final_four_analysis
ERM
2024-09-27
Last updated: 2024-09-27
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
were in that past version.
| File | Version | Author | Date | Message |
|---|---|---|---|---|
| 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)## 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))
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'))
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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... 2024-09-27 11:08:47 AM b1 <- plotAvgProf(Ind2_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 2" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:09:01 AM c1 <- plotAvgProf(Ind3_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 3" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:09:11 AM d1 <- plotAvgProf(Ind6_TSS_peaks_plot[c(1,3,5,7,9,11)], xlim=c(-3000, 3000), ylab = "Count Frequency")+ ggtitle("3 hour Individual 6" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:09:16 AM a2 <- plotAvgProf(Ind1_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 1" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:09:16 AM b2 <- plotAvgProf(Ind2_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 2" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:09:34 AM c2 <- plotAvgProf(Ind3_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 3" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:10:05 AM d2 <- plotAvgProf(Ind6_TSS_peaks_plot[c(2,4,6,8,10,12)], xlim=c(-3000, 3000),ylab = "Count Frequency")+ ggtitle("24 hour Individual 6" )+coord_cartesian(xlim=c(-2000,2000))>> plotting figure... 2024-09-27 11:10:20 AM plot_grid(a1,a2, b1,b2,c1,c2,d1,d2, axis="l",align = "hv",nrow=4, ncol=2)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
# 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(all_four_filt_counts, "data/all_four_filtered_counts.txt",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")
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 |
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 <- HeatmapAnnotation(df = counts_corr_mat, col = mat_colors)
Heatmap(FCmatrix_full, top_annotation = htanno_full)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
# 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 |
| chr1.6925805.6926224 | chr1.6925805.6926158 | 354 | 421 | FALSE | smaller | 67 |
| chr1.6962028.6963541 | chr1.6962149.6963541 | 1393 | 1515 | FALSE | smaller | 122 |
| chr1.7075459.7075727 | chr1.7075463.7075727 | 265 | 270 | FALSE | smaller | 5 |
| chr1.7207782.7208486 | chr1.7207782.7208352 | 571 | 706 | FALSE | smaller | 135 |
| chr1.7438711.7439094 | chr1.7438777.7439094 | 318 | 385 | FALSE | smaller | 67 |
| chr1.7500578.7501069 | chr1.7500713.7501069 | 357 | 493 | FALSE | smaller | 136 |
| chr1.7526392.7526950 | chr1.7526394.7526950 | 557 | 560 | FALSE | smaller | 3 |
| chr1.7539588.7540154 | chr1.7539588.7540093 | 506 | 568 | FALSE | smaller | 62 |
| chr1.7614248.7615016 | chr1.7614248.7614910 | 663 | 770 | FALSE | smaller | 107 |
| chr1.7704462.7704924 | chr1.7704512.7704924 | 413 | 464 | FALSE | smaller | 51 |
| chr1.7719629.7719973 | chr1.7719660.7719967 | 308 | 346 | FALSE | smaller | 38 |
| chr1.7990049.7990678 | chr1.7990106.7990678 | 573 | 631 | FALSE | smaller | 58 |
| chr1.8025629.8027358 | chr1.8025629.8027230 | 1602 | 1731 | FALSE | smaller | 129 |
| chr1.8062015.8062412 | chr1.8062015.8062372 | 358 | 399 | FALSE | smaller | 41 |
| chr1.8202264.8202849 | chr1.8202264.8202839 | 576 | 587 | FALSE | smaller | 11 |
| chr1.8276278.8276855 | chr1.8276278.8276811 | 534 | 579 | FALSE | smaller | 45 |
| chr1.8317889.8318454 | chr1.8317983.8318454 | 472 | 567 | FALSE | smaller | 95 |
| chr1.8357465.8358203 | chr1.8357465.8358089 | 625 | 740 | FALSE | smaller | 115 |
| chr1.8367445.8368077 | chr1.8367456.8368077 | 622 | 634 | FALSE | smaller | 12 |
| chr1.8371025.8371725 | chr1.8371027.8371725 | 699 | 702 | FALSE | smaller | 3 |
| chr1.8373527.8374304 | chr1.8373542.8374304 | 763 | 779 | FALSE | smaller | 16 |
| chr1.8402136.8402645 | chr1.8402144.8402645 | 502 | 511 | FALSE | smaller | 9 |
| chr1.8409093.8409572 | chr1.8409093.8409465 | 373 | 481 | FALSE | smaller | 108 |
| chr1.8447733.8448153 | chr1.8447781.8448153 | 373 | 422 | FALSE | smaller | 49 |
| chr1.8464406.8464711 | chr1.8464406.8464704 | 299 | 307 | FALSE | smaller | 8 |
| chr1.8552777.8553506 | chr1.8552816.8553506 | 691 | 731 | FALSE | smaller | 40 |
| chr1.8816891.8818206 | chr1.8816900.8818206 | 1307 | 1317 | FALSE | smaller | 10 |
| chr1.8862587.8862832 | chr1.8862612.8862832 | 221 | 247 | FALSE | smaller | 26 |
| chr1.8943305.8943820 | chr1.8943491.8943820 | 330 | 517 | FALSE | smaller | 187 |
| chr1.9112795.9113342 | chr1.9112795.9113308 | 514 | 549 | FALSE | smaller | 35 |
| chr1.9121887.9122488 | chr1.9122044.9122488 | 445 | 603 | FALSE | smaller | 158 |
| chr1.9192634.9193134 | chr1.9192638.9193134 | 497 | 502 | FALSE | smaller | 5 |
| chr1.9249509.9250292 | chr1.9249509.9250291 | 783 | 785 | FALSE | smaller | 2 |
| chr1.9270444.9270753 | chr1.9270585.9270753 | 169 | 311 | FALSE | smaller | 142 |
| chr1.9280437.9280883 | chr1.9280439.9280883 | 445 | 448 | FALSE | smaller | 3 |
| chr1.9294939.9295417 | chr1.9294939.9295230 | 292 | 480 | FALSE | smaller | 188 |
| chr1.9349886.9350815 | chr1.9349901.9350815 | 915 | 931 | FALSE | smaller | 16 |
| chr1.9384465.9385244 | chr1.9384465.9385222 | 758 | 781 | FALSE | smaller | 23 |
| chr1.9412968.9414140 | chr1.9412968.9414073 | 1106 | 1174 | FALSE | smaller | 68 |
| chr1.9477638.9478334 | chr1.9477638.9478064 | 427 | 698 | FALSE | smaller | 271 |
| chr1.9588320.9589673 | chr1.9588430.9589673 | 1244 | 1355 | FALSE | smaller | 111 |
| chr1.9687212.9688612 | chr1.9687212.9688546 | 1335 | 1402 | FALSE | smaller | 67 |
| chr1.9690004.9690404 | chr1.9690010.9690404 | 395 | 402 | FALSE | smaller | 7 |
| chr1.9861272.9861633 | chr1.9861272.9861615 | 344 | 363 | FALSE | smaller | 19 |
| chr1.9867260.9867962 | chr1.9867260.9867936 | 677 | 704 | FALSE | smaller | 27 |
| chr1.9874716.9875727 | chr1.9874716.9875655 | 940 | 1013 | FALSE | smaller | 73 |
| chr1.10046037.10046857 | chr1.10046067.10046857 | 791 | 822 | FALSE | smaller | 31 |
| chr1.10057444.10058186 | chr1.10057444.10057924 | 481 | 744 | FALSE | smaller | 263 |
| chr1.10108148.10109234 | chr1.10108148.10108997 | 850 | 1088 | FALSE | smaller | 238 |
| chr1.10225669.10225944 | chr1.10225669.10225934 | 266 | 277 | FALSE | smaller | 11 |
| chr1.10337444.10337792 | chr1.10337444.10337778 | 335 | 350 | FALSE | smaller | 15 |
| chr1.10415310.10415689 | chr1.10415310.10415675 | 366 | 381 | FALSE | smaller | 15 |
| chr1.10430094.10431279 | chr1.10430118.10431279 | 1162 | 1187 | FALSE | smaller | 25 |
| chr1.10450221.10450657 | chr1.10450223.10450657 | 435 | 438 | FALSE | smaller | 3 |
| chr1.10530388.10531196 | chr1.10530388.10531178 | 791 | 810 | FALSE | smaller | 19 |
| chr1.10639387.10641914 | chr1.10639524.10641887 | 2364 | 2529 | FALSE | smaller | 165 |
| chr1.10676833.10677740 | chr1.10676833.10677719 | 887 | 909 | FALSE | smaller | 22 |
| chr1.10705625.10706383 | chr1.10705793.10706383 | 591 | 760 | FALSE | smaller | 169 |
| chr1.10788609.10789054 | chr1.10788609.10789048 | 440 | 447 | FALSE | smaller | 7 |
| chr1.10835880.10836301 | chr1.10835885.10836301 | 417 | 423 | FALSE | smaller | 6 |
| chr1.11187424.11188227 | chr1.11187464.11188227 | 764 | 805 | FALSE | smaller | 41 |
| chr1.11262074.11262946 | chr1.11262074.11262931 | 858 | 874 | FALSE | smaller | 16 |
| chr1.11287024.11287231 | chr1.11287053.11287231 | 179 | 209 | FALSE | smaller | 30 |
| chr1.11331503.11332031 | chr1.11331503.11332009 | 507 | 530 | FALSE | smaller | 23 |
| chr1.11374481.11375152 | chr1.11374579.11375152 | 574 | 673 | FALSE | smaller | 99 |
| chr1.11653121.11654958 | chr1.11653182.11654958 | 1777 | 1839 | FALSE | smaller | 62 |
| chr1.11702025.11702641 | chr1.11702043.11702625 | 583 | 618 | FALSE | smaller | 35 |
| chr1.11957125.11957472 | chr1.11957147.11957472 | 326 | 349 | FALSE | smaller | 23 |
| chr1.11990456.11991565 | chr1.11990456.11991507 | 1052 | 1111 | FALSE | smaller | 59 |
| chr1.12038619.12039412 | chr1.12038619.12039354 | 736 | 795 | FALSE | smaller | 59 |
| chr1.12076503.12076909 | chr1.12076523.12076909 | 387 | 408 | FALSE | smaller | 21 |
| chr1.12079442.12079874 | chr1.12079452.12079874 | 423 | 434 | FALSE | smaller | 11 |
| chr1.12124730.12125435 | chr1.12124730.12125430 | 701 | 707 | FALSE | smaller | 6 |
| chr1.12276366.12276746 | chr1.12276366.12276663 | 298 | 382 | FALSE | smaller | 84 |
| chr1.12397501.12398046 | chr1.12397501.12398002 | 502 | 547 | FALSE | smaller | 45 |
| chr1.12511561.12512466 | chr1.12511561.12512409 | 849 | 907 | FALSE | smaller | 58 |
| chr1.12584779.12586273 | chr1.12584820.12586190 | 1371 | 1496 | FALSE | smaller | 125 |
| chr1.12636532.12636959 | chr1.12636532.12636952 | 421 | 429 | FALSE | smaller | 8 |
| chr1.13543536.13544390 | chr1.13543536.13544366 | 831 | 856 | FALSE | smaller | 25 |
| chr1.13725712.13726316 | chr1.13725712.13726267 | 556 | 606 | FALSE | smaller | 50 |
| chr1.13828628.13829298 | chr1.13828628.13829247 | 620 | 672 | FALSE | smaller | 52 |
| chr1.14098494.14098908 | chr1.14098494.14098892 | 399 | 416 | FALSE | smaller | 17 |
| chr1.14346092.14346513 | chr1.14346104.14346490 | 387 | 423 | FALSE | smaller | 36 |
| chr1.14479781.14480379 | chr1.14479837.14480379 | 543 | 600 | FALSE | smaller | 57 |
| chr1.14515230.14515633 | chr1.14515230.14515572 | 343 | 405 | FALSE | smaller | 62 |
| chr1.14910102.14910514 | chr1.14910112.14910514 | 403 | 414 | FALSE | smaller | 11 |
| chr1.14942784.14943479 | chr1.14942784.14943454 | 671 | 697 | FALSE | smaller | 26 |
| chr1.15046373.15047153 | chr1.15046373.15047093 | 721 | 782 | FALSE | smaller | 61 |
| chr1.15106354.15107288 | chr1.15106354.15107281 | 928 | 936 | FALSE | smaller | 8 |
| chr1.15139584.15140520 | chr1.15139584.15140472 | 889 | 938 | FALSE | smaller | 49 |
| chr1.15168566.15169038 | chr1.15168566.15168945 | 380 | 474 | FALSE | smaller | 94 |
| chr1.15243310.15243831 | chr1.15243332.15243831 | 500 | 523 | FALSE | smaller | 23 |
| chr1.15272341.15272967 | chr1.15272355.15272967 | 613 | 628 | FALSE | smaller | 15 |
| chr1.15337579.15338469 | chr1.15337579.15338214 | 636 | 892 | FALSE | smaller | 256 |
| chr1.15337579.15338469 | chr1.15338279.15338469 | 191 | 892 | FALSE | smaller | 701 |
| chr1.15377324.15377665 | chr1.15377324.15377592 | 269 | 343 | FALSE | smaller | 74 |
| chr1.15444247.15444866 | chr1.15444247.15444825 | 579 | 621 | FALSE | smaller | 42 |
| chr1.15492433.15493101 | chr1.15492486.15493101 | 616 | 670 | FALSE | smaller | 54 |
| chr1.15584537.15585349 | chr1.15584583.15585349 | 767 | 814 | FALSE | smaller | 47 |
| chr1.15681096.15681510 | chr1.15681114.15681505 | 392 | 416 | FALSE | smaller | 24 |
| chr1.15684056.15684415 | chr1.15684056.15684404 | 349 | 361 | FALSE | smaller | 12 |
| chr1.15700408.15700790 | chr1.15700415.15700790 | 376 | 384 | FALSE | smaller | 8 |
| chr1.15771851.15772471 | chr1.15772211.15772471 | 261 | 622 | FALSE | smaller | 361 |
| chr1.15792047.15792404 | chr1.15792061.15792404 | 344 | 359 | FALSE | smaller | 15 |
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| chr1.230136304.230137094 | chr1.230136304.230137011 | 708 | 792 | FALSE | smaller | 84 |
| chr1.230191089.230191744 | chr1.230191089.230191731 | 643 | 657 | FALSE | smaller | 14 |
| chr1.230204907.230205237 | chr1.230204907.230205234 | 328 | 332 | FALSE | smaller | 4 |
| chr1.230277278.230277722 | chr1.230277278.230277694 | 417 | 446 | FALSE | smaller | 29 |
| chr1.230334098.230335108 | chr1.230334181.230334976 | 796 | 1012 | FALSE | smaller | 216 |
