final_four_analysis
ERM
2024-10-02
Last updated: 2024-10-02
Checks: 7 0
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
Modified: analysis/Raodah_mycount.Rmd
Modified: analysis/Smaller_set_DAR.Rmd
Modified: analysis/TE_analysis.Rmd
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
These are the previous versions of the repository in which changes were
made to the R Markdown (analysis/final_four_analysis.Rmd)
and HTML (docs/final_four_analysis.html) files. If you’ve
configured a remote Git repository (see ?wflow_git_remote),
click on the hyperlinks in the table below to view the files as they
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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,
...
)
}Counts summary
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))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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))
| Version | Author | Date |
|---|---|---|
| 7b65928 | reneeisnowhere | 2024-09-27 |
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'))
cardiomyocyte purity
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))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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... 2024-10-02 8:43:36 PM 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... 2024-10-02 8:43:46 PM 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... 2024-10-02 8:43:51 PM 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... 2024-10-02 8:43:52 PM 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... 2024-10-02 8:43:53 PM 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... 2024-10-02 8:44:08 PM 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... 2024-10-02 8:44:18 PM 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... 2024-10-02 8:44:24 PM 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'))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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")
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
pca_plot(PCA4_info_anno, col_var='trt', shape_var = 'indv')+ggtitle("PCA of raw counts by treatment and individual")
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
PCA analysis 155560 peaks
# 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)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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"))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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"))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
We go from 172481, 48 peaks to155560, 48 peaks using rowMeans (log2cpm) > 0 across all samples. This is my new high confidence peak set.
Contribution of variance in PCA plot
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))
}
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
Comparing the peak sets, old and new
# 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 |
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| chr2.55056069.55056348 | chr2.55056069.55056338 | 270 | 281 | FALSE | smaller | 11 |
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| chr2.56199503.56200053 | chr2.56199503.56200023 | 521 | 552 | FALSE | smaller | 31 |
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| chr2.239444208.239444684 | chr2.239444249.239444684 | 436 | 478 | FALSE | smaller | 42 |
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| chr2.239866399.239867992 | chr2.239867044.239867992 | 949 | 1595 | FALSE | smaller | 646 |
| chr2.239979288.239979879 | chr2.239979288.239979662 | 375 | 593 | FALSE | smaller | 218 |
| chr2.240135409.240135695 | chr2.240135409.240135686 | 278 | 288 | FALSE | smaller | 10 |
| chr2.240156987.240157618 | chr2.240156987.240157463 | 477 | 633 | FALSE | smaller | 156 |
| chr2.240160324.240160889 | chr2.240160409.240160823 | 415 | 567 | FALSE | smaller | 152 |
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| chr2.240385568.240386185 | chr2.240385568.240386080 | 513 | 619 | FALSE | smaller | 106 |
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| chr2.240457631.240458018 | chr2.240457663.240457982 | 320 | 389 | FALSE | smaller | 69 |
| chr2.240463925.240465533 | chr2.240464241.240465348 | 1108 | 1610 | FALSE | smaller | 502 |
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| chr2.240568597.240569137 | chr2.240568597.240569127 | 531 | 542 | FALSE | smaller | 11 |
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| chr2.240595509.240596198 | chr2.240595695.240596198 | 504 | 691 | FALSE | smaller | 187 |
| chr2.240612796.240613310 | chr2.240612796.240613275 | 480 | 516 | FALSE | smaller | 36 |
| chr2.240728752.240729242 | chr2.240728848.240729242 | 395 | 492 | FALSE | smaller | 97 |
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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 |
| chr2.241190129.241190985 | chr2.241190208.241190985 | 778 | 858 | FALSE | smaller | 80 |
| chr2.241216073.241216505 | chr2.241216144.241216488 | 345 | 434 | FALSE | smaller | 89 |
| chr2.241223747.241224311 | chr2.241223762.241224311 | 550 | 566 | FALSE | smaller | 16 |
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| chr2.241413338.241413648 | chr2.241413338.241413604 | 267 | 312 | FALSE | smaller | 45 |
| chr2.241627712.241628225 | chr2.241627718.241628225 | 508 | 515 | FALSE | smaller | 7 |
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| chr2.241655875.241656684 | chr2.241655925.241656684 | 760 | 811 | FALSE | smaller | 51 |
| chr2.241666606.241667004 | chr2.241666606.241666996 | 391 | 400 | FALSE | smaller | 9 |
| chr2.241719584.241720626 | chr2.241719584.241720507 | 924 | 1044 | FALSE | smaller | 120 |
| chr2.241770470.241770815 | chr2.241770470.241770760 | 291 | 347 | FALSE | smaller | 56 |
| 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 |
| chr3.3023163.3023819 | chr3.3023163.3023408 | 246 | 658 | FALSE | smaller | 412 |
| chr3.3110726.3111123 | chr3.3110726.3111028 | 303 | 399 | FALSE | smaller | 96 |
| chr3.4303088.4304131 | chr3.4303088.4304108 | 1021 | 1045 | FALSE | smaller | 24 |
| chr3.4533660.4534147 | chr3.4533662.4534147 | 486 | 489 | FALSE | smaller | 3 |
| 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 |
| chr3.4729102.4729664 | chr3.4729102.4729649 | 548 | 564 | FALSE | smaller | 16 |
| chr3.4746604.4746843 | chr3.4746631.4746833 | 203 | 241 | FALSE | smaller | 38 |
| chr3.4906270.4906826 | chr3.4906270.4906719 | 450 | 558 | FALSE | smaller | 108 |
| 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 |
| chr3.5784636.5785010 | chr3.5784712.5784954 | 243 | 376 | FALSE | smaller | 133 |
| 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 |
| chr3.9396114.9397767 | chr3.9396114.9397762 | 1649 | 1655 | FALSE | smaller | 6 |
| 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 |
| chr3.12572055.12572257 | chr3.12572082.12572239 | 158 | 204 | FALSE | smaller | 46 |
| 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 |
| chr3.12952982.12953740 | chr3.12953083.12953740 | 658 | 760 | FALSE | smaller | 102 |
| chr3.12969458.12969775 | chr3.12969459.12969775 | 317 | 319 | FALSE | smaller | 2 |
| chr3.12984022.12984725 | chr3.12984077.12984725 | 649 | 705 | FALSE | smaller | 56 |
| chr3.12992640.12994037 | chr3.12992640.12994035 | 1396 | 1399 | FALSE | smaller | 3 |
| chr3.13008146.13008415 | chr3.13008146.13008400 | 255 | 271 | FALSE | smaller | 16 |
| chr3.13072102.13073366 | chr3.13072102.13073361 | 1260 | 1266 | FALSE | smaller | 6 |
| 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 |
| chr3.13308411.13308891 | chr3.13308411.13308785 | 375 | 482 | FALSE | smaller | 107 |
