Overlap APAqtls with CTCF qtls

Last updated: 2019-03-19

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Knit directory: threeprimeseq/analysis/

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/NewPeakPostMP.Rmd
    Modified:   analysis/NuclearSpecQTL.Rmd
    Modified:   analysis/PeakToXper.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
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    Modified:   analysis/initialPacBioQuant.Rmd
    Modified:   analysis/mispriming_approach.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlapMolQTL.opposite.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   analysis/unexplainedeQTL_analysis.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
Rmd	d924da6	Briana Mittleman	2019-03-19	add ctcf analysis

I will look at ctcf data too see if this insulator element could act as a mechanism for apa qtls. This is in line with the kinetic model. We know CTCF binding slows polymerase. We are testing if this slow down is associated with APA as well.

The ctcf data I will use can be found at https://www.ncbi.nlm.nih.gov/pubmed/27010758

I will download the normalized phenotype file each row a binding region and each column a sample.

The regions are defined as chromosome, start, end in hg19. I will format this file so I have an ID like i do for the APA analysis.

library(tidyverse)

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✔ readr   1.3.1       ✔ forcats 0.4.0

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library(workflowr)

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library(data.table)

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Attaching package: 'data.table'

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library(cowplot)

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Attaching package: 'cowplot'

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    ggsave

PAS Enrichment at CTCF

First, I will look at overlap between the PAS and these CTCF sites. I can do this with deep tools by making a bed file.

CTCF2bed.py

CTCF=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbinding.csv", "r")
bedFile=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbindingLoc.bed", "w")

for i,ln in enumerate(CTCF):
    if i >0: 
        chrm=ln.split(",")[0]
        start=ln.split(",")[1]
        end=ln.split(",")[2]
        bedFile.write("%s\t%s\t%s\n"%(chrm, start, end))
bedFile.close()

Deeptools plots:

TotandNucAtCTCF_DTPlot_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=TotandNucAtCTCF_DTPlot_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TotandNucAtCTCF_DTPlot_noMPFilt.out
#SBATCH --error=TotandNucAtCTCF_DTPlot_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbindingLoc.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/CTCF/TotalandNucAtCTCF.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/CTCF/TotalandNucAtCTCF.gz --refPointLabel "CTCF" --plotTitle "Combined 3' at CTCF" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/CTCF/TotalandNucAtCTCF.png

No enrichemnt

Call CTCF QTL

I want to reformat the phenotypes, this is easiest in R.

CTCF=read.csv("../data/CTCF/CTCFbinding.csv", header=T) %>% mutate(ID= paste(chrm,start, end, sep=":")) %>% select(chrm, start, end, ID, contains("NA"))


write.table(CTCF, file="../data/CTCF/CTCFbinding.pheno.bed",col.names = T, row.names = F, quote = F, sep="\t" )

put on midway

#remove header
sort -k1,1 -k2,2n CTCFbinding.pheno.bed > CTCFbinding.pheno.sort.bed
#add header

bgzip CTCFbinding.pheno.sort.bed
tabix CTCFbinding.pheno.sort.bed.gz  

#get the PCs 
#midway1
#export PATH=/project/gilad/software/midway1/qtltools-1.0:$PATH


QTLtools pca --bed /project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbinding.pheno.sort.bed.gz  --scale --center --out /project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbinding.pheno.sort.bed.PC.out


head -n 6 CTCFbinding.pheno.sort.bed.PC.out.pca > CTCFbinding.pheno.sort.bed.5PCs.out.pca

Make samples file:

smaplesCTCF.py

ctcf=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbinding.csv", "r")
sampleFile=open("/project2/gilad/briana/threeprimeseq/data/CTCF/samples.txt", "w")

samplesVCF=open("/project2/gilad/briana/YRI_geno_hg19/vcf.samples.txt", "r")

samplesoK={}
for ln in samplesVCF:
  samList=ln.split()
  for i in samList:
      samplesoK[i]=""
      
print(samplesoK)
for i, ln in enumerate(ctcf):
    if i >0:
        lnList=ln.split(",")
        for each in lnList:
            if each in samplesoK.keys():
                sampleFile.write("%s\n"%(each))
            else: 
                print("notInvcf")
                
sampleFile.close()

VCF file does not have these samples.

CTCFqtl_nom.sh

#!/bin/bash


#SBATCH --job-name=CTCFqtl_nom
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=CTCFqtl_nom.out
#SBATCH --error=CTCFqtl_nom.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
  
for i in $(seq 1 30)
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf.gz  --cov /project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbinding.pheno.sort.bed.5PCs.out.pca --bed /project2/gilad/briana/threeprimeseq/data/CTCF/CTCFbinding.pheno.sort.bed.gz --out /project2/gilad/briana/threeprimeseq/data/CTCF/nom/fastqtl_CTCFbinding.nominal.out --chunk $i 30  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/CTCF/samples.txt
done

Use QTLs they called

Info from site:
This is the called QTLs 1% FDR threshold (q value <= 0.01) and kept only cluster variants defined as having P value within one order of magnitude to the P value of the lead variant for the same binding region.

