALSE | smaller | 75 |
| chr6.21870303.21870629 | chr6.21870354.21870629 | 276 | 328 | FALSE | smaller | 52 |
| chr6.22108235.22108640 | chr6.22108315.22108640 | 326 | 407 | FALSE | smaller | 81 |
| chr6.22658859.22659264 | chr6.22658859.22659240 | 382 | 407 | FALSE | smaller | 25 |
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| chr6.22917180.22917649 | chr6.22917180.22917619 | 440 | 471 | FALSE | smaller | 31 |
| chr6.23228329.23228792 | chr6.23228382.23228789 | 408 | 465 | FALSE | smaller | 57 |
| chr6.23454071.23455028 | chr6.23454575.23455028 | 454 | 959 | FALSE | smaller | 505 |
| chr6.23981004.23981385 | chr6.23981004.23981363 | 360 | 383 | FALSE | smaller | 23 |
| chr6.24052552.24053035 | chr6.24052595.24052933 | 339 | 485 | FALSE | smaller | 146 |
| chr6.24389184.24389517 | chr6.24389184.24389510 | 327 | 335 | FALSE | smaller | 8 |
| chr6.24402784.24403747 | chr6.24402797.24403747 | 951 | 965 | FALSE | smaller | 14 |
| chr6.24854056.24854744 | chr6.24854349.24854744 | 396 | 690 | FALSE | smaller | 294 |
| chr6.25346081.25346318 | chr6.25346081.25346314 | 234 | 239 | FALSE | smaller | 5 |
| chr6.25717950.25718385 | chr6.25717955.25718385 | 431 | 437 | FALSE | smaller | 6 |
| chr6.25748684.25749191 | chr6.25748689.25749191 | 503 | 509 | FALSE | smaller | 6 |
| chr6.26043151.26044677 | chr6.26043219.26044677 | 1459 | 1528 | FALSE | smaller | 69 |
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| chr6.26165762.26166545 | chr6.26165762.26166457 | 696 | 785 | FALSE | smaller | 89 |
| chr6.26224901.26226228 | chr6.26224901.26226181 | 1281 | 1329 | FALSE | smaller | 48 |
| chr6.26272131.26273547 | chr6.26272170.26273547 | 1378 | 1418 | FALSE | smaller | 40 |
| chr6.26284576.26286563 | chr6.26284638.26286563 | 1926 | 1989 | FALSE | smaller | 63 |
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| chr6.26595623.26596200 | chr6.26595623.26596198 | 576 | 579 | FALSE | smaller | 3 |
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| chr6.27019527.27020530 | chr6.27019527.27020491 | 965 | 1005 | FALSE | smaller | 40 |
| chr6.27139125.27140143 | chr6.27139125.27140142 | 1018 | 1020 | FALSE | smaller | 2 |
| chr6.27237231.27237657 | chr6.27237236.27237657 | 422 | 428 | FALSE | smaller | 6 |
| chr6.27374431.27375603 | chr6.27374431.27375477 | 1047 | 1174 | FALSE | smaller | 127 |
| chr6.27472327.27473562 | chr6.27472337.27473562 | 1226 | 1237 | FALSE | smaller | 11 |
| chr6.27693611.27694839 | chr6.27693752.27694839 | 1088 | 1230 | FALSE | smaller | 142 |
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| chr6.27807188.27808731 | chr6.27807262.27808696 | 1435 | 1545 | FALSE | smaller | 110 |
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| chr6.27814587.27815508 | chr6.27814587.27815488 | 902 | 923 | FALSE | smaller | 21 |
| chr6.27830709.27831489 | chr6.27830709.27831383 | 675 | 782 | FALSE | smaller | 107 |
| chr6.28080033.28081613 | chr6.28080033.28081578 | 1546 | 1582 | FALSE | smaller | 36 |
| chr6.28161443.28162026 | chr6.28161443.28161941 | 499 | 585 | FALSE | smaller | 86 |
| chr6.28207217.28208125 | chr6.28207217.28207923 | 707 | 910 | FALSE | smaller | 203 |
| chr6.28281277.28282175 | chr6.28281325.28282175 | 851 | 900 | FALSE | smaller | 49 |
| chr6.33512063.33512904 | chr6.33512063.33512750 | 688 | 843 | FALSE | smaller | 155 |
| chr6.33552926.33553400 | chr6.33552926.33553395 | 470 | 476 | FALSE | smaller | 6 |
| chr6.33562250.33562839 | chr6.33562250.33562650 | 401 | 591 | FALSE | smaller | 190 |
| chr6.33632589.33633014 | chr6.33632616.33633014 | 399 | 427 | FALSE | smaller | 28 |
| chr6.33751117.33751488 | chr6.33751172.33751488 | 317 | 373 | FALSE | smaller | 56 |
| chr6.33830552.33831086 | chr6.33830552.33831040 | 489 | 536 | FALSE | smaller | 47 |
| chr6.33839367.33839667 | chr6.33839446.33839667 | 222 | 302 | FALSE | smaller | 80 |
| chr6.33864657.33865239 | chr6.33864657.33865208 | 552 | 584 | FALSE | smaller | 32 |
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| chr6.33984062.33984590 | chr6.33984089.33984527 | 439 | 530 | FALSE | smaller | 91 |
| chr6.34043548.34044985 | chr6.34043643.34044985 | 1343 | 1439 | FALSE | smaller | 96 |
| chr6.34073322.34073993 | chr6.34073322.34073946 | 625 | 673 | FALSE | smaller | 48 |
| chr6.34143966.34144604 | chr6.34143994.34144604 | 611 | 640 | FALSE | smaller | 29 |
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| chr6.34359579.34360201 | chr6.34359579.34360032 | 454 | 624 | FALSE | smaller | 170 |
| chr6.34465515.34465781 | chr6.34465606.34465781 | 176 | 268 | FALSE | smaller | 92 |
| chr6.34465930.34466381 | chr6.34465940.34466381 | 442 | 453 | FALSE | smaller | 11 |
| chr6.34851436.34851930 | chr6.34851436.34851853 | 418 | 496 | FALSE | smaller | 78 |
| chr6.34858753.34859194 | chr6.34858753.34859131 | 379 | 443 | FALSE | smaller | 64 |
| chr6.34901433.34902524 | chr6.34901492.34902524 | 1033 | 1093 | FALSE | smaller | 60 |
| chr6.35128510.35128798 | chr6.35128525.35128798 | 274 | 290 | FALSE | smaller | 16 |
| chr6.35168480.35169382 | chr6.35168499.35169382 | 884 | 904 | FALSE | smaller | 20 |
| chr6.35209966.35210184 | chr6.35209977.35210184 | 208 | 220 | FALSE | smaller | 12 |
| chr6.35281074.35281479 | chr6.35281074.35281375 | 302 | 407 | FALSE | smaller | 105 |
| chr6.35327361.35327744 | chr6.35327361.35327722 | 362 | 385 | FALSE | smaller | 23 |
| chr6.35382971.35383472 | chr6.35382971.35383421 | 451 | 503 | FALSE | smaller | 52 |
| chr6.35397003.35397355 | chr6.35397003.35397283 | 281 | 354 | FALSE | smaller | 73 |
| chr6.35449340.35449885 | chr6.35449340.35449884 | 545 | 547 | FALSE | smaller | 2 |
| chr6.35601228.35602903 | chr6.35601269.35602903 | 1635 | 1677 | FALSE | smaller | 42 |
| chr6.35691324.35691817 | chr6.35691324.35691774 | 451 | 495 | FALSE | smaller | 44 |
| chr6.35731418.35732560 | chr6.35731418.35732541 | 1124 | 1144 | FALSE | smaller | 20 |
| chr6.35736400.35736551 | chr6.35736400.35736522 | 123 | 153 | FALSE | smaller | 30 |
| chr6.35931021.35931708 | chr6.35931021.35931672 | 652 | 689 | FALSE | smaller | 37 |
| chr6.36083264.36083882 | chr6.36083288.36083882 | 595 | 620 | FALSE | smaller | 25 |
| chr6.36123628.36124107 | chr6.36123633.36124107 | 475 | 481 | FALSE | smaller | 6 |
| chr6.36127554.36127796 | chr6.36127589.36127796 | 208 | 244 | FALSE | smaller | 36 |
| chr6.36235388.36235649 | chr6.36235388.36235599 | 212 | 263 | FALSE | smaller | 51 |
| chr6.36345693.36345954 | chr6.36345754.36345951 | 198 | 263 | FALSE | smaller | 65 |
| chr6.36396182.36396939 | chr6.36396182.36396874 | 693 | 759 | FALSE | smaller | 66 |
| chr6.36578369.36578700 | chr6.36578513.36578700 | 188 | 333 | FALSE | smaller | 145 |
| chr6.36590292.36590557 | chr6.36590292.36590555 | 264 | 267 | FALSE | smaller | 3 |
| chr6.36623324.36624312 | chr6.36623324.36624122 | 799 | 990 | FALSE | smaller | 191 |
| chr6.36660953.36661896 | chr6.36660953.36661819 | 867 | 945 | FALSE | smaller | 78 |
| chr6.36682423.36683048 | chr6.36682505.36683048 | 544 | 627 | FALSE | smaller | 83 |
| chr6.36693050.36693556 | chr6.36693052.36693426 | 375 | 508 | FALSE | smaller | 133 |
| chr6.36699196.36699692 | chr6.36699196.36699673 | 478 | 498 | FALSE | smaller | 20 |
| chr6.36711662.36712181 | chr6.36711662.36712105 | 444 | 521 | FALSE | smaller | 77 |
| chr6.36839145.36839666 | chr6.36839368.36839666 | 299 | 523 | FALSE | smaller | 224 |
| chr6.36840540.36841316 | chr6.36840576.36841316 | 741 | 778 | FALSE | smaller | 37 |
| chr6.36858495.36858977 | chr6.36858495.36858964 | 470 | 484 | FALSE | smaller | 14 |
| chr6.36885598.36886323 | chr6.36885598.36886309 | 712 | 727 | FALSE | smaller | 15 |
| chr6.36907869.36908288 | chr6.36907880.36908288 | 409 | 421 | FALSE | smaller | 12 |
| chr6.36927678.36928349 | chr6.36927678.36928332 | 655 | 673 | FALSE | smaller | 18 |
| chr6.36970438.36971496 | chr6.36970548.36971496 | 949 | 1060 | FALSE | smaller | 111 |
| chr6.36971681.36972505 | chr6.36971699.36972505 | 807 | 826 | FALSE | smaller | 19 |
| chr6.36975780.36976369 | chr6.36975780.36976286 | 507 | 591 | FALSE | smaller | 84 |
| chr6.37006825.37007483 | chr6.37006865.37007483 | 619 | 660 | FALSE | smaller | 41 |
| chr6.37029354.37029797 | chr6.37029362.37029797 | 436 | 445 | FALSE | smaller | 9 |
| chr6.37077613.37078425 | chr6.37077695.37078425 | 731 | 814 | FALSE | smaller | 83 |
| chr6.37141402.37141800 | chr6.37141404.37141747 | 344 | 400 | FALSE | smaller | 56 |
| chr6.37172091.37172608 | chr6.37172091.37172530 | 440 | 519 | FALSE | smaller | 79 |
| chr6.37207924.37208460 | chr6.37207924.37208362 | 439 | 538 | FALSE | smaller | 99 |
| chr6.37325676.37326680 | chr6.37325676.37326676 | 1001 | 1006 | FALSE | smaller | 5 |
| chr6.37481613.37482068 | chr6.37481613.37482055 | 443 | 457 | FALSE | smaller | 14 |
| chr6.37512367.37512857 | chr6.37512367.37512709 | 343 | 492 | FALSE | smaller | 149 |
| chr6.37565061.37566793 | chr6.37565061.37566728 | 1668 | 1734 | FALSE | smaller | 66 |
| chr6.37585575.37586466 | chr6.37585575.37586261 | 687 | 893 | FALSE | smaller | 206 |
| chr6.37657283.37658663 | chr6.37657283.37658000 | 718 | 1382 | FALSE | smaller | 664 |
| chr6.37657283.37658663 | chr6.37658038.37658472 | 435 | 1382 | FALSE | smaller | 947 |
| chr6.37664183.37665701 | chr6.37664229.37665701 | 1473 | 1520 | FALSE | smaller | 47 |
| chr6.37691895.37692390 | chr6.37691895.37692375 | 481 | 497 | FALSE | smaller | 16 |
| chr6.37713584.37713994 | chr6.37713762.37713994 | 233 | 412 | FALSE | smaller | 179 |
| chr6.37725717.37726493 | chr6.37725739.37726493 | 755 | 778 | FALSE | smaller | 23 |
| chr6.37820253.37820545 | chr6.37820253.37820504 | 252 | 294 | FALSE | smaller | 42 |
| chr6.37823361.37823645 | chr6.37823367.37823645 | 279 | 286 | FALSE | smaller | 7 |
| chr6.37931105.37931382 | chr6.37931163.37931382 | 220 | 279 | FALSE | smaller | 59 |
| chr6.37994234.37994605 | chr6.37994262.37994605 | 344 | 373 | FALSE | smaller | 29 |
| chr6.38115164.38115558 | chr6.38115266.38115558 | 293 | 396 | FALSE | smaller | 103 |
| chr6.38124290.38124740 | chr6.38124290.38124694 | 405 | 452 | FALSE | smaller | 47 |
| chr6.38239932.38240386 | chr6.38239932.38240376 | 445 | 456 | FALSE | smaller | 11 |
| chr6.38346682.38347050 | chr6.38346762.38347048 | 287 | 370 | FALSE | smaller | 83 |
| chr6.38571687.38571933 | chr6.38571697.38571933 | 237 | 248 | FALSE | smaller | 11 |
| chr6.38639064.38640397 | chr6.38639064.38640395 | 1332 | 1335 | FALSE | smaller | 3 |
| chr6.38974515.38975104 | chr6.38974523.38975104 | 582 | 591 | FALSE | smaller | 9 |
| chr6.39063024.39063917 | chr6.39063024.39063875 | 852 | 895 | FALSE | smaller | 43 |
| chr6.39295346.39295961 | chr6.39295597.39295961 | 365 | 617 | FALSE | smaller | 252 |
| chr6.39303121.39303650 | chr6.39303140.39303650 | 511 | 531 | FALSE | smaller | 20 |
| chr6.39425888.39426566 | chr6.39425888.39426436 | 549 | 680 | FALSE | smaller | 131 |
| chr6.39573122.39573308 | chr6.39573122.39573303 | 182 | 188 | FALSE | smaller | 6 |
| chr6.39849422.39850092 | chr6.39849422.39850090 | 669 | 672 | FALSE | smaller | 3 |
| chr6.39887451.39888503 | chr6.39887656.39888503 | 848 | 1054 | FALSE | smaller | 206 |
| chr6.40171101.40172080 | chr6.40171108.40172080 | 973 | 981 | FALSE | smaller | 8 |
| chr6.40196857.40197391 | chr6.40196857.40197304 | 448 | 536 | FALSE | smaller | 88 |
| chr6.40210594.40211191 | chr6.40210596.40211191 | 596 | 599 | FALSE | smaller | 3 |