| chr1.230377859.230378238 | chr1.230377859.230378231 | 373 | 381 | FALSE | smaller | 8 |
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| chr1.230424447.230424877 | chr1.230424454.230424877 | 424 | 432 | FALSE | smaller | 8 |
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| chr1.230857192.230857454 | chr1.230857192.230857400 | 209 | 264 | FALSE | smaller | 55 |
| chr1.230857850.230858183 | chr1.230857877.230858183 | 307 | 335 | FALSE | smaller | 28 |
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| chr1.231278140.231278478 | chr1.231278143.231278478 | 336 | 340 | FALSE | smaller | 4 |
| chr1.231315788.231316162 | chr1.231315788.231316115 | 328 | 376 | FALSE | smaller | 48 |
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| chr1.231799388.231799804 | chr1.231799396.231799804 | 409 | 418 | FALSE | smaller | 9 |
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| chr1.232340476.232340813 | chr1.232340504.232340813 | 310 | 339 | FALSE | smaller | 29 |
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| chr1.232620098.232620846 | chr1.232620107.232620846 | 740 | 750 | FALSE | smaller | 10 |
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| chr1.233543500.233544163 | chr1.233543500.233544146 | 647 | 665 | FALSE | smaller | 18 |
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| chr1.234276287.234276647 | chr1.234276287.234276598 | 312 | 362 | FALSE | smaller | 50 |
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| chr1.234405756.234406079 | chr1.234405756.234406031 | 276 | 325 | FALSE | smaller | 49 |
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| chr1.235336836.235337456 | chr1.235336921.235337456 | 536 | 622 | FALSE | smaller | 86 |
| chr1.235411623.235412129 | chr1.235411623.235412124 | 502 | 508 | FALSE | smaller | 6 |
| chr1.235607381.235607742 | chr1.235607381.235607623 | 243 | 363 | FALSE | smaller | 120 |
| chr1.235903012.235903522 | chr1.235903012.235903479 | 468 | 512 | FALSE | smaller | 44 |
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| chr1.236085649.236086112 | chr1.236085748.236086112 | 365 | 465 | FALSE | smaller | 100 |
| chr1.236097126.236097409 | chr1.236097266.236097409 | 144 | 285 | FALSE | smaller | 141 |
| chr1.236101050.236102117 | chr1.236101050.236102026 | 977 | 1069 | FALSE | smaller | 92 |
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| chr1.236394966.236395989 | chr1.236394966.236395891 | 926 | 1025 | FALSE | smaller | 99 |
| chr1.236503135.236503595 | chr1.236503135.236503561 | 427 | 462 | FALSE | smaller | 35 |
| chr1.236639057.236639276 | chr1.236639057.236639246 | 190 | 221 | FALSE | smaller | 31 |
| chr1.236688731.236689178 | chr1.236688731.236689166 | 436 | 449 | FALSE | smaller | 13 |
| chr1.236785123.236785684 | chr1.236785123.236785513 | 391 | 563 | FALSE | smaller | 172 |
| chr1.237091639.237091977 | chr1.237091747.237091977 | 231 | 340 | FALSE | smaller | 109 |
| chr1.237108250.237108782 | chr1.237108250.237108695 | 446 | 534 | FALSE | smaller | 88 |
| chr1.237204836.237206165 | chr1.237204836.237206142 | 1307 | 1331 | FALSE | smaller | 24 |
| chr1.237244826.237245384 | chr1.237244826.237245341 | 516 | 560 | FALSE | smaller | 44 |
| chr1.237817181.237817792 | chr1.237817260.237817792 | 533 | 613 | FALSE | smaller | 80 |
| chr1.237838632.237839606 | chr1.237838632.237839545 | 914 | 976 | FALSE | smaller | 62 |
| chr1.238062228.238062659 | chr1.238062239.238062534 | 296 | 433 | FALSE | smaller | 137 |
| chr1.238135854.238136641 | chr1.238135873.238136641 | 769 | 789 | FALSE | smaller | 20 |
| chr1.238485663.238486372 | chr1.238485675.238486372 | 698 | 711 | FALSE | smaller | 13 |
| chr1.238892688.238892975 | chr1.238892701.238892975 | 275 | 289 | FALSE | smaller | 14 |
| chr1.239898096.239898448 | chr1.239898096.239898446 | 351 | 354 | FALSE | smaller | 3 |
| chr1.240206809.240207626 | chr1.240206809.240207544 | 736 | 819 | FALSE | smaller | 83 |
| chr1.240498464.240499409 | chr1.240498548.240499409 | 862 | 947 | FALSE | smaller | 85 |
| chr1.240692022.240692377 | chr1.240692027.240692377 | 351 | 357 | FALSE | smaller | 6 |
| chr1.240809936.240810518 | chr1.240809936.240810499 | 564 | 584 | FALSE | smaller | 20 |
| chr1.241306136.241306784 | chr1.241306148.241306784 | 637 | 650 | FALSE | smaller | 13 |
| chr1.241423633.241424876 | chr1.241423633.241424856 | 1224 | 1245 | FALSE | smaller | 21 |
| chr1.241612620.241612917 | chr1.241612663.241612917 | 255 | 299 | FALSE | smaller | 44 |
| chr1.242467643.242467882 | chr1.242467643.242467845 | 203 | 241 | FALSE | smaller | 38 |
| chr1.242763792.242764457 | chr1.242763946.242764457 | 512 | 667 | FALSE | smaller | 155 |
| chr1.243931200.243931670 | chr1.243931200.243931663 | 464 | 472 | FALSE | smaller | 8 |
| chr1.244730126.244730927 | chr1.244730126.244730879 | 754 | 803 | FALSE | smaller | 49 |
| chr1.244813329.244813633 | chr1.244813329.244813628 | 300 | 306 | FALSE | smaller | 6 |
| chr1.245085528.245086105 | chr1.245085528.245086026 | 499 | 579 | FALSE | smaller | 80 |
| chr1.245108695.245109405 | chr1.245109005.245109405 | 401 | 712 | FALSE | smaller | 311 |
| chr1.245791093.245792199 | chr1.245791135.245792199 | 1065 | 1108 | FALSE | smaller | 43 |
| chr1.245797870.245799370 | chr1.245797870.245798330 | 461 | 1502 | FALSE | smaller | 1041 |
| chr1.245797870.245799370 | chr1.245798515.245799370 | 856 | 1502 | FALSE | smaller | 646 |
| chr1.245856719.245857387 | chr1.245856719.245857380 | 662 | 670 | FALSE | smaller | 8 |
| chr1.246139728.246140020 | chr1.246139782.246140020 | 239 | 294 | FALSE | smaller | 55 |
| chr1.246176130.246176891 | chr1.246176130.246176876 | 747 | 763 | FALSE | smaller | 16 |
| chr1.246180959.246181689 | chr1.246181100.246181689 | 590 | 732 | FALSE | smaller | 142 |
| chr1.246200002.246200494 | chr1.246200002.246200472 | 471 | 494 | FALSE | smaller | 23 |
| chr1.246428498.246429001 | chr1.246428573.246429001 | 429 | 505 | FALSE | smaller | 76 |
| chr1.246444400.246445185 | chr1.246444471.246445185 | 715 | 787 | FALSE | smaller | 72 |
| chr1.246565847.246567056 | chr1.246565847.246567051 | 1205 | 1211 | FALSE | smaller | 6 |
| chr1.246620685.246620977 | chr1.246620685.246620973 | 289 | 294 | FALSE | smaller | 5 |
| chr1.246794326.246795125 | chr1.246794331.246795125 | 795 | 801 | FALSE | smaller | 6 |
| chr1.246798513.246798833 | chr1.246798571.246798833 | 263 | 322 | FALSE | smaller | 59 |
| chr1.246837737.246838134 | chr1.246837737.246838129 | 393 | 399 | FALSE | smaller | 6 |
| chr1.246931047.246932359 | chr1.246931047.246932355 | 1309 | 1314 | FALSE | smaller | 5 |
| chr1.247273050.247273423 | chr1.247273086.247273423 | 338 | 375 | FALSE | smaller | 37 |
| chr1.247371181.247371489 | chr1.247371192.247371489 | 298 | 310 | FALSE | smaller | 12 |
| chr1.247415285.247416090 | chr1.247415285.247416029 | 745 | 807 | FALSE | smaller | 62 |
| chr1.248752033.248753410 | chr1.248752033.248752594 | 562 | 1379 | FALSE | smaller | 817 |
| chr1.248821120.248821555 | chr1.248821120.248821533 | 414 | 437 | FALSE | smaller | 23 |
| chr2.11281.12035 | chr2.11281.11975 | 695 | 756 | FALSE | smaller | 61 |
| chr2.37992.38392 | chr2.38092.38392 | 301 | 402 | FALSE | smaller | 101 |
| chr2.114722.115816 | chr2.114722.114911 | 190 | 1096 | FALSE | smaller | 906 |
| chr2.114722.115816 | chr2.114944.115816 | 873 | 1096 | FALSE | smaller | 223 |
| chr2.263782.264467 | chr2.263806.264467 | 662 | 687 | FALSE | smaller | 25 |
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| chr2.393630.394434 | chr2.393701.394434 | 734 | 806 | FALSE | smaller | 72 |
| chr2.425792.426343 | chr2.425825.426324 | 500 | 553 | FALSE | smaller | 53 |
| chr2.526969.527968 | chr2.526969.527578 | 610 | 1001 | FALSE | smaller | 391 |
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| chr2.950068.951084 | chr2.950068.950966 | 899 | 1018 | FALSE | smaller | 119 |
| chr2.960114.960592 | chr2.960114.960577 | 464 | 480 | FALSE | smaller | 16 |
| chr2.1306211.1307532 | chr2.1306211.1307483 | 1273 | 1323 | FALSE | smaller | 50 |
| chr2.1583442.1583774 | chr2.1583459.1583774 | 316 | 334 | FALSE | smaller | 18 |
| chr2.1704844.1705417 | chr2.1704844.1705313 | 470 | 575 | FALSE | smaller | 105 |
| chr2.1766245.1766976 | chr2.1766557.1766976 | 420 | 733 | FALSE | smaller | 313 |
| chr2.1819026.1819481 | chr2.1819026.1819425 | 400 | 457 | FALSE | smaller | 57 |
| chr2.2025175.2025554 | chr2.2025175.2025410 | 236 | 381 | FALSE | smaller | 145 |
| chr2.2572390.2572898 | chr2.2572390.2572893 | 504 | 510 | FALSE | smaller | 6 |
| chr2.2573276.2574719 | chr2.2573544.2574719 | 1176 | 1445 | FALSE | smaller | 269 |
| chr2.2664045.2664893 | chr2.2664159.2664893 | 735 | 850 | FALSE | smaller | 115 |
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| chr2.2769917.2770405 | chr2.2769917.2770373 | 457 | 490 | FALSE | smaller | 33 |
| chr2.3045290.3046142 | chr2.3045430.3046142 | 713 | 854 | FALSE | smaller | 141 |
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| chr2.3170298.3172083 | chr2.3170866.3172083 | 1218 | 1787 | FALSE | smaller | 569 |
| chr2.3189221.3189671 | chr2.3189343.3189671 | 329 | 452 | FALSE | smaller | 123 |
| chr2.3200341.3200926 | chr2.3200341.3200873 | 533 | 587 | FALSE | smaller | 54 |
| chr2.3314534.3315065 | chr2.3314534.3315059 | 526 | 533 | FALSE | smaller | 7 |
| chr2.3316786.3317087 | chr2.3316793.3317087 | 295 | 303 | FALSE | smaller | 8 |
| chr2.3387764.3388879 | chr2.3387764.3388830 | 1067 | 1117 | FALSE | smaller | 50 |
| chr2.3527201.3528779 | chr2.3527201.3528712 | 1512 | 1580 | FALSE | smaller | 68 |
| chr2.3535719.3536978 | chr2.3535786.3536978 | 1193 | 1261 | FALSE | smaller | 68 |
| chr2.3650855.3651292 | chr2.3650855.3651277 | 423 | 439 | FALSE | smaller | 16 |
| chr2.3966005.3966474 | chr2.3966051.3966474 | 424 | 471 | FALSE | smaller | 47 |
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| chr2.4012199.4012516 | chr2.4012199.4012492 | 294 | 319 | FALSE | smaller | 25 |
| chr2.4499385.4499859 | chr2.4499385.4499852 | 468 | 476 | FALSE | smaller | 8 |
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| chr2.4903798.4904357 | chr2.4903804.4904357 | 554 | 561 | FALSE | smaller | 7 |
| chr2.4937512.4938007 | chr2.4937512.4937881 | 370 | 497 | FALSE | smaller | 127 |
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| chr2.5250456.5250833 | chr2.5250517.5250833 | 317 | 379 | FALSE | smaller | 62 |
| chr2.5392756.5393029 | chr2.5392771.5393029 | 259 | 275 | FALSE | smaller | 16 |
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| chr2.6694608.6694901 | chr2.6694608.6694894 | 287 | 295 | FALSE | smaller | 8 |
| chr2.6823114.6823611 | chr2.6823114.6823596 | 483 | 499 | FALSE | smaller | 16 |
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| chr2.8596366.8597790 | chr2.8596366.8597689 | 1324 | 1426 | FALSE | smaller | 102 |
| chr2.8626652.8627056 | chr2.8626652.8627026 | 375 | 406 | FALSE | smaller | 31 |
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| chr2.8796376.8796915 | chr2.8796376.8796890 | 515 | 541 | FALSE | smaller | 26 |
| chr2.8893548.8893796 | chr2.8893585.8893796 | 212 | 250 | FALSE | smaller | 38 |
| chr2.8938645.8939270 | chr2.8938662.8939270 | 609 | 627 | FALSE | smaller | 18 |
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| chr2.9071027.9071447 | chr2.9071027.9071394 | 368 | 422 | FALSE | smaller | 54 |
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| chr2.9124174.9124583 | chr2.9124174.9124494 | 321 | 411 | FALSE | smaller | 90 |
| chr2.9191002.9191434 | chr2.9191002.9191424 | 423 | 434 | FALSE | smaller | 11 |
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| chr2.9364952.9366207 | chr2.9364952.9365922 | 971 | 1257 | FALSE | smaller | 286 |