I can make a file with the snp positions and I will look for these in my nominal APA data

ctcfQTL=read.csv("../data/CTCF/CTCFQTLS.csv")

ctcfQTL_snponly=ctcfQTL %>% select(VARIANT_CHRM, VARIANT_POS) %>% mutate(snp_loc=paste(VARIANT_CHRM,VARIANT_POS,sep= ":")) %>% select(snp_loc)


write.table(ctcfQTL_snponly, file="../data/CTCF/CTCFqtl_snps.txt", col.names = F, row.names = F, quote = F)

Look for these snps in nominal data:

CTCFqtlinAPA.py

def main(apa, ctcfQTL, outFile):
    fout=open(outFile,"w")
    ctcfdic={}
    for ln in open(ctcfQTL,"r"):
        snp=ln.split()[0]
        ctcfdic[snp]=""
    for ln in open(apa, "r"):
            snpApa =ln.split()[1]   
            if snpApa in ctcfdic.keys():
                fout.write(ln)
    fout.close()
                

if __name__ == "__main__":
    import sys
    fraction=sys.argv[1]
    OutFile=sys.argv[2]
    ctcfQTL="/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFqtl_snps.txt"
    if fraction=="Total":
        nomFile="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"
    else:
        nomFile="/project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_allNomRes.txt"
    main(nomFile, ctcfQTL, OutFile)

Run: run_CTCFqtlinAPA.sh

#!/bin/bash


#SBATCH --job-name=run_CTCFqtlinAPA
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_CTCFqtlinAPA.out
#SBATCH --error=run_CTCFqtlinAPA.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env

python CTCFqtlinAPA.py "Total" "/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFQtlinTotalAPA.txt"
python CTCFqtlinAPA.py "Nuclear" "/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFQtlinNuclearAPA.txt"

Make empirical distribution:

I can do empirical distribution based on genes not in this set. I will make a list of the genes with] an overlap in total and in nuclear.

I can then find the matched peak numbers based on the genes that do have an overlap.

getCTCFgenes.py



apaNuc=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFQtlinNuclearAPA.txt", "r")
apaTot=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFQtlinTotalAPA.txt","r")

nucGenes=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFQtlinNuclearAPA_Genes.txt", "w")
totGenes=open("/project2/gilad/briana/threeprimeseq/data/CTCF/CTCFQtlinTotalAPA_Genes.txt", "w")


def overlapGenes(inFile, outFile):
     #make dictionary with gene (this will have unique)
     geneDic={}
     for ln in inFile:
          gene=ln.split()[0].split(":")[-1].split("_")[0]
          if gene not in geneDic.keys():
              geneDic[gene]=""
     for k,v in geneDic.items():
         outFile.write("%s\n"%(k))
     outFile.close()
     
overlapGenes(apaTot, totGenes)
overlapGenes(apaNuc,nucGenes)

Make the empirical dist:

Problem: which snp do i choose? CTCF not associated with a gene so i can pick the best snp for the non ctcf gene

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] cowplot_0.9.4     data.table_1.12.0 workflowr_1.2.0  
 [4] forcats_0.4.0     stringr_1.4.0     dplyr_0.8.0.1    
 [7] purrr_0.3.1       readr_1.3.1       tidyr_0.8.3      
[10] tibble_2.0.1      ggplot2_3.1.0     tidyverse_1.2.1  

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0       cellranger_1.1.0 plyr_1.8.4       pillar_1.3.1    
 [5] compiler_3.5.1   git2r_0.24.0     tools_3.5.1      digest_0.6.18   
 [9] lubridate_1.7.4  jsonlite_1.6     evaluate_0.13    nlme_3.1-137    
[13] gtable_0.2.0     lattice_0.20-38  pkgconfig_2.0.2  rlang_0.3.1     
[17] cli_1.0.1        rstudioapi_0.9.0 yaml_2.2.0       haven_2.1.0     
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[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.5 glue_1.3.0      
[33] R6_2.4.0         readxl_1.3.0     rmarkdown_1.11   modelr_0.1.4    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.3  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.4-0
[45] stringi_1.3.1    lazyeval_0.2.1   munsell_0.5.0    broom_0.5.1     
[49] crayon_1.3.4

Overlap APAqtls with CTCF qtls

Briana Mittleman

3/19/2019

PAS Enrichment at CTCF

Call CTCF QTL

Use QTLs they called