| chr6.40308490.40309130 | chr6.40308490.40309125 | 636 | 642 | FALSE | smaller | 6 |
| chr6.40629887.40630585 | chr6.40629887.40630551 | 665 | 700 | FALSE | smaller | 35 |
| chr6.40775697.40776108 | chr6.40775727.40776108 | 382 | 413 | FALSE | smaller | 31 |
| chr6.40779483.40779817 | chr6.40779483.40779811 | 329 | 336 | FALSE | smaller | 7 |
| chr6.40848073.40848448 | chr6.40848113.40848443 | 331 | 377 | FALSE | smaller | 46 |
| chr6.40867514.40867870 | chr6.40867526.40867870 | 345 | 358 | FALSE | smaller | 13 |
| chr6.41039171.41039745 | chr6.41039171.41039658 | 488 | 576 | FALSE | smaller | 88 |
| chr6.41071825.41074166 | chr6.41071845.41074166 | 2322 | 2343 | FALSE | smaller | 21 |
| chr6.41373738.41374775 | chr6.41373914.41374775 | 862 | 1039 | FALSE | smaller | 177 |
| chr6.41424363.41424913 | chr6.41424396.41424913 | 518 | 552 | FALSE | smaller | 34 |
| chr6.41625472.41625894 | chr6.41625472.41625882 | 411 | 424 | FALSE | smaller | 13 |
| chr6.41713102.41714286 | chr6.41713102.41714099 | 998 | 1186 | FALSE | smaller | 188 |
| chr6.41734671.41735944 | chr6.41734671.41735940 | 1270 | 1275 | FALSE | smaller | 5 |
| chr6.41779558.41780654 | chr6.41779575.41780654 | 1080 | 1098 | FALSE | smaller | 18 |
| chr6.41786200.41788119 | chr6.41786234.41788119 | 1886 | 1921 | FALSE | smaller | 35 |
| chr6.41804235.41804474 | chr6.41804235.41804466 | 232 | 241 | FALSE | smaller | 9 |
| chr6.41805577.41806974 | chr6.41805577.41806947 | 1371 | 1399 | FALSE | smaller | 28 |
| chr6.41920656.41922063 | chr6.41920656.41922018 | 1363 | 1409 | FALSE | smaller | 46 |
| chr6.41936087.41936636 | chr6.41936087.41936631 | 545 | 551 | FALSE | smaller | 6 |
| chr6.41966385.41967577 | chr6.41966385.41967532 | 1148 | 1194 | FALSE | smaller | 46 |
| chr6.42010059.42010802 | chr6.42010059.42010714 | 656 | 745 | FALSE | smaller | 89 |
| chr6.42067974.42068579 | chr6.42067974.42068542 | 569 | 607 | FALSE | smaller | 38 |
| chr6.42099355.42100709 | chr6.42099379.42100709 | 1331 | 1356 | FALSE | smaller | 25 |
| chr6.42124313.42124792 | chr6.42124418.42124792 | 375 | 481 | FALSE | smaller | 106 |
| chr6.42166398.42167156 | chr6.42166398.42167089 | 692 | 760 | FALSE | smaller | 68 |
| chr6.42251609.42252578 | chr6.42251700.42252578 | 879 | 971 | FALSE | smaller | 92 |
| chr6.42278982.42279938 | chr6.42279138.42279938 | 801 | 958 | FALSE | smaller | 157 |
| chr6.42315774.42317087 | chr6.42315774.42317084 | 1311 | 1315 | FALSE | smaller | 4 |
| chr6.42377451.42378097 | chr6.42377475.42378097 | 623 | 648 | FALSE | smaller | 25 |
| chr6.42451524.42452059 | chr6.42451524.42452057 | 534 | 537 | FALSE | smaller | 3 |
| chr6.42553907.42554410 | chr6.42554036.42554410 | 375 | 505 | FALSE | smaller | 130 |
| chr6.42702143.42702552 | chr6.42702170.42702552 | 383 | 411 | FALSE | smaller | 28 |
| chr6.42901452.42902065 | chr6.42901452.42902044 | 593 | 615 | FALSE | smaller | 22 |
| chr6.42923626.42924251 | chr6.42923626.42924221 | 596 | 627 | FALSE | smaller | 31 |
| chr6.43075507.43077360 | chr6.43075507.43077339 | 1833 | 1855 | FALSE | smaller | 22 |
| chr6.43174131.43174920 | chr6.43174131.43174892 | 762 | 791 | FALSE | smaller | 29 |
| chr6.43181846.43182659 | chr6.43181858.43182562 | 705 | 815 | FALSE | smaller | 110 |
| chr6.43257167.43257474 | chr6.43257270.43257423 | 154 | 309 | FALSE | smaller | 155 |
| chr6.43280855.43281089 | chr6.43280855.43281046 | 192 | 236 | FALSE | smaller | 44 |
| chr6.43454442.43455565 | chr6.43454453.43455565 | 1113 | 1125 | FALSE | smaller | 12 |
| chr6.43516087.43517723 | chr6.43516217.43517723 | 1507 | 1638 | FALSE | smaller | 131 |
| chr6.43575279.43576535 | chr6.43575285.43576535 | 1251 | 1258 | FALSE | smaller | 7 |
| chr6.43635278.43636807 | chr6.43635479.43636807 | 1329 | 1531 | FALSE | smaller | 202 |
| chr6.43681348.43682042 | chr6.43681709.43682042 | 334 | 696 | FALSE | smaller | 362 |
| chr6.43807288.43808074 | chr6.43807321.43808074 | 754 | 788 | FALSE | smaller | 34 |
| chr6.43813207.43813440 | chr6.43813207.43813436 | 230 | 235 | FALSE | smaller | 5 |
| chr6.43851021.43851670 | chr6.43851046.43851670 | 625 | 651 | FALSE | smaller | 26 |
| chr6.43855989.43856639 | chr6.43856058.43856639 | 582 | 652 | FALSE | smaller | 70 |
| chr6.43903840.43904149 | chr6.43903842.43904149 | 308 | 311 | FALSE | smaller | 3 |
| chr6.43943758.43944259 | chr6.43943787.43944259 | 473 | 503 | FALSE | smaller | 30 |
| chr6.44051586.44051809 | chr6.44051588.44051809 | 222 | 225 | FALSE | smaller | 3 |
| chr6.44166489.44166865 | chr6.44166623.44166865 | 243 | 378 | FALSE | smaller | 135 |
| chr6.44180855.44181411 | chr6.44180855.44181405 | 551 | 558 | FALSE | smaller | 7 |
| chr6.44221243.44221836 | chr6.44221260.44221836 | 577 | 595 | FALSE | smaller | 18 |
| chr6.44245898.44248087 | chr6.44245959.44248087 | 2129 | 2191 | FALSE | smaller | 62 |
| chr6.44264898.44265815 | chr6.44264910.44265815 | 906 | 919 | FALSE | smaller | 13 |
| chr6.44270865.44271218 | chr6.44270865.44271211 | 347 | 355 | FALSE | smaller | 8 |
| chr6.44275171.44277282 | chr6.44275171.44276081 | 911 | 2113 | FALSE | smaller | 1202 |
| chr6.44275171.44277282 | chr6.44276248.44277282 | 1035 | 2113 | FALSE | smaller | 1078 |
| chr6.44309069.44309439 | chr6.44309086.44309401 | 316 | 372 | FALSE | smaller | 56 |
| chr6.44328336.44328787 | chr6.44328336.44328758 | 423 | 453 | FALSE | smaller | 30 |
| chr6.44339926.44340210 | chr6.44339949.44340210 | 262 | 286 | FALSE | smaller | 24 |
| chr6.44430219.44430849 | chr6.44430238.44430849 | 612 | 632 | FALSE | smaller | 20 |
| chr6.44477344.44477741 | chr6.44477355.44477741 | 387 | 399 | FALSE | smaller | 12 |
| chr6.44593360.44594065 | chr6.44593457.44594065 | 609 | 707 | FALSE | smaller | 98 |
| chr6.44668034.44668412 | chr6.44668042.44668412 | 371 | 380 | FALSE | smaller | 9 |
| chr6.44696218.44696556 | chr6.44696218.44696529 | 312 | 340 | FALSE | smaller | 28 |
| chr6.44738682.44739165 | chr6.44738682.44738917 | 236 | 485 | FALSE | smaller | 249 |
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| chr6.45520317.45520994 | chr6.45520389.45520957 | 569 | 679 | FALSE | smaller | 110 |
| chr6.45681828.45682200 | chr6.45681949.45682200 | 252 | 374 | FALSE | smaller | 122 |
| chr6.45697523.45697963 | chr6.45697523.45697919 | 397 | 442 | FALSE | smaller | 45 |
| chr6.45986665.45987405 | chr6.45986665.45987398 | 734 | 742 | FALSE | smaller | 8 |
| chr6.45997877.45998290 | chr6.45997877.45998208 | 332 | 415 | FALSE | smaller | 83 |
| chr6.46008328.46008554 | chr6.46008345.46008554 | 210 | 228 | FALSE | smaller | 18 |
| chr6.46473904.46474278 | chr6.46473904.46474236 | 333 | 376 | FALSE | smaller | 43 |
| chr6.46491099.46492337 | chr6.46491116.46492337 | 1222 | 1240 | FALSE | smaller | 18 |
| chr6.46756645.46757620 | chr6.46756645.46757560 | 916 | 977 | FALSE | smaller | 61 |
| chr6.46964692.46965030 | chr6.46964754.46965022 | 269 | 340 | FALSE | smaller | 71 |
| chr6.46993909.46994166 | chr6.46993909.46994149 | 241 | 259 | FALSE | smaller | 18 |
| chr6.47132731.47133080 | chr6.47132731.47133079 | 349 | 351 | FALSE | smaller | 2 |
| chr6.47264590.47265139 | chr6.47264590.47265072 | 483 | 551 | FALSE | smaller | 68 |
| chr6.47788000.47788665 | chr6.47788010.47788665 | 656 | 667 | FALSE | smaller | 11 |
| chr6.47853254.47853999 | chr6.47853254.47853964 | 711 | 747 | FALSE | smaller | 36 |
| chr6.48641609.48641976 | chr6.48641611.48641976 | 366 | 369 | FALSE | smaller | 3 |
| chr6.48887543.48887874 | chr6.48887543.48887861 | 319 | 333 | FALSE | smaller | 14 |
| chr6.49336566.49337142 | chr6.49336566.49337125 | 560 | 578 | FALSE | smaller | 18 |
| chr6.49582513.49582919 | chr6.49582513.49582912 | 400 | 408 | FALSE | smaller | 8 |
| chr6.49913636.49913985 | chr6.49913636.49913983 | 348 | 351 | FALSE | smaller | 3 |
| chr6.49982178.49982664 | chr6.49982193.49982664 | 472 | 488 | FALSE | smaller | 16 |
| chr6.50269085.50269555 | chr6.50269107.50269555 | 449 | 472 | FALSE | smaller | 23 |
| chr6.50707678.50708152 | chr6.50707693.50708152 | 460 | 476 | FALSE | smaller | 16 |
| chr6.50845501.50845885 | chr6.50845501.50845801 | 301 | 386 | FALSE | smaller | 85 |
| chr6.51998924.51999165 | chr6.51998924.51999106 | 183 | 243 | FALSE | smaller | 60 |
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| chr6.52499853.52500314 | chr6.52499853.52500175 | 323 | 463 | FALSE | smaller | 140 |
| chr6.52514581.52514894 | chr6.52514581.52514886 | 306 | 315 | FALSE | smaller | 9 |
| chr6.52550042.52550401 | chr6.52550042.52550400 | 359 | 361 | FALSE | smaller | 2 |
| chr6.52551450.52552720 | chr6.52551450.52552576 | 1127 | 1272 | FALSE | smaller | 145 |
| chr6.52566267.52567381 | chr6.52566458.52567361 | 904 | 1116 | FALSE | smaller | 212 |
| chr6.52598128.52598618 | chr6.52598128.52598524 | 397 | 492 | FALSE | smaller | 95 |
| chr6.52663859.52665072 | chr6.52663859.52664950 | 1092 | 1215 | FALSE | smaller | 123 |
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| chr6.52839365.52839980 | chr6.52839511.52839980 | 470 | 617 | FALSE | smaller | 147 |
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| chr6.52929304.52929838 | chr6.52929319.52929838 | 520 | 536 | FALSE | smaller | 16 |
| chr6.53161657.53162031 | chr6.53161657.53162013 | 357 | 376 | FALSE | smaller | 19 |
| chr6.53171760.53172392 | chr6.53171760.53172300 | 541 | 634 | FALSE | smaller | 93 |
| chr6.53325950.53326292 | chr6.53325950.53326290 | 341 | 344 | FALSE | smaller | 3 |
| chr6.53358750.53360020 | chr6.53358844.53360020 | 1177 | 1272 | FALSE | smaller | 95 |
| chr6.53411458.53411784 | chr6.53411458.53411774 | 317 | 328 | FALSE | smaller | 11 |
| chr6.53436829.53438104 | chr6.53436829.53438070 | 1242 | 1277 | FALSE | smaller | 35 |
| chr6.53493135.53493610 | chr6.53493140.53493610 | 471 | 477 | FALSE | smaller | 6 |
| chr6.53520050.53520865 | chr6.53520393.53520865 | 473 | 817 | FALSE | smaller | 344 |
| chr6.53524238.53524732 | chr6.53524238.53524723 | 486 | 496 | FALSE | smaller | 10 |
| chr6.53883321.53883586 | chr6.53883367.53883586 | 220 | 267 | FALSE | smaller | 47 |
| chr6.53894132.53894502 | chr6.53894134.53894422 | 289 | 372 | FALSE | smaller | 83 |
| chr6.54003131.54003734 | chr6.54003161.54003734 | 574 | 605 | FALSE | smaller | 31 |
| chr6.54273239.54274094 | chr6.54273239.54274057 | 819 | 857 | FALSE | smaller | 38 |
| chr6.54824496.54824936 | chr6.54824506.54824880 | 375 | 442 | FALSE | smaller | 67 |
| chr6.55868837.55869358 | chr6.55868863.55869358 | 496 | 523 | FALSE | smaller | 27 |
| chr6.56246782.56247876 | chr6.56246782.56247854 | 1073 | 1096 | FALSE | smaller | 23 |
| chr6.56664120.56664472 | chr6.56664131.56664472 | 342 | 354 | FALSE | smaller | 12 |
| chr6.56749338.56750184 | chr6.56749338.56750093 | 756 | 848 | FALSE | smaller | 92 |
| chr6.56757285.56757958 | chr6.56757285.56757861 | 577 | 675 | FALSE | smaller | 98 |
| chr6.56763734.56764477 | chr6.56763734.56764459 | 726 | 745 | FALSE | smaller | 19 |
| chr6.56820890.56821468 | chr6.56820890.56821390 | 501 | 580 | FALSE | smaller | 79 |
| chr6.56842668.56844469 | chr6.56842698.56844469 | 1772 | 1803 | FALSE | smaller | 31 |
| chr6.56938548.56939123 | chr6.56938548.56939121 | 574 | 577 | FALSE | smaller | 3 |
| chr6.57961037.57961662 | chr6.57961073.57961662 | 590 | 627 | FALSE | smaller | 37 |
| chr6.57983698.57984451 | chr6.57983698.57984238 | 541 | 755 | FALSE | smaller | 214 |
| chr6.62492467.62492844 | chr6.62492467.62492749 | 283 | 379 | FALSE | smaller | 96 |
