Quantify reads per Peaks

Last updated: 2018-08-24

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In this analysis I will run feature counts on the human and chimp,total and nuclear threeprime seq libraries agaisnt the orthologous peaks I called with liftover.

First I will need to convert the bed files to saf files. This File is GeneID, Chr, Start, End, Strand. In my case it is peak ID.

#human
from misc_helper import *

fout = file("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.bed"):
    chrom, start, end, name = ln.split()
    start=int(start)
    end=int(end)
    ID = "%s_%s_%s_%s"%(name, chrom ,start, end)
    fout.write("%s\t%s\t%d\t%d\t.\n"%(ID, chrom, start, end))

fout.close()


#chimp
from misc_helper import *

fout = file("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.bed"):
    chrom, start, end, name = ln.split()
    start=int(start)
    end=int(end)
    ID = "%s_%s_%s_%s"%(name, chrom ,start, end)
    fout.write("%s\t%s\t%d\t%d\t.\n"%(ID, chrom, start, end))

fout.close()

The resulting files are:

• /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.saf

• /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.saf

#!/bin/bash

#SBATCH --job-name=fc_orthopeaks
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=fc_orthopeaks.out
#SBATCH --error=fc_orthopeaks.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END


module load Anaconda3
source activate comp_threeprime_env

# outdir: /project2/gilad/briana/comparitive_threeprime/data/Peak_quant

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.saf -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/HumanTotal_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/human/data/sort/*T-sort.bam -s 1

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.saf -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/HumanNuclear_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/human/data/sort/*N-sort.bam -s 1


featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.saf -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/ChimpTotal_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/chimp/data/sort/*T-sort.bam -s 1

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.saf -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/Peak_quant/ChimpNuclear_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/chimp/data/sort/*N-sort.bam -s 1

I need the matching peaks from human and chimps from the liftover pipeline data.

PeakNames=read.table(file = "../data/liftover/HumanChimpPeaknames.txt", header=T)

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.6.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.1.19       compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20       

This reproducible R Markdown analysis was created with workflowr 1.1.1