| chr2.9378329.9379060 | chr2.9378329.9378989 | 661 | 733 | FALSE | smaller | 72 |
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| chr2.9654292.9654881 | chr2.9654301.9654881 | 581 | 591 | FALSE | smaller | 10 |
| chr2.9699359.9699769 | chr2.9699494.9699769 | 276 | 412 | FALSE | smaller | 136 |
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| chr2.9972539.9973082 | chr2.9972652.9973082 | 431 | 545 | FALSE | smaller | 114 |
| chr2.10015519.10017144 | chr2.10015960.10017144 | 1185 | 1627 | FALSE | smaller | 442 |
| chr2.10029028.10029631 | chr2.10029028.10029598 | 571 | 605 | FALSE | smaller | 34 |
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| chr2.10097100.10097812 | chr2.10097100.10097805 | 706 | 714 | FALSE | smaller | 8 |
| chr2.10120042.10120883 | chr2.10120110.10120883 | 774 | 843 | FALSE | smaller | 69 |
| chr2.10122316.10123065 | chr2.10122316.10123056 | 741 | 751 | FALSE | smaller | 10 |
| chr2.10149691.10150094 | chr2.10149697.10150036 | 340 | 405 | FALSE | smaller | 65 |
| chr2.10270579.10271052 | chr2.10270579.10271021 | 443 | 475 | FALSE | smaller | 32 |
| chr2.10367781.10368530 | chr2.10367833.10368530 | 698 | 751 | FALSE | smaller | 53 |
| chr2.10369391.10370441 | chr2.10369533.10370441 | 909 | 1052 | FALSE | smaller | 143 |
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| chr2.10433048.10433838 | chr2.10433048.10433806 | 759 | 792 | FALSE | smaller | 33 |
| chr2.10518107.10518898 | chr2.10518122.10518843 | 722 | 793 | FALSE | smaller | 71 |
| chr2.10565996.10566287 | chr2.10566024.10566287 | 264 | 293 | FALSE | smaller | 29 |
| chr2.10634184.10634951 | chr2.10634436.10634951 | 516 | 769 | FALSE | smaller | 253 |
| chr2.10654109.10654318 | chr2.10654116.10654318 | 203 | 211 | FALSE | smaller | 8 |
| chr2.10709091.10709852 | chr2.10709091.10709810 | 720 | 763 | FALSE | smaller | 43 |
| chr2.10832200.10832816 | chr2.10832221.10832816 | 596 | 618 | FALSE | smaller | 22 |
| chr2.10911260.10912609 | chr2.10911260.10912442 | 1183 | 1351 | FALSE | smaller | 168 |
| chr2.10936097.10936545 | chr2.10936097.10936535 | 439 | 450 | FALSE | smaller | 11 |
| chr2.11175314.11175830 | chr2.11175405.11175830 | 426 | 518 | FALSE | smaller | 92 |
| chr2.11342666.11343149 | chr2.11342666.11343144 | 479 | 485 | FALSE | smaller | 6 |
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| chr2.11389803.11390203 | chr2.11389803.11390193 | 391 | 402 | FALSE | smaller | 11 |
| chr2.11444724.11445012 | chr2.11444741.11445012 | 272 | 290 | FALSE | smaller | 18 |
| chr2.11446738.11447379 | chr2.11446738.11447215 | 478 | 643 | FALSE | smaller | 165 |
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| chr2.11563350.11563941 | chr2.11563408.11563941 | 534 | 593 | FALSE | smaller | 59 |
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| chr2.240877984.240878330 | chr2.240878012.240878330 | 319 | 348 | FALSE | smaller | 29 |
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| chr3.5426241.5426801 | chr3.5426241.5426791 | 551 | 562 | FALSE | smaller | 11 |
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| chr3.8001714.8002027 | chr3.8001724.8002027 | 304 | 315 | FALSE | smaller | 11 |
| chr3.8136859.8137685 | chr3.8136886.8137685 | 800 | 828 | FALSE | smaller | 28 |
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| chr3.8497690.8497902 | chr3.8497690.8497893 | 204 | 214 | FALSE | smaller | 10 |
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| 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 |
| chr3.29330128.29330627 | chr3.29330198.29330627 | 430 | 501 | FALSE | smaller | 71 |
| chr3.29639654.29640261 | chr3.29639654.29640223 | 570 | 609 | FALSE | smaller | 39 |
| chr3.29701362.29702054 | chr3.29701408.29702054 | 647 | 694 | FALSE | smaller | 47 |
| chr3.29718584.29719018 | chr3.29718589.29719015 | 427 | 436 | FALSE | smaller | 9 |
| chr3.29763954.29764875 | chr3.29763954.29764871 | 918 | 923 | FALSE | smaller | 5 |
| chr3.30606203.30607465 | chr3.30606203.30607442 | 1240 | 1264 | FALSE | smaller | 24 |
| chr3.31021597.31021822 | chr3.31021597.31021804 | 208 | 227 | FALSE | smaller | 19 |
| chr3.31196951.31197297 | chr3.31196953.31197297 | 345 | 348 | FALSE | smaller | 3 |
| chr3.31356980.31357202 | chr3.31357013.31357202 | 190 | 224 | FALSE | smaller | 34 |
| chr3.31361424.31362248 | chr3.31361424.31362232 | 809 | 826 | FALSE | smaller | 17 |
| chr3.31531803.31534314 | chr3.31531846.31534314 | 2469 | 2513 | FALSE | smaller | 44 |
| chr3.31546243.31546508 | chr3.31546244.31546508 | 265 | 267 | FALSE | smaller | 2 |
| chr3.31732488.31733302 | chr3.31732094.31732487 | 1 | 816 | FALSE | smaller | 815 |
| chr3.31771083.31771490 | chr3.31771083.31771370 | 288 | 409 | FALSE | smaller | 121 |
| chr3.31980366.31982043 | chr3.31980366.31982017 | 1652 | 1679 | FALSE | smaller | 27 |
| chr3.32143868.32144738 | chr3.32143902.32144738 | 837 | 872 | FALSE | smaller | 35 |
| chr3.32177179.32178310 | chr3.32177179.32178304 | 1126 | 1133 | FALSE | smaller | 7 |
| chr3.32864559.32864872 | chr3.32864559.32864829 | 271 | 315 | FALSE | smaller | 44 |
| chr3.33170482.33170898 | chr3.33170499.33170898 | 400 | 418 | FALSE | smaller | 18 |
| chr3.33299491.33300570 | chr3.33299493.33300570 | 1078 | 1081 | FALSE | smaller | 3 |
| chr3.33377846.33378400 | chr3.33377913.33378400 | 488 | 556 | FALSE | smaller | 68 |
| chr3.33446386.33447739 | chr3.33446390.33447739 | 1350 | 1355 | FALSE | smaller | 5 |
| chr3.34178352.34178880 | chr3.34178352.34178871 | 520 | 530 | FALSE | smaller | 10 |
| chr3.34207031.34208160 | chr3.34207057.34208160 | 1104 | 1131 | FALSE | smaller | 27 |
| chr3.34260705.34261430 | chr3.34260705.34261391 | 687 | 727 | FALSE | smaller | 40 |
| chr3.34357466.34357982 | chr3.34357493.34357982 | 490 | 518 | FALSE | smaller | 28 |
| chr3.34862471.34862885 | chr3.34862471.34862863 | 393 | 416 | FALSE | smaller | 23 |
| chr3.35505046.35505737 | chr3.35505046.35505717 | 672 | 693 | FALSE | smaller | 21 |
| chr3.35664360.35665172 | chr3.35664473.35665172 | 700 | 814 | FALSE | smaller | 114 |
| chr3.35745833.35746222 | chr3.35745833.35746043 | 211 | 391 | FALSE | smaller | 180 |
| chr3.35954261.35955082 | chr3.35954261.35955059 | 799 | 823 | FALSE | smaller | 24 |
| chr3.36362016.36362519 | chr3.36362016.36362473 | 458 | 505 | FALSE | smaller | 47 |
| chr3.36446285.36446716 | chr3.36446288.36446716 | 429 | 433 | FALSE | smaller | 4 |
| chr3.36759270.36759514 | chr3.36759277.36759514 | 238 | 246 | FALSE | smaller | 8 |
| chr3.36763800.36765082 | chr3.36763800.36764972 | 1173 | 1284 | FALSE | smaller | 111 |
| chr3.36893099.36893451 | chr3.36893134.36893451 | 318 | 354 | FALSE | smaller | 36 |
| chr3.36923465.36924009 | chr3.36923525.36924009 | 485 | 546 | FALSE | smaller | 61 |
| chr3.36943831.36944977 | chr3.36943831.36944936 | 1106 | 1148 | FALSE | smaller | 42 |
| chr3.37031316.37031652 | chr3.37031317.37031652 | 336 | 338 | FALSE | smaller | 2 |
| chr3.37055179.37055399 | chr3.37055205.37055399 | 195 | 222 | FALSE | smaller | 27 |
| chr3.37208605.37208859 | chr3.37208605.37208858 | 254 | 256 | FALSE | smaller | 2 |
| chr3.37274453.37274850 | chr3.37274453.37274795 | 343 | 399 | FALSE | smaller | 56 |
| chr3.37453360.37454309 | chr3.37453420.37454309 | 890 | 951 | FALSE | smaller | 61 |
| chr3.37479929.37480366 | chr3.37479929.37480350 | 422 | 439 | FALSE | smaller | 17 |
| chr3.37484610.37485306 | chr3.37484610.37485182 | 573 | 698 | FALSE | smaller | 125 |
| chr3.37491381.37492087 | chr3.37491381.37492065 | 685 | 708 | FALSE | smaller | 23 |
| chr3.37499090.37500048 | chr3.37499202.37500048 | 847 | 960 | FALSE | smaller | 113 |
| chr3.37765691.37765942 | chr3.37765691.37765937 | 247 | 253 | FALSE | smaller | 6 |
| chr3.37825861.37826686 | chr3.37825872.37826686 | 815 | 827 | FALSE | smaller | 12 |
| chr3.37860278.37861386 | chr3.37860278.37861305 | 1028 | 1110 | FALSE | smaller | 82 |
| chr3.38004191.38005050 | chr3.38004257.38004900 | 644 | 861 | FALSE | smaller | 217 |
| chr3.38029398.38029953 | chr3.38029398.38029920 | 523 | 557 | FALSE | smaller | 34 |
| chr3.38084629.38085778 | chr3.38084629.38085607 | 979 | 1151 | FALSE | smaller | 172 |
| chr3.38099150.38099774 | chr3.38099190.38099774 | 585 | 626 | FALSE | smaller | 41 |
| chr3.38264060.38265273 | chr3.38264060.38265229 | 1170 | 1215 | FALSE | smaller | 45 |
| chr3.38270715.38270950 | chr3.38270715.38270914 | 200 | 237 | FALSE | smaller | 37 |
| chr3.38397308.38397854 | chr3.38397367.38397854 | 488 | 548 | FALSE | smaller | 60 |
| chr3.38454129.38456258 | chr3.38454129.38456132 | 2004 | 2131 | FALSE | smaller | 127 |
| chr3.38472843.38473816 | chr3.38473040.38473816 | 777 | 975 | FALSE | smaller | 198 |
| chr3.38479612.38480088 | chr3.38479618.38480088 | 471 | 478 | FALSE | smaller | 7 |
| chr3.38500763.38501788 | chr3.38500763.38501699 | 937 | 1027 | FALSE | smaller | 90 |
| chr3.38574783.38575111 | chr3.38574783.38575088 | 306 | 330 | FALSE | smaller | 24 |
| chr3.38598258.38598814 | chr3.38598416.38598814 | 399 | 558 | FALSE | smaller | 159 |
| chr3.39125359.39126986 | chr3.39125359.39126917 | 1559 | 1629 | FALSE | smaller | 70 |
| chr3.39146741.39147558 | chr3.39146768.39147558 | 791 | 819 | FALSE | smaller | 28 |
| chr3.39180340.39181309 | chr3.39180407.39181309 | 903 | 971 | FALSE | smaller | 68 |
| chr3.39808718.39810263 | chr3.39808718.39810260 | 1543 | 1547 | FALSE | smaller | 4 |
| chr3.39978273.39978569 | chr3.39978273.39978551 | 279 | 298 | FALSE | smaller | 19 |
| chr3.40211452.40211887 | chr3.40211540.40211887 | 348 | 437 | FALSE | smaller | 89 |
| chr3.40457179.40457806 | chr3.40457179.40457767 | 589 | 629 | FALSE | smaller | 40 |
| chr3.40497288.40497748 | chr3.40497403.40497744 | 342 | 462 | FALSE | smaller | 120 |
| chr3.40504652.40505056 | chr3.40504652.40505038 | 387 | 406 | FALSE | smaller | 19 |
| chr3.40580453.40580978 | chr3.40580453.40580934 | 482 | 527 | FALSE | smaller | 45 |
| chr3.40602518.40603111 | chr3.40602872.40603111 | 240 | 595 | FALSE | smaller | 355 |
| chr3.40615308.40616582 | chr3.40615317.40616582 | 1266 | 1276 | FALSE | smaller | 10 |
| chr3.40930124.40930638 | chr3.40930124.40930627 | 504 | 516 | FALSE | smaller | 12 |
| chr3.40971170.40972120 | chr3.40971192.40972120 | 929 | 952 | FALSE | smaller | 23 |
| chr3.40989506.40990070 | chr3.40989754.40990070 | 317 | 566 | FALSE | smaller | 249 |
| chr3.41075912.41076349 | chr3.41075941.41076349 | 409 | 439 | FALSE | smaller | 30 |
| chr3.41101935.41102558 | chr3.41102018.41102558 | 541 | 625 | FALSE | smaller | 84 |
| chr3.41247172.41247956 | chr3.41247172.41247896 | 725 | 786 | FALSE | smaller | 61 |
| chr3.41265798.41266419 | chr3.41265798.41266398 | 601 | 623 | FALSE | smaller | 22 |
| chr3.41304629.41305884 | chr3.41304629.41305641 | 1013 | 1257 | FALSE | smaller | 244 |
| chr3.41352314.41353189 | chr3.41352314.41352971 | 658 | 877 | FALSE | smaller | 219 |
| chr3.41362434.41363692 | chr3.41362434.41363550 | 1117 | 1260 | FALSE | smaller | 143 |
| chr3.41527119.41527605 | chr3.41527119.41527580 | 462 | 488 | FALSE | smaller | 26 |
| chr3.41710844.41711372 | chr3.41710844.41711335 | 492 | 530 | FALSE | smaller | 38 |
| chr3.41961368.41962637 | chr3.41961373.41962633 | 1261 | 1271 | FALSE | smaller | 10 |
| chr3.41998067.41998508 | chr3.41998067.41998487 | 421 | 443 | FALSE | smaller | 22 |
| chr3.42035890.42036864 | chr3.42035890.42036736 | 847 | 976 | FALSE | smaller | 129 |
| chr3.42051717.42053205 | chr3.42051717.42052418 | 702 | 1490 | FALSE | smaller | 788 |
| chr3.42118815.42119268 | chr3.42118829.42119268 | 440 | 455 | FALSE | smaller | 15 |
| chr3.42399085.42399603 | chr3.42399140.42399589 | 450 | 520 | FALSE | smaller | 70 |
| chr3.42407865.42408131 | chr3.42407882.42408131 | 250 | 268 | FALSE | smaller | 18 |