| chr6.63347615.63347930 | chr6.63347615.63347915 | 301 | 317 | FALSE | smaller | 16 |
| chr6.64926056.64926555 | chr6.64926056.64926471 | 416 | 501 | FALSE | smaller | 85 |
| chr6.64987173.64987747 | chr6.64987173.64987745 | 573 | 576 | FALSE | smaller | 3 |
| chr6.65538753.65538917 | chr6.65538769.65538917 | 149 | 166 | FALSE | smaller | 17 |
| chr6.65554470.65554861 | chr6.65554484.65554861 | 378 | 393 | FALSE | smaller | 15 |
| chr6.66103557.66104853 | chr6.66103557.66104686 | 1130 | 1298 | FALSE | smaller | 168 |
| chr6.68195015.68195469 | chr6.68195015.68195409 | 395 | 456 | FALSE | smaller | 61 |
| chr6.68572628.68572953 | chr6.68572646.68572953 | 308 | 327 | FALSE | smaller | 19 |
| chr6.68777947.68778933 | chr6.68778000.68778933 | 934 | 988 | FALSE | smaller | 54 |
| chr6.69796158.69797452 | chr6.69796162.69797452 | 1291 | 1296 | FALSE | smaller | 5 |
| chr6.69866407.69867878 | chr6.69866413.69867878 | 1466 | 1473 | FALSE | smaller | 7 |
| chr6.70248782.70249530 | chr6.70248782.70249335 | 554 | 750 | FALSE | smaller | 196 |
| chr6.70566635.70567336 | chr6.70566635.70567333 | 699 | 703 | FALSE | smaller | 4 |
| chr6.70709949.70710552 | chr6.70709949.70710434 | 486 | 605 | FALSE | smaller | 119 |
| chr6.71028068.71028643 | chr6.71028068.71028518 | 451 | 577 | FALSE | smaller | 126 |
| chr6.71404198.71405196 | chr6.71404240.71405196 | 957 | 1000 | FALSE | smaller | 43 |
| chr6.71446320.71446812 | chr6.71446385.71446812 | 428 | 494 | FALSE | smaller | 66 |
| chr6.71531903.71532279 | chr6.71531945.71532279 | 335 | 378 | FALSE | smaller | 43 |
| chr6.72288443.72288993 | chr6.72288461.72288974 | 514 | 552 | FALSE | smaller | 38 |
| chr6.72418516.72418835 | chr6.72418516.72418833 | 318 | 321 | FALSE | smaller | 3 |
| chr6.72465054.72465597 | chr6.72465059.72465597 | 539 | 545 | FALSE | smaller | 6 |
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| chr6.163665247.163665837 | chr6.163665281.163665837 | 557 | 592 | FALSE | smaller | 35 |
| chr6.163759682.163759891 | chr6.163759682.163759835 | 154 | 211 | FALSE | smaller | 57 |
| chr6.163821123.163821442 | chr6.163821130.163821442 | 313 | 321 | FALSE | smaller | 8 |
| chr6.163955038.163955693 | chr6.163955240.163955693 | 454 | 657 | FALSE | smaller | 203 |
| chr6.163962645.163963803 | chr6.163962645.163963665 | 1021 | 1160 | FALSE | smaller | 139 |
| chr6.164275209.164275551 | chr6.164275209.164275548 | 340 | 344 | FALSE | smaller | 4 |
| chr6.165516076.165516538 | chr6.165516118.165516538 | 421 | 464 | FALSE | smaller | 43 |
| chr6.165605326.165605728 | chr6.165605326.165605678 | 353 | 404 | FALSE | smaller | 51 |
| chr6.165677239.165677902 | chr6.165676634.165677238 | 1 | 665 | FALSE | smaller | 664 |
| chr6.165705977.165706327 | chr6.165706014.165706327 | 314 | 352 | FALSE | smaller | 38 |
| chr6.165707305.165708442 | chr6.165707305.165708367 | 1063 | 1139 | FALSE | smaller | 76 |
| chr6.165794121.165794633 | chr6.165794121.165794608 | 488 | 514 | FALSE | smaller | 26 |
| chr6.166307171.166308796 | chr6.166307209.166308796 | 1588 | 1627 | FALSE | smaller | 39 |
| chr6.166448551.166448920 | chr6.166448557.166448920 | 364 | 371 | FALSE | smaller | 7 |
| chr6.166491000.166491475 | chr6.166491023.166491475 | 453 | 477 | FALSE | smaller | 24 |
| chr6.166526951.166527422 | chr6.166526951.166527344 | 394 | 473 | FALSE | smaller | 79 |
| chr6.166583284.166583763 | chr6.166583336.166583763 | 428 | 481 | FALSE | smaller | 53 |
| chr6.166626899.166628536 | chr6.166626918.166628536 | 1619 | 1639 | FALSE | smaller | 20 |
| chr6.166633545.166634057 | chr6.166633555.166634047 | 493 | 514 | FALSE | smaller | 21 |
| chr6.166640358.166640850 | chr6.166640358.166640767 | 410 | 494 | FALSE | smaller | 84 |
| chr6.166660262.166660789 | chr6.166660289.166660789 | 501 | 529 | FALSE | smaller | 28 |
| chr6.166743898.166744556 | chr6.166743898.166744527 | 630 | 660 | FALSE | smaller | 30 |
| chr6.166861961.166863313 | chr6.166862057.166863313 | 1257 | 1354 | FALSE | smaller | 97 |
| chr6.166900958.166902572 | chr6.166901803.166902572 | 770 | 1616 | FALSE | smaller | 846 |
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| chr6.167058330.167058663 | chr6.167058344.167058663 | 320 | 335 | FALSE | smaller | 15 |
| chr6.167091690.167092043 | chr6.167091690.167092012 | 323 | 355 | FALSE | smaller | 32 |
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| chr6.167470867.167471402 | chr6.167470940.167471402 | 463 | 537 | FALSE | smaller | 74 |
| chr6.167524706.167525410 | chr6.167524706.167525344 | 639 | 706 | FALSE | smaller | 67 |
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| chr6.167588352.167589176 | chr6.167588352.167589138 | 787 | 826 | FALSE | smaller | 39 |
| chr6.167650867.167652393 | chr6.167650867.167652339 | 1473 | 1528 | FALSE | smaller | 55 |
| chr6.167669694.167670508 | chr6.167669694.167670459 | 766 | 816 | FALSE | smaller | 50 |
| chr6.167745019.167745986 | chr6.167745019.167745831 | 813 | 969 | FALSE | smaller | 156 |
| chr6.167757169.167757858 | chr6.167757227.167757858 | 632 | 691 | FALSE | smaller | 59 |
| chr6.167766995.167767581 | chr6.167766995.167767242 | 248 | 588 | FALSE | smaller | 340 |
| chr6.167777696.167778146 | chr6.167777730.167778146 | 417 | 452 | FALSE | smaller | 35 |
| chr6.167796092.167796538 | chr6.167796092.167796447 | 356 | 448 | FALSE | smaller | 92 |
| chr6.167825458.167827245 | chr6.167825458.167827189 | 1732 | 1789 | FALSE | smaller | 57 |
| chr6.167827489.167828107 | chr6.167827613.167828107 | 495 | 620 | FALSE | smaller | 125 |
| chr6.167936668.167936896 | chr6.167936668.167936853 | 186 | 230 | FALSE | smaller | 44 |
| chr6.168035110.168036537 | chr6.168035185.168036537 | 1353 | 1429 | FALSE | smaller | 76 |
| chr6.168054790.168055367 | chr6.168054790.168055335 | 546 | 579 | FALSE | smaller | 33 |
| chr6.168062823.168063230 | chr6.168062882.168063230 | 349 | 409 | FALSE | smaller | 60 |
| chr6.168318981.168319988 | chr6.168319281.168319988 | 708 | 1009 | FALSE | smaller | 301 |
| chr6.168370178.168370759 | chr6.168370178.168370731 | 554 | 583 | FALSE | smaller | 29 |
| chr6.168518636.168519344 | chr6.168518814.168519219 | 406 | 710 | FALSE | smaller | 304 |
| chr6.169023643.169024605 | chr6.169023797.169024605 | 809 | 964 | FALSE | smaller | 155 |
| chr6.169146646.169146959 | chr6.169146646.169146893 | 248 | 315 | FALSE | smaller | 67 |
| chr6.169247656.169248845 | chr6.169247656.169248658 | 1003 | 1191 | FALSE | smaller | 188 |
| chr6.169292476.169293043 | chr6.169292481.169293043 | 563 | 569 | FALSE | smaller | 6 |
| chr6.169371180.169372333 | chr6.169371180.169372091 | 912 | 1155 | FALSE | smaller | 243 |
| chr6.169421645.169421860 | chr6.169421645.169421833 | 189 | 217 | FALSE | smaller | 28 |
| chr6.169564397.169565034 | chr6.169564403.169564856 | 454 | 639 | FALSE | smaller | 185 |
| chr6.169621212.169621673 | chr6.169621234.169621573 | 340 | 463 | FALSE | smaller | 123 |
| chr6.169624068.169625597 | chr6.169624334.169625587 | 1254 | 1531 | FALSE | smaller | 277 |
| chr6.169638652.169639261 | chr6.169638673.169639261 | 589 | 611 | FALSE | smaller | 22 |
| chr6.169701466.169702936 | chr6.169701466.169702908 | 1443 | 1472 | FALSE | smaller | 29 |
| chr6.170087977.170089100 | chr6.170088001.170088523 | 523 | 1125 | FALSE | smaller | 602 |
| chr6.170087977.170089100 | chr6.170088649.170089100 | 452 | 1125 | FALSE | smaller | 673 |
| chr6.170096657.170097534 | chr6.170096657.170097404 | 748 | 879 | FALSE | smaller | 131 |
| chr6.170129696.170130592 | chr6.170129696.170130368 | 673 | 898 | FALSE | smaller | 225 |
| chr6.170134003.170134562 | chr6.170134003.170134438 | 436 | 561 | FALSE | smaller | 125 |
| chr6.170163381.170163695 | chr6.170163388.170163681 | 294 | 316 | FALSE | smaller | 22 |
| chr6.170177392.170177877 | chr6.170177394.170177877 | 484 | 487 | FALSE | smaller | 3 |
| chr6.170231760.170232170 | chr6.170231830.170232170 | 341 | 412 | FALSE | smaller | 71 |
| chr7.216508.217161 | chr7.216508.216976 | 469 | 655 | FALSE | smaller | 186 |
| chr7.222143.223625 | chr7.222143.223476 | 1334 | 1484 | FALSE | smaller | 150 |
| chr7.234776.235087 | chr7.234776.235019 | 244 | 313 | FALSE | smaller | 69 |
| chr7.236937.237548 | chr7.236937.237432 | 496 | 613 | FALSE | smaller | 117 |
| chr7.266751.267369 | chr7.266751.267334 | 584 | 620 | FALSE | smaller | 36 |
| chr7.486071.486655 | chr7.486071.486521 | 451 | 586 | FALSE | smaller | 135 |
| chr7.512862.513231 | chr7.512953.513168 | 216 | 371 | FALSE | smaller | 155 |
| chr7.516885.517381 | chr7.516885.517341 | 457 | 498 | FALSE | smaller | 41 |
| chr7.587694.588079 | chr7.587705.588079 | 375 | 387 | FALSE | smaller | 12 |
| chr7.604212.605042 | chr7.604212.604916 | 705 | 832 | FALSE | smaller | 127 |
| chr7.663730.664524 | chr7.663740.664524 | 785 | 796 | FALSE | smaller | 11 |
| chr7.726269.727338 | chr7.726397.727338 | 942 | 1071 | FALSE | smaller | 129 |
| chr7.768996.769511 | chr7.769073.769511 | 439 | 517 | FALSE | smaller | 78 |
| chr7.867137.868058 | chr7.867337.868058 | 722 | 923 | FALSE | smaller | 201 |
| chr7.893534.894207 | chr7.893693.894207 | 515 | 675 | FALSE | smaller | 160 |
| chr7.923175.923884 | chr7.923175.923872 | 698 | 711 | FALSE | smaller | 13 |
| chr7.935748.936995 | chr7.935888.936995 | 1108 | 1249 | FALSE | smaller | 141 |
| chr7.954388.955146 | chr7.954388.955129 | 742 | 760 | FALSE | smaller | 18 |
| chr7.962441.962831 | chr7.962441.962771 | 331 | 392 | FALSE | smaller | 61 |
| chr7.999862.1000434 | chr7.999862.1000259 | 398 | 574 | FALSE | smaller | 176 |
| chr7.1007558.1009311 | chr7.1007558.1009238 | 1681 | 1755 | FALSE | smaller | 74 |
| chr7.1016298.1017540 | chr7.1016469.1017374 | 906 | 1244 | FALSE | smaller | 338 |
| chr7.1057540.1058012 | chr7.1057556.1058012 | 457 | 474 | FALSE | smaller | 17 |
| chr7.1070434.1071037 | chr7.1070521.1071037 | 517 | 605 | FALSE | smaller | 88 |
| chr7.1095650.1095977 | chr7.1095650.1095918 | 269 | 329 | FALSE | smaller | 60 |
| chr7.1100877.1101259 | chr7.1100877.1101142 | 266 | 384 | FALSE | smaller | 118 |
| chr7.1182895.1183781 | chr7.1182935.1183781 | 847 | 888 | FALSE | smaller | 41 |
| chr7.1210289.1210808 | chr7.1210289.1210770 | 482 | 521 | FALSE | smaller | 39 |
| chr7.1224332.1224771 | chr7.1224332.1224738 | 407 | 441 | FALSE | smaller | 34 |
| chr7.1231218.1231665 | chr7.1231226.1231664 | 439 | 449 | FALSE | smaller | 10 |
| chr7.1289838.1290613 | chr7.1289838.1290610 | 773 | 777 | FALSE | smaller | 4 |
| chr7.1425689.1425976 | chr7.1425725.1425976 | 252 | 289 | FALSE | smaller | 37 |
| chr7.1443585.1444011 | chr7.1443657.1443983 | 327 | 428 | FALSE | smaller | 101 |
| chr7.1487919.1488402 | chr7.1487919.1488325 | 407 | 485 | FALSE | smaller | 78 |
| chr7.1520631.1521032 | chr7.1520631.1520977 | 347 | 403 | FALSE | smaller | 56 |
| chr7.1618301.1619604 | chr7.1619135.1619590 | 456 | 1305 | FALSE | smaller | 849 |
| chr7.1693229.1695224 | chr7.1693229.1693883 | 655 | 1997 | FALSE | smaller | 1342 |
| chr7.1693229.1695224 | chr7.1693970.1695224 | 1255 | 1997 | FALSE | smaller | 742 |
| chr7.1744779.1746394 | chr7.1745368.1746394 | 1027 | 1617 | FALSE | smaller | 590 |
| chr7.1749512.1750352 | chr7.1749627.1750352 | 726 | 842 | FALSE | smaller | 116 |
| chr7.1825460.1826454 | chr7.1825460.1826187 | 728 | 996 | FALSE | smaller | 268 |
| chr7.1871984.1872561 | chr7.1871991.1872561 | 571 | 579 | FALSE | smaller | 8 |