| 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 |
| chr3.17157535.17158129 | chr3.17157535.17158116 | 582 | 596 | FALSE | smaller | 14 |
| chr3.17179580.17180101 | chr3.17179621.17180101 | 481 | 523 | FALSE | smaller | 42 |
| chr3.17190366.17191052 | chr3.17190530.17191052 | 523 | 688 | FALSE | smaller | 165 |
| chr3.17494218.17494877 | chr3.17494316.17494867 | 552 | 661 | FALSE | smaller | 109 |
| 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 |
| chr3.20080963.20081579 | chr3.20080963.20081578 | 616 | 618 | FALSE | smaller | 2 |
| 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 |
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| chr3.29639654.29640261 | chr3.29639654.29640223 | 570 | 609 | FALSE | smaller | 39 |
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| chr3.43769367.43771282 | chr3.43769372.43771282 | 1911 | 1917 | FALSE | smaller | 6 |
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| chr3.50590770.50591794 | chr3.50590770.50591657 | 888 | 1026 | FALSE | smaller | 138 |
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| chr3.51390744.51392582 | chr3.51390834.51392582 | 1749 | 1840 | FALSE | smaller | 91 |
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| chr3.51607999.51608862 | chr3.51607999.51608625 | 627 | 865 | FALSE | smaller | 238 |
| chr3.51669161.51669633 | chr3.51669161.51669575 | 415 | 474 | FALSE | smaller | 59 |
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| chr3.52032918.52033349 | chr3.52032919.52033349 | 431 | 433 | FALSE | smaller | 2 |
| chr3.52245679.52246596 | chr3.52245679.52246563 | 885 | 919 | FALSE | smaller | 34 |
| chr3.52253275.52254117 | chr3.52253311.52254117 | 807 | 844 | FALSE | smaller | 37 |
| chr3.52287022.52288301 | chr3.52287033.52288301 | 1269 | 1281 | FALSE | smaller | 12 |
| chr3.52299898.52301072 | chr3.52299898.52301020 | 1123 | 1176 | FALSE | smaller | 53 |
| chr3.52444952.52445468 | chr3.52444986.52445468 | 483 | 518 | FALSE | smaller | 35 |
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| chr3.53780263.53780498 | chr3.53780272.53780498 | 227 | 237 | FALSE | smaller | 10 |
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| chr3.60303225.60303572 | chr3.60303225.60303507 | 283 | 349 | FALSE | smaller | 66 |
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| chr3.61315982.61316271 | chr3.61315989.61316271 | 283 | 291 | FALSE | smaller | 8 |
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| chr3.63048576.63049290 | chr3.63048576.63049145 | 570 | 716 | FALSE | smaller | 146 |
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| chr3.63872146.63872692 | chr3.63872214.63872692 | 479 | 548 | FALSE | smaller | 69 |
| chr3.63954971.63955507 | chr3.63954971.63955497 | 527 | 538 | FALSE | smaller | 11 |
| chr3.63960150.63960702 | chr3.63960160.63960702 | 543 | 554 | FALSE | smaller | 11 |
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| chr3.65996634.65997053 | chr3.65996634.65996957 | 324 | 421 | FALSE | smaller | 97 |
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| chr3.100734016.100734413 | chr3.100734016.100734401 | 386 | 399 | FALSE | smaller | 13 |
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| chr3.101924393.101924970 | chr3.101924393.101924885 | 493 | 579 | FALSE | smaller | 86 |
| chr3.101928822.101929429 | chr3.101928921.101929429 | 509 | 609 | FALSE | smaller | 100 |
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| chr3.133693491.133694584 | chr3.133693660.133694584 | 925 | 1095 | FALSE | smaller | 170 |
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| chr3.134039097.134039690 | chr3.134039097.134039475 | 379 | 595 | FALSE | smaller | 216 |
| chr3.134056527.134057160 | chr3.134056527.134057100 | 574 | 635 | FALSE | smaller | 61 |
| chr3.134092090.134092685 | chr3.134092092.134092685 | 594 | 597 | FALSE | smaller | 3 |
| chr3.134134198.134134691 | chr3.134134198.134134674 | 477 | 495 | FALSE | smaller | 18 |
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| chr5.464778.466163 | chr5.464832.465851 | 1020 | 1387 | FALSE | smaller | 367 |
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| chr5.863072.863372 | chr5.863072.863283 | 212 | 302 | FALSE | smaller | 90 |
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| chr5.926625.927210 | chr5.926625.927159 | 535 | 587 | FALSE | smaller | 52 |
| chr5.1185668.1186282 | chr5.1185673.1186282 | 610 | 616 | FALSE | smaller | 6 |
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| chr5.1952341.1953119 | chr5.1952381.1953119 | 739 | 780 | FALSE | smaller | 41 |
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| chr5.4924581.4925131 | chr5.4924589.4925131 | 543 | 552 | FALSE | smaller | 9 |
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| chr5.6510233.6510644 | chr5.6510233.6510586 | 354 | 413 | FALSE | smaller | 59 |
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| chr5.8340096.8340980 | chr5.8340675.8340980 | 306 | 886 | FALSE | smaller | 580 |
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| chr5.10555411.10555689 | chr5.10555483.10555689 | 207 | 280 | FALSE | smaller | 73 |
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| chr5.11012261.11012829 | chr5.11012261.11012715 | 455 | 570 | FALSE | smaller | 115 |
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| chr5.11466977.11467499 | chr5.11467001.11467499 | 499 | 524 | FALSE | smaller | 25 |
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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 |
| chr5.41924885.41925812 | chr5.41924885.41925748 | 864 | 929 | FALSE | smaller | 65 |
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| chr5.42621833.42622228 | chr5.42621833.42622134 | 302 | 397 | FALSE | smaller | 95 |
| 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 |
| chr5.43120676.43122445 | chr5.43120676.43122437 | 1762 | 1771 | FALSE | smaller | 9 |
| chr5.43191753.43192949 | chr5.43191773.43192949 | 1177 | 1198 | FALSE | smaller | 21 |
| chr5.43312610.43313882 | chr5.43312759.43313882 | 1124 | 1274 | FALSE | smaller | 150 |
| chr5.43432376.43432992 | chr5.43432486.43432992 | 507 | 618 | FALSE | smaller | 111 |
| chr5.43555882.43557743 | chr5.43555882.43557554 | 1673 | 1863 | FALSE | smaller | 190 |
| chr5.43635300.43635687 | chr5.43635335.43635687 | 353 | 389 | FALSE | smaller | 36 |
| chr5.43664982.43666719 | chr5.43665111.43666719 | 1609 | 1739 | FALSE | smaller | 130 |
| chr5.43888573.43889857 | chr5.43888591.43889857 | 1267 | 1286 | FALSE | smaller | 19 |
| chr5.44789260.44789748 | chr5.44789331.44789748 | 418 | 490 | FALSE | smaller | 72 |
| chr5.44875714.44876223 | chr5.44875714.44876221 | 508 | 511 | FALSE | smaller | 3 |
| chr5.44935288.44935778 | chr5.44935288.44935709 | 422 | 492 | FALSE | smaller | 70 |
| chr5.49599440.49600761 | chr5.49599489.49600761 | 1273 | 1323 | FALSE | smaller | 50 |
| 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 |
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| 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 |
| chr5.54295990.54296596 | chr5.54295996.54296586 | 591 | 608 | FALSE | smaller | 17 |
| chr5.54517247.54519063 | chr5.54517247.54518917 | 1671 | 1818 | FALSE | smaller | 147 |
| chr5.54637647.54637991 | chr5.54637647.54637984 | 338 | 346 | FALSE | smaller | 8 |
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| chr5.55176187.55176889 | chr5.55176209.55176889 | 681 | 704 | FALSE | smaller | 23 |
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| chr5.55633216.55634441 | chr5.55633261.55634441 | 1181 | 1227 | FALSE | smaller | 46 |