| chr3.42489880.42490915 | chr3.42490007.42490915 | 909 | 1037 | FALSE | smaller | 128 |
| chr3.42515386.42516025 | chr3.42515452.42516025 | 574 | 641 | FALSE | smaller | 67 |
| chr3.42520925.42521224 | chr3.42520950.42521224 | 275 | 301 | FALSE | smaller | 26 |
| chr3.42772472.42773593 | chr3.42772573.42773593 | 1021 | 1123 | FALSE | smaller | 102 |
| chr3.42780790.42781278 | chr3.42780790.42781250 | 461 | 490 | FALSE | smaller | 29 |
| chr3.42805122.42805513 | chr3.42805122.42805511 | 390 | 393 | FALSE | smaller | 3 |
| chr3.42839734.42840088 | chr3.42839734.42840063 | 330 | 356 | FALSE | smaller | 26 |
| chr3.43094510.43095121 | chr3.43094510.43095063 | 554 | 613 | FALSE | smaller | 59 |
| chr3.43102794.43103625 | chr3.43102794.43103548 | 755 | 833 | FALSE | smaller | 78 |
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| chr3.43769367.43771282 | chr3.43769372.43771282 | 1911 | 1917 | FALSE | smaller | 6 |
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| chr3.44712358.44712998 | chr3.44712358.44712975 | 618 | 642 | FALSE | smaller | 24 |
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| chr3.46895300.46895457 | chr3.46895320.46895457 | 138 | 159 | FALSE | smaller | 21 |
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| chr3.47835932.47836197 | chr3.47835932.47836170 | 239 | 267 | FALSE | smaller | 28 |
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| chr3.48015952.48017064 | chr3.48015952.48016992 | 1041 | 1114 | FALSE | smaller | 73 |
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| chr3.48223155.48223585 | chr3.48223317.48223488 | 172 | 432 | FALSE | smaller | 260 |
| chr3.48433852.48434221 | chr3.48433861.48434221 | 361 | 371 | FALSE | smaller | 10 |
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| chr3.48507576.48508092 | chr3.48507625.48508092 | 468 | 518 | FALSE | smaller | 50 |
| chr3.48535691.48536373 | chr3.48535691.48536325 | 635 | 684 | FALSE | smaller | 49 |
| chr3.48536723.48537167 | chr3.48536723.48537078 | 356 | 446 | FALSE | smaller | 90 |
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| chr3.48661171.48662287 | chr3.48661237.48662287 | 1051 | 1118 | FALSE | smaller | 67 |
| chr3.48694320.48695172 | chr3.48694320.48695097 | 778 | 854 | FALSE | smaller | 76 |
| chr3.48715320.48715513 | chr3.48715320.48715503 | 184 | 195 | FALSE | smaller | 11 |
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| chr3.48820309.48821314 | chr3.48820309.48821309 | 1001 | 1007 | FALSE | smaller | 6 |
| chr3.48881040.48882165 | chr3.48881040.48882139 | 1100 | 1127 | FALSE | smaller | 27 |
| chr3.48898133.48899196 | chr3.48898133.48899146 | 1014 | 1065 | FALSE | smaller | 51 |
| chr3.48918125.48920058 | chr3.48918141.48920058 | 1918 | 1935 | FALSE | smaller | 17 |
| chr3.49012583.49012851 | chr3.49012591.49012851 | 261 | 270 | FALSE | smaller | 9 |
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| chr3.49294468.49294803 | chr3.49294468.49294675 | 208 | 337 | FALSE | smaller | 129 |
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| chr3.49456354.49456721 | chr3.49456407.49456721 | 315 | 369 | FALSE | smaller | 54 |
| chr3.49607319.49607610 | chr3.49607391.49607610 | 220 | 293 | FALSE | smaller | 73 |
| chr3.49718018.49718873 | chr3.49718018.49718709 | 692 | 857 | FALSE | smaller | 165 |
| chr3.49719113.49719863 | chr3.49719364.49719863 | 500 | 752 | FALSE | smaller | 252 |
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| chr3.49856206.49857040 | chr3.49856269.49857040 | 772 | 836 | FALSE | smaller | 64 |
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| chr3.50057968.50058212 | chr3.50057987.50058212 | 226 | 246 | FALSE | smaller | 20 |
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| chr3.50259842.50260942 | chr3.50259863.50260942 | 1080 | 1102 | FALSE | smaller | 22 |
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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 |
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| chr3.52253275.52254117 | chr3.52253311.52254117 | 807 | 844 | FALSE | smaller | 37 |
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| chr3.52449732.52450646 | chr3.52449734.52450519 | 786 | 916 | FALSE | smaller | 130 |
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| chr3.53235959.53236688 | chr3.53235959.53236574 | 616 | 731 | FALSE | smaller | 115 |
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| chr3.53328008.53328320 | chr3.53328034.53328293 | 260 | 314 | FALSE | smaller | 54 |
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| chr3.55077773.55078082 | chr3.55077839.55078082 | 244 | 311 | FALSE | smaller | 67 |
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| chr3.59300711.59301135 | chr3.59300727.59301135 | 409 | 426 | FALSE | smaller | 17 |
| chr3.59829510.59829772 | chr3.59829517.59829772 | 256 | 264 | FALSE | smaller | 8 |
| chr3.60013132.60013665 | chr3.60013132.60013624 | 493 | 535 | FALSE | smaller | 42 |
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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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| chr3.63872146.63872692 | 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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| chr3.64072225.64072621 | chr3.64072246.64072621 | 376 | 398 | FALSE | smaller | 22 |
| chr3.64137986.64138407 | chr3.64138029.64138407 | 379 | 423 | FALSE | smaller | 44 |
| chr3.64209681.64210136 | chr3.64209681.64210128 | 448 | 457 | FALSE | smaller | 9 |
| chr3.64351976.64352826 | chr3.64352043.64352826 | 784 | 852 | FALSE | smaller | 68 |
| chr3.64355506.64355739 | chr3.64355584.64355739 | 156 | 235 | FALSE | smaller | 79 |
| chr3.64489192.64490398 | chr3.64489192.64490271 | 1080 | 1208 | FALSE | smaller | 128 |
| chr3.64687525.64688504 | chr3.64687561.64688488 | 928 | 981 | FALSE | smaller | 53 |
| chr3.64694052.64694527 | chr3.64694103.64694527 | 425 | 477 | FALSE | smaller | 52 |
| chr3.64900097.64900737 | chr3.64900109.64900737 | 629 | 642 | FALSE | smaller | 13 |
| chr3.64906487.64907026 | chr3.64906487.64907025 | 539 | 541 | FALSE | smaller | 2 |
| chr3.65190586.65191305 | chr3.65190619.65191305 | 687 | 721 | FALSE | smaller | 34 |
| chr3.65194282.65194645 | chr3.65194312.65194645 | 334 | 365 | FALSE | smaller | 31 |
| chr3.65342127.65342429 | chr3.65342189.65342356 | 168 | 304 | FALSE | smaller | 136 |
| chr3.65356411.65357138 | chr3.65356411.65356802 | 392 | 729 | FALSE | smaller | 337 |
| chr3.65508824.65508986 | chr3.65508824.65508982 | 159 | 164 | FALSE | smaller | 5 |
| chr3.65732142.65732617 | chr3.65732194.65732617 | 424 | 477 | FALSE | smaller | 53 |
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| chr5.67806757.67806989 | chr5.67806757.67806956 | 200 | 234 | FALSE | smaller | 34 |
| chr5.68225622.68226699 | chr5.68225622.68226696 | 1075 | 1079 | FALSE | smaller | 4 |
| chr5.68261833.68262250 | chr5.68261833.68262246 | 414 | 419 | FALSE | smaller | 5 |
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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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| chr5.71883137.71884025 | chr5.71883137.71884004 | 868 | 890 | FALSE | smaller | 22 |
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| 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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| chr5.72925399.72926188 | chr5.72925435.72926188 | 754 | 791 | FALSE | smaller | 37 |
| chr5.72953735.72953976 | chr5.72953735.72953952 | 218 | 243 | FALSE | smaller | 25 |
| chr5.73119854.73121017 | chr5.73120202.73121017 | 816 | 1165 | FALSE | smaller | 349 |
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| chr5.74865491.74867135 | chr5.74865491.74867070 | 1580 | 1646 | FALSE | smaller | 66 |
| chr5.74916845.74917213 | chr5.74916845.74917189 | 345 | 370 | FALSE | smaller | 25 |
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| chr5.74949155.74949421 | chr5.74949156.74949421 | 266 | 268 | FALSE | smaller | 2 |
| chr5.74954113.74954764 | chr5.74954239.74954764 | 526 | 653 | FALSE | smaller | 127 |
| chr5.75046283.75047046 | chr5.75046307.75047046 | 740 | 765 | FALSE | smaller | 25 |
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| chr5.75510565.75512194 | chr5.75510565.75512175 | 1611 | 1631 | FALSE | smaller | 20 |
| chr5.75536787.75537191 | chr5.75536787.75537163 | 377 | 406 | FALSE | smaller | 29 |
| chr5.75708017.75708350 | chr5.75708059.75708344 | 286 | 335 | FALSE | smaller | 49 |
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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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| chr5.180208588.180209386 | chr5.180209019.180209386 | 368 | 800 | FALSE | smaller | 432 |
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| chr6.2397030.2397567 | chr6.2397030.2397559 | 530 | 539 | FALSE | smaller | 9 |
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| chr6.2790616.2791503 | chr6.2790625.2791503 | 879 | 889 | FALSE | smaller | 10 |
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| chr6.12031649.12031988 | chr6.12031738.12031988 | 251 | 341 | FALSE | smaller | 90 |
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| 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 |
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| chr6.13385012.13385484 | chr6.13385017.13385484 | 468 | 474 | FALSE | smaller | 6 |
| chr6.13400680.13401134 | chr6.13400743.13401134 | 392 | 456 | FALSE | smaller | 64 |
| 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 |
| chr6.13480086.13481630 | chr6.13480086.13481427 | 1342 | 1546 | FALSE | smaller | 204 |
| 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 |
| chr6.14270836.14271363 | chr6.14270967.14271363 | 397 | 529 | FALSE | smaller | 132 |
| 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 |
| chr6.15245275.15246079 | chr6.15245275.15246078 | 804 | 806 | FALSE | smaller | 2 |
| chr6.15247905.15248338 | chr6.15247915.15248338 | 424 | 435 | FALSE | smaller | 11 |
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| chr6.15323033.15323424 | chr6.15323239.15323424 | 186 | 393 | FALSE | smaller | 207 |
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| 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 |
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| chr6.15793090.15793639 | chr6.15793090.15793615 | 526 | 551 | FALSE | smaller | 25 |
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| chr6.15977011.15977448 | chr6.15977023.15977448 | 426 | 439 | FALSE | smaller | 13 |
| chr6.15997685.15998307 | chr6.15997685.15998258 | 574 | 624 | FALSE | smaller | 50 |
| chr6.16215350.16215920 | chr6.16215350.16215919 | 570 | 572 | FALSE | smaller | 2 |
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| chr6.16494802.16495172 | chr6.16494858.16495172 | 315 | 372 | FALSE | smaller | 57 |
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| chr6.16564016.16564757 | chr6.16564139.16564757 | 619 | 743 | FALSE | smaller | 124 |
| chr6.16646102.16646773 | chr6.16646102.16646715 | 614 | 673 | FALSE | smaller | 59 |
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| 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 |
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| chr6.19180671.19181104 | chr6.19180716.19181104 | 389 | 435 | FALSE | smaller | 46 |
| chr6.19205057.19205667 | chr6.19205057.19205566 | 510 | 612 | FALSE | smaller | 102 |
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| 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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| chr6.21826033.21826495 | chr6.21826033.21826348 | 316 | 464 | FALSE | smaller | 148 |
| chr6.21846707.21848579 | chr6.21846707.21848505 | 1799 | 1874 | FALSE | smaller | 75 |