| chr7.1888637.1889941 | chr7.1888720.1889941 | 1222 | 1306 | FALSE | smaller | 84 |
| chr7.1928875.1929748 | chr7.1928953.1929748 | 796 | 875 | FALSE | smaller | 79 |
| chr7.1938807.1939887 | chr7.1938938.1939887 | 950 | 1082 | FALSE | smaller | 132 |
| chr7.1979443.1980110 | chr7.1979443.1979996 | 554 | 669 | FALSE | smaller | 115 |
| chr7.2041894.2042432 | chr7.2041941.2042324 | 384 | 540 | FALSE | smaller | 156 |
| chr7.2082231.2082604 | chr7.2082278.2082604 | 327 | 375 | FALSE | smaller | 48 |
| chr7.2099727.2100576 | chr7.2099740.2100576 | 837 | 851 | FALSE | smaller | 14 |
| chr7.2102361.2103630 | chr7.2102406.2103630 | 1225 | 1271 | FALSE | smaller | 46 |
| chr7.2146732.2147660 | chr7.2146732.2147133 | 402 | 930 | FALSE | smaller | 528 |
| chr7.2165301.2165733 | chr7.2165301.2165566 | 266 | 434 | FALSE | smaller | 168 |
| chr7.2172666.2173201 | chr7.2172688.2173201 | 514 | 537 | FALSE | smaller | 23 |
| chr7.2174997.2175804 | chr7.2175002.2175804 | 803 | 809 | FALSE | smaller | 6 |
| chr7.2241707.2242988 | chr7.2241707.2242946 | 1240 | 1283 | FALSE | smaller | 43 |
| chr7.2309850.2310386 | chr7.2309850.2310339 | 490 | 538 | FALSE | smaller | 48 |
| chr7.2321092.2321469 | chr7.2321184.2321469 | 286 | 379 | FALSE | smaller | 93 |
| chr7.2380127.2380457 | chr7.2380127.2380349 | 223 | 332 | FALSE | smaller | 109 |
| chr7.2403143.2404655 | chr7.2403158.2404655 | 1498 | 1514 | FALSE | smaller | 16 |
| chr7.2468607.2469339 | chr7.2468800.2469339 | 540 | 734 | FALSE | smaller | 194 |
| chr7.2523959.2524331 | chr7.2523995.2524331 | 337 | 374 | FALSE | smaller | 37 |
| chr7.2577008.2577543 | chr7.2577008.2577530 | 523 | 537 | FALSE | smaller | 14 |
| chr7.2577975.2578821 | chr7.2577984.2578821 | 838 | 848 | FALSE | smaller | 10 |
| chr7.2605869.2606417 | chr7.2605929.2606417 | 489 | 550 | FALSE | smaller | 61 |
| chr7.2612969.2613733 | chr7.2612969.2613598 | 630 | 766 | FALSE | smaller | 136 |
| chr7.2639852.2640396 | chr7.2639928.2640396 | 469 | 546 | FALSE | smaller | 77 |
| chr7.2647127.2647773 | chr7.2647133.2647773 | 641 | 648 | FALSE | smaller | 7 |
| chr7.2717309.2717823 | chr7.2717315.2717823 | 509 | 516 | FALSE | smaller | 7 |
| chr7.2789699.2790128 | chr7.2789761.2790128 | 368 | 431 | FALSE | smaller | 63 |
| chr7.2803115.2803371 | chr7.2803170.2803371 | 202 | 258 | FALSE | smaller | 56 |
| chr7.2815034.2815679 | chr7.2815034.2815565 | 532 | 647 | FALSE | smaller | 115 |
| chr7.2843898.2844809 | chr7.2843898.2844106 | 209 | 913 | FALSE | smaller | 704 |
| chr7.2843898.2844809 | chr7.2844145.2844809 | 665 | 913 | FALSE | smaller | 248 |
| chr7.3272617.3273293 | chr7.3272617.3273166 | 550 | 678 | FALSE | smaller | 128 |
| chr7.3399952.3400223 | chr7.3399958.3400223 | 266 | 273 | FALSE | smaller | 7 |
| chr7.3482342.3482740 | chr7.3482342.3482704 | 363 | 400 | FALSE | smaller | 37 |
| chr7.3537387.3537961 | chr7.3537387.3537920 | 534 | 576 | FALSE | smaller | 42 |
| chr7.4011220.4011603 | chr7.4011274.4011603 | 330 | 385 | FALSE | smaller | 55 |
| chr7.4077141.4077634 | chr7.4077268.4077631 | 364 | 495 | FALSE | smaller | 131 |
| chr7.4134551.4134989 | chr7.4134551.4134971 | 421 | 440 | FALSE | smaller | 19 |
| chr7.4139754.4140353 | chr7.4139754.4140290 | 537 | 601 | FALSE | smaller | 64 |
| chr7.4264354.4265302 | chr7.4264572.4265256 | 685 | 950 | FALSE | smaller | 265 |
| chr7.4282808.4283217 | chr7.4282944.4283217 | 274 | 411 | FALSE | smaller | 137 |
| chr7.4603991.4604550 | chr7.4603993.4604550 | 558 | 561 | FALSE | smaller | 3 |
| chr7.4681980.4682873 | chr7.4681981.4682863 | 883 | 895 | FALSE | smaller | 12 |
| chr7.4709245.4709541 | chr7.4709289.4709541 | 253 | 298 | FALSE | smaller | 45 |
| chr7.4764626.4765056 | chr7.4764626.4765039 | 414 | 432 | FALSE | smaller | 18 |
| chr7.4791882.4793070 | chr7.4791882.4792833 | 952 | 1190 | FALSE | smaller | 238 |
| chr7.4800608.4802252 | chr7.4800608.4802218 | 1611 | 1646 | FALSE | smaller | 35 |
| chr7.4849545.4849728 | chr7.4849554.4849728 | 175 | 185 | FALSE | smaller | 10 |
| chr7.4882176.4884008 | chr7.4882176.4883983 | 1808 | 1834 | FALSE | smaller | 26 |
| chr7.4952111.4952666 | chr7.4952122.4952666 | 545 | 557 | FALSE | smaller | 12 |
| chr7.5073059.5073958 | chr7.5073276.5073958 | 683 | 901 | FALSE | smaller | 218 |
| chr7.5085332.5086002 | chr7.5085337.5086002 | 666 | 672 | FALSE | smaller | 6 |
| chr7.5307831.5308826 | chr7.5307831.5308816 | 986 | 997 | FALSE | smaller | 11 |
| chr7.5345617.5346250 | chr7.5345710.5346235 | 526 | 635 | FALSE | smaller | 109 |
| chr7.5354518.5355368 | chr7.5354518.5355258 | 741 | 852 | FALSE | smaller | 111 |
| chr7.5361036.5362687 | chr7.5361036.5362435 | 1400 | 1653 | FALSE | smaller | 253 |
| chr7.5387852.5389745 | chr7.5387852.5388758 | 907 | 1895 | FALSE | smaller | 988 |
| chr7.5387852.5389745 | chr7.5388912.5389745 | 834 | 1895 | FALSE | smaller | 1061 |
| chr7.5412933.5413560 | chr7.5412933.5413410 | 478 | 629 | FALSE | smaller | 151 |
| chr7.5418462.5419393 | chr7.5418462.5419370 | 909 | 933 | FALSE | smaller | 24 |
| chr7.5448610.5449046 | chr7.5448610.5449022 | 413 | 438 | FALSE | smaller | 25 |
| chr7.5538748.5539222 | chr7.5538748.5539045 | 298 | 476 | FALSE | smaller | 178 |
| chr7.5595159.5595926 | chr7.5595235.5595926 | 692 | 769 | FALSE | smaller | 77 |
| chr7.5604846.5605395 | chr7.5604846.5605337 | 492 | 551 | FALSE | smaller | 59 |
| chr7.5608318.5608710 | chr7.5608318.5608701 | 384 | 394 | FALSE | smaller | 10 |
| chr7.5667437.5667790 | chr7.5667437.5667756 | 320 | 355 | FALSE | smaller | 35 |
| chr7.6015830.6016181 | chr7.6015854.6016181 | 328 | 353 | FALSE | smaller | 25 |
| chr7.6026092.6026814 | chr7.6026092.6026780 | 689 | 724 | FALSE | smaller | 35 |
| chr7.6104268.6105152 | chr7.6104268.6105129 | 862 | 886 | FALSE | smaller | 24 |
| chr7.6172043.6172535 | chr7.6172186.6172535 | 350 | 494 | FALSE | smaller | 144 |
| chr7.6179323.6180494 | chr7.6179323.6180159 | 837 | 1173 | FALSE | smaller | 336 |
| chr7.6197876.6198620 | chr7.6197876.6198499 | 624 | 746 | FALSE | smaller | 122 |
| chr7.6392899.6394013 | chr7.6392924.6394013 | 1090 | 1116 | FALSE | smaller | 26 |
| chr7.6421619.6422836 | chr7.6421627.6422836 | 1210 | 1219 | FALSE | smaller | 9 |
| chr7.6503406.6504608 | chr7.6503417.6504608 | 1192 | 1204 | FALSE | smaller | 12 |
| chr7.6516418.6516921 | chr7.6516423.6516921 | 499 | 505 | FALSE | smaller | 6 |
| chr7.6607255.6608583 | chr7.6607255.6608498 | 1244 | 1330 | FALSE | smaller | 86 |
| chr7.6614932.6616414 | chr7.6614932.6615494 | 563 | 1484 | FALSE | smaller | 921 |
| chr7.6614932.6616414 | chr7.6615727.6616414 | 688 | 1484 | FALSE | smaller | 796 |
| chr7.6621690.6622976 | chr7.6622138.6622976 | 839 | 1288 | FALSE | smaller | 449 |
| chr7.6636821.6637381 | chr7.6636876.6637381 | 506 | 562 | FALSE | smaller | 56 |
| chr7.6663854.6664766 | chr7.6663854.6664490 | 637 | 914 | FALSE | smaller | 277 |
| chr7.7416065.7416516 | chr7.7416065.7416484 | 420 | 453 | FALSE | smaller | 33 |
| chr7.7564103.7564742 | chr7.7564103.7564714 | 612 | 641 | FALSE | smaller | 29 |
| chr7.7640217.7641437 | chr7.7640256.7641242 | 987 | 1222 | FALSE | smaller | 235 |
| chr7.7805185.7805966 | chr7.7805185.7805961 | 777 | 783 | FALSE | smaller | 6 |
| chr7.7861307.7861631 | chr7.7861307.7861625 | 319 | 326 | FALSE | smaller | 7 |
| chr7.8034804.8036246 | chr7.8034804.8035482 | 679 | 1444 | FALSE | smaller | 765 |
| chr7.8034804.8036246 | chr7.8035528.8036246 | 719 | 1444 | FALSE | smaller | 725 |
| chr7.8143600.8143804 | chr7.8143600.8143799 | 200 | 206 | FALSE | smaller | 6 |
| chr7.8214228.8214728 | chr7.8214246.8214728 | 483 | 502 | FALSE | smaller | 19 |
| chr7.8404785.8405039 | chr7.8404797.8405039 | 243 | 256 | FALSE | smaller | 13 |
| chr7.8443887.8444431 | chr7.8443887.8444430 | 544 | 546 | FALSE | smaller | 2 |
| chr7.10237354.10237923 | chr7.10237356.10237923 | 568 | 571 | FALSE | smaller | 3 |
| chr7.10779660.10780518 | chr7.10779672.10780518 | 847 | 860 | FALSE | smaller | 13 |
| chr7.11259681.11260344 | chr7.11259681.11260343 | 663 | 665 | FALSE | smaller | 2 |
| chr7.11604618.11605093 | chr7.11604618.11605020 | 403 | 477 | FALSE | smaller | 74 |
| chr7.11698482.11698955 | chr7.11698514.11698955 | 442 | 475 | FALSE | smaller | 33 |
| chr7.12518659.12519942 | chr7.12518659.12519790 | 1132 | 1285 | FALSE | smaller | 153 |
| chr7.12569890.12571352 | chr7.12570320.12571352 | 1033 | 1464 | FALSE | smaller | 431 |
| chr7.12686492.12687881 | chr7.12686502.12687881 | 1380 | 1391 | FALSE | smaller | 11 |
| chr7.12757499.12757783 | chr7.12757500.12757783 | 284 | 286 | FALSE | smaller | 2 |
| chr7.12770301.12770782 | chr7.12770301.12770756 | 456 | 483 | FALSE | smaller | 27 |
| chr7.13092033.13092642 | chr7.13092061.13092642 | 582 | 611 | FALSE | smaller | 29 |
| chr7.13717782.13718494 | chr7.13717801.13718483 | 683 | 714 | FALSE | smaller | 31 |
| chr7.13851767.13852103 | chr7.13851851.13852103 | 253 | 338 | FALSE | smaller | 85 |
| chr7.13898913.13899479 | chr7.13898919.13899449 | 531 | 568 | FALSE | smaller | 37 |
| chr7.14834450.14834752 | chr7.14834450.14834669 | 220 | 304 | FALSE | smaller | 84 |
| chr7.14851595.14851984 | chr7.14851595.14851977 | 383 | 391 | FALSE | smaller | 8 |
| chr7.15007590.15007854 | chr7.15007590.15007834 | 245 | 266 | FALSE | smaller | 21 |
| chr7.16644642.16647066 | chr7.16644652.16646525 | 1874 | 2426 | FALSE | smaller | 552 |
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| chr7.45689444.45689961 | chr7.45689444.45689864 | 421 | 519 | FALSE | smaller | 98 |
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| chr7.45974314.45974849 | chr7.45974339.45974839 | 501 | 537 | FALSE | smaller | 36 |
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| chr7.46290467.46291394 | chr7.46290467.46291326 | 860 | 929 | FALSE | smaller | 69 |
| chr7.46539938.46540567 | chr7.46539938.46540557 | 620 | 631 | FALSE | smaller | 11 |
| chr7.46713966.46714429 | chr7.46713980.46714429 | 450 | 465 | FALSE | smaller | 15 |
| chr7.46811131.46812058 | chr7.46811241.46812058 | 818 | 929 | FALSE | smaller | 111 |
| chr7.46859974.46860466 | chr7.46859974.46860456 | 483 | 494 | FALSE | smaller | 11 |
| chr7.46930446.46930649 | chr7.46930446.46930643 | 198 | 205 | FALSE | smaller | 7 |
| chr7.46937912.46938182 | chr7.46937912.46938177 | 266 | 272 | FALSE | smaller | 6 |
| chr7.47180136.47180645 | chr7.47180136.47180601 | 466 | 511 | FALSE | smaller | 45 |
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| chr7.47370435.47370988 | chr7.47370435.47370983 | 549 | 555 | FALSE | smaller | 6 |
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| chr7.47604608.47605391 | chr7.47604608.47605317 | 710 | 785 | FALSE | smaller | 75 |
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| chr7.47799617.47799894 | chr7.47799664.47799894 | 231 | 279 | FALSE | smaller | 48 |
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| chr7.158828311.158830324 | chr7.158828311.158830313 | 2003 | 2015 | FALSE | smaller | 12 |
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| chr9.39809397.39810104 | chr9.39809456.39810104 | 649 | 709 | FALSE | smaller | 60 |
| chr9.40584024.40584322 | chr9.40584104.40584322 | 219 | 300 | FALSE | smaller | 81 |
| chr9.40592017.40592242 | chr9.40592017.40592220 | 204 | 227 | FALSE | smaller | 23 |
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| chr9.81558478.81558825 | chr9.81558485.81558825 | 341 | 349 | FALSE | smaller | 8 |