| chr5.55864321.55865041 | chr5.55864419.55865041 | 623 | 722 | FALSE | smaller | 99 |
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| chr5.56089102.56089841 | chr5.56089103.56089841 | 739 | 741 | FALSE | smaller | 2 |
| chr5.56211288.56211868 | chr5.56211288.56211802 | 515 | 582 | FALSE | smaller | 67 |
| chr5.56264358.56264700 | chr5.56264358.56264666 | 309 | 344 | FALSE | smaller | 35 |
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| chr5.56471744.56472096 | chr5.56471744.56472053 | 310 | 354 | FALSE | smaller | 44 |
| chr5.56480287.56481329 | chr5.56480298.56481329 | 1032 | 1044 | FALSE | smaller | 12 |
| chr5.56517023.56517411 | chr5.56517023.56517403 | 381 | 390 | FALSE | smaller | 9 |
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| chr5.57004777.57005088 | chr5.57004777.57005079 | 303 | 313 | FALSE | smaller | 10 |
| chr5.57028246.57028523 | chr5.57028246.57028500 | 255 | 279 | FALSE | smaller | 24 |
| chr5.57040792.57041130 | chr5.57040792.57041093 | 302 | 340 | FALSE | smaller | 38 |
| chr5.57173167.57174879 | chr5.57173167.57174853 | 1687 | 1714 | FALSE | smaller | 27 |
| chr5.57207487.57208488 | chr5.57207487.57208172 | 686 | 1003 | FALSE | smaller | 317 |
| chr5.57316787.57317663 | chr5.57316827.57317663 | 837 | 878 | FALSE | smaller | 41 |
| chr5.57403356.57403670 | chr5.57403376.57403670 | 295 | 316 | FALSE | smaller | 21 |
| chr5.57412073.57412413 | chr5.57412073.57412369 | 297 | 342 | FALSE | smaller | 45 |
| chr5.57482173.57483119 | chr5.57482456.57483119 | 664 | 948 | FALSE | smaller | 284 |
| chr5.57504456.57504695 | chr5.57504463.57504695 | 233 | 241 | FALSE | smaller | 8 |
| chr5.57690823.57691353 | chr5.57690823.57691293 | 471 | 532 | FALSE | smaller | 61 |
| chr5.57778791.57779225 | chr5.57778793.57779225 | 433 | 436 | FALSE | smaller | 3 |
| chr5.57780232.57780756 | chr5.57780327.57780756 | 430 | 526 | FALSE | smaller | 96 |
| chr5.58239781.58240026 | chr5.58239790.58240026 | 237 | 247 | FALSE | smaller | 10 |
| chr5.58459216.58461152 | chr5.58459216.58461147 | 1932 | 1938 | FALSE | smaller | 6 |
| 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 |
| chr5.59199720.59200316 | chr5.59199720.59200299 | 580 | 598 | FALSE | smaller | 18 |
| chr5.59287287.59288334 | chr5.59287308.59288334 | 1027 | 1049 | FALSE | smaller | 22 |
| chr5.59487756.59488437 | chr5.59487756.59488384 | 629 | 683 | FALSE | smaller | 54 |
| chr5.59562916.59563696 | chr5.59562941.59563696 | 756 | 782 | FALSE | smaller | 26 |
| chr5.59800000.59800768 | chr5.59800000.59800697 | 698 | 770 | FALSE | smaller | 72 |
| chr5.61279589.61280922 | chr5.61279760.61280922 | 1163 | 1335 | FALSE | smaller | 172 |
| 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 |
| chr5.61658640.61659715 | chr5.61658642.61659715 | 1074 | 1077 | FALSE | smaller | 3 |
| chr5.61963819.61965062 | chr5.61963827.61965062 | 1236 | 1245 | FALSE | smaller | 9 |
| chr5.62152370.62152887 | chr5.62152384.62152887 | 504 | 519 | FALSE | smaller | 15 |
| chr5.62432161.62432480 | chr5.62432161.62432457 | 297 | 321 | FALSE | smaller | 24 |
| chr5.62977672.62978584 | chr5.62977688.62978584 | 897 | 914 | FALSE | smaller | 17 |
| chr5.63131587.63131961 | chr5.63131587.63131935 | 349 | 376 | FALSE | smaller | 27 |
| chr5.63747398.63747647 | chr5.63747398.63747646 | 249 | 251 | FALSE | smaller | 2 |
| chr5.64147810.64148286 | chr5.64147810.64148265 | 456 | 478 | FALSE | smaller | 22 |
| chr5.64299806.64300101 | chr5.64299813.64300101 | 289 | 297 | FALSE | smaller | 8 |
| chr5.64384793.64385046 | chr5.64384800.64385046 | 247 | 255 | FALSE | smaller | 8 |
| chr5.64505808.64507475 | chr5.64505808.64507470 | 1663 | 1669 | FALSE | smaller | 6 |
| chr5.64566439.64566836 | chr5.64566439.64566826 | 388 | 399 | FALSE | smaller | 11 |
| chr5.64922917.64923588 | chr5.64922973.64923588 | 616 | 673 | FALSE | smaller | 57 |
| chr5.64939515.64940358 | chr5.64939515.64940209 | 695 | 845 | FALSE | smaller | 150 |
| chr5.65128251.65128649 | chr5.65128328.65128649 | 322 | 400 | FALSE | smaller | 78 |
| chr5.65213964.65214313 | chr5.65213964.65214311 | 348 | 351 | FALSE | smaller | 3 |
| chr5.65480294.65480896 | chr5.65480299.65480896 | 598 | 604 | FALSE | smaller | 6 |
| chr5.65488351.65488807 | chr5.65488371.65488807 | 437 | 458 | FALSE | smaller | 21 |
| chr5.65623764.65625421 | chr5.65623933.65625421 | 1489 | 1659 | FALSE | smaller | 170 |
| chr5.65707499.65707960 | chr5.65707559.65707960 | 402 | 463 | FALSE | smaller | 61 |
| 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 |
| chr5.66989258.66989595 | chr5.66989258.66989585 | 328 | 339 | FALSE | smaller | 11 |
| 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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| chr5.68420492.68421017 | chr5.68420492.68420908 | 417 | 527 | FALSE | smaller | 110 |
| chr5.68433880.68434810 | chr5.68433981.68434810 | 830 | 932 | FALSE | smaller | 102 |
| chr5.68898886.68899509 | chr5.68898893.68899509 | 617 | 625 | FALSE | smaller | 8 |
| chr5.68964424.68966288 | chr5.68964424.68965136 | 713 | 1866 | FALSE | smaller | 1153 |
| chr5.68964424.68966288 | chr5.68965304.68966288 | 985 | 1866 | FALSE | smaller | 881 |
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| chr5.71891740.71892259 | chr5.71891742.71892259 | 518 | 521 | FALSE | smaller | 3 |
| chr5.71931956.71933513 | chr5.71932117.71933513 | 1397 | 1559 | FALSE | smaller | 162 |
| chr5.72079197.72079780 | chr5.72079329.72079780 | 452 | 585 | FALSE | smaller | 133 |
| chr5.72167981.72168127 | chr5.72167986.72168127 | 142 | 148 | FALSE | smaller | 6 |
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| chr5.72556631.72557248 | chr5.72556638.72557248 | 611 | 619 | FALSE | smaller | 8 |
| chr5.72599458.72599802 | chr5.72599458.72599794 | 337 | 346 | FALSE | smaller | 9 |
| chr5.72624555.72625111 | chr5.72624579.72625111 | 533 | 558 | FALSE | smaller | 25 |
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| chr5.72953735.72953976 | chr5.72953735.72953952 | 218 | 243 | FALSE | smaller | 25 |
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| chr5.73447533.73448387 | chr5.73448150.73448387 | 238 | 856 | FALSE | smaller | 618 |
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| chr5.74865491.74867135 | chr5.74865491.74867070 | 1580 | 1646 | FALSE | smaller | 66 |
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| chr5.75046283.75047046 | chr5.75046307.75047046 | 740 | 765 | FALSE | smaller | 25 |
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| chr5.80034983.80035746 | chr5.80035074.80035738 | 665 | 765 | FALSE | smaller | 100 |
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| chr5.80466692.80467562 | chr5.80466692.80467493 | 802 | 872 | FALSE | smaller | 70 |
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| chr5.80871460.80872083 | chr5.80871460.80872054 | 595 | 625 | FALSE | smaller | 30 |
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| chr5.83404251.83404851 | chr5.83404257.83404851 | 595 | 602 | FALSE | smaller | 7 |
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| chr5.83744704.83745111 | chr5.83744764.83745111 | 348 | 409 | FALSE | smaller | 61 |
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| chr5.84393944.84394489 | chr5.84393944.84394341 | 398 | 547 | FALSE | smaller | 149 |
| chr5.84437734.84438122 | chr5.84437734.84438117 | 384 | 390 | FALSE | smaller | 6 |
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| chr5.85440893.85442065 | chr5.85440893.85441879 | 987 | 1174 | FALSE | smaller | 187 |
| chr5.85587850.85588230 | chr5.85587921.85588230 | 310 | 382 | FALSE | smaller | 72 |