| chr6.21870303.21870629 | chr6.21870354.21870629 | 276 | 328 | FALSE | smaller | 52 |
| chr6.22108235.22108640 | chr6.22108315.22108640 | 326 | 407 | FALSE | smaller | 81 |
| chr6.22658859.22659264 | chr6.22658859.22659240 | 382 | 407 | FALSE | smaller | 25 |
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| chr6.22917180.22917649 | chr6.22917180.22917619 | 440 | 471 | FALSE | smaller | 31 |
| chr6.23228329.23228792 | chr6.23228382.23228789 | 408 | 465 | FALSE | smaller | 57 |
| chr6.23454071.23455028 | chr6.23454575.23455028 | 454 | 959 | FALSE | smaller | 505 |
| chr6.23981004.23981385 | chr6.23981004.23981363 | 360 | 383 | FALSE | smaller | 23 |
| chr6.24052552.24053035 | chr6.24052595.24052933 | 339 | 485 | FALSE | smaller | 146 |
| chr6.24389184.24389517 | chr6.24389184.24389510 | 327 | 335 | FALSE | smaller | 8 |
| chr6.24402784.24403747 | chr6.24402797.24403747 | 951 | 965 | FALSE | smaller | 14 |
| chr6.24854056.24854744 | chr6.24854349.24854744 | 396 | 690 | FALSE | smaller | 294 |
| chr6.25346081.25346318 | chr6.25346081.25346314 | 234 | 239 | FALSE | smaller | 5 |
| chr6.25717950.25718385 | chr6.25717955.25718385 | 431 | 437 | FALSE | smaller | 6 |
| chr6.25748684.25749191 | chr6.25748689.25749191 | 503 | 509 | FALSE | smaller | 6 |
| chr6.26043151.26044677 | chr6.26043219.26044677 | 1459 | 1528 | FALSE | smaller | 69 |
| 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 |
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| chr6.26595623.26596200 | chr6.26595623.26596198 | 576 | 579 | FALSE | smaller | 3 |
| chr6.26659244.26660327 | chr6.26659258.26660327 | 1070 | 1085 | FALSE | smaller | 15 |
| 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 |
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| chr6.27807188.27808731 | chr6.27807262.27808696 | 1435 | 1545 | FALSE | smaller | 110 |
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| 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 |
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| chr6.33830552.33831086 | chr6.33830552.33831040 | 489 | 536 | FALSE | smaller | 47 |
| chr6.33839367.33839667 | chr6.33839446.33839667 | 222 | 302 | FALSE | smaller | 80 |
| chr6.33864657.33865239 | chr6.33864657.33865208 | 552 | 584 | FALSE | smaller | 32 |
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| chr6.33984062.33984590 | chr6.33984089.33984527 | 439 | 530 | FALSE | smaller | 91 |
| chr6.34043548.34044985 | chr6.34043643.34044985 | 1343 | 1439 | FALSE | smaller | 96 |
| chr6.34073322.34073993 | chr6.34073322.34073946 | 625 | 673 | FALSE | smaller | 48 |
| chr6.34143966.34144604 | chr6.34143994.34144604 | 611 | 640 | FALSE | smaller | 29 |
| 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 |
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| chr6.34901433.34902524 | chr6.34901492.34902524 | 1033 | 1093 | FALSE | smaller | 60 |
| chr6.35128510.35128798 | chr6.35128525.35128798 | 274 | 290 | FALSE | smaller | 16 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| chr6.37207924.37208460 | chr6.37207924.37208362 | 439 | 538 | FALSE | smaller | 99 |
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| chr6.37512367.37512857 | chr6.37512367.37512709 | 343 | 492 | FALSE | smaller | 149 |
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| 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 |
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| 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 |
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| 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 |
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| chr6.44430219.44430849 | chr6.44430238.44430849 | 612 | 632 | FALSE | smaller | 20 |
| chr6.44477344.44477741 | chr6.44477355.44477741 | 387 | 399 | FALSE | smaller | 12 |
| chr6.44593360.44594065 | chr6.44593457.44594065 | 609 | 707 | FALSE | smaller | 98 |
| chr6.44668034.44668412 | chr6.44668042.44668412 | 371 | 380 | FALSE | smaller | 9 |
| chr6.44696218.44696556 | chr6.44696218.44696529 | 312 | 340 | FALSE | smaller | 28 |
| chr6.44738682.44739165 | chr6.44738682.44738917 | 236 | 485 | FALSE | smaller | 249 |
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| chr6.45520317.45520994 | chr6.45520389.45520957 | 569 | 679 | FALSE | smaller | 110 |
| chr6.45681828.45682200 | chr6.45681949.45682200 | 252 | 374 | FALSE | smaller | 122 |
| chr6.45697523.45697963 | chr6.45697523.45697919 | 397 | 442 | FALSE | smaller | 45 |
| chr6.45986665.45987405 | chr6.45986665.45987398 | 734 | 742 | FALSE | smaller | 8 |
| chr6.45997877.45998290 | chr6.45997877.45998208 | 332 | 415 | FALSE | smaller | 83 |
| chr6.46008328.46008554 | chr6.46008345.46008554 | 210 | 228 | FALSE | smaller | 18 |
| chr6.46473904.46474278 | chr6.46473904.46474236 | 333 | 376 | FALSE | smaller | 43 |
| chr6.46491099.46492337 | chr6.46491116.46492337 | 1222 | 1240 | FALSE | smaller | 18 |
| chr6.46756645.46757620 | chr6.46756645.46757560 | 916 | 977 | FALSE | smaller | 61 |
| chr6.46964692.46965030 | chr6.46964754.46965022 | 269 | 340 | FALSE | smaller | 71 |
| chr6.46993909.46994166 | chr6.46993909.46994149 | 241 | 259 | FALSE | smaller | 18 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| 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 |
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| chr6.56757285.56757958 | chr6.56757285.56757861 | 577 | 675 | FALSE | smaller | 98 |
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| 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 |
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| chr6.57983698.57984451 | chr6.57983698.57984238 | 541 | 755 | FALSE | smaller | 214 |
| chr6.62492467.62492844 | chr6.62492467.62492749 | 283 | 379 | FALSE | smaller | 96 |
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| chr6.64926056.64926555 | chr6.64926056.64926471 | 416 | 501 | FALSE | smaller | 85 |
| chr6.64987173.64987747 | chr6.64987173.64987745 | 573 | 576 | FALSE | smaller | 3 |
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| chr6.66103557.66104853 | chr6.66103557.66104686 | 1130 | 1298 | FALSE | smaller | 168 |
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| chr6.68777947.68778933 | chr6.68778000.68778933 | 934 | 988 | FALSE | smaller | 54 |
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| chr6.69866407.69867878 | chr6.69866413.69867878 | 1466 | 1473 | FALSE | smaller | 7 |
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| chr6.70709949.70710552 | chr6.70709949.70710434 | 486 | 605 | FALSE | smaller | 119 |
| chr6.71028068.71028643 | chr6.71028068.71028518 | 451 | 577 | FALSE | smaller | 126 |
| chr6.71404198.71405196 | chr6.71404240.71405196 | 957 | 1000 | FALSE | smaller | 43 |
| chr6.71446320.71446812 | chr6.71446385.71446812 | 428 | 494 | FALSE | smaller | 66 |
| chr6.71531903.71532279 | chr6.71531945.71532279 | 335 | 378 | FALSE | smaller | 43 |
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| chr6.74137860.74138291 | chr6.74137860.74138244 | 385 | 433 | FALSE | smaller | 48 |
| chr6.74166862.74167309 | chr6.74166862.74167280 | 419 | 449 | FALSE | smaller | 30 |
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| chr6.75215192.75215562 | chr6.75215192.75215439 | 248 | 372 | FALSE | smaller | 124 |
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| chr6.75386091.75386747 | chr6.75386144.75386747 | 604 | 658 | FALSE | smaller | 54 |
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| chr6.75609805.75610173 | chr6.75609818.75610173 | 356 | 370 | FALSE | smaller | 14 |
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| chr6.79077820.79078939 | chr6.79077825.79078939 | 1115 | 1121 | FALSE | smaller | 6 |
| chr6.79203863.79204116 | chr6.79203863.79204094 | 232 | 255 | FALSE | smaller | 23 |
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| chr6.81759899.81760201 | chr6.81759902.81760199 | 298 | 304 | FALSE | smaller | 6 |
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| chr6.83807201.83807605 | chr6.83807223.83807605 | 383 | 406 | FALSE | smaller | 23 |
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| chr6.85464143.85465143 | chr6.85464807.85465143 | 337 | 1002 | FALSE | smaller | 665 |
| chr6.85468308.85468941 | chr6.85468308.85468659 | 352 | 635 | FALSE | smaller | 283 |
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| chr6.85629862.85630301 | chr6.85629862.85630294 | 433 | 441 | FALSE | smaller | 8 |
| chr6.85922927.85923592 | chr6.85922968.85923592 | 625 | 667 | FALSE | smaller | 42 |
| chr6.87079100.87079440 | chr6.87079110.87079425 | 316 | 342 | FALSE | smaller | 26 |
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| chr6.87154852.87156703 | chr6.87154852.87156664 | 1813 | 1853 | FALSE | smaller | 40 |
| chr6.87333934.87334275 | chr6.87333934.87334265 | 332 | 343 | FALSE | smaller | 11 |
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| chr6.87397659.87398138 | chr6.87397659.87398128 | 470 | 481 | FALSE | smaller | 11 |
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| chr6.88359770.88361016 | chr6.88359770.88360897 | 1128 | 1248 | FALSE | smaller | 120 |
| chr6.88639760.88640513 | chr6.88639760.88640500 | 741 | 755 | FALSE | smaller | 14 |
| chr6.89035310.89036204 | chr6.89035346.89036204 | 859 | 896 | FALSE | smaller | 37 |
| chr6.89273724.89274336 | chr6.89273759.89274336 | 578 | 614 | FALSE | smaller | 36 |
| chr6.89346304.89346929 | chr6.89346495.89346929 | 435 | 627 | FALSE | smaller | 192 |
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| chr6.89553133.89553793 | chr6.89553287.89553793 | 507 | 662 | FALSE | smaller | 155 |
| chr6.89602043.89603115 | chr6.89602115.89603115 | 1001 | 1074 | FALSE | smaller | 73 |
| chr6.89792888.89793876 | chr6.89792888.89793859 | 972 | 990 | FALSE | smaller | 18 |
| chr6.89887255.89888109 | chr6.89887312.89888109 | 798 | 856 | FALSE | smaller | 58 |
| chr6.89895265.89895863 | chr6.89895265.89895770 | 506 | 600 | FALSE | smaller | 94 |
| chr6.89950841.89951700 | chr6.89950841.89951513 | 673 | 861 | FALSE | smaller | 188 |
| chr6.89977658.89978424 | chr6.89977658.89978323 | 666 | 768 | FALSE | smaller | 102 |
| chr6.90086554.90086924 | chr6.90086554.90086907 | 354 | 372 | FALSE | smaller | 18 |
| chr6.90135458.90136211 | chr6.90135594.90136211 | 618 | 755 | FALSE | smaller | 137 |
| chr6.90168725.90169856 | chr6.90168725.90169816 | 1092 | 1133 | FALSE | smaller | 41 |
| chr6.90175792.90176262 | chr6.90175792.90176200 | 409 | 472 | FALSE | smaller | 63 |
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| chr6.148806553.148806929 | chr6.148806553.148806845 | 293 | 378 | FALSE | smaller | 85 |
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| chr6.149058199.149058473 | chr6.149058219.149058473 | 255 | 276 | FALSE | smaller | 21 |
| chr6.149082276.149082564 | chr6.149082360.149082564 | 205 | 290 | FALSE | smaller | 85 |
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| chr6.149334771.149335125 | chr6.149334866.149335125 | 260 | 356 | FALSE | smaller | 96 |
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| chr6.149648223.149649212 | chr6.149648271.149649212 | 942 | 991 | FALSE | smaller | 49 |
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| chr7.234776.235087 | chr7.234776.235019 | 244 | 313 | FALSE | smaller | 69 |
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| chr7.893534.894207 | chr7.893693.894207 | 515 | 675 | FALSE | smaller | 160 |
| chr7.923175.923884 | chr7.923175.923872 | 698 | 711 | FALSE | smaller | 13 |
| chr7.935748.936995 | chr7.935888.936995 | 1108 | 1249 | FALSE | smaller | 141 |
| chr7.954388.955146 | chr7.954388.955129 | 742 | 760 | FALSE | smaller | 18 |