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| chr9.83543957.83544853 | chr9.83544007.83544853 | 847 | 898 | FALSE | smaller | 51 |
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| chr11.57514110.57516117 | chr11.57514159.57516117 | 1959 | 2009 | FALSE | smaller | 50 |
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| chr11.59614771.59616788 | chr11.59614792.59616788 | 1997 | 2019 | FALSE | smaller | 22 |
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| chr11.72321475.72322006 | chr11.72321520.72321986 | 467 | 533 | FALSE | smaller | 66 |
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| chr11.72838690.72839181 | chr11.72838695.72839181 | 487 | 493 | FALSE | smaller | 6 |
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| chr11.132008989.132009300 | chr11.132008989.132009236 | 248 | 313 | FALSE | smaller | 65 |
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b <- filter_by_overlaps(old_peaks_gr,new_peaks_gr)
b.a <- filter_by_overlaps(new_peaks_gr, old_peaks_gr)
b.b <- filter_by_non_overlaps(new_peaks_gr, old_peaks_gr)
b.c <- filter_by_non_overlaps(old_peaks_gr, new_peaks_gr)
new_peaks_gr %>% as.data.frame %>% summarize(mean =mean(width)) mean
1 606.7886number of peaks in old data set: 151666
number of peaks in new data set: 155557
number of overlaps in between both sets: 149895
number of peaks in new set that do not overlap old set: 5678
number of peaks in old set that do not overlap new set: 1985
Snyder_41peaks <- read.delim("data/other_papers/ENCFF966JZT_bed_Snyder_41peaks.bed",header=TRUE) %>%
GRanges()
heart_overlap <- join_overlap_intersect(new_peaks_gr,Snyder_41peaks)
ol_count <- heart_overlap %>%
as.data.frame %>%
distinct(peakid) %>% tallynumber of peaks in Snyder heart ATAC set: 218982
number of peaks in my data set: 155557
number of unique peaks that overlap the Snyder data: 66927
percent of my peaks overlapping Snyder Heart left ventricle data: 43.02
%
group <- c( rep(c(1,2,3,4,5,6,7,8,9,10,11,12),4))
group <- factor(group, levels =c("1","2","3","4","5","6","7","8","9","10","11","12"))
short_names <- paste0(pca_final_four_anno$indv,"_",pca_final_four_anno$trt,"_",pca_final_four_anno$time)
dge <- DGEList.data.frame(counts = x4_filtered, group = group, genes = row.names(x4_filtered))
group_1 <- c(rep(c("DNR_24","DNR_3","DOX_24","DOX_3","EPI_24","EPI_3","MTX_24","MTX_3","TRZ_24","TRZ_3","VEH_24", "VEH_3"),4))
dge$group$indv <- pca_final_four_anno$indv
dge$group$time <- pca_final_four_anno$time
dge$group$trt <- pca_final_four_anno$trt
indv <- pca_final_four_anno$indv
efit4 <- readRDS("data/Final_four_data/efit4_filt_bl.RDS")
# mm <- model.matrix(~0 +group_1)
# colnames(mm) <- c("DNR_24", "DNR_3", "DOX_24","DOX_3","EPI_24", "EPI_3","MTX_24", "MTX_3", "TRZ_24","TRZ_3","VEH_24", "VEH_3")
#
# y <- voom(dge$counts, mm,plot =TRUE)
# #
# corfit <- duplicateCorrelation(y, mm, block = indv)
# #
# v <- voom(dge$counts, mm, block = indv, correlation = corfit$consensus)
# #
# fit <- lmFit(v, mm, block = indv, correlation = corfit$consensus)
# colnames(mm) <- c("DNR_24","DNR_3","DOX_24","DOX_3","EPI_24","EPI_3","MTX_24","MTX_3","TRZ_24","TRZ_3","VEH_24", "VEH_3")
#
#
# cm <- makeContrasts(
# DNR_3.VEH_3 = DNR_3-VEH_3,
# DOX_3.VEH_3 = DOX_3-VEH_3,
# EPI_3.VEH_3 = EPI_3-VEH_3,
# MTX_3.VEH_3 = MTX_3-VEH_3,
# TRZ_3.VEH_3 = TRZ_3-VEH_3,
# DNR_24.VEH_24 =DNR_24-VEH_24,
# DOX_24.VEH_24= DOX_24-VEH_24,
# EPI_24.VEH_24= EPI_24-VEH_24,
# MTX_24.VEH_24= MTX_24-VEH_24,
# TRZ_24.VEH_24= TRZ_24-VEH_24,
# levels = mm)
# vfit <- lmFit(y, mm)
# vfit<- contrasts.fit(vfit, contrasts=cm)
#
# efit4 <- eBayes(vfit)
# saveRDS(efit4,"data/Final_four_data/efit4_filt_bl.RDS")
results = decideTests(efit4)
summary(results) DNR_3.VEH_3 DOX_3.VEH_3 EPI_3.VEH_3 MTX_3.VEH_3 TRZ_3.VEH_3
Down 5925 448 2959 22 0
NotSig 146648 155046 151480 155536 155560
Up 2987 66 1121 2 0
DNR_24.VEH_24 DOX_24.VEH_24 EPI_24.VEH_24 MTX_24.VEH_24 TRZ_24.VEH_24
Down 27539 21704 23244 1585 0
NotSig 92353 107936 107084 149730 155560
Up 35668 25920 25232 4245 0# V.DNR_3.top_ff= topTable(efit4, coef=1, adjust.method="BH", number=Inf, sort.by="p") %>% rownames_to_column("peak")
# V.DOX_3.top_ff= topTable(efit4, coef=2, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.EPI_3.top_ff= topTable(efit4, coef=3, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.MTX_3.top_ff= topTable(efit4, coef=4, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.TRZ_3.top_ff= topTable(efit4, coef=5, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.DNR_24.top_ff= topTable(efit4, coef=6, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.DOX_24.top_ff= topTable(efit4, coef=7, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.EPI_24.top_ff= topTable(efit4, coef=8, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.MTX_24.top_ff= topTable(efit4, coef=9, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
# V.TRZ_24.top_ff= topTable(efit4, coef=10, adjust.method="BH", number=Inf, sort.by="p")%>% rownames_to_column("peak")
#
# toplist_full_ff <- list(V.DNR_3.top_ff, V.DOX_3.top_ff,V.EPI_3.top_ff,V.MTX_3.top_ff,V.TRZ_3.top_ff,V.DNR_24.top_ff, V.DOX_24.top_ff,V.EPI_24.top_ff,V.MTX_24.top_ff,V.TRZ_24.top_ff)
# names(toplist_full_ff) <- c("DNR_3", "DOX_3","EPI_3","MTX_3","TRZ_3","DNR_24", "DOX_24","EPI_24","MTX_24","TRZ_24")
# toplist_ff <-map_df(toplist_full_ff, ~as.data.frame(.x), .id="trt_time")
# #
# toplist_ff <- toplist_ff %>%
# separate(trt_time, into= c("trt","time"), sep = "_") %>%
# mutate(trt=factor(trt, levels = c("DOX","EPI","DNR","MTX","TRZ"))) %>%
# mutate(time = factor(time, levels = c("3", "24"), labels = c("3 hours", "24 hours")))
# saveRDS(toplist_ff,"data/Final_four_data/toplist_ff.RDS")
toplist_ff <- readRDS("data/Final_four_data/toplist_ff.RDS")
median_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")
median_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")### making median lfc
median_df <- toplist_ff %>%
pivot_wider(., id_cols=c(time,peak), names_from = trt, values_from = logFC) %>%
rowwise() %>%
mutate(median_lfc= median(c_across(DNR:TRZ)))
median_3_lfc <- median_df %>%
dplyr::filter(time == "3 hours") %>%
ungroup() %>%
dplyr::select(time, peak,median_lfc) %>%
dplyr::rename("med_3h_lfc"=median_lfc)
median_24_lfc <- median_df %>%
dplyr::filter(time == "24 hours") %>%
ungroup() %>%
dplyr::select(time, peak,median_lfc) %>%
dplyr::rename("med_24h_lfc"=median_lfc)
write_csv(median_3_lfc, "data/Final_four_data/median_3_lfc.csv")
write_csv(median_24_lfc, "data/Final_four_data/median_24_lfc.csv")FCmatrix_ff <- subset(efit4$coefficients)
colnames(FCmatrix_ff) <-
c("DNR\n3h",
"DOX\n3h",
"EPI\n3h",
"MTX\n3h",
"TRZ\n3h",
"DNR\n24h",
"DOX\n24h",
"EPI\n24h",
"MTX\n24h",
"TRZ\n24h"
)
mat_col_ff <-
data.frame(
time = c(rep("3 hours", 5), rep("24 hours", 5)),
class = (c(
"AC", "AC", "AC", "nAC","nAC", "AC", "AC", "AC", "nAC","nAC"
)))
rownames(mat_col_ff) <- colnames(FCmatrix_ff)
mat_colors_ff <-
list(
time = c("pink", "chocolate4"),
class = c("yellow1", "lightgreen"))
names(mat_colors_ff$time) <- unique(mat_col_ff$time)
names(mat_colors_ff$class) <- unique(mat_col_ff$class)
# names(mat_colors_FC$TOP2i) <- unique(mat_col_FC$TOP2i)
corrFC_ff <- cor(FCmatrix_ff)
htanno_ff <- HeatmapAnnotation(df = mat_col_ff, col = mat_colors_ff)
Heatmap(corrFC_ff, top_annotation = htanno_ff)V.DNR_3.top= topTable(efit4, coef=1, adjust.method="BH", number=Inf, sort.by="p")
V.DOX_3.top= topTable(efit4, coef=2, adjust.method="BH", number=Inf, sort.by="p")
V.EPI_3.top= topTable(efit4, coef=3, adjust.method="BH", number=Inf, sort.by="p")
V.MTX_3.top= topTable(efit4, coef=4, adjust.method="BH", number=Inf, sort.by="p")
V.TRZ_3.top= topTable(efit4, coef=5, adjust.method="BH", number=Inf, sort.by="p")
V.DNR_24.top= topTable(efit4, coef=6, adjust.method="BH", number=Inf, sort.by="p")
V.DOX_24.top= topTable(efit4, coef=7, adjust.method="BH", number=Inf, sort.by="p")
V.EPI_24.top= topTable(efit4, coef=8, adjust.method="BH", number=Inf, sort.by="p")
V.MTX_24.top= topTable(efit4, coef=9, adjust.method="BH", number=Inf, sort.by="p")
V.TRZ_24.top= topTable(efit4, coef=10, adjust.method="BH", number=Inf, sort.by="p")
# volcanoplot(efit2,coef = c(1,2,3,4,5,6),style = "p-value",
# # highlight = 8,
# # names = efit2$genes$SYMBOL,
# hl.col = "red",xlab = "Log2 Fold Change",
# ylab = NULL,pch = 16,cex = 0.35,
# main = "test")
#
plot_filenames <- c("V.DNR_3.top","V.DOX_3.top","V.EPI_3.top","V.MTX_3.top",
"V.TRZ_.top","V.DNR_24.top","V.DOX_24.top","V.EPI_24.top",
"V.MTX_24.top","V.TRZ_24.top")
plot_files <- c( V.DNR_3.top,V.DOX_3.top,V.EPI_3.top,V.MTX_3.top,
V.TRZ_3.top,V.DNR_24.top,V.DOX_24.top,V.EPI_24.top,
V.MTX_24.top,V.TRZ_24.top)
volcanosig <- function(df, psig.lvl) {
df <- df %>%
mutate(threshold = ifelse(adj.P.Val > psig.lvl, "A", ifelse(adj.P.Val <= psig.lvl & logFC<=0,"B","C")))
# ifelse(adj.P.Val <= psig.lvl & logFC >= 0,"B", "C")))
##This is where I could add labels, but I have taken out
# df <- df %>% mutate(genelabels = "")
# df$genelabels[1:topg] <- df$rownames[1:topg]
ggplot(df, aes(x=logFC, y=-log10(P.Value))) +
geom_point(aes(color=threshold))+
# geom_text_repel(aes(label = genelabels), segment.curvature = -1e-20,force = 1,size=2.5,
# arrow = arrow(length = unit(0.015, "npc")), max.overlaps = Inf) +
#geom_hline(yintercept = -log10(psig.lvl))+
xlab(expression("Log"[2]*" FC"))+
ylab(expression("-log"[10]*"P Value"))+
scale_color_manual(values = c("black", "red","blue"))+
theme_cowplot()+
ylim(0,20)+
xlim(-6,6)+
theme(legend.position = "none",
plot.title = element_text(size = rel(1.5), hjust = 0.5),
axis.title = element_text(size = rel(0.8)))
}
#v1<- volcanosig(V.DA24.top, 0.01,0)
v1 <- volcanosig(V.DNR_3.top, 0.01)+ ggtitle("DNR 3 hour")
v2 <- volcanosig(V.DNR_24.top, 0.01)+ ggtitle("DNR 24 hour")+ylab("")
v3 <- volcanosig(V.DOX_3.top, 0.01)+ ggtitle("DOX 3 hour")
v4 <- volcanosig(V.DOX_24.top, 0.01)+ ggtitle("DOX 24 hour")+ylab("")
v5 <- volcanosig(V.EPI_3.top, 0.01)+ ggtitle("EPI 3 hour")
v6 <- volcanosig(V.EPI_24.top, 0.01)+ ggtitle("EPI 24 hour")+ylab("")
v7 <- volcanosig(V.MTX_3.top, 0.01)+ ggtitle("MTX 3 hour")
v8 <- volcanosig(V.MTX_24.top, 0.01)+ ggtitle("MTX 24 hour")+ylab("")
v9 <- volcanosig(V.TRZ_3.top, 0.01)+ ggtitle("TRZ 3 hour")
v10 <- volcanosig(V.TRZ_24.top, 0.01)+ ggtitle("TRZ 24 hour")+ylab("")
# volcanoplot(efit2,coef = 10,style = "p-value",
# highlight = 8,
# names = efit2$genes$SYMBOL,
# hl.col = "red",xlab = "Log2 Fold Change",
# ylab = NULL,pch = 16,cex = 0.35,
# main = "Using Trastuzumab 24 hour data and volcanoplot function")
plot_grid(v1,v2, rel_widths =c(.8,1))plot_grid(v3,v4, rel_widths =c(.8,1))plot_grid(v5,v6, rel_widths =c(.8,1))plot_grid(v7,v8, rel_widths =c(.8,1))plot_grid(v9,v10, rel_widths =c(.8,1))DNR_3_top3_ff <- row.names(V.DNR_3.top[1:3,])
log_filt_ff <- filt4_matrix_lcpm %>%
as.data.frame() %>%
rename_with(.,~gsub(pattern = "Ind1_75", replacement = "1_",.)) %>%
rename_with(.,~gsub(pattern = "Ind2_87", replacement = "2_",.)) %>%
rename_with(.,~gsub(pattern = "Ind3_77", replacement = "3_",.)) %>%
rename_with(.,~gsub(pattern = "Ind6_71", replacement = "6_",.)) %>%
rename_with(.,~gsub( "DX" ,'DOX',.)) %>%
rename_with(.,~gsub( "DA" ,'DNR',.)) %>%
rename_with(.,~gsub( "E" ,'EPI',.)) %>%
rename_with(.,~gsub( "T" ,'TRZ',.)) %>%
rename_with(.,~gsub( "M" ,'MTX',.)) %>%
rename_with(.,~gsub( "V" ,'VEH',.)) %>%
rename_with(.,~gsub("24h","_24h",.)) %>%
rename_with(.,~gsub("3h","_3h",.))