| chr5.86526810.86527158 | chr5.86526835.86527158 | 324 | 350 | FALSE | smaller | 26 |
| chr5.87029753.87030724 | chr5.87029753.87030705 | 953 | 973 | FALSE | smaller | 20 |
| chr5.87420613.87421165 | chr5.87420613.87421073 | 461 | 554 | FALSE | smaller | 93 |
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| chr5.88601266.88601821 | chr5.88601298.88601821 | 524 | 557 | FALSE | smaller | 33 |
| chr5.88683650.88683985 | chr5.88683650.88683901 | 252 | 337 | FALSE | smaller | 85 |
| chr5.88925849.88926779 | chr5.88925903.88926779 | 877 | 932 | FALSE | smaller | 55 |
| chr5.88956375.88956723 | chr5.88956375.88956713 | 339 | 350 | FALSE | smaller | 11 |
| chr5.89071228.89071560 | chr5.89071237.89071560 | 324 | 334 | FALSE | smaller | 10 |
| chr5.89770052.89770294 | chr5.89770052.89770290 | 239 | 244 | FALSE | smaller | 5 |
| chr5.89982151.89982638 | chr5.89982163.89982638 | 476 | 489 | FALSE | smaller | 13 |
| chr5.90093929.90094386 | chr5.90093929.90094378 | 450 | 459 | FALSE | smaller | 9 |
| chr5.90161264.90161850 | chr5.90161264.90161742 | 479 | 588 | FALSE | smaller | 109 |
| chr5.90982588.90983502 | chr5.90982598.90983502 | 905 | 916 | FALSE | smaller | 11 |
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| chr6.2908113.2909173 | chr6.2908231.2909173 | 943 | 1062 | FALSE | smaller | 119 |
| chr6.2911815.2912126 | chr6.2911815.2911943 | 129 | 313 | FALSE | smaller | 184 |
| chr6.2960135.2960793 | chr6.2960206.2960793 | 588 | 660 | FALSE | smaller | 72 |
| chr6.3013444.3013948 | chr6.3013444.3013935 | 492 | 506 | FALSE | smaller | 14 |
| chr6.3109093.3109459 | chr6.3109093.3109333 | 241 | 368 | FALSE | smaller | 127 |
| chr6.3158241.3158532 | chr6.3158241.3158502 | 262 | 293 | FALSE | smaller | 31 |
| chr6.3324237.3324767 | chr6.3324237.3324761 | 525 | 532 | FALSE | smaller | 7 |
| chr6.3345744.3346379 | chr6.3345744.3346332 | 589 | 637 | FALSE | smaller | 48 |
| chr6.3363237.3363649 | chr6.3363237.3363623 | 387 | 414 | FALSE | smaller | 27 |
| chr6.3487721.3488353 | chr6.3487721.3488273 | 553 | 634 | FALSE | smaller | 81 |
| chr6.3510552.3511088 | chr6.3510552.3511011 | 460 | 538 | FALSE | smaller | 78 |
| chr6.3511334.3511851 | chr6.3511334.3511841 | 508 | 519 | FALSE | smaller | 11 |
| chr6.3739455.3739845 | chr6.3739455.3739828 | 374 | 392 | FALSE | smaller | 18 |
| chr6.3748316.3748689 | chr6.3748316.3748649 | 334 | 375 | FALSE | smaller | 41 |
| chr6.3801088.3801631 | chr6.3801088.3801603 | 516 | 545 | FALSE | smaller | 29 |
| chr6.3808764.3809365 | chr6.3808773.3809365 | 593 | 603 | FALSE | smaller | 10 |
| chr6.4274493.4275649 | chr6.4274493.4275252 | 760 | 1158 | FALSE | smaller | 398 |
| chr6.4274493.4275649 | chr6.4275276.4275649 | 374 | 1158 | FALSE | smaller | 784 |
| chr6.4295414.4295917 | chr6.4295453.4295917 | 465 | 505 | FALSE | smaller | 40 |
| chr6.4297137.4297800 | chr6.4297137.4297791 | 655 | 665 | FALSE | smaller | 10 |
| 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 |
| chr6.12012723.12013513 | chr6.12012723.12013508 | 786 | 792 | FALSE | smaller | 6 |
| 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 |
| chr6.12107534.12108503 | chr6.12107573.12108465 | 893 | 971 | FALSE | smaller | 78 |
| chr6.12275045.12275449 | chr6.12275098.12275449 | 352 | 406 | FALSE | smaller | 54 |
| chr6.12301013.12301409 | chr6.12301013.12301364 | 352 | 398 | FALSE | smaller | 46 |
| 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 |
| chr6.13044256.13044720 | chr6.13044256.13044680 | 425 | 466 | FALSE | smaller | 41 |
| chr6.13079144.13079814 | chr6.13079180.13079814 | 635 | 672 | FALSE | smaller | 37 |
| chr6.13303596.13304390 | chr6.13303596.13304359 | 764 | 796 | FALSE | smaller | 32 |
| chr6.13334745.13335324 | chr6.13334745.13335317 | 573 | 581 | FALSE | smaller | 8 |
| chr6.13351170.13351650 | chr6.13351170.13351616 | 447 | 482 | FALSE | smaller | 35 |
| 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 |
| chr6.15264975.15265907 | chr6.15264975.15265805 | 831 | 934 | FALSE | smaller | 103 |
| 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 |
| chr6.16244479.16245283 | chr6.16244598.16245283 | 686 | 806 | FALSE | smaller | 120 |
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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 |
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| 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 |
| chr6.26044929.26045734 | chr6.26044929.26045671 | 743 | 807 | FALSE | smaller | 64 |
| 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 |
| chr6.26388047.26388701 | chr6.26388110.26388701 | 592 | 656 | FALSE | smaller | 64 |
| 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 |
| chr6.27791753.27792711 | chr6.27791816.27792711 | 896 | 960 | FALSE | smaller | 64 |
| 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 |
| chr6.33904599.33905228 | chr6.33904600.33905228 | 629 | 631 | FALSE | smaller | 2 |
| 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 |
| chr6.34324736.34325176 | chr6.34324736.34325153 | 418 | 442 | FALSE | smaller | 24 |
| 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 |
| chr6.45422561.45424290 | chr6.45422561.45424136 | 1576 | 1731 | FALSE | smaller | 155 |
| 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 |
| chr6.52361550.52363313 | chr6.52361583.52363313 | 1731 | 1765 | FALSE | smaller | 34 |
| 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 |
| chr6.52670734.52671456 | chr6.52670734.52671414 | 681 | 724 | FALSE | smaller | 43 |
| chr6.52839365.52839980 | chr6.52839511.52839980 | 470 | 617 | FALSE | smaller | 147 |
| chr6.52856151.52856554 | chr6.52856151.52856552 | 402 | 405 | FALSE | smaller | 3 |
| 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 |
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| chr6.161139731.161140763 | chr6.161139813.161140763 | 951 | 1034 | FALSE | smaller | 83 |
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| chr6.161354234.161354701 | chr6.161354234.161354695 | 462 | 469 | FALSE | smaller | 7 |
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| chr6.170087977.170089100 | chr6.170088001.170088523 | 523 | 1125 | FALSE | smaller | 602 |
| chr6.170087977.170089100 | chr6.170088649.170089100 | 452 | 1125 | FALSE | smaller | 673 |
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| chr6.170231760.170232170 | chr6.170231830.170232170 | 341 | 412 | FALSE | smaller | 71 |
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| chr7.234776.235087 | chr7.234776.235019 | 244 | 313 | FALSE | smaller | 69 |
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| chr7.512862.513231 | chr7.512953.513168 | 216 | 371 | FALSE | smaller | 155 |
| chr7.516885.517381 | chr7.516885.517341 | 457 | 498 | FALSE | smaller | 41 |
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| chr7.604212.605042 | chr7.604212.604916 | 705 | 832 | FALSE | smaller | 127 |
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| chr7.867137.868058 | chr7.867337.868058 | 722 | 923 | FALSE | smaller | 201 |
| chr7.893534.894207 | chr7.893693.894207 | 515 | 675 | FALSE | smaller | 160 |
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| 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 |
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| chr7.1100877.1101259 | chr7.1100877.1101142 | 266 | 384 | FALSE | smaller | 118 |
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| chr7.1224332.1224771 | chr7.1224332.1224738 | 407 | 441 | FALSE | smaller | 34 |
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| chr7.1487919.1488402 | chr7.1487919.1488325 | 407 | 485 | FALSE | smaller | 78 |