| chr7.962441.962831 | chr7.962441.962771 | 331 | 392 | FALSE | smaller | 61 |
| chr7.999862.1000434 | chr7.999862.1000259 | 398 | 574 | FALSE | smaller | 176 |
| chr7.1007558.1009311 | chr7.1007558.1009238 | 1681 | 1755 | FALSE | smaller | 74 |
| chr7.1016298.1017540 | chr7.1016469.1017374 | 906 | 1244 | FALSE | smaller | 338 |
| chr7.1057540.1058012 | chr7.1057556.1058012 | 457 | 474 | FALSE | smaller | 17 |
| chr7.1070434.1071037 | chr7.1070521.1071037 | 517 | 605 | FALSE | smaller | 88 |
| chr7.1095650.1095977 | chr7.1095650.1095918 | 269 | 329 | FALSE | smaller | 60 |
| chr7.1100877.1101259 | chr7.1100877.1101142 | 266 | 384 | FALSE | smaller | 118 |
| chr7.1182895.1183781 | chr7.1182935.1183781 | 847 | 888 | FALSE | smaller | 41 |
| chr7.1210289.1210808 | chr7.1210289.1210770 | 482 | 521 | FALSE | smaller | 39 |
| chr7.1224332.1224771 | chr7.1224332.1224738 | 407 | 441 | FALSE | smaller | 34 |
| chr7.1231218.1231665 | chr7.1231226.1231664 | 439 | 449 | FALSE | smaller | 10 |
| chr7.1289838.1290613 | chr7.1289838.1290610 | 773 | 777 | FALSE | smaller | 4 |
| chr7.1425689.1425976 | chr7.1425725.1425976 | 252 | 289 | FALSE | smaller | 37 |
| chr7.1443585.1444011 | chr7.1443657.1443983 | 327 | 428 | FALSE | smaller | 101 |
| chr7.1487919.1488402 | chr7.1487919.1488325 | 407 | 485 | FALSE | smaller | 78 |
| chr7.1520631.1521032 | chr7.1520631.1520977 | 347 | 403 | FALSE | smaller | 56 |
| chr7.1618301.1619604 | chr7.1619135.1619590 | 456 | 1305 | FALSE | smaller | 849 |
| chr7.1693229.1695224 | chr7.1693229.1693883 | 655 | 1997 | FALSE | smaller | 1342 |
| chr7.1693229.1695224 | chr7.1693970.1695224 | 1255 | 1997 | FALSE | smaller | 742 |
| chr7.1744779.1746394 | chr7.1745368.1746394 | 1027 | 1617 | FALSE | smaller | 590 |
| chr7.1749512.1750352 | chr7.1749627.1750352 | 726 | 842 | FALSE | smaller | 116 |
| chr7.1825460.1826454 | chr7.1825460.1826187 | 728 | 996 | FALSE | smaller | 268 |
| chr7.1871984.1872561 | chr7.1871991.1872561 | 571 | 579 | FALSE | smaller | 8 |
| chr7.1888637.1889941 | chr7.1888720.1889941 | 1222 | 1306 | FALSE | smaller | 84 |
| chr7.1928875.1929748 | chr7.1928953.1929748 | 796 | 875 | FALSE | smaller | 79 |
| chr7.1938807.1939887 | chr7.1938938.1939887 | 950 | 1082 | FALSE | smaller | 132 |
| chr7.1979443.1980110 | chr7.1979443.1979996 | 554 | 669 | FALSE | smaller | 115 |
| chr7.2041894.2042432 | chr7.2041941.2042324 | 384 | 540 | FALSE | smaller | 156 |
| chr7.2082231.2082604 | chr7.2082278.2082604 | 327 | 375 | FALSE | smaller | 48 |
| chr7.2099727.2100576 | chr7.2099740.2100576 | 837 | 851 | FALSE | smaller | 14 |
| chr7.2102361.2103630 | chr7.2102406.2103630 | 1225 | 1271 | FALSE | smaller | 46 |
| chr7.2146732.2147660 | chr7.2146732.2147133 | 402 | 930 | FALSE | smaller | 528 |
| chr7.2165301.2165733 | chr7.2165301.2165566 | 266 | 434 | FALSE | smaller | 168 |
| chr7.2172666.2173201 | chr7.2172688.2173201 | 514 | 537 | FALSE | smaller | 23 |
| chr7.2174997.2175804 | chr7.2175002.2175804 | 803 | 809 | FALSE | smaller | 6 |
| chr7.2241707.2242988 | chr7.2241707.2242946 | 1240 | 1283 | FALSE | smaller | 43 |
| chr7.2309850.2310386 | chr7.2309850.2310339 | 490 | 538 | FALSE | smaller | 48 |
| chr7.2321092.2321469 | chr7.2321184.2321469 | 286 | 379 | FALSE | smaller | 93 |
| chr7.2380127.2380457 | chr7.2380127.2380349 | 223 | 332 | FALSE | smaller | 109 |
| chr7.2403143.2404655 | chr7.2403158.2404655 | 1498 | 1514 | FALSE | smaller | 16 |
| chr7.2468607.2469339 | chr7.2468800.2469339 | 540 | 734 | FALSE | smaller | 194 |
| chr7.2523959.2524331 | chr7.2523995.2524331 | 337 | 374 | FALSE | smaller | 37 |
| chr7.2577008.2577543 | chr7.2577008.2577530 | 523 | 537 | FALSE | smaller | 14 |
| chr7.2577975.2578821 | chr7.2577984.2578821 | 838 | 848 | FALSE | smaller | 10 |
| chr7.2605869.2606417 | chr7.2605929.2606417 | 489 | 550 | FALSE | smaller | 61 |
| chr7.2612969.2613733 | chr7.2612969.2613598 | 630 | 766 | FALSE | smaller | 136 |
| chr7.2639852.2640396 | chr7.2639928.2640396 | 469 | 546 | FALSE | smaller | 77 |
| chr7.2647127.2647773 | chr7.2647133.2647773 | 641 | 648 | FALSE | smaller | 7 |
| chr7.2717309.2717823 | chr7.2717315.2717823 | 509 | 516 | FALSE | smaller | 7 |
| chr7.2789699.2790128 | chr7.2789761.2790128 | 368 | 431 | FALSE | smaller | 63 |
| chr7.2803115.2803371 | chr7.2803170.2803371 | 202 | 258 | FALSE | smaller | 56 |
| chr7.2815034.2815679 | chr7.2815034.2815565 | 532 | 647 | FALSE | smaller | 115 |
| chr7.2843898.2844809 | chr7.2843898.2844106 | 209 | 913 | FALSE | smaller | 704 |
| chr7.2843898.2844809 | chr7.2844145.2844809 | 665 | 913 | FALSE | smaller | 248 |
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| chr7.3399952.3400223 | chr7.3399958.3400223 | 266 | 273 | FALSE | smaller | 7 |
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| 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 |
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| 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 |
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| 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 |
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| chr7.4952111.4952666 | chr7.4952122.4952666 | 545 | 557 | FALSE | smaller | 12 |
| chr7.5073059.5073958 | chr7.5073276.5073958 | 683 | 901 | FALSE | smaller | 218 |
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| 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 |
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| chr7.8143600.8143804 | chr7.8143600.8143799 | 200 | 206 | FALSE | smaller | 6 |
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| chr7.13851767.13852103 | chr7.13851851.13852103 | 253 | 338 | FALSE | smaller | 85 |
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| chr7.14851595.14851984 | chr7.14851595.14851977 | 383 | 391 | FALSE | smaller | 8 |
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| chr7.16644642.16647066 | chr7.16646645.16647066 | 422 | 2426 | FALSE | smaller | 2004 |
| chr7.16660888.16661411 | chr7.16660888.16661407 | 520 | 525 | FALSE | smaller | 5 |
| chr7.16688220.16688550 | chr7.16688310.16688543 | 234 | 332 | FALSE | smaller | 98 |
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| chr7.17556153.17556719 | chr7.17556187.17556719 | 533 | 568 | FALSE | smaller | 35 |
| chr7.17600061.17600668 | chr7.17600061.17600658 | 598 | 609 | FALSE | smaller | 11 |
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| chr7.17773251.17773524 | chr7.17773251.17773519 | 269 | 275 | FALSE | smaller | 6 |
| chr7.17901161.17901686 | chr7.17901327.17901639 | 313 | 527 | FALSE | smaller | 214 |
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| chr7.18803970.18804509 | chr7.18803970.18804489 | 520 | 541 | FALSE | smaller | 21 |
| chr7.19145507.19145705 | chr7.19145507.19145691 | 185 | 200 | FALSE | smaller | 15 |
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| chr7.23298183.23300094 | chr7.23298762.23300094 | 1333 | 1913 | FALSE | smaller | 580 |
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| chr7.23984657.23985064 | chr7.23984657.23984954 | 298 | 409 | FALSE | smaller | 111 |
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| chr7.24698687.24699067 | chr7.24698772.24699067 | 296 | 382 | FALSE | smaller | 86 |
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| chr7.25636076.25637380 | chr7.25636661.25637380 | 720 | 1306 | FALSE | smaller | 586 |
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| chr7.28998058.28998769 | chr7.28998083.28998769 | 687 | 713 | FALSE | smaller | 26 |
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| chr7.29237297.29237940 | chr7.29237297.29237935 | 639 | 645 | FALSE | smaller | 6 |
| chr7.29281688.29282393 | chr7.29281688.29282390 | 703 | 707 | FALSE | smaller | 4 |
| chr7.29345517.29346194 | chr7.29345533.29346194 | 662 | 679 | FALSE | smaller | 17 |
| chr7.29463717.29464276 | chr7.29463741.29464276 | 536 | 561 | FALSE | smaller | 25 |
| chr7.29469953.29470588 | chr7.29470011.29470588 | 578 | 637 | FALSE | smaller | 59 |
| chr7.29476677.29477462 | chr7.29476695.29477462 | 768 | 787 | FALSE | smaller | 19 |
| chr7.29771237.29771847 | chr7.29771296.29771847 | 552 | 612 | FALSE | smaller | 60 |
| chr7.29932130.29932460 | chr7.29932130.29932447 | 318 | 332 | FALSE | smaller | 14 |
| chr7.29976518.29976986 | chr7.29976518.29976984 | 467 | 470 | FALSE | smaller | 3 |
| chr7.30115128.30116315 | chr7.30115128.30116276 | 1149 | 1189 | FALSE | smaller | 40 |
| chr7.30116687.30117298 | chr7.30116687.30117295 | 609 | 613 | FALSE | smaller | 4 |
| chr7.30159838.30160366 | chr7.30159838.30160154 | 317 | 530 | FALSE | smaller | 213 |
| chr7.30162699.30163837 | chr7.30162699.30163832 | 1134 | 1140 | FALSE | smaller | 6 |
| chr7.30238840.30239229 | chr7.30238921.30239229 | 309 | 391 | FALSE | smaller | 82 |
| chr7.30285645.30286183 | chr7.30285663.30286183 | 521 | 540 | FALSE | smaller | 19 |
| chr7.30406721.30407421 | chr7.30406721.30407418 | 698 | 702 | FALSE | smaller | 4 |
| chr7.30593853.30595592 | chr7.30593853.30595590 | 1738 | 1741 | FALSE | smaller | 3 |
| chr7.30755448.30756061 | chr7.30755448.30755964 | 517 | 615 | FALSE | smaller | 98 |
| chr7.30879255.30879633 | chr7.30879279.30879633 | 355 | 380 | FALSE | smaller | 25 |
| chr7.30920136.30920304 | chr7.30920136.30920282 | 147 | 170 | FALSE | smaller | 23 |
| chr7.31077112.31078468 | chr7.31077112.31078002 | 891 | 1358 | FALSE | smaller | 467 |
| chr7.31335951.31336969 | chr7.31335995.31336969 | 975 | 1020 | FALSE | smaller | 45 |
| chr7.31488698.31488930 | chr7.31488703.31488930 | 228 | 234 | FALSE | smaller | 6 |
| chr7.32030818.32031608 | chr7.32030834.32031608 | 775 | 792 | FALSE | smaller | 17 |
| chr7.32154860.32155101 | chr7.32154860.32155089 | 230 | 243 | FALSE | smaller | 13 |
| chr7.32162837.32163800 | chr7.32162837.32163799 | 963 | 965 | FALSE | smaller | 2 |
| chr7.32298096.32299418 | chr7.32298096.32299386 | 1291 | 1324 | FALSE | smaller | 33 |
| chr7.32489652.32490638 | chr7.32489724.32490638 | 915 | 988 | FALSE | smaller | 73 |
| chr7.32587436.32587951 | chr7.32587436.32587918 | 483 | 517 | FALSE | smaller | 34 |
| chr7.32588725.32589305 | chr7.32588725.32589258 | 534 | 582 | FALSE | smaller | 48 |
| chr7.32871891.32872451 | chr7.32872061.32872396 | 336 | 562 | FALSE | smaller | 226 |
| chr7.33043431.33043782 | chr7.33043431.33043765 | 335 | 353 | FALSE | smaller | 18 |
| chr7.33602772.33603228 | chr7.33602772.33603213 | 442 | 458 | FALSE | smaller | 16 |
| chr7.33796021.33796757 | chr7.33796021.33796686 | 666 | 738 | FALSE | smaller | 72 |
| chr7.33979479.33980572 | chr7.33979504.33980572 | 1069 | 1095 | FALSE | smaller | 26 |
| chr7.34069605.34070148 | chr7.34069605.34070106 | 502 | 545 | FALSE | smaller | 43 |
| chr7.34133421.34133902 | chr7.34133421.34133861 | 441 | 483 | FALSE | smaller | 42 |
| chr7.34296326.34296995 | chr7.34296433.34296995 | 563 | 671 | FALSE | smaller | 108 |
| chr7.34320356.34321643 | chr7.34320356.34321622 | 1267 | 1289 | FALSE | smaller | 22 |
| chr7.34338960.34339506 | chr7.34338960.34339482 | 523 | 548 | FALSE | smaller | 25 |
| chr7.34844755.34845278 | chr7.34844870.34845278 | 409 | 525 | FALSE | smaller | 116 |
| chr7.35185667.35187252 | chr7.35185667.35187199 | 1533 | 1587 | FALSE | smaller | 54 |
| chr7.35227347.35227842 | chr7.35227370.35227842 | 473 | 497 | FALSE | smaller | 24 |
| chr7.35253385.35255086 | chr7.35253385.35255071 | 1687 | 1703 | FALSE | smaller | 16 |
| chr7.35263444.35263865 | chr7.35263488.35263865 | 378 | 423 | FALSE | smaller | 45 |
| chr7.35415224.35415736 | chr7.35415224.35415712 | 489 | 514 | FALSE | smaller | 25 |
| chr7.35467389.35468006 | chr7.35467389.35467955 | 567 | 619 | FALSE | smaller | 52 |