row.names(log_filt_ff) <- row.names(filt4_matrix_lcpm)
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DNR_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour DNR")+
scale_fill_manual(values = drug_pal)+
theme_bw()DOX_3_top3_ff <- row.names(V.DOX_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DOX_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour DOX")+
scale_fill_manual(values = drug_pal)+
theme_bw()EPI_3_top3_ff <- row.names(V.EPI_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% EPI_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour EPI")+
scale_fill_manual(values = drug_pal)+
theme_bw()MTX_3_top3_ff <- row.names(V.MTX_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% MTX_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour MTX")+
scale_fill_manual(values = drug_pal)+
theme_bw()TRZ_3_top3_ff <- row.names(V.TRZ_3.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% TRZ_3_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 3 hour TRZ")+
scale_fill_manual(values = drug_pal)+
theme_bw()DNR_24_top3_ff <- row.names(V.DNR_24.top[1:3,])
# row.names(log_filt_23s) <- row.names(lcpm_h3_23s_filtered)
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DNR_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour DNR")+
scale_fill_manual(values = drug_pal)+
theme_bw()DOX_24_top3_ff <- row.names(V.DOX_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% DOX_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour DOX")+
scale_fill_manual(values = drug_pal)+
theme_bw()EPI_24_top3_ff <- row.names(V.EPI_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% EPI_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour EPI")+
scale_fill_manual(values = drug_pal)+
theme_bw()MTX_24_top3_ff <- row.names(V.MTX_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% MTX_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour MTX")+
scale_fill_manual(values = drug_pal)+
theme_bw()TRZ_24_top3_ff <- row.names(V.TRZ_24.top[1:3,])
log_filt_ff %>%
dplyr::filter(row.names(.) %in% TRZ_24_top3_ff) %>%
mutate(Peak = row.names(.)) %>%
pivot_longer(cols = !Peak, names_to = "sample", values_to = "counts") %>%
separate("sample", into = c("indv","trt","time")) %>%
mutate(time=factor(time, levels = c("3h","24h"))) %>%
mutate(trt=factor(trt, levels= c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
ggplot(., aes (x = time, y=counts))+
geom_boxplot(aes(fill=trt))+
facet_wrap(Peak~.)+
ggtitle("top 3 DAR in 24 hour TRZ")+
scale_fill_manual(values = drug_pal)+
theme_bw()hour3_lfc <- toplist_ff %>%
dplyr::filter(time=="3 hours") %>%
dplyr::select(trt:logFC) %>%
pivot_wider(id_cols = c(time, peak), names_from = trt, values_from = logFC)
hour3_lfc %>%
ggplot(.,aes(x=DOX, y=DNR))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("3 hour logFC, no filtering peaks")hour3_lfc %>%
ggplot(.,aes(x=DOX, y=EPI))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("3 hour logFC, no filtering peaks")hour3_lfc %>%
ggplot(.,aes(x=DOX, y=MTX))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("3 hour logFC, no filtering peaks")hour24_lfc <- toplist_ff %>%
dplyr::filter(time=="24 hours") %>%
dplyr::select(trt:logFC) %>%
pivot_wider(id_cols = c(time, peak), names_from = trt, values_from = logFC)
hour24_lfc %>%
ggplot(.,aes(x=DOX, y=DNR))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("24 hour logFC, no filtering peaks")hour24_lfc %>%
ggplot(.,aes(x=DOX, y=EPI))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("24 hour logFC, no filtering peaks")hour24_lfc %>%
ggplot(.,aes(x=DOX, y=MTX))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("24 hour logFC, no filtering peaks")# indv_n45 <- df_names_n45$indv
# indv <- factor(indv, levels = c(1,2,3,4,5,6))
# time_n45 <- df_names_n45$time
# time <- factor(time, levels =c("3h","24"))
# trt_n45 <- df_names_n45$trt
# label_n45 <- paste0(indv_n45,"_",trt_n45,"_",time_n45)
group_fac_ff <- group
groupid_ff <- as.numeric(group_fac_ff)
compid_ff <- data.frame(c1= c(2,4,6,8,10,1,3,5,7,9), c2 = c( 12,12,12,12,12,11,11,11,11,11))
y_TMM_cpm_ff <- cpm(x4_filtered, log = TRUE)
colnames(y_TMM_cpm_ff) <- short_names
# y_TMM_cpm
set.seed(31415)
# cormotif_initial_ff <- cormotiffit(exprs = y_TMM_cpm_ff, groupid = groupid_ff, compid = compid_ff, K=1:8, max.iter = 500, runtype = "logCPM")
#
# saveRDS(cormotif_initial_ff,"data/Final_four_data/cormotif_ff_4_run.RDS")
# cormotif_four_ff <- cormotiffit(exprs = y_TMM_cpm_ff, groupid = groupid_ff, compid = compid_ff, K=4, max.iter = 500, runtype = "logCPM")
# saveRDS(cormotif_four_ff,"data/Final_four_data/cormotif_only4_run.RDS")
cormotif_initial_ff <- readRDS("data/Final_four_data/cormotif_ff_4_run.RDS")
gene_prob_tran_ff <- cormotif_initial_ff$bestmotif$p.post
rownames(gene_prob_tran_ff) <- rownames(x4_filtered)
motif_prob_ff <- cormotif_initial_ff$bestmotif$clustlike
rownames(motif_prob_ff) <- rownames(gene_prob_tran_ff)
# write.csv(motif_prob_n45_he,"data/cormotif_probability_45_list_he.csv")myColors <- rev(c("#FFFFFF", "#E6E6E6" ,"#CCCCCC", "#B3B3B3", "#999999", "#808080", "#666666","#4C4C4C", "#333333", "#191919","#000000"))
Cormotif::plotIC(cormotif_initial_ff)plot.new()
legend('center',fill=myColors, legend =rev(c("0", "0.1", "0.2", "0.3", "0.4", "0.5", "0.6", "0.7", "0.8","0.9", "1")), box.col="white",title = "Probability\nlegend", horiz=FALSE,title.cex=.8)
Cormotif::plotMotif(cormotif_initial_ff)Study breakdown: 1 = DNR_3, 2 = DOX_3, 3 = EPI_3, 4 = MTX_3, 5 =
TRZ_3
6 = DNR_24, 7 = DOX_24, 8 = EPI_24, 9 = MTX_24, 10 = TRZ_24
# motif_prob_ff <- read.csv("data/cormotif_probability_45_list_he.csv")
# motif_prob_ff %>%
# as.data.frame() %>%
# dplyr::filter(V1<.5 & V2>.5 & V3 <.5& V4<0.5)
background_peaks <- motif_prob_ff %>%
as.data.frame() %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
NR_ff <- motif_prob_ff %>%
as.data.frame() %>%
dplyr::filter(V1>.5 & V2<.5 & V3 <.5& V4<0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
LR_ff <- motif_prob_ff %>%
as.data.frame() %>%
dplyr::filter(V1<.5 & V2>.5 & V3 <.5& V4<0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
ESR_ff <- motif_prob_ff %>%
as.data.frame() %>%
dplyr::filter(V1<.5 & V2<.5 & V3 >.5& V4<0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)
EAR_ff <- motif_prob_ff %>%
as.data.frame() %>%
dplyr::filter(V1<.5 & V2<.5 & V3 <.5& V4>0.5) %>%
rownames_to_column("Peakid") %>%
dplyr::select(Peakid) %>%
separate(Peakid, into=c("chr","start","end"),remove = FALSE)Number of Peaks by main grouping:
-EAR group has 7454 peaks.
-ESR group has 15781 peaks.
-LR group has 42609 peaks.
-NR group has 86331 peaks.
–Total number of peaks is 155560 –Total number of peaks in clusters is
152175
-percent of peaks classified into clusters: 97.8239907
-number of peaks not classified: 3385
txdb <- TxDb.Hsapiens.UCSC.hg38.knownGene
EAR_ff_gr <- EAR_ff %>% GRanges()
ESR_ff_gr <- ESR_ff %>% GRanges()
LR_ff_gr <- LR_ff %>% GRanges()
NR_ff_gr <- NR_ff %>% GRanges()
background_gr <- background_peaks %>% GRanges()
# mylist <- list(EAR_ff_gr,ESR_ff_gr,LR_ff_gr,NR_ff_gr,background_gr)
# peakAnnoList<- lapply(mylist, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# names(peakAnnoList) <- c("EAR","ESR","LR","NR","background")
# saveRDS(peakAnnoList, "data/Final_four_data/peakAnnoList_ff_motif.RDS")
peakAnnoList_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_motif.RDS")
plotAnnoBar(peakAnnoList_motif[c(4,1:3)])+
ggtitle ("Genomic Feature Distribution, Four groups")#### HOw I assigned expressed NG TSS to new peak file
### export new peak file as .bed file
a4gr <- all_four_filt_counts %>%
dplyr::select(seqnames:Geneid) %>%
dplyr::rename("Peakid"=Geneid) %>%
GRanges()
# write_delim((as.data.frame(a4gr)),delim = "\t",file = "data/Final_four_data/all_four_filt.bed", col_name=FALSE)## write tab delim datafile without column headers
###Do the same with TSS file
# ensembl <- useEnsembl(biomart = "genes", dataset = "hsapiens_gene_ensembl")
# my_attributes <- c('chromosome_name', 'start_position', 'end_position','entrezgene_id', 'ensembl_gene_id', 'hgnc_symbol')
