Last updated: 2018-08-14

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Unstaged changes:
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    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
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    File Version Author Date Message
    Rmd ba9a74f brimittleman 2018-08-14 add phenotype leafcutter analysis

Like I did on the first 16 individuals, I want to prepare a phenotype file for leafcutter. I will use this to start calling QTLs. I am using the filtered peaks called with Yang’s script. I need a file that has the peak and the coverage per individual. The phenotype per peak per individual is coverage at peak/coverage for all peaks in the same gene. First step is to map the peaks to a gene. I am going to use the refseq genes because they look like that have better annotated UTRs. I am going to subset to only the NM tagged mRNAs.

/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm.sort.bed

awk '$4 ~ /NM/ {print}' ncbiRefSeq_sm.sort.bed > ncbiRefSeq_sm.sort.mRNA.bed

I will use bedtools intersect and have it write peak and the gene that it intersects with. A is the peaks and B is the genes. I want to write out A with -wa and -wb because I want all of the info. I can then subset the parts I care about after. I want to force strandedness with -s. I say it is sorted with -sorted

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

bedtools intersect -wa -wb -sorted -s -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.bed

The result of this file has both files. I want to keep columns 1-6 and 10. This will be the peaks and the gene that overlaped it.

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $10}' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes_sm.bed

Now I can run feature counts on this file. In need to make the file into a saf file. This file has GeneID, Chr, Start, End, Strand. I want the ID to be peak#:chr1:start:end:strand:gene

from misc_helper import *

fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes_sm.bed"):
    chrom, start, end, name, score, strand, gene = ln.split()
    name_i=int(name)
    start_i=int(start)
    end_i=int(end)
    ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, 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()

ref_gene_peak_fc.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env


featureCounts -a /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant.fc /project2/gilad/briana/threeprimeseq/data/sort/*-sort.bam -s 1

The header of this file will need to be changed. I can do this by writing it out in python.

Session information

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