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| chr7.1618301.1619604 | chr7.1619135.1619590 | 456 | 1305 | FALSE | smaller | 849 |
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| chr7.1693229.1695224 | chr7.1693970.1695224 | 1255 | 1997 | FALSE | smaller | 742 |
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| chr7.1749512.1750352 | chr7.1749627.1750352 | 726 | 842 | FALSE | smaller | 116 |
| chr7.1825460.1826454 | chr7.1825460.1826187 | 728 | 996 | FALSE | smaller | 268 |
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| 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 |
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| chr7.2082231.2082604 | chr7.2082278.2082604 | 327 | 375 | FALSE | smaller | 48 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| chr7.43491911.43492380 | chr7.43491911.43492329 | 419 | 471 | FALSE | smaller | 52 |
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| chr7.44184453.44185641 | chr7.44184453.44185400 | 948 | 1190 | FALSE | smaller | 242 |
| chr7.44246292.44247083 | chr7.44246417.44247083 | 667 | 793 | FALSE | smaller | 126 |
| chr7.44397099.44397450 | chr7.44397183.44397450 | 268 | 353 | FALSE | smaller | 85 |
| chr7.44565284.44566017 | chr7.44565322.44566017 | 696 | 735 | FALSE | smaller | 39 |
| chr7.44615014.44615540 | chr7.44615116.44615540 | 425 | 528 | FALSE | smaller | 103 |
| chr7.44634758.44635632 | chr7.44634758.44635625 | 868 | 876 | FALSE | smaller | 8 |
| chr7.44639208.44640393 | chr7.44639239.44640393 | 1155 | 1187 | FALSE | smaller | 32 |
| chr7.44693775.44694578 | chr7.44693813.44694525 | 713 | 805 | FALSE | smaller | 92 |
| chr7.44979711.44980154 | chr7.44979732.44980154 | 423 | 445 | FALSE | smaller | 22 |
| chr7.44999251.45000788 | chr7.44999251.45000418 | 1168 | 1539 | FALSE | smaller | 371 |
| chr7.44999251.45000788 | chr7.45000504.45000788 | 285 | 1539 | FALSE | smaller | 1254 |
| chr7.45156862.45157114 | chr7.45156862.45157102 | 241 | 254 | FALSE | smaller | 13 |
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| chr7.45363470.45364210 | chr7.45363514.45364210 | 697 | 742 | FALSE | smaller | 45 |
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| chr7.45663048.45663520 | chr7.45663077.45663520 | 444 | 474 | FALSE | smaller | 30 |
| 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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| chr7.46539938.46540567 | 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 |
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| chr7.46930446.46930649 | chr7.46930446.46930643 | 198 | 205 | FALSE | smaller | 7 |
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| chr7.47370435.47370988 | chr7.47370435.47370983 | 549 | 555 | FALSE | smaller | 6 |
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| chr7.47464811.47465806 | chr7.47464872.47465806 | 935 | 997 | FALSE | smaller | 62 |
| chr7.47481994.47483022 | chr7.47482009.47483022 | 1014 | 1030 | FALSE | smaller | 16 |
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| chr7.47604608.47605391 | chr7.47604608.47605317 | 710 | 785 | FALSE | smaller | 75 |
| chr7.47635110.47635682 | chr7.47635114.47635682 | 569 | 574 | FALSE | smaller | 5 |
| chr7.47651399.47651979 | chr7.47651404.47651979 | 576 | 582 | FALSE | smaller | 6 |
| chr7.47799617.47799894 | chr7.47799664.47799894 | 231 | 279 | FALSE | smaller | 48 |
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| chr7.49038608.49039198 | chr7.49038608.49039182 | 575 | 592 | FALSE | smaller | 17 |
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| chr7.50436266.50436819 | chr7.50436266.50436744 | 479 | 555 | FALSE | smaller | 76 |
| chr7.50502356.50502765 | chr7.50502382.50502765 | 384 | 411 | FALSE | smaller | 27 |
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| chr7.50821534.50821966 | chr7.50821564.50821966 | 403 | 434 | FALSE | smaller | 31 |
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| chr7.51141336.51142274 | chr7.51141336.51142244 | 909 | 940 | FALSE | smaller | 31 |
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| chr7.51477189.51477899 | chr7.51477230.51477899 | 670 | 712 | FALSE | smaller | 42 |
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| chr7.53350155.53350518 | chr7.53350260.53350518 | 259 | 365 | FALSE | smaller | 106 |
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| chr7.54543052.54543453 | chr7.54543154.54543453 | 300 | 403 | FALSE | smaller | 103 |
| chr7.54546604.54547104 | chr7.54546604.54547099 | 496 | 502 | FALSE | smaller | 6 |
| chr7.54589501.54590243 | chr7.54589680.54590243 | 564 | 744 | FALSE | smaller | 180 |
| chr7.54697663.54698374 | chr7.54697715.54698374 | 660 | 713 | FALSE | smaller | 53 |
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| chr7.55078865.55079435 | chr7.55079008.55079434 | 427 | 572 | FALSE | smaller | 145 |
| chr7.55106061.55106300 | chr7.55106061.55106244 | 184 | 241 | FALSE | smaller | 57 |
| chr7.55186088.55186599 | chr7.55186147.55186599 | 453 | 513 | FALSE | smaller | 60 |
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| chr7.55307166.55307475 | chr7.55307235.55307475 | 241 | 311 | FALSE | smaller | 70 |
| chr7.55343993.55345318 | chr7.55344052.55345318 | 1267 | 1327 | FALSE | smaller | 60 |
| chr7.55426589.55427056 | chr7.55426589.55427054 | 466 | 469 | FALSE | smaller | 3 |
| chr7.55438594.55439387 | chr7.55438679.55439387 | 709 | 795 | FALSE | smaller | 86 |
| chr7.55514682.55515136 | chr7.55514689.55514984 | 296 | 456 | FALSE | smaller | 160 |
| chr7.55546800.55547175 | chr7.55546807.55547175 | 369 | 377 | FALSE | smaller | 8 |
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| chr7.56106155.56106799 | chr7.56106192.56106799 | 608 | 646 | FALSE | smaller | 38 |
| chr7.56371605.56372205 | chr7.56371695.56372205 | 511 | 602 | FALSE | smaller | 91 |
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| chr7.64307132.64307809 | chr7.64307188.64307785 | 598 | 679 | FALSE | smaller | 81 |
| chr7.64313778.64314395 | chr7.64313778.64314142 | 365 | 619 | FALSE | smaller | 254 |
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| chr7.87316769.87317629 | chr7.87316769.87317606 | 838 | 862 | FALSE | smaller | 24 |
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| chr7.87567101.87567454 | chr7.87567101.87567414 | 314 | 355 | FALSE | smaller | 41 |
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| chr7.88218880.88219487 | chr7.88218914.88219487 | 574 | 609 | FALSE | smaller | 35 |
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| chr9.96523733.96524382 | chr9.96523967.96524382 | 416 | 651 | FALSE | smaller | 235 |
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| chr11.45880176.45880779 | chr11.45880176.45880642 | 467 | 605 | FALSE | smaller | 138 |
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| chr11.45928551.45928830 | chr11.45928556.45928803 | 248 | 281 | FALSE | smaller | 33 |
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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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| chr11.70323667.70325056 | chr11.70323928.70325056 | 1129 | 1391 | FALSE | smaller | 262 |
| chr11.70333195.70333733 | chr11.70333249.70333733 | 485 | 540 | FALSE | smaller | 55 |
| chr11.70424043.70424694 | chr11.70424074.70424694 | 621 | 653 | FALSE | smaller | 32 |
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| chr11.70556932.70557204 | chr11.70556932.70557163 | 232 | 274 | FALSE | smaller | 42 |
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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
Overlapping peaks with ATAC-seq heart, left ventricle data, Snyder ENCFF966JZT.