| chr7.35487232.35487861 | chr7.35487244.35487861 | 618 | 631 | FALSE | smaller | 13 |
| chr7.35578314.35578900 | chr7.35578508.35578900 | 393 | 588 | FALSE | smaller | 195 |
| chr7.35593668.35594151 | chr7.35593789.35594141 | 353 | 485 | FALSE | smaller | 132 |
| chr7.35596095.35596541 | chr7.35596148.35596541 | 394 | 448 | FALSE | smaller | 54 |
| chr7.35613200.35613469 | chr7.35613203.35613469 | 267 | 271 | FALSE | smaller | 4 |
| chr7.35820810.35821192 | chr7.35820810.35821172 | 363 | 384 | FALSE | smaller | 21 |
| chr7.35875382.35875567 | chr7.35875389.35875567 | 179 | 187 | FALSE | smaller | 8 |
| chr7.35967468.35968355 | chr7.35967512.35968353 | 842 | 889 | FALSE | smaller | 47 |
| chr7.36084071.36084879 | chr7.36084071.36084819 | 749 | 810 | FALSE | smaller | 61 |
| chr7.36214742.36215483 | chr7.36214774.36215483 | 710 | 743 | FALSE | smaller | 33 |
| chr7.36303199.36304103 | chr7.36303199.36304075 | 877 | 906 | FALSE | smaller | 29 |
| chr7.36400630.36401045 | chr7.36400638.36401045 | 408 | 417 | FALSE | smaller | 9 |
| chr7.36459049.36459918 | chr7.36459124.36459918 | 795 | 871 | FALSE | smaller | 76 |
| chr7.36514536.36515484 | chr7.36514536.36515002 | 467 | 950 | FALSE | smaller | 483 |
| chr7.36522008.36522199 | chr7.36522008.36522144 | 137 | 193 | FALSE | smaller | 56 |
| chr7.36535762.36536253 | chr7.36535983.36536253 | 271 | 493 | FALSE | smaller | 222 |
| chr7.36601184.36601444 | chr7.36601231.36601444 | 214 | 262 | FALSE | smaller | 48 |
| chr7.36763085.36763674 | chr7.36763085.36763590 | 506 | 591 | FALSE | smaller | 85 |
| chr7.36789758.36790753 | chr7.36789800.36790753 | 954 | 997 | FALSE | smaller | 43 |
| chr7.37117275.37117716 | chr7.37117344.37117716 | 373 | 443 | FALSE | smaller | 70 |
| chr7.37118193.37119094 | chr7.37118193.37118955 | 763 | 903 | FALSE | smaller | 140 |
| chr7.37342902.37343303 | chr7.37342920.37343303 | 384 | 403 | FALSE | smaller | 19 |
| chr7.37447451.37449303 | chr7.37447479.37449303 | 1825 | 1854 | FALSE | smaller | 29 |
| chr7.37491975.37492215 | chr7.37491975.37492135 | 161 | 242 | FALSE | smaller | 81 |
| chr7.37973856.37974425 | chr7.37973882.37974425 | 544 | 571 | FALSE | smaller | 27 |
| chr7.38363372.38363725 | chr7.38363493.38363725 | 233 | 355 | FALSE | smaller | 122 |
| chr7.38368092.38368568 | chr7.38368092.38368451 | 360 | 478 | FALSE | smaller | 118 |
| chr7.38369924.38370409 | chr7.38369931.38370409 | 479 | 487 | FALSE | smaller | 8 |
| chr7.38893164.38893419 | chr7.38893186.38893419 | 234 | 257 | FALSE | smaller | 23 |
| chr7.39259614.39261162 | chr7.39259616.39261162 | 1547 | 1550 | FALSE | smaller | 3 |
| chr7.39413982.39414891 | chr7.39414035.39414891 | 857 | 911 | FALSE | smaller | 54 |
| chr7.39501020.39501597 | chr7.39501020.39501420 | 401 | 579 | FALSE | smaller | 178 |
| chr7.39754161.39755097 | chr7.39754212.39755097 | 886 | 938 | FALSE | smaller | 52 |
| chr7.39833207.39833882 | chr7.39833222.39833882 | 661 | 677 | FALSE | smaller | 16 |
| chr7.39910073.39910894 | chr7.39910201.39910894 | 694 | 823 | FALSE | smaller | 129 |
| chr7.39949044.39951104 | chr7.39949044.39951064 | 2021 | 2062 | FALSE | smaller | 41 |
| chr7.39951341.39951973 | chr7.39951341.39951938 | 598 | 634 | FALSE | smaller | 36 |
| chr7.40239842.40240952 | chr7.40239842.40240946 | 1105 | 1112 | FALSE | smaller | 7 |
| chr7.40701035.40701522 | chr7.40701035.40701490 | 456 | 489 | FALSE | smaller | 33 |
| chr7.40815160.40816032 | chr7.40815395.40816032 | 638 | 874 | FALSE | smaller | 236 |
| chr7.40915077.40915445 | chr7.40915113.40915445 | 333 | 370 | FALSE | smaller | 37 |
| chr7.41472588.41473047 | chr7.41472664.41473047 | 384 | 461 | FALSE | smaller | 77 |
| chr7.41520380.41520878 | chr7.41520383.41520878 | 496 | 500 | FALSE | smaller | 4 |
| chr7.41579845.41580560 | chr7.41579856.41580560 | 705 | 717 | FALSE | smaller | 12 |
| chr7.41693386.41693635 | chr7.41693486.41693635 | 150 | 251 | FALSE | smaller | 101 |
| chr7.41965208.41966857 | chr7.41965224.41966857 | 1634 | 1651 | FALSE | smaller | 17 |
| chr7.41993040.41993906 | chr7.41993177.41993906 | 730 | 868 | FALSE | smaller | 138 |
| chr7.42045674.42046925 | chr7.42045674.42046774 | 1101 | 1253 | FALSE | smaller | 152 |
| chr7.42081097.42081601 | chr7.42081115.42081601 | 487 | 506 | FALSE | smaller | 19 |
| chr7.42082087.42082466 | chr7.42082154.42082466 | 313 | 381 | FALSE | smaller | 68 |
| chr7.42247332.42247876 | chr7.42247332.42247822 | 491 | 546 | FALSE | smaller | 55 |
| chr7.42858684.42858919 | chr7.42858684.42858916 | 233 | 237 | FALSE | smaller | 4 |
| chr7.42887816.42889380 | chr7.42887817.42889380 | 1564 | 1566 | FALSE | smaller | 2 |
| chr7.43161377.43161840 | chr7.43161379.43161840 | 462 | 465 | FALSE | smaller | 3 |
| chr7.43197864.43199017 | chr7.43197873.43199017 | 1145 | 1155 | FALSE | smaller | 10 |
| chr7.43444718.43445474 | chr7.43444718.43445432 | 715 | 758 | FALSE | smaller | 43 |
| chr7.43491911.43492380 | chr7.43491911.43492329 | 419 | 471 | FALSE | smaller | 52 |
| chr7.43868925.43869852 | chr7.43869278.43869852 | 575 | 929 | FALSE | smaller | 354 |
| chr7.44073892.44074298 | chr7.44073892.44074277 | 386 | 408 | FALSE | smaller | 22 |
| 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 |
| chr7.45267189.45267546 | chr7.45267224.45267546 | 323 | 359 | FALSE | smaller | 36 |
| chr7.45363470.45364210 | chr7.45363514.45364210 | 697 | 742 | FALSE | smaller | 45 |
| chr7.45384568.45384827 | chr7.45384568.45384810 | 243 | 261 | FALSE | smaller | 18 |
| chr7.45663048.45663520 | chr7.45663077.45663520 | 444 | 474 | FALSE | smaller | 30 |
| chr7.45689444.45689961 | chr7.45689444.45689864 | 421 | 519 | FALSE | smaller | 98 |
| chr7.45768513.45769383 | chr7.45768513.45769378 | 866 | 872 | FALSE | smaller | 6 |
| chr7.45974314.45974849 | chr7.45974339.45974839 | 501 | 537 | FALSE | smaller | 36 |
| chr7.46162529.46163477 | chr7.46162529.46163323 | 795 | 950 | FALSE | smaller | 155 |
| chr7.46290467.46291394 | chr7.46290467.46291326 | 860 | 929 | FALSE | smaller | 69 |
| chr7.46539938.46540567 | chr7.46539938.46540557 | 620 | 631 | FALSE | smaller | 11 |
| chr7.46713966.46714429 | chr7.46713980.46714429 | 450 | 465 | FALSE | smaller | 15 |
| chr7.46811131.46812058 | chr7.46811241.46812058 | 818 | 929 | FALSE | smaller | 111 |
| chr7.46859974.46860466 | chr7.46859974.46860456 | 483 | 494 | FALSE | smaller | 11 |
| chr7.46930446.46930649 | chr7.46930446.46930643 | 198 | 205 | FALSE | smaller | 7 |
| chr7.46937912.46938182 | chr7.46937912.46938177 | 266 | 272 | FALSE | smaller | 6 |
| chr7.47180136.47180645 | chr7.47180136.47180601 | 466 | 511 | FALSE | smaller | 45 |
| chr7.47342475.47343309 | chr7.47342475.47343267 | 793 | 836 | FALSE | smaller | 43 |
| chr7.47351828.47352482 | chr7.47352010.47352482 | 473 | 656 | FALSE | smaller | 183 |
| chr7.47370435.47370988 | chr7.47370435.47370983 | 549 | 555 | FALSE | smaller | 6 |
| chr7.47441934.47442252 | chr7.47441934.47442220 | 287 | 320 | FALSE | smaller | 33 |
| chr7.47454624.47455067 | chr7.47454624.47454965 | 342 | 445 | FALSE | smaller | 103 |
| chr7.47464811.47465806 | chr7.47464872.47465806 | 935 | 997 | FALSE | smaller | 62 |
| chr7.47481994.47483022 | chr7.47482009.47483022 | 1014 | 1030 | FALSE | smaller | 16 |
| chr7.47534348.47534663 | chr7.47534349.47534663 | 315 | 317 | FALSE | smaller | 2 |
| chr7.47551681.47551927 | chr7.47551681.47551922 | 242 | 248 | FALSE | smaller | 6 |
| chr7.47581033.47582518 | chr7.47581034.47582518 | 1485 | 1487 | FALSE | smaller | 2 |
| 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 |
| chr7.47852603.47852982 | chr7.47852617.47852965 | 349 | 381 | FALSE | smaller | 32 |
| chr7.47923064.47923441 | chr7.47923064.47923440 | 377 | 379 | FALSE | smaller | 2 |
| chr7.47931616.47933216 | chr7.47931676.47933216 | 1541 | 1602 | FALSE | smaller | 61 |
| chr7.47979001.47979766 | chr7.47979001.47979751 | 751 | 767 | FALSE | smaller | 16 |
| chr7.49038608.49039198 | chr7.49038608.49039182 | 575 | 592 | FALSE | smaller | 17 |
| chr7.49291917.49292209 | chr7.49291917.49292091 | 175 | 294 | FALSE | smaller | 119 |
| chr7.49438275.49438956 | chr7.49438339.49438956 | 618 | 683 | FALSE | smaller | 65 |
| chr7.49638191.49638508 | chr7.49638236.49638508 | 273 | 319 | FALSE | smaller | 46 |
| chr7.49765350.49765834 | chr7.49765350.49765808 | 459 | 486 | FALSE | smaller | 27 |
| chr7.49777142.49777620 | chr7.49777142.49777613 | 472 | 480 | FALSE | smaller | 8 |
| chr7.49783386.49783782 | chr7.49783386.49783775 | 390 | 398 | FALSE | smaller | 8 |
| chr7.49788431.49789444 | chr7.49788446.49789347 | 902 | 1015 | FALSE | smaller | 113 |
| chr7.49852667.49853328 | chr7.49852740.49853263 | 524 | 663 | FALSE | smaller | 139 |
| chr7.50206529.50206959 | chr7.50206556.50206959 | 404 | 432 | FALSE | smaller | 28 |
| chr7.50303947.50305179 | chr7.50303956.50305179 | 1224 | 1234 | FALSE | smaller | 10 |
| chr7.50436266.50436819 | chr7.50436266.50436744 | 479 | 555 | FALSE | smaller | 76 |
| chr7.50502356.50502765 | chr7.50502382.50502765 | 384 | 411 | FALSE | smaller | 27 |
| chr7.50632431.50633288 | chr7.50632431.50633238 | 808 | 859 | FALSE | smaller | 51 |
| chr7.50755114.50756027 | chr7.50755114.50755748 | 635 | 915 | FALSE | smaller | 280 |
| chr7.50821534.50821966 | chr7.50821564.50821966 | 403 | 434 | FALSE | smaller | 31 |
| chr7.50940547.50940988 | chr7.50940547.50940957 | 411 | 443 | FALSE | smaller | 32 |
| chr7.51141336.51142274 | chr7.51141336.51142244 | 909 | 940 | FALSE | smaller | 31 |
| chr7.51162652.51163169 | chr7.51162654.51163169 | 516 | 519 | FALSE | smaller | 3 |
| chr7.51277972.51278516 | chr7.51277972.51278478 | 507 | 546 | FALSE | smaller | 39 |
| chr7.51315490.51317625 | chr7.51315528.51317625 | 2098 | 2137 | FALSE | smaller | 39 |
| chr7.51344130.51344920 | chr7.51344130.51344907 | 778 | 792 | FALSE | smaller | 14 |
| chr7.51477189.51477899 | chr7.51477230.51477899 | 670 | 712 | FALSE | smaller | 42 |
| chr7.52311913.52312469 | chr7.52311913.52312393 | 481 | 558 | FALSE | smaller | 77 |
| chr7.52355145.52355520 | chr7.52355145.52355514 | 370 | 377 | FALSE | smaller | 7 |
| chr7.52634954.52635424 | chr7.52634954.52635421 | 468 | 472 | FALSE | smaller | 4 |
| chr7.53350155.53350518 | chr7.53350260.53350518 | 259 | 365 | FALSE | smaller | 106 |
| chr7.53794695.53795211 | chr7.53794905.53795211 | 307 | 518 | FALSE | smaller | 211 |
| chr7.54541868.54542646 | chr7.54541868.54542633 | 766 | 780 | FALSE | smaller | 14 |
| 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 |
| chr7.54967309.54967838 | chr7.54967309.54967785 | 477 | 531 | FALSE | smaller | 54 |
| chr7.55063700.55063925 | chr7.55063714.55063925 | 212 | 227 | FALSE | smaller | 15 |
| 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 |
| chr7.55204213.55205139 | chr7.55204216.55205139 | 924 | 928 | FALSE | smaller | 4 |
| 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 |
| chr7.55975956.55976861 | chr7.55975975.55976861 | 887 | 907 | FALSE | smaller | 20 |
| chr7.56106155.56106799 | chr7.56106192.56106799 | 608 | 646 | FALSE | smaller | 38 |
| chr7.56371605.56372205 | chr7.56371695.56372205 | 511 | 602 | FALSE | smaller | 91 |
| chr7.57424869.57425569 | chr7.57425001.57425569 | 569 | 702 | FALSE | smaller | 133 |
| chr7.64307132.64307809 | chr7.64307188.64307785 | 598 | 679 | FALSE | smaller | 81 |
| chr7.64313778.64314395 | chr7.64313778.64314142 | 365 | 619 | FALSE | smaller | 254 |
| chr7.64551359.64551689 | chr7.64551424.64551671 | 248 | 332 | FALSE | smaller | 84 |
| chr7.64569443.64570328 | chr7.64569512.64570328 | 817 | 887 | FALSE | smaller | 70 |
| chr7.64877241.64877837 | chr7.64877241.64877833 | 593 | 598 | FALSE | smaller | 5 |
| chr7.64907535.64908018 | chr7.64907578.64908018 | 441 | 485 | FALSE | smaller | 44 |