# searchAttributes(mart = ensembl, pattern = "refseq")
# searchDatasets(mart=ensembl,pattern="NCBI")
# gene_list <- getBM(attributes=my_attributes,
# filters = "hgnc_symbol",
# values = S13Table$SYMBOL,
# mart = ensembl)
# write.csv(gene_ref,"data/gene_ref.csv")
# listFilters(ensembl)
####
# gene_ref <- read.csv("data/gene_ref.csv")
# gene_full_list <- gene_list %>%
# mutate(chromosome_name=as.numeric(chromosome_name))%>%
# # filter(!is.na(chromosome_name)) #%>%
# na.omit(.)
# dplyr::select(chromosome_name,start_position,end_position,entrezgene_id,ensembl_gene_id)
### I performed the bedtools using cli. I moved the files to the correct directory on my hardrive to do this.
# bedtools closest -a all_four_filt.bed -b All_TSS_NGs.bed -D b >assigned_newNGbyTSS.bed
#### This file only has the tss, I had to link the geneIDs and symbols and names using the lookup_table (contains TSS, Start and end, ENTREZID, ensembl_id,and SYBOL)
lookup_table <- read.csv("data/Final_four_data/expressedgene_lookuptable.csv", row.names = 1)
NG_assigned_table <- read_delim("data/Final_four_data/assigned_newNGbyTSS.bed", col_names = FALSE)
TSS_NG_table <- NG_assigned_table %>%
dplyr::rename("chr"=X1,
"start"=X2,
"end"= X3,
"Peakid"=X6,
"NG_chr"=X7,
"NG_start"=X8 ,
"NG_end"=X9 ,
"dist_to_NG"=X13) %>%
dplyr::select(chr:end,Peakid:NG_end,dist_to_NG) %>%
left_join(., lookup_table, by= c("NG_chr"="NG_chr","NG_end"="NG_start")) %>%
dplyr::rename("NG_end"="NG_end.y", "NG_TSS"=NG_end) %>%
dplyr::select(chr:NG_start,NG_end,NG_TSS,ENTREZID:SYMBOL,dist_to_NG) #%>%
# write_delim(.,"data/Final_four_data/TSS_assigned_NG.txt",delim = "\t",col_names = TRUE)
Collapsed_new_peaks <- TSS_NG_table %>%
group_by(chr, start, end, Peakid) %>%
summarise(chr=paste(unique(chr)),
start=paste(unique(start)),
end=paste(unique(end)),
NG_chr=paste(NG_chr,collapse=":"),
NG_start=paste(NG_start,collapse=":"),
NG_end=paste(NG_end,collapse=":"),
NG_TSS=paste(NG_TSS,collapse=":"),
NCBI_gene = paste(unique(ENTREZID),collapse=","),
ensembl_ID= paste(unique(ensembl_id),collapse = ","),
SYMBOL= paste(unique(SYMBOL),collapse = ",") ,
dist_to_NG =min(dist_to_NG))
# write_delim(Collapsed_new_peaks,"data/Final_four_data/collapsed_new_peaks.txt",delim = "\t",col_names = TRUE)TSS_assigned_NG <- read_delim("data/Final_four_data/TSS_assigned_NG.txt", delim = "\t", col_names = TRUE)
Collapsed_new_peaks <- read_delim("data/Final_four_data/collapsed_new_peaks.txt", delim = "\t", col_names = TRUE)
median_24_lfc <- read_csv("data/Final_four_data/median_24_lfc.csv")
median_3_lfc <- read_csv("data/Final_four_data/median_3_lfc.csv")
open_3med <- median_3_lfc %>%
dplyr::filter(med_3h_lfc > 0)
close_3med <- median_3_lfc %>%
dplyr::filter(med_3h_lfc < 0)
open_24med <- median_24_lfc %>%
dplyr::filter(med_24h_lfc > 0)
close_24med <- median_24_lfc %>%
dplyr::filter(med_24h_lfc < 0)
medA <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc > 0 & med_24h_lfc>0)
medB <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc < 0 & med_24h_lfc < 0)
medC <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak")) %>%
dplyr::filter(med_3h_lfc > 0& med_24h_lfc <0)
medD <- median_3_lfc %>%
left_join(median_24_lfc, by=c("peak"="peak"))%>%
dplyr::filter(med_3h_lfc < 0 & med_24h_lfc > 0)
EAR_open <- EAR_ff %>%
dplyr::filter(Peakid %in% open_3med$peak)
EAR_open_gr <- EAR_open %>% GRanges()
EAR_close <- EAR_ff %>%
dplyr::filter(Peakid %in% close_3med$peak)
EAR_close_gr <- EAR_close %>% GRanges()
LR_open <- LR_ff %>%
dplyr::filter(Peakid %in% open_24med$peak)
LR_open_gr <- LR_open %>% GRanges()
LR_close <- LR_ff %>%
dplyr::filter(Peakid %in% close_24med$peak)
LR_close_gr <- LR_close %>% GRanges()
NR_gr <- NR_ff %>%
GRanges()
ESR_open <- ESR_ff %>%
dplyr::filter(Peakid %in% medA$peak)
ESR_open_gr <- ESR_open %>% GRanges()
ESR_close <- ESR_ff %>%
dplyr::filter(Peakid %in% medB$peak)
ESR_close_gr <- ESR_close %>% GRanges()
ESR_C <- ESR_ff %>%
dplyr::filter(Peakid %in% medC$peak)
ESR_D <- ESR_ff %>%
dplyr::filter(Peakid %in% medD$peak)
ESR_OC <- ESR_C %>%
rbind(ESR_D)
ESR_OC_gr <- ESR_OC %>% GRanges()
mylist_new <- list(EAR_open_gr, EAR_close_gr,ESR_open_gr, ESR_close_gr,ESR_OC_gr, LR_open_gr, LR_close_gr,NR_gr, background_gr)
names(mylist_new) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open", "LR_close","NR","background")
# peakAnnoList<- lapply(mylist_new, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# names(peakAnnoList) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open", "LR_close","NR","background")
# saveRDS(peakAnnoList, "data/Final_four_data/peakAnnoList_ff_8motif.RDS")
peakAnnoList_8_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_8motif.RDS")
plotAnnoBar(peakAnnoList_8_motif[c(8,1:7)])+
ggtitle ("Genomic Feature Distribution, eight groups")Snyder_41peaks <- read.delim("data/other_papers/ENCFF966JZT_bed_Snyder_41peaks.bed",header=TRUE) %>%
GRanges()
raodah_fullpeaks <- readRDS( "data/Final_four_data/All_Raodahpeaks.RDS") %>%
GRanges %>%
GenomeInfoDb::keepStandardChromosomes(., pruning.mode = "coarse") %>%
dplyr::filter(seqnames!="chrM") %>%
dplyr::filter(seqnames!="chrY")
# rtracklayer::export.bed(raodah_fullpeaks,con= "data/Final_four_data/H3K27ac_files/H3k27ac.bed")
overlap_ac <- join_overlap_intersect((new_peaks_gr %>% dplyr::filter(seqnames !="chrY")),raodah_fullpeaks) %>% as.data.frame()
overlap_ac %>%
distinct(Geneid) Geneid
1 chr1.632859.633563
2 chr1.778314.779281
3 chr1.998953.1001296
4 chr1.1004520.1006056
5 chr1.1019367.1021991
6 chr1.1024554.1025391
7 chr1.1079476.1080507
8 chr1.1230663.1232629
9 chr1.1264653.1265715
10 chr1.1272894.1274563
11 chr1.1307293.1309390
12 chr1.1324229.1325527
13 chr1.1347782.1349961
14 chr1.1374687.1375810
15 chr1.1398577.1400659
16 chr1.1406563.1407944
17 chr1.1511390.1512209
18 chr1.1573888.1575504
19 chr1.1614893.1617117
20 chr1.1624714.1626013
21 chr1.1723973.1724836
22 chr1.1777644.1779045
23 chr1.1889404.1892325
24 chr1.1908290.1909438
25 chr1.1919107.1920164
26 chr1.2049658.2050904
27 chr1.2104353.2105397
28 chr1.2133170.2133980
29 chr1.2135035.2136217
30 chr1.2189427.2190229
31 chr1.2193999.2195640
32 chr1.2212183.2213752
33 chr1.2213837.2215326
34 chr1.2226580.2228340
35 chr1.2228794.2230628
36 chr1.2270670.2271233
37 chr1.2313888.2317202
38 chr1.2329998.2331434
39 chr1.2391271.2392593
40 chr1.2411839.2413080
41 chr1.3067985.3073149
42 chr1.3170183.3171800
43 chr1.3291840.3293572
44 chr1.3312318.3314317
45 chr1.3327810.3328958
46 chr1.3329194.3330574
47 chr1.3347252.3350255
48 chr1.3350259.3351446
49 chr1.3351942.3353766
50 chr1.3393136.3394618
51 chr1.3423098.3423747
52 chr1.3542877.3543880
53 chr1.3624190.3625835
54 chr1.3772178.3773144
55 chr1.3795629.3796684
56 chr1.3856613.3857650
57 chr1.3899449.3901494
58 chr1.4605037.4605968
59 chr1.5991893.5992657
60 chr1.6198593.6200250
61 chr1.6204868.6208050
62 chr1.6224397.6225838
63 chr1.6275892.6276727
64 chr1.6353741.6354227
65 chr1.6358738.6360121
66 chr1.6393053.6394151
67 chr1.6490330.6491930
68 chr1.6496615.6499077
69 chr1.6515565.6516436
70 chr1.6519537.6520618
71 chr1.6553935.6554640
72 chr1.6579114.6580873
73 chr1.6600273.6605987
74 chr1.6612541.6615407
75 chr1.6624808.6626048
76 chr1.6700413.6702196
77 chr1.6784254.6786817
78 chr1.6801764.6803295
79 chr1.6948481.6949728
80 chr1.6961967.6963960
81 chr1.7771008.7772319
82 chr1.7783843.7784239
83 chr1.7960842.7962107
84 chr1.8004284.8005759
85 chr1.8025466.8027468
86 chr1.8076851.8079271
87 chr1.8118084.8118455
88 chr1.8121073.8122178
89 chr1.8373519.8374597
90 chr1.8395857.8397367
91 chr1.8422521.8425401
92 chr1.8455609.8456942
93 chr1.8525393.8526259
94 chr1.8626279.8627094
95 chr1.8701612.8703906
96 chr1.8706299.8707871
97 chr1.8712056.8712995
98 chr1.8816545.8818689
99 chr1.8857619.8858525
100 chr1.8873050.8874313
101 chr1.8877214.8879774
102 chr1.9128286.9129869
103 chr1.9162472.9163399
104 chr1.9180461.9184174
105 chr1.9234294.9235470
106 chr1.9249625.9250390
107 chr1.9291787.9292826
108 chr1.9384236.9385825
109 chr1.9412466.9414052
110 chr1.9539185.9540331
111 chr1.9588570.9590017
112 chr1.9885876.9886722
113 chr1.9889893.9891333
114 chr1.9900323.9901503
115 chr1.9909414.9911251
116 chr1.9922184.9922961
117 chr1.9942521.9943953
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119 chr1.10108148.10108837
120 chr1.10209689.10212632
121 chr1.10398466.10400378
122 chr1.10456029.10457443
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124 chr1.10474503.10475881
125 chr1.10528022.10529276
126 chr1.10662478.10665423
127 chr1.10677114.10678196
128 chr1.10684246.10687048
129 chr1.10691404.10693107
130 chr1.10693865.10695247
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133 chr1.10720008.10721257
134 chr1.10722717.10724831
135 chr1.10733334.10734138
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138 chr1.10793688.10796810
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140 chr1.11012236.11013375
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146 chr1.11680738.11681773
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149 chr1.11786968.11788442
150 chr1.11788673.11793441
151 chr1.11802677.11804025
152 chr1.11804609.11806983
153 chr1.11840775.11842769
154 chr1.11846271.11848859
155 chr1.11856587.11859905
156 chr1.11880127.11882164
157 chr1.11882802.11883616
158 chr1.11907626.11910200
159 chr1.11925933.11926616
160 chr1.11934149.11935305
161 chr1.11979589.11981179
162 chr1.11988850.11992480
163 chr1.12014236.12015200
164 chr1.12019077.12020145
165 chr1.12229886.12231652
166 chr1.12453846.12455046
167 chr1.12495569.12497013
168 chr1.12594901.12596653
169 chr1.12615883.12617296
170 chr1.12618055.12620052
171 chr1.13583689.13584731
172 chr1.13748923.13749994
173 chr1.14597956.14599684
174 chr1.14627063.14628207
175 chr1.14731987.14733951
176 chr1.15139568.15140471
177 chr1.15152267.15154222
178 chr1.15523884.15525681
179 chr1.15526382.15527434
180 chr1.15603261.15603730
181 chr1.15616744.15618545
182 chr1.15683308.15685298
183 chr1.15703199.15704194
184 chr1.15714216.15715127
185 chr1.15758478.15759574
186 chr1.15795113.15795699
187 chr1.15800017.15800981
188 chr1.15833988.15836370
189 chr1.15847236.15850609
190 chr1.15916337.15917449
191 chr1.15949274.15950418
192 chr1.15975509.15976374
193 chr1.16012562.16020865
194 chr1.16048182.16049408
195 chr1.16111241.16112462
196 chr1.16138069.16142211
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198 chr1.16148399.16156958
199 chr1.16167447.16168042
200 chr1.16172923.16174201
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202 chr1.16181437.16183022
203 chr1.16206546.16207140
204 chr1.16351536.16353019
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206 chr1.16375121.16376382
207 chr1.16440258.16441551
208 chr1.16498785.16499769
209 chr1.16612605.16613986
210 chr1.16643836.16645321
211 chr1.16903448.16905695
212 chr1.16913561.16914531
213 chr1.17053236.17054460
214 chr1.17128117.17129676
215 chr1.17155192.17156345
216 chr1.17212370.17213613
217 chr1.17437372.17440529
218 chr1.17443059.17444290
219 chr1.17539247.17541041
220 chr1.17568725.17570496
221 chr1.17587540.17588917
222 chr1.17617781.17619807
223 chr1.17874181.17876379
224 chr1.18480388.18481515
225 chr1.18902103.18903207
226 chr1.18921822.18923549
227 chr1.18948873.18950795
228 chr1.18955614.18957593
229 chr1.18965335.18966213
230 chr1.19009937.19011737
231 chr1.19151415.19152212
232 chr1.19209156.19211329
233 chr1.19251154.19252268
234 chr1.19311169.19313099
235 chr1.19484249.19486373
236 chr1.19596212.19597764
237 chr1.19882146.19883120
238 chr1.20507505.20508347
239 chr1.20613333.20614447
240 chr1.20633257.20634352
241 chr1.20660634.20662176
242 chr1.20696377.20697328
243 chr1.20731660.20733878
244 chr1.20785934.20788152
245 chr1.21151862.21152867
246 chr1.21175396.21177410
247 chr1.21261152.21263179
248 chr1.21289346.21290468
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250 chr1.21345168.21346069
251 chr1.21781672.21784146
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290 chr1.23790009.23793170
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294 chr1.23979592.23980917
295 chr1.24018356.24019631
296 chr1.24022402.24023198
297 chr1.24120155.24120983
298 chr1.24126097.24126570
299 chr1.24149934.24151973
300 chr1.24416384.24417861
301 chr1.24502224.24503399
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305 chr1.24744768.24746961
306 chr1.25022122.25023366
307 chr1.25231958.25233500
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310 chr1.25338204.25339168
311 chr1.25429910.25431699
312 chr1.25549255.25550390
313 chr1.25566538.25567748
314 chr1.25616224.25618452
315 chr1.25694948.25697215
316 chr1.25698147.25701481
317 chr1.25727137.25729162
318 chr1.25743413.25744751
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322 chr1.25859084.25859781
323 chr1.25874833.25876190
324 chr1.25891954.25893412
325 chr1.25905170.25905846
326 chr1.25997062.25998576
327 chr1.26016447.26018476
328 chr1.26020302.26022739
329 chr1.26035010.26037300
330 chr1.26064500.26068472
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332 chr1.26101736.26102460
333 chr1.26110652.26112678
334 chr1.26158461.26159822
335 chr1.26161075.26161948
336 chr1.26169415.26170531
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339 chr1.26275695.26277061
340 chr1.26278959.26280870
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342 chr1.26431975.26432774
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353 chr1.26826349.26827104
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399 chr1.28258957.28261055
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401 chr1.28369131.28370606
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403 chr1.28505486.28508235
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405 chr1.28552784.28553571
406 chr1.28581244.28582274
407 chr1.28642244.28643735
408 chr1.28647753.28649080
409 chr1.28668227.28669963
410 chr1.28735922.28738230
411 chr1.28884363.28885567
412 chr1.28886772.28888330
413 chr1.29122778.29125475
414 chr1.29156609.29157422
415 chr1.29180285.29182351