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
%
Differential analysis
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")Correlation of LFC
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)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
plot_grid(v3,v4, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
plot_grid(v5,v6, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
plot_grid(v7,v8, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
plot_grid(v9,v10, rel_widths =c(.8,1))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
examples of top DAR peaks by treatment
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()
#### Correlations of LFC by treatment
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")
Cormotif
# 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)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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
Peak distribution characteristics
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")
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
#### 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)Eight group peak characteristics
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")
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
Overlapping peaks with H3K27ac data
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
118 chr1.10031790.10034238
119 chr1.10108148.10108837
120 chr1.10209689.10212632
121 chr1.10398466.10400378
122 chr1.10456029.10457443
123 chr1.10471939.10472660
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
131 chr1.10714179.10715577
132 chr1.10716041.10718452
133 chr1.10720008.10721257
134 chr1.10722717.10724831
135 chr1.10733334.10734138
136 chr1.10736800.10738525
137 chr1.10744722.10746245
138 chr1.10793688.10796810
139 chr1.10819797.10821342
140 chr1.11012236.11013375
141 chr1.11059370.11060733
142 chr1.11099207.11100158
143 chr1.11175475.11177256
144 chr1.11262109.11262839
145 chr1.11272919.11274078
146 chr1.11680738.11681773
147 chr1.11707483.11708247
148 chr1.11735803.11736833
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
197 chr1.16143219.16147654
198 chr1.16148399.16156958
199 chr1.16167447.16168042
200 chr1.16172923.16174201
201 chr1.16175438.16176930
202 chr1.16181437.16183022
203 chr1.16206546.16207140
204 chr1.16351536.16353019
205 chr1.16366993.16368681
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
249 chr1.21323964.21325270
250 chr1.21345168.21346069
251 chr1.21781672.21784146
252 chr1.21896167.21897738
253 chr1.21901320.21902319
254 chr1.21909208.21910423
255 chr1.21911039.21912024
256 chr1.21912833.21915589
257 chr1.21919410.21922784
258 chr1.21930536.21934521
259 chr1.21935195.21938085
260 chr1.21939008.21940497
261 chr1.21942000.21945116
262 chr1.21955192.21957316
263 chr1.22024891.22026363
264 chr1.22051992.22054102
265 chr1.22112333.22113406
266 chr1.22418263.22419709
267 chr1.22446906.22448426
268 chr1.22451342.22453031
269 chr1.22710044.22711386
270 chr1.22750009.22751524
271 chr1.23019003.23021255
272 chr1.23166569.23168970
273 chr1.23343108.23345242
274 chr1.23367678.23371556
275 chr1.23393728.23395467
276 chr1.23402611.23404161
277 chr1.23422891.23425765
278 chr1.23482934.23485009
279 chr1.23553736.23557536
280 chr1.23557657.23559996
281 chr1.23561732.23563193
282 chr1.23567770.23569388
283 chr1.23575460.23575945
284 chr1.23576703.23577923
285 chr1.23592282.23593890
286 chr1.23691521.23692684
287 chr1.23742818.23744277
288 chr1.23758745.23759442
289 chr1.23778005.23778975
290 chr1.23790009.23793170
291 chr1.23799433.23801336
292 chr1.23824773.23825572
293 chr1.23958760.23961060
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
302 chr1.24641951.24644426
303 chr1.24683302.24684536
304 chr1.24693719.24694977
305 chr1.24744768.24746961
306 chr1.25022122.25023366
307 chr1.25231958.25233500
308 chr1.25239469.25240703
309 chr1.25246575.25248251
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
319 chr1.25771132.25772263
320 chr1.25799835.25801199
321 chr1.25818768.25821850
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
331 chr1.26093538.26095406
332 chr1.26101736.26102460
333 chr1.26110652.26112678
334 chr1.26158461.26159822
335 chr1.26161075.26161948
336 chr1.26169415.26170531
337 chr1.26233727.26235328
338 chr1.26254250.26254842
339 chr1.26275695.26277061
340 chr1.26278959.26280870
341 chr1.26284365.26285322
342 chr1.26431975.26432774
343 chr1.26471588.26473491
344 chr1.26500099.26501714
345 chr1.26531019.26531992
346 chr1.26620019.26621747
347 chr1.26692228.26693518
348 chr1.26694909.26698553
349 chr1.26723178.26724198
350 chr1.26744105.26745325
351 chr1.26771204.26772346
352 chr1.26786165.26789287
353 chr1.26826349.26827104
354 chr1.26864330.26865907
355 chr1.26889738.26890764
356 chr1.26899559.26900488
357 chr1.26921440.26922349
358 chr1.26993238.26995328
359 chr1.26998763.26999985
360 chr1.27004341.27005117
361 chr1.27010048.27011668
362 chr1.27012465.27013401
363 chr1.27098988.27100323
364 chr1.27111969.27112606
365 chr1.27233969.27235654
366 chr1.27298835.27301743
367 chr1.27322036.27322995
368 chr1.27342044.27343723
369 chr1.27365343.27367262
370 chr1.27418459.27419118
371 chr1.27427894.27428993
372 chr1.27489538.27490841
373 chr1.27503706.27505676
374 chr1.27519400.27520064
375 chr1.27525052.27529985
376 chr1.27536463.27539340
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1798 chr1.212030395.212031161
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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
1809 chr1.213014831.213015980
1810 chr1.213051013.213051799
1811 chr1.213399309.213400539
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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
1820 chr1.214446761.214447327
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
1828 chr1.217089514.217090344
1829 chr1.217453752.217454375
1830 chr1.217630613.217631453
1831 chr1.218284721.218285576
1832 chr1.218304105.218305242
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1834 chr1.218344479.218348307
1835 chr1.218371599.218372439
1836 chr1.218373935.218375344
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1849 chr1.220271919.220273017
1850 chr1.220528086.220529443
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1860 chr1.223704955.223705782
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1865 chr1.223731201.223732336
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1870 chr1.223992231.223993154
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1879 chr1.225627487.225627828
1880 chr1.225651691.225654242
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1886 chr1.225923293.225924477
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1892 chr1.226121616.226123049
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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
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1903 chr1.226870348.226871539
1904 chr1.226939044.226940652
1905 chr1.226973987.226975348
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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
2092 chr10.3500279.3500831
2093 chr10.3574251.3575541
2094 chr10.3749653.3750481
2095 chr10.3754887.3756002
2096 chr10.3775003.3776506
2097 chr10.3778907.3787106
2098 chr10.3809710.3810660
2099 chr10.3811289.3811871
2100 chr10.3827308.3828752
2101 chr10.3852144.3853118
2102 chr10.3887922.3888379
2103 chr10.3911463.3912820
2104 chr10.4196531.4197466
2105 chr10.4241422.4244158
2106 chr10.4343930.4345240
2107 chr10.4469800.4470889
2108 chr10.4661382.4664097
2109 chr10.4867376.4868357
2110 chr10.5071167.5072233
2111 chr10.5446350.5446842
2112 chr10.5466269.5466828
2113 chr10.5684538.5686215
2114 chr10.5812454.5814278
2115 chr10.5888590.5891281
2116 chr10.5977198.5978386
2117 chr10.6088804.6089971
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2119 chr10.6163220.6165566
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2121 chr10.6221552.6223376
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2123 chr10.7787033.7789632
2124 chr10.7797247.7797972
2125 chr10.7818335.7819521
2126 chr10.9824686.9826009
2127 chr10.10792232.10793347
2128 chr10.10871921.10872890
2129 chr10.10891198.10891914
2130 chr10.10910846.10912578
2131 chr10.10913897.10914809
2132 chr10.11017034.11020120
2133 chr10.11164840.11166893
2134 chr10.11177592.11178970
2135 chr10.11224791.11225504