| chr7.64946655.64947888 | chr7.64946655.64947842 | 1188 | 1235 | FALSE | smaller | 47 |
| chr7.65037813.65038659 | chr7.65037813.65038654 | 842 | 848 | FALSE | smaller | 6 |
| chr7.65080767.65081591 | chr7.65080795.65081591 | 797 | 826 | FALSE | smaller | 29 |
| chr7.65239317.65240329 | chr7.65239317.65240328 | 1012 | 1014 | FALSE | smaller | 2 |
| chr7.65429675.65431139 | chr7.65429692.65430612 | 921 | 1466 | FALSE | smaller | 545 |
| chr7.66047457.66047786 | chr7.66047464.66047775 | 312 | 331 | FALSE | smaller | 19 |
| chr7.66075488.66076530 | chr7.66075488.66076502 | 1015 | 1044 | FALSE | smaller | 29 |
| chr7.66110863.66111247 | chr7.66110914.66111247 | 334 | 386 | FALSE | smaller | 52 |
| chr7.66142102.66142642 | chr7.66142186.66142642 | 457 | 542 | FALSE | smaller | 85 |
| chr7.66556156.66558338 | chr7.66556448.66558066 | 1619 | 2184 | FALSE | smaller | 565 |
| chr7.66558593.66559130 | chr7.66558647.66559130 | 484 | 539 | FALSE | smaller | 55 |
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| chr8.42411392.42413214 | chr8.42412019.42413214 | 1196 | 1824 | FALSE | smaller | 628 |
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| chr8.55540488.55540870 | chr8.55540488.55540685 | 198 | 384 | FALSE | smaller | 186 |
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| chr9.132236357.132236754 | chr9.132236357.132236690 | 334 | 399 | FALSE | smaller | 65 |
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| chr9.132459452.132460047 | chr9.132459510.132460047 | 538 | 597 | FALSE | smaller | 59 |
| chr9.132669491.132671390 | chr9.132669514.132671386 | 1873 | 1901 | FALSE | smaller | 28 |
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| chr9.132901701.132902342 | chr9.132901782.132902342 | 561 | 643 | FALSE | smaller | 82 |
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| chr9.133403881.133404118 | chr9.133403900.133404118 | 219 | 239 | FALSE | smaller | 20 |
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| chr9.136007351.136007658 | chr9.136007373.136007653 | 281 | 309 | FALSE | smaller | 28 |
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| chr9.136902069.136902897 | chr9.136902399.136902897 | 499 | 830 | FALSE | smaller | 331 |
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| chr9.137114651.137117076 | chr9.137115059.137117076 | 2018 | 2427 | FALSE | smaller | 409 |
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| chr10.5576488.5577589 | chr10.5576488.5577226 | 739 | 1103 | FALSE | smaller | 364 |
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| chr10.10886397.10886654 | chr10.10886397.10886624 | 228 | 259 | FALSE | smaller | 31 |
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| chr10.11242345.11242913 | chr10.11242345.11242883 | 539 | 570 | FALSE | smaller | 31 |
| chr10.11283929.11284744 | chr10.11284355.11284744 | 390 | 817 | FALSE | smaller | 427 |
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| chr10.11893100.11893484 | chr10.11893100.11893351 | 252 | 386 | FALSE | smaller | 134 |
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| chr10.11915880.11916520 | chr10.11915903.11916520 | 618 | 642 | FALSE | smaller | 24 |
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| chr10.12063312.12063712 | chr10.12063312.12063682 | 371 | 402 | FALSE | smaller | 31 |
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| chr10.12201499.12202019 | chr10.12201528.12202019 | 492 | 522 | FALSE | smaller | 30 |
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| chr10.12264128.12264912 | chr10.12264180.12264912 | 733 | 786 | FALSE | smaller | 53 |
| chr10.12348629.12350879 | chr10.12348702.12350879 | 2178 | 2252 | FALSE | smaller | 74 |
| chr10.12482716.12482980 | chr10.12482716.12482934 | 219 | 266 | FALSE | smaller | 47 |
| chr10.12501106.12502454 | chr10.12501106.12502380 | 1275 | 1350 | FALSE | smaller | 75 |
| chr10.12615431.12616130 | chr10.12615431.12616098 | 668 | 701 | FALSE | smaller | 33 |
| chr10.12727902.12728204 | chr10.12727907.12728204 | 298 | 304 | FALSE | smaller | 6 |
| chr10.12984020.12984523 | chr10.12984020.12984517 | 498 | 505 | FALSE | smaller | 7 |
| chr10.13439199.13440867 | chr10.13439451.13440867 | 1417 | 1670 | FALSE | smaller | 253 |
| chr10.13505145.13505557 | chr10.13505172.13505557 | 386 | 414 | FALSE | smaller | 28 |
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| chr10.133442732.133443414 | chr10.133442888.133443414 | 527 | 684 | FALSE | smaller | 157 |
| chr11.209368.209801 | chr11.209441.209801 | 361 | 435 | FALSE | smaller | 74 |
| chr11.250801.251191 | chr11.250809.251191 | 383 | 392 | FALSE | smaller | 9 |
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| chr11.429284.429700 | chr11.429302.429700 | 399 | 418 | FALSE | smaller | 19 |
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| chr11.926401.926885 | chr11.926502.926885 | 384 | 486 | FALSE | smaller | 102 |
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| chr11.27305101.27306006 | chr11.27305167.27306006 | 840 | 907 | FALSE | smaller | 67 |
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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
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 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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()
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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)
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$peak)
ESR_OC <- ESR_C %>%
rbind(ESR_D)
ESR_OC_gr <- ESR_OC %>% GRanges()
mylist_new <- list(EAR_open_gr, EAR_close_gr,ESR_open_gr, ESR_close_gr,ESR_OC_gr, LR_open_gr, LR_close_gr,NR_gr, background_gr)
names(mylist_new) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open", "LR_close","NR","background")
# peakAnnoList<- lapply(mylist_new, annotatePeak, tssRegion =c(-2000,2000), TxDb= txdb)
# names(peakAnnoList) <- c("EAR_open", "EAR_close","ESR_open", "ESR_close","ESR_OC", "LR_open", "LR_close","NR","background")
# saveRDS(peakAnnoList, "data/Final_four_data/peakAnnoList_ff_8motif.RDS")
peakAnnoList_8_motif <- readRDS("data/Final_four_data/peakAnnoList_ff_8motif.RDS")
plotAnnoBar(peakAnnoList_8_motif[c(8,1:7)])+
ggtitle ("Genomic Feature Distribution, eight groups")
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
Overlapping peaks with H3K27ac data
raodah_fullpeaks <- readRDS( "data/Final_four_data/All_Raodahpeaks.RDS") %>%
GRanges %>%
GenomeInfoDb::keepStandardChromosomes(., pruning.mode = "coarse") %>%
dplyr::filter(seqnames!="chrM") %>%
dplyr::filter(seqnames!="chrY")
overlap_ac <- join_overlap_intersect((new_peaks_gr %>% dplyr::filter(seqnames !="chrY")),raodah_fullpeaks) %>% as.data.frame()
# overlap_ac %>%
# distinct(peakid)
# saveRDS(overlap_ac, "data/Final_four_data/overlapping_ac_atac_peaks.RDS")abs LFC across clusters
toplist_ff <- readRDS("data/Final_four_data/toplist_ff.RDS")
mean_lfc <- toplist_ff %>%
mutate(EAR_open = if_else(peak %in% EAR_open$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"
)
)
| Version | Author | Date |
|---|---|---|
| 8acb1e8 | reneeisnowhere | 2024-09-23 |
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] cowplot_1.1.3
[2] ComplexHeatmap_2.20.0
[3] ggrepel_0.9.6
[4] plyranges_1.24.0
[5] ggsignif_0.6.4
[6] genomation_1.36.0
[7] eulerr_7.0.2
[8] devtools_2.4.5
[9] usethis_3.0.0
[10] ggpubr_0.6.0
[11] BiocParallel_1.38.0
[12] Cormotif_1.50.0
[13] affy_1.82.0
[14] scales_1.3.0
[15] VennDiagram_1.7.3
[16] futile.logger_1.4.3
[17] gridExtra_2.3
[18] ggfortify_0.4.17
[19] edgeR_4.2.1
[20] limma_3.60.4
[21] rtracklayer_1.64.0
[22] org.Hs.eg.db_3.19.1
[23] TxDb.Hsapiens.UCSC.hg38.knownGene_3.18.0
[24] GenomicFeatures_1.56.0
[25] AnnotationDbi_1.66.0
[26] Biobase_2.64.0
[27] GenomicRanges_1.56.1
[28] GenomeInfoDb_1.40.1
[29] IRanges_2.38.1
[30] S4Vectors_0.42.1
[31] BiocGenerics_0.50.0
[32] ChIPseeker_1.40.0
[33] RColorBrewer_1.1-3
[34] broom_1.0.6
[35] kableExtra_1.4.0
[36] lubridate_1.9.3
[37] forcats_1.0.0
[38] stringr_1.5.1
[39] dplyr_1.1.4
[40] purrr_1.0.2
[41] readr_2.1.5
[42] tidyr_1.3.1
[43] tibble_3.2.1
[44] ggplot2_3.5.1
[45] tidyverse_2.0.0
[46] workflowr_1.7.1
loaded via a namespace (and not attached):
[1] fs_1.6.4
[2] matrixStats_1.4.1
[3] bitops_1.0-8
[4] enrichplot_1.24.4
[5] doParallel_1.0.17
[6] httr_1.4.7
[7] profvis_0.3.8
[8] tools_4.4.1
[9] backports_1.5.0
[10] utf8_1.2.4
[11] R6_2.5.1
[12] lazyeval_0.2.2
[13] GetoptLong_1.0.5
[14] urlchecker_1.0.1
[15] withr_3.0.1
[16] preprocessCore_1.66.0
[17] cli_3.6.3
[18] formatR_1.14
[19] scatterpie_0.2.4
[20] labeling_0.4.3
[21] sass_0.4.9
[22] Rsamtools_2.20.0
[23] systemfonts_1.1.0
[24] yulab.utils_0.1.7
[25] DOSE_3.30.5
[26] svglite_2.1.3
[27] R.utils_2.12.3
[28] sessioninfo_1.2.2
[29] plotrix_3.8-4
[30] BSgenome_1.72.0
[31] impute_1.78.0
[32] rstudioapi_0.16.0
[33] RSQLite_2.3.7
[34] shape_1.4.6.1
[35] generics_0.1.3
[36] gridGraphics_0.5-1
[37] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
[38] BiocIO_1.14.0
[39] vroom_1.6.5
[40] gtools_3.9.5
[41] car_3.1-2
[42] GO.db_3.19.1
[43] Matrix_1.7-0
[44] fansi_1.0.6
[45] abind_1.4-8
[46] R.methodsS3_1.8.2
[47] lifecycle_1.0.4
[48] whisker_0.4.1
[49] yaml_2.3.10
[50] carData_3.0-5
[51] SummarizedExperiment_1.34.0
[52] gplots_3.1.3.1
[53] qvalue_2.36.0
[54] SparseArray_1.4.8
[55] blob_1.2.4
[56] promises_1.3.0
[57] crayon_1.5.3
[58] miniUI_0.1.1.1
[59] lattice_0.22-6
[60] KEGGREST_1.44.1
[61] magick_2.8.5
[62] pillar_1.9.0
[63] knitr_1.48
[64] fgsea_1.30.0
[65] rjson_0.2.23
[66] boot_1.3-31
[67] codetools_0.2-20
[68] fastmatch_1.1-4
[69] glue_1.7.0
[70] getPass_0.2-4
[71] ggfun_0.1.6
[72] remotes_2.5.0
[73] data.table_1.16.0
[74] vctrs_0.6.5
[75] png_0.1-8
[76] treeio_1.28.0
[77] gtable_0.3.5
[78] cachem_1.1.0
[79] xfun_0.47
[80] mime_0.12
[81] S4Arrays_1.4.1
[82] tidygraph_1.3.1
[83] iterators_1.0.14
[84] statmod_1.5.0
[85] ellipsis_0.3.2
[86] nlme_3.1-166
[87] ggtree_3.12.0
[88] bit64_4.0.5
[89] rprojroot_2.0.4
[90] bslib_0.8.0
[91] affyio_1.74.0
[92] KernSmooth_2.23-24
[93] colorspace_2.1-1
[94] DBI_1.2.3
[95] seqPattern_1.36.0
[96] tidyselect_1.2.1
[97] processx_3.8.4
[98] bit_4.0.5
[99] compiler_4.4.1
[100] curl_5.2.2
[101] git2r_0.33.0
[102] httr2_1.0.4
[103] xml2_1.3.6
[104] DelayedArray_0.30.1
[105] shadowtext_0.1.4
[106] caTools_1.18.3
[107] callr_3.7.6
[108] rappdirs_0.3.3
[109] digest_0.6.37
[110] rmarkdown_2.28
[111] XVector_0.44.0
[112] htmltools_0.5.8.1
[113] pkgconfig_2.0.3
[114] MatrixGenerics_1.16.0
[115] highr_0.11
[116] fastmap_1.2.0
[117] GlobalOptions_0.1.2
[118] htmlwidgets_1.6.4
[119] rlang_1.1.4
[120] UCSC.utils_1.0.0
[121] shiny_1.9.1
[122] farver_2.1.2
[123] jquerylib_0.1.4
[124] jsonlite_1.8.8
[125] GOSemSim_2.30.2
[126] R.oo_1.26.0
[127] RCurl_1.98-1.16
[128] magrittr_2.0.3
[129] GenomeInfoDbData_1.2.12
[130] ggplotify_0.1.2
[131] patchwork_1.3.0
[132] munsell_0.5.1
[133] Rcpp_1.0.13
[134] ape_5.8
[135] viridis_0.6.5
[136] stringi_1.8.4
[137] ggraph_2.2.1
[138] zlibbioc_1.50.0
[139] MASS_7.3-61
[140] pkgbuild_1.4.4
[141] plyr_1.8.9
[142] parallel_4.4.1
[143] Biostrings_2.72.1
[144] graphlayouts_1.1.1
[145] splines_4.4.1
[146] circlize_0.4.16
[147] hms_1.1.3
[148] locfit_1.5-9.10
[149] ps_1.8.0
[150] igraph_2.0.3
[151] pkgload_1.4.0
[152] reshape2_1.4.4
[153] futile.options_1.0.1
[154] XML_3.99-0.17
[155] evaluate_1.0.0
[156] lambda.r_1.2.4
[157] BiocManager_1.30.25
[158] foreach_1.5.2
[159] tzdb_0.4.0
[160] tweenr_2.0.3
[161] httpuv_1.6.15
[162] polyclip_1.10-7
[163] clue_0.3-65
[164] gridBase_0.4-7
[165] ggforce_0.4.2
[166] xtable_1.8-4
[167] restfulr_0.0.15
[168] tidytree_0.4.6
[169] rstatix_0.7.2
[170] later_1.3.2
[171] viridisLite_0.4.2
[172] aplot_0.2.3
[173] memoise_2.0.1
[174] GenomicAlignments_1.40.0
[175] cluster_2.1.6
[176] timechange_0.3.0