416 chr1.29202538.29203108
417 chr1.29237014.29240557
418 chr1.29271452.29272710
419 chr1.29282779.29283598
420 chr1.29688371.29689260
421 chr1.29962377.29963206
422 chr1.29972572.29975184
423 chr1.30718110.30719721
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1799 chr1.212034600.212036015
1800 chr1.212087962.212088973
1801 chr1.212219710.212220414
1802 chr1.212234295.212235367
1803 chr1.212284613.212287021
1804 chr1.212558100.212559233
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1807 chr1.212791328.212792541
1808 chr1.212857676.212859279
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1810 chr1.213051013.213051799
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1814 chr1.214028530.214031690
1815 chr1.214106074.214108513
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1817 chr1.214277097.214278847
1818 chr1.214279953.214282140
1819 chr1.214439185.214440334
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1821 chr1.214550351.214552973
1822 chr1.214603060.214603607
1823 chr1.215566987.215568416
1824 chr1.216600815.216601345
1825 chr1.216616518.216617677
1826 chr1.216847157.216848365
1827 chr1.216902724.216903983
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1829 chr1.217453752.217454375
1830 chr1.217630613.217631453
1831 chr1.218284721.218285576
1832 chr1.218304105.218305242
1833 chr1.218305471.218306452
1834 chr1.218344479.218348307
1835 chr1.218371599.218372439
1836 chr1.218373935.218375344
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1838 chr1.218458975.218460794
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1840 chr1.218476222.218477311
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1849 chr1.220271919.220273017
1850 chr1.220528086.220529443
1851 chr1.220654450.220656161
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1855 chr1.221741432.221743815
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1860 chr1.223704955.223705782
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1870 chr1.223992231.223993154
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1872 chr1.224182824.224184283
1873 chr1.224329370.224330794
1874 chr1.224356720.224357850
1875 chr1.224432706.224435544
1876 chr1.224503295.224503922
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1878 chr1.225427127.225428732
1879 chr1.225627487.225627828
1880 chr1.225651691.225654242
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1883 chr1.225777239.225778403
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1886 chr1.225923293.225924477
1887 chr1.225940873.225942068
1888 chr1.225998667.226000158
1889 chr1.226061314.226063786
1890 chr1.226083059.226084007
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1892 chr1.226121616.226123049
1893 chr1.226127855.226129302
1894 chr1.226181884.226182830
1895 chr1.226185312.226187098
1896 chr1.226271611.226273890
1897 chr1.226307142.226310731
1898 chr1.226364295.226365328
1899 chr1.226407674.226408248
1900 chr1.226526453.226527581
1901 chr1.226532158.226533778
1902 chr1.226604282.226605619
1903 chr1.226870348.226871539
1904 chr1.226939044.226940652
1905 chr1.226973987.226975348
1906 chr1.226979319.226980399
1907 chr1.227317726.227319450
1908 chr1.227357075.227357936
1909 chr1.227563499.227564245
1910 chr1.227734301.227736066
1911 chr1.227757979.227760179
1912 chr1.227787107.227788791
1913 chr1.227895720.227896686
1914 chr1.227945772.227947231
1915 chr1.228081610.228084287
1916 chr1.228085291.228086115
1917 chr1.228102242.228103492
1918 chr1.228108874.228109934
1919 chr1.228139679.228141067
1920 chr1.228165198.228166719
1921 chr1.228207234.228210489
1922 chr1.228213052.228214053
1923 chr1.228367414.228370515
1924 chr1.228405948.228407676
1925 chr1.228457220.228458305
1926 chr1.228486328.228488298
1927 chr1.228963897.228964841
1928 chr1.229271014.229272053
1929 chr1.229296129.229297332
1930 chr1.229342082.229343002
1931 chr1.229507803.229508915
1932 chr1.229557394.229559488
1933 chr1.229563368.229564170
1934 chr1.229625071.229626838
1935 chr1.229863924.229864725
1936 chr1.230066772.230068630
1937 chr1.230104654.230105870
1938 chr1.230208446.230208954
1939 chr1.230642187.230643417
1940 chr1.230714061.230714622
1941 chr1.230772176.230773361
1942 chr1.230824169.230825468
1943 chr1.230847887.230852187
1944 chr1.230865890.230870179
1945 chr1.230978107.230979491
1946 chr1.231240603.231241906
1947 chr1.231336874.231338979
1948 chr1.231394997.231396098
1949 chr1.231407628.231408332
1950 chr1.231419968.231423246
1951 chr1.231527895.231529150
1952 chr1.231827543.231828880
1953 chr1.231845749.231848421
1954 chr1.231849946.231851487
1955 chr1.231929239.231930104
1956 chr1.232318830.232319998
1957 chr1.232327784.232329557
1958 chr1.232355112.232356154
1959 chr1.232486001.232487737
1960 chr1.232508338.232510099
1961 chr1.232537764.232538907
1962 chr1.232552906.232554003
1963 chr1.232555514.232556585
1964 chr1.232589474.232590529
1965 chr1.232593848.232596020
1966 chr1.232619861.232620930
1967 chr1.232622305.232623431
1968 chr1.232628804.232630906
1969 chr1.232804831.232806082
1970 chr1.232950010.232951817
1971 chr1.232952329.232953489
1972 chr1.233112994.233113977
1973 chr1.233543261.233544302
1974 chr1.233664500.233665549
1975 chr1.233706991.233708314
1976 chr1.234372954.234374384
1977 chr1.234478043.234479262
1978 chr1.234495280.234495797
1979 chr1.234522952.234524122
1980 chr1.234538467.234540756
1981 chr1.234542998.234546359
1982 chr1.234553129.234554240
1983 chr1.234554572.234555547
1984 chr1.234564389.234565400
1985 chr1.234582282.234583995
1986 chr1.234584178.234585885
1987 chr1.234598879.234601219
1988 chr1.234604536.234614096
1989 chr1.234617463.234619405
1990 chr1.234699668.234700998
1991 chr1.234709541.234711924
1992 chr1.234722965.234728625
1993 chr1.234745601.234747158
1994 chr1.234771316.234773005
1995 chr1.234879340.234879975
1996 chr1.234925578.234926422
1997 chr1.234974572.234975892
1998 chr1.234987303.234988106
1999 chr1.234997043.234998509
2000 chr1.235073828.235076020
2001 chr1.235079222.235079938
2002 chr1.235082914.235084519
2003 chr1.235127560.235129131
2004 chr1.235160676.235161580
2005 chr1.235326705.235329871
2006 chr1.235367068.235368144
2007 chr1.235502829.235505780
2008 chr1.235866121.235867739
2009 chr1.235910334.235911110
2010 chr1.235971546.235972836
2011 chr1.236063608.236065711
2012 chr1.236066639.236067662
2013 chr1.236067726.236068386
2014 chr1.236507026.236508165
2015 chr1.236523619.236524656
2016 chr1.236604101.236605007
2017 chr1.236623263.236624456
2018 chr1.236686139.236688128
2019 chr1.236688392.236691210
2020 chr1.236699003.236702244
2021 chr1.236704121.236705090
2022 chr1.236726886.236728014
2023 chr1.236794965.236796090
2024 chr1.236889872.236891420
2025 chr1.236997450.236998609
2026 chr1.237012810.237013281
2027 chr1.237041784.237045288
2028 chr1.237186415.237188368
2029 chr1.237199136.237199701
2030 chr1.237258727.237259497
2031 chr1.237542628.237543204
2032 chr1.237695639.237696636
2033 chr1.238647239.238648570
2034 chr1.238658165.238659159
2035 chr1.239730627.239731622
2036 chr1.241518688.241520038
2037 chr1.241639560.241640599
2038 chr1.241847500.241848557
2039 chr1.242745003.242745496
2040 chr1.242876982.242878361
2041 chr1.244047166.244050431
2042 chr1.244162974.244165187
2043 chr1.244239377.244239935
2044 chr1.244361800.244362695
2045 chr1.244451061.244452523
2046 chr1.244652522.244653976
2047 chr1.244758441.244759450
2048 chr1.244833829.244836416
2049 chr1.244862597.244865731
2050 chr1.244969559.244971746
2051 chr1.245153226.245157576
2052 chr1.245787913.245788929
2053 chr1.245790096.245792094
2054 chr1.245822599.245823336
2055 chr1.245873717.245876390
2056 chr1.245997368.245998604
2057 chr1.246176131.246176826
2058 chr1.246356932.246357661
2059 chr1.246565922.246567046
2060 chr1.246724065.246725013
2061 chr1.246930958.246932357
2062 chr1.247007657.247008175
2063 chr1.247078140.247078873
2064 chr1.247103727.247104531
2065 chr1.247111056.247112498
2066 chr1.247210419.247211067
2067 chr1.247330969.247333321
2068 chr1.248820891.248821676
2069 chr1.248824918.248826974
2070 chr1.248837768.248839343
2071 chr1.248857697.248859872
2072 chr1.248872536.248874228
2073 chr1.248905820.248907505
2074 chr10.133114.137250
2075 chr10.372802.374439
2076 chr10.598307.599122
2077 chr10.624119.625733
2078 chr10.688590.690295
2079 chr10.909744.910506
2080 chr10.930314.932482
2081 chr10.986298.988940
2082 chr10.1048103.1050105
2083 chr10.1055836.1057546
2084 chr10.3045345.3047251
2085 chr10.3065690.3070028
2086 chr10.3090302.3091703
2087 chr10.3092720.3096988
2088 chr10.3098904.3100280
2089 chr10.3111478.3112302
2090 chr10.3125793.3127616
2091 chr10.3172189.3173328
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19894 chrX.155719034.155719529# saveRDS(overlap_ac, "data/Final_four_data/overlapping_ac_atac_peaks.RDS")
# H3K27ac_NG_df <- read.delim("data/Final_four_data/H3K27ac_files/H3k27ac.bed", header = FALSE)
H3K27ac_NG_df <- read.delim("data/Final_four_data/H3K27ac_files/H3K27ac_NG_df.bed")toplist_ff <- readRDS("data/Final_four_data/toplist_ff.RDS")
mean_lfc <- toplist_ff %>%
mutate(EAR_open = if_else(peak %in% EAR_open$Peakid, "y", "no")) %>%
mutate(EAR_close = if_else(peak %in% EAR_close$Peakid, "y", "no")) %>%
mutate(ESR_open = if_else(peak %in% ESR_open$Peakid, "y", "no")) %>%
mutate(ESR_close= if_else(peak %in% ESR_close$Peakid, "y", "no")) %>%
# mutate(ESR_OC= if_else(peak %in% ESR_OC$Peakid, "y", "no")) %>%
mutate(ESR_C= if_else(peak %in% ESR_C$Peakid, "y", "no")) %>%
mutate(ESR_D= if_else(peak %in% ESR_D$Peakid, "y", "no")) %>%
mutate(LR_open= if_else(peak %in% LR_open$Peakid, "y", "no")) %>%
mutate(LR_close= if_else(peak %in% LR_close$Peakid, "y", "no")) %>%
mutate(NR = if_else(peak %in% NR_ff$Peakid, "y", "no"))%>%
mutate(trt = factor(trt,levels=c("DOX","EPI","DNR","MTX","TRZ","VEH"))) %>%
group_by(trt, time) %>%
mutate(absFC = (logFC)) %>%
dplyr::select(trt, time, absFC, EAR_open:NR) %>%
dplyr::summarize(
EAR_op = mean(absFC[EAR_open == "y"]),
EAR_cl = mean(absFC[EAR_close == "y"]),
ESR_op = mean(absFC[ESR_open == "y"]),
ESR_cl = mean(absFC[ESR_close == "y"]),
# ESR_OC = mean(absFC[ESR_OC == "y"]),
ESR_c = mean(absFC[ESR_C == "y"]),
ESR_d = mean(absFC[ESR_D == "y"]),
LR_op = mean(absFC[LR_open == "y"]),
LR_cl = mean(absFC[LR_close == "y"]),
NR = mean(absFC[NR == "y"])
) %>%
as.data.frame()
mean_lfc %>%
ungroup() %>%
pivot_longer(!c(trt, time), names_to = "Motif",
values_to = "meanLFC") %>%
# mutate(Set = dplyr::case_match(Motif,
# "EAR open"~ EAR_op ,
# EAR_cm ~"EAR close" ,
# ESR_om ~"ESR open",
# ESR_cm ~ "ESR close",
# ESR_ocm ~ "ESR open/close",
# LR_om~ "LR open",
# LR_cm~ "LR close",
# NRm ~ "No Response",
# .default = Motif)) %>%
ggplot(., aes(x = time,y = meanLFC,col = trt,
group = trt
)) +
geom_point(size = 2) +
geom_line(linewidth = 2) +
ggpubr::fill_palette(drug_pal) +
# guides(fill=guide_legend(title = "Treatment"))+
facet_wrap( ~ Motif, nrow = 2) +
theme_bw() +
xlab("Time (hours)") +
scale_color_manual(values = drug_pal) +
ylab(" Avg. Log Fold Change") +
theme_bw() +
# ggtitle(" average |Log Fold| for genes across treatment each motif")+
theme(
plot.title = element_text(size = rel(1.0), hjust = 0.5),
axis.title = element_text(size = 15, color = "black"),
axis.line = element_line(linewidth = 1.0),
strip.background = element_rect(fill = "transparent"),
axis.text = element_text(
size = 10,
color = "black",
angle = 0
),
strip.text.x = element_text(
size = 11,
color = "black",
face = "bold"
)
)# Collapsed_H3k27ac_NG <- H3K27ac_NG_df %>%
# group_by(seqnames, start, end, Geneid) %>%
# summarise(seqnames=paste(unique(seqnames)),
# start=paste(unique(start)),
# end=paste(unique(end)),
# NG_chr=paste(NG_chr,collapse=":"),
# NG_start=paste(NG_start,collapse=":"),
# NG_end=paste(NG_end,collapse=":"),
# # NG_TSS=paste(NG_TSS,collapse=":"),
# ENTREZID = paste(unique(ENTREZID),collapse=","),
# ensembl_ID= paste(unique(ensembl_id),collapse = ","),
# SYMBOL= paste(unique(SYMBOL),collapse = ",") ,
# dist_to_NG =min(dist_to_NG))
# write_delim(Collapsed_H3k27ac_NG,"data/Final_four_data/H3K27ac_files/Collapsed_H3k27ac_NG.txt",delim = "\t",col_names = TRUE)
Collapsed_H3k27ac_NG <- read_delim("data/Final_four_data/H3K27ac_files/Collapsed_H3k27ac_NG.txt",delim = "\t",col_names = TRUE)
RNA_median_3_lfc <- readRDS("data/other_papers/RNA_median_3_lfc.RDS")
RNA_median_24_lfc <- readRDS("data/other_papers/RNA_median_24_lfc.RDS")
overlap_df_ggplot <- readRDS("data/Final_four_data/LFC_ATAC_K27ac.RDS")
AC_median_3_lfc <- read_csv("data/Final_four_data/AC_median_3_lfc.csv")
AC_median_24_lfc <- read_csv("data/Final_four_data/AC_median_24_lfc.csv")
anti_joined_lfc <- median_24_lfc %>%
dplyr::filter(!peak %in% overlap_df_ggplot$peakid) %>%
left_join(., (Collapsed_new_peaks %>% dplyr::select(Peakid, NCBI_gene:dist_to_NG)), by = c("peak"="Peakid")) %>%
left_join(.,median_3_lfc,by = c("peak"="peak")) %>%
left_join(., RNA_median_3_lfc ,
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID")) %>%
left_join(., RNA_median_24_lfc,
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID"))
joined_LFC_df <- overlap_df_ggplot %>%
left_join(.,(Collapsed_new_peaks %>%
dplyr::select(Peakid,dist_to_NG, NCBI_gene:SYMBOL)),
by=c("peakid"="Peakid")) %>%
left_join(., RNA_median_3_lfc ,
# %>%
# dplyr::select(SYMBOL,RNA_3h_lfc)),
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID")) %>%
left_join(., RNA_median_24_lfc,# %>%
# dplyr::select(SYMBOL,RNA_24h_lfc)),
by=c("SYMBOL"="SYMBOL", "NCBI_gene"="ENTREZID"))
anti_joined_H3k27ac <-Collapsed_H3k27ac_NG%>%
dplyr::filter(!Geneid %in% overlap_df_ggplot$Geneid) %>%
left_join(.,(RNA_median_24_lfc %>% mutate(ENTREZID=as.numeric(ENTREZID))),by=c("ENTREZID"="ENTREZID","SYMBOL"="SYMBOL")) %>%
left_join(.,(RNA_median_3_lfc %>% mutate(ENTREZID=as.numeric(ENTREZID))),by=c("ENTREZID"="ENTREZID","SYMBOL"="SYMBOL")) %>%
left_join(.,AC_median_24_lfc, by=c("Geneid"="Peakid")) %>%
left_join(.,AC_median_3_lfc, by=c("Geneid"="Peakid")) %>%
dplyr::select(Geneid,ENTREZID:RNA_3h_lfc,AC_24h_lfc,AC_3h_lfc)