2136 chr10.11335113.11336011
2137 chr10.11338316.11339418
2138 chr10.11610905.11612096
2139 chr10.11757226.11758566
2140 chr10.11793501.11794677
2141 chr10.11823017.11824232
2142 chr10.11868595.11872443
2143 chr10.11874148.11876805
2144 chr10.11881510.11882548
2145 chr10.11897665.11899031
2146 chr10.12041735.12043637
2147 chr10.12068637.12069242
2148 chr10.12129083.12130247
2149 chr10.12194511.12196759
2150 chr10.12234624.12236874
2151 chr10.12241798.12242652
2152 chr10.12484709.12485725
2153 chr10.13099180.13100926
2154 chr10.13299308.13300240
2155 chr10.13346547.13349004
2156 chr10.13685687.13686604
2157 chr10.13707252.13707995
2158 chr10.13757609.13759148
2159 chr10.14404597.14405684
2160 chr10.14449844.14450783
2161 chr10.14603537.14604590
2162 chr10.14648337.14649527
2163 chr10.14712651.14713970
2164 chr10.14836904.14839262
2165 chr10.14876645.14877301
2166 chr10.14877880.14879557
2167 chr10.14953060.14954463
2168 chr10.14959211.14960485
2169 chr10.15096613.15098160
2170 chr10.15167659.15169305
2171 chr10.15369679.15371442
2172 chr10.15859857.15860615
2173 chr10.16816803.16818180
2174 chr10.17010510.17011274
2175 chr10.17200870.17202236
2176 chr10.17227441.17230937
2177 chr10.17616147.17617913
2178 chr10.17643182.17645004
2179 chr10.17673512.17674491
2180 chr10.17835632.17837434
2181 chr10.17912400.17913895
2182 chr10.17914218.17915154
2183 chr10.18140037.18142231
2184 chr10.18178768.18180935
2185 chr10.18340497.18341406
2186 chr10.18465962.18466939
2187 chr10.18473117.18474845
2188 chr10.18627463.18628056
2189 chr10.18658820.18660444
2190 chr10.19815570.19817765
2191 chr10.20596293.20598009
2192 chr10.20598544.20599241
2193 chr10.20889908.20898138
2194 chr10.20898639.20899446
2195 chr10.20954412.20955622
2196 chr10.20963609.20964323
2197 chr10.21290675.21291599
2198 chr10.21292749.21294401
2199 chr10.21313898.21316761
2200 chr10.21336207.21337471
2201 chr10.21349739.21350769
2202 chr10.21364163.21365360
2203 chr10.21368893.21370302
2204 chr10.21399206.21400201
2205 chr10.21494310.21497664
2206 chr10.21508013.21510737
2207 chr10.21515254.21519514
2208 chr10.21524422.21528210
2209 chr10.21532770.21535693
2210 chr10.21727519.21728522
2211 chr10.22002693.22003922
2212 chr10.22229122.22229566
2213 chr10.22315775.22317186
2214 chr10.22320325.22322030
2215 chr10.22340413.22341467
2216 chr10.22617423.22619156
2217 chr10.22713684.22714727
2218 chr10.23047052.23047897
2219 chr10.23095339.23096545
2220 chr10.23399353.23400499
2221 chr10.23438463.23440923
2222 chr10.24008629.24009705
2223 chr10.24208623.24209489
2224 chr10.24255180.24257175
2225 chr10.24525909.24526714
2226 chr10.24721216.24725223
2227 chr10.24762328.24763436
2228 chr10.25016156.25017527
2229 chr10.25059744.25060668
2230 chr10.25068065.25068948
2231 chr10.25080939.25083230
2232 chr10.25493132.25494037
2233 chr10.25495854.25496694
2234 chr10.25501442.25503148
2235 chr10.25681771.25683003
2236 chr10.26697017.26698634
2237 chr10.26857608.26861314
2238 chr10.27099916.27100671
2239 chr10.27153567.27156534
2240 chr10.27239764.27243493
2241 chr10.27503672.27505266
2242 chr10.28291327.28292582
2243 chr10.28302204.28303387
2244 chr10.28326397.28328260
2245 chr10.28531915.28534485
2246 chr10.28558769.28559906
2247 chr10.28668323.28669364
2248 chr10.28676695.28680572
2249 chr10.28680744.28682821
2250 chr10.28704584.28705601
2251 chr10.28721285.28722804
2252 chr10.28806716.28808545
2253 chr10.28838660.28840364
2254 chr10.28990828.28992390
2255 chr10.29063729.29065158
2256 chr10.29105234.29106645
2257 chr10.29112929.29114016
2258 chr10.29231441.29232339
2259 chr10.29237345.29238399
2260 chr10.29252832.29255327
2261 chr10.29256046.29258026
2262 chr10.29322647.29323277
2263 chr10.29574362.29575417
2264 chr10.29598653.29599266
2265 chr10.29632764.29636345
2266 chr10.29658935.29659551
2267 chr10.29724760.29726059
2268 chr10.29734876.29737304
2269 chr10.29865393.29866537
2270 chr10.30058461.30060041
2271 chr10.30182332.30183481
2272 chr10.30348519.30349354
2273 chr10.30433062.30434938
2274 chr10.31031092.31032734
2275 chr10.31133847.31134383
2276 chr10.31318212.31320795
2277 chr10.31754556.31755977
2278 chr10.31759604.31761074
2279 chr10.31816013.31817417
2280 chr10.31927443.31929781
2281 chr10.32054916.32056663
2282 chr10.32269633.32270207
2283 chr10.32346000.32347704
2284 chr10.32378373.32380023
2285 chr10.32445740.32446913
2286 chr10.32938397.32939121
2287 chr10.32939497.32940006
2288 chr10.32940623.32941968
2289 chr10.32956446.32959276
2290 chr10.32961810.32962591
2291 chr10.32962976.32963654
2292 chr10.32980123.32981553
2293 chr10.33004969.33006290
2294 chr10.33237628.33239094
2295 chr10.33332381.33333115
2296 chr10.33333528.33337859
2297 chr10.33459960.33461506
2298 chr10.33616749.33617402
2299 chr10.34739590.34740331
2300 chr10.34813721.34816481
2301 chr10.35089262.35090925
2302 chr10.35126194.35128290
2303 chr10.35149080.35150012
2304 chr10.35153009.35154361
2305 chr10.35195085.35196514
2306 chr10.35331402.35332486
2307 chr10.35335200.35337684
2308 chr10.35372168.35372991
2309 chr10.35413170.35414833
2310 chr10.35421146.35422051
2311 chr10.35447886.35448991
2312 chr10.35638327.35643897
2313 chr10.35770649.35771450
2314 chr10.35874650.35876604
2315 chr10.36113821.36117582
2316 chr10.36280400.36281094
2317 chr10.36349377.36350429
2318 chr10.36441091.36442184
2319 chr10.36449982.36450739
2320 chr10.37856978.37858378
2321 chr10.37976063.37976882
2322 chr10.38010413.38011717
2323 chr10.38094307.38095119
2324 chr10.42637880.42638850
2325 chr10.42782027.42783482
2326 chr10.43137389.43138980
2327 chr10.43395734.43397548
2328 chr10.43406766.43409757
2329 chr10.43436330.43437520
2330 chr10.43455114.43456366
2331 chr10.43648174.43649369
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2335 chr10.44952390.44953399
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2338 chr10.44973795.44976529
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2340 chr10.44996161.44996994
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2346 chr10.46018648.46021013
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2358 chr10.47402710.47404131
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2360 chr10.47552912.47553505
2361 chr10.47988300.47989767
2362 chr10.48250946.48252236
2363 chr10.48306348.48307646
2364 chr10.48453805.48454742
2365 chr10.48523483.48524425
2366 chr10.48707475.48708777
2367 chr10.49224990.49226112
2368 chr10.49277910.49278862
2369 chr10.49297961.49301142
2370 chr10.49538736.49539801
2371 chr10.49941530.49942313
2372 chr10.50418058.50418738
2373 chr10.50550408.50550836
2374 chr10.50577285.50578402
2375 chr10.50603701.50604819
2376 chr10.50604977.50605827
2377 chr10.50621838.50626067
2378 chr10.50739491.50740636
2379 chr10.50990474.50994139
2380 chr10.51073804.51074949
2381 chr10.51244424.51245042
2382 chr10.51698667.51700692
2383 chr10.52258877.52259565
2384 chr10.52313231.52315349
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2386 chr10.54868714.54869985
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2390 chr10.57846141.57847335
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2392 chr10.58268824.58270060
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2396 chr10.58757448.58758522
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2398 chr10.58856259.58857441
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2414 chr10.60827908.60829003
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19893 chrX.155612083.155613144
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")abs LFC across clusters
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"
)
)
making median dataframes
# 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)Correlations
Correlation between k27ac/atac shared peak median LFC and RNA median LFC
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared H3K27ac peaks and RNA-NG at 3 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared H3K27ac peaks and RNA-NG at 24 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared ATAC peaks and RNA-NG at 3 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared ATAC peaks and RNA-NG at 24 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
Correlation between atac no- shared peak median LFC and RNA median LFC
anti_joined_H3k27ac %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of H3K27ac peaks not shared by ATAC peaks and RNA-NG at 3 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
