prepare APA phenotype

Last updated: 2018-08-28

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In this analysis I want to assign all of the orthologus peaks to the human and chimp mRNAs. Once I do this I will be able to create a PAS usage phenotype. This is x/y. X is the read count in a peak and y is the number of reads in any peak for the same gene. This analysis will follow a similar pipeline to https://brimittleman.github.io/threeprimeseq/pheno.leaf.comb.html. I will run all of this seperatly for the human and chimp peaks. Unfortunately I cannot forse strandedness here like I did in the human LCL. I lost the strand information in the liftover.

#!/bin/bash

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

module load Anaconda3
source activate comp_threeprime_env


#human
bedtools intersect -wa -wb -sorted -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/humanOrthoPeaks.sort.bed  -b /project2/gilad/briana/genome_anotation_data/comp_genomes/gene_annos/humanGene_ncbiRefSeq_mRNA_sort.bed > /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/humanOrthoPeaks.sort.refseqgenes.bed 


#chimp
bedtools intersect -wa -wb -sorted -a /project2/gilad/briana/comparitive_threeprime/data/ortho_peaks/chimpOrthoPeaks.sort.bed -b /project2/gilad/briana/genome_anotation_data/comp_genomes/gene_annos/chimpGene_refGene_mRNA_sort.bed > /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/chimpOrthoPeaks.sort.refgenes.bed

The resulting file has info from everything. I need to use awk to keep just the colums I want. chr, start, end, peak name, “.”, gene strand, gene name. The first . is for a score and the second . is strand.

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t " "." "\t" $10 "\t" $8}' /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/chimpOrthoPeaks.sort.refgenes.bed > /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/chimpOrthoPeaks.sort.refgenes.sm.bed

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t " "." "\t" $10 "\t" $8}' /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/humanOrthoPeaks.sort.refseqgenes.bed  > /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/humanOrthoPeaks.sort.refseqgenes.sm.bed 

Before I run feature count I want to create an ID for each peak and transform the data into a SAF file. The ID will be peak#:ch#:start:end:strand:gene

#peakwGene2Saf_C.py  
from misc_helper import *

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

#peakwGene2Saf_H.py
from misc_helper import *

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

Feature counts on these peaks.

fc_orthopeaksWgene.sh

#!/bin/bash

#SBATCH --job-name=fc_orthopeaksWgene
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=fc_orthopeaksWgene.out
#SBATCH --error=fc_orthopeaksWgene.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/PeakwGene_quant

featureCounts -a /project2/gilad/briana/comparitive_threeprime/data/PeaksGeneAnno/humanOrthoPeaks.sort.refseqgenes.sm.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/PeakwGene_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/PeaksGeneAnno/humanOrthoPeaks.sort.refseqgenes.sm.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/PeakwGene_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/PeaksGeneAnno/chimpOrthoPeaks.sort.refgenes.sm.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/PeakwGene_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/PeaksGeneAnno/chimpOrthoPeaks.sort.refgenes.sm.SAF -F SAF -o /project2/gilad/briana/comparitive_threeprime/data/PeakwGene_quant/ChimpNuclear_Orthopeak.quant /project2/gilad/briana/comparitive_threeprime/chimp/data/sort/*N-sort.bam -s 1

This leads to loss a large number of reads most likely because they fall outside of the orthologous peaks or in peaks not assigned to genes. This is a huge problem in the chimps because there are only 2000 annotated genes.

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_0.6.16    
 [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_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20       

This reproducible R Markdown analysis was created with workflowr 1.1.1