#
anti_joined_H3k27ac %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of H3K27ac peaks not shared by ATAC peaks and RNA-NG at 24 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
anti_joined_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of ATAC peaks not shared with H3K27ac and RNA-NG at 3 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
anti_joined_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of ATAC peaks not shared with H3K27ac and RNA-NG at 24 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
fullpeaks_H3k27ac_lfc <- Collapsed_H3k27ac_NG%>%
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)Correlations of ATAC peaks within 2kb and RNA
fullpeaks_joined_lfc <- median_24_lfc %>%
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"))
fullpeaks_joined_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_3h_lfc,y=RNA_3h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of 2kb ATAC peaks and RNA-NGat 3 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
fullpeaks_joined_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=med_24h_lfc,y=RNA_24h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of 2kb ATAC peaks and RNA-NGat 24 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
fullpeaks_H3k27ac_lfc <- Collapsed_H3k27ac_NG%>%
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)
fullpeaks_H3k27ac_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_3h_lfc,y=RNA_3h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of 2kb H3K27ac peaks and RNA-NG at 3 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
fullpeaks_H3k27ac_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_24h_lfc,y=RNA_24h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of 2kb H3K27ac peaks and RNA-NG at 24 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-20000 & dist_to_NG<20000) %>%
ggplot(., aes(x=AC_3h_lfc,y=RNA_3h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of H3K27ac shared peaks (20kb) and RNA-NG at 3 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-20000 & dist_to_NG<20000) %>%
ggplot(., aes(x=AC_24h_lfc,y=RNA_24h_lfc))+
geom_point()+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of H3K27ac shared peaks(20kb) and RNA-NG at 24 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-20000 & dist_to_NG<20000) %>%
ggplot(., aes(x=med_3h_lfc,y=RNA_3h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared ATAC peaks (20kb) and RNA-NG at 3 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
joined_LFC_df %>%
dplyr::filter(dist_to_NG>-20000 & dist_to_NG<20000) %>%
ggplot(., aes(x=med_24h_lfc,y=RNA_24h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of shared ATAC peaks(20kb) and RNA-NG at 24 hours")+
xlab("ATAC peak LFC")+
ylab("RNA LFC")
fullpeaks_H3k27ac_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_3h_lfc,y=RNA_3h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of all H3k27ac peaks(2kb) and RNA-NG at 3 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
fullpeaks_H3k27ac_lfc %>%
dplyr::filter(dist_to_NG>-2000 & dist_to_NG<2000) %>%
ggplot(., aes(x=AC_24h_lfc,y=RNA_24h_lfc))+
geom_point(col="cornflowerblue")+
sm_statCorr(corr_method = 'pearson')+
ggtitle("Correlation of all H3k27ac peaks(2kb) and RNA-NG at 24 hours")+
xlab("H3K27ac peak LFC")+
ylab("RNA LFC")
sessionInfo()R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 10 x64 (build 19045)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/Chicago
tzcode source: internal
attached base packages:
[1] grid stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] smplot2_0.2.4
[2] cowplot_1.1.3
[3] ComplexHeatmap_2.20.0
[4] ggrepel_0.9.6
[5] plyranges_1.24.0
[6] ggsignif_0.6.4
[7] genomation_1.36.0
[8] eulerr_7.0.2
[9] devtools_2.4.5
[10] usethis_3.0.0
[11] ggpubr_0.6.0
[12] BiocParallel_1.38.0
[13] Cormotif_1.50.0
[14] affy_1.82.0
[15] scales_1.3.0
[16] VennDiagram_1.7.3
[17] futile.logger_1.4.3
[18] gridExtra_2.3
[19] ggfortify_0.4.17
[20] edgeR_4.2.1
[21] limma_3.60.4
[22] rtracklayer_1.64.0
[23] org.Hs.eg.db_3.19.1
[24] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[25] GenomicFeatures_1.56.0
[26] AnnotationDbi_1.66.0
[27] Biobase_2.64.0
[28] GenomicRanges_1.56.1
[29] GenomeInfoDb_1.40.1
[30] IRanges_2.38.1
[31] S4Vectors_0.42.1
[32] BiocGenerics_0.50.0
[33] ChIPseeker_1.40.0
[34] RColorBrewer_1.1-3
[35] broom_1.0.7
[36] kableExtra_1.4.0
[37] lubridate_1.9.3
[38] forcats_1.0.0
[39] stringr_1.5.1
[40] dplyr_1.1.4
[41] purrr_1.0.2
[42] readr_2.1.5
[43] tidyr_1.3.1
[44] tibble_3.2.1
[45] ggplot2_3.5.1
[46] tidyverse_2.0.0
[47] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] R.methodsS3_1.8.2
[2] vroom_1.6.5
[3] urlchecker_1.0.1
[4] nnet_7.3-19
[5] Biostrings_2.72.1
[6] vctrs_0.6.5
[7] digest_0.6.37
[8] png_0.1-8
[9] shape_1.4.6.1
[10] git2r_0.33.0
[11] magick_2.8.5
[12] MASS_7.3-61
[13] reshape2_1.4.4
[14] httpuv_1.6.15
[15] foreach_1.5.2
[16] qvalue_2.36.0
[17] withr_3.0.1
[18] xfun_0.47
[19] ggfun_0.1.6
[20] ellipsis_0.3.2
[21] memoise_2.0.1
[22] profvis_0.4.0
[23] systemfonts_1.1.0
[24] tidytree_0.4.6
[25] zoo_1.8-12
[26] GlobalOptions_0.1.2
[27] gtools_3.9.5
[28] R.oo_1.26.0
[29] Formula_1.2-5
[30] KEGGREST_1.44.1
[31] promises_1.3.0
[32] httr_1.4.7
[33] rstatix_0.7.2
[34] restfulr_0.0.15
[35] ps_1.8.0
[36] rstudioapi_0.16.0
[37] UCSC.utils_1.0.0
[38] miniUI_0.1.1.1
[39] generics_0.1.3
[40] DOSE_3.30.5
[41] base64enc_0.1-3
[42] processx_3.8.4
[43] curl_5.2.3
[44] zlibbioc_1.50.0
[45] ggraph_2.2.1
[46] polyclip_1.10-7
[47] GenomeInfoDbData_1.2.12
[48] SparseArray_1.4.8
[49] xtable_1.8-4
[50] doParallel_1.0.17
[51] evaluate_1.0.0
[52] S4Arrays_1.4.1
[53] preprocessCore_1.66.0
[54] hms_1.1.3
[55] colorspace_2.1-1
[56] magrittr_2.0.3
[57] later_1.3.2
[58] viridis_0.6.5
[59] ggtree_3.12.0
[60] lattice_0.22-6
[61] getPass_0.2-4
[62] XML_3.99-0.17
[63] shadowtext_0.1.4
[64] matrixStats_1.4.1
[65] Hmisc_5.1-3
[66] pillar_1.9.0
[67] nlme_3.1-166
[68] iterators_1.0.14
[69] gridBase_0.4-7
[70] caTools_1.18.3
[71] compiler_4.4.1
[72] stringi_1.8.4
[73] SummarizedExperiment_1.34.0
[74] GenomicAlignments_1.40.0
[75] plyr_1.8.9
[76] crayon_1.5.3
[77] abind_1.4-8
[78] BiocIO_1.14.0
[79] gridGraphics_0.5-1
[80] locfit_1.5-9.10
[81] graphlayouts_1.2.0
[82] bit_4.5.0
[83] fastmatch_1.1-4
[84] whisker_0.4.1
[85] codetools_0.2-20
[86] bslib_0.8.0
[87] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[88] GetoptLong_1.0.5
[89] mime_0.12
[90] splines_4.4.1
[91] circlize_0.4.16
[92] Rcpp_1.0.13
[93] knitr_1.48
[94] blob_1.2.4
[95] utf8_1.2.4
[96] clue_0.3-65
[97] fs_1.6.4
[98] checkmate_2.3.2
[99] pkgbuild_1.4.4
[100] ggplotify_0.1.2
[101] Matrix_1.7-0
[102] callr_3.7.6
[103] statmod_1.5.0
[104] tzdb_0.4.0
[105] svglite_2.1.3
[106] tweenr_2.0.3
[107] pkgconfig_2.0.3
[108] tools_4.4.1
[109] cachem_1.1.0
[110] RSQLite_2.3.7
[111] viridisLite_0.4.2
[112] DBI_1.2.3
[113] impute_1.78.0
[114] fastmap_1.2.0
[115] rmarkdown_2.28
[116] Rsamtools_2.20.0
[117] sass_0.4.9
[118] patchwork_1.3.0
[119] BiocManager_1.30.25
[120] carData_3.0-5
[121] rpart_4.1.23
[122] farver_2.1.2
[123] mgcv_1.9-1
[124] tidygraph_1.3.1
[125] scatterpie_0.2.4
[126] yaml_2.3.10
[127] MatrixGenerics_1.16.0
[128] foreign_0.8-87
[129] cli_3.6.3
[130] lifecycle_1.0.4
[131] lambda.r_1.2.4
[132] sessioninfo_1.2.2
[133] backports_1.5.0
[134] timechange_0.3.0
[135] gtable_0.3.5
[136] rjson_0.2.23
[137] parallel_4.4.1
[138] ape_5.8
[139] jsonlite_1.8.9
[140] bitops_1.0-8
[141] bit64_4.5.2
[142] pwr_1.3-0
[143] yulab.utils_0.1.7
[144] futile.options_1.0.1
[145] highr_0.11
[146] jquerylib_0.1.4
[147] GOSemSim_2.30.2
[148] R.utils_2.12.3
[149] lazyeval_0.2.2
[150] shiny_1.9.1
[151] htmltools_0.5.8.1
[152] enrichplot_1.24.4
[153] GO.db_3.19.1
[154] rappdirs_0.3.3
[155] formatR_1.14
[156] glue_1.8.0
[157] httr2_1.0.5
[158] XVector_0.44.0
[159] RCurl_1.98-1.16
[160] rprojroot_2.0.4
[161] treeio_1.28.0
[162] BSgenome_1.72.0
[163] boot_1.3-31
[164] igraph_2.0.3
[165] R6_2.5.1
[166] gplots_3.1.3.1
[167] labeling_0.4.3
[168] cluster_2.1.6
[169] pkgload_1.4.0
[170] aplot_0.2.3
[171] DelayedArray_0.30.1
[172] tidyselect_1.2.1
[173] plotrix_3.8-4
[174] htmlTable_2.4.3
[175] ggforce_0.4.2
[176] xml2_1.3.6
[177] car_3.1-3
[178] seqPattern_1.36.0
[179] munsell_0.5.1
[180] KernSmooth_2.23-24
[181] affyio_1.74.0
[182] data.table_1.16.0
[183] htmlwidgets_1.6.4
[184] fgsea_1.30.0
[185] rlang_1.1.4
[186] remotes_2.5.0
[187] fansi_1.0.6