New Peaks After Misprime Approach

Last updated: 2019-01-14

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    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
Rmd	6bc9243	Briana Mittleman	2019-01-14	evaluate clean reads, make new file for misprime filter

In the previous analysis I looked at a mispriming approach. Now I am going to use these filtered reads to create new BAM files, BW files, coverage files, and finally a peak list. After, I will evaluate the differences in the peak lists.

Now I need to filter the sorted bed files based on these clean reads.

I can make an R script that uses filter join:

Infile1 is the sorted bed, Infile2 is cleaned bed, Filter on read name

I can sue the number_T/N as the identifer.

filterSortBedbyCleanedBed.R

#!/bin/rscripts

# usage: Rscirpt --vanilla  filterSortBedbyCleanedBed.R identifier

#this script takes in the sorted bed file and the clean reads, it will clean the bed file   


library(dplyr)
library(tidyr)
library(data.table)


args = commandArgs(trailingOnly=TRUE)
identifier=args[1]


sortBedName= paste("/project2/gilad/briana/threeprimeseq/data/bed_sort/YL-SP-", identifier, "-combined-sort.bed", sep="")

CleanName= paste("/project2/gilad/briana/threeprimeseq/data/nuc_10up_CleanReads/TenBaseUP.", identifier, ".CleanReads.bed", sep="")

outFile= paste("/project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP/YL-SP-", identifier, "-combined-sort.clean.bed", sep="")  

bedFile=fread(sortBedName, col.names = c("Chrom", "start", "end", "name", "score", "strand"))

cleanFile=fread(CleanName, col.names = c("Chrom", "start", "end", "name", "score", "strand"))

intersection=bedFile %>% semi_join(cleanFile, by="name")

fwrite(intersection, file=outFile,quote = F, col.names = F, row.names = F)

I need to call this in a bash script that gets just the identifier:

run_filterSortBedbyCleanedBed.sh

#!/bin/bash

#SBATCH --job-name=run_filterSortBedbyCleanedBed
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=run_filterSortBedbyCleanedBed.out
#SBATCH --error=run_filterSortBedbyCleanedBed.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  

for i in $(ls /project2/gilad/briana/threeprimeseq/data/nuc_10up_CleanReads/*);do
   describer=$(echo ${i} | sed -e 's/.*TenBaseUP.//' | sed -e "s/.CleanReads.bed//")
   Rscript --vanilla  filterSortBedbyCleanedBed.R  ${describer}
done

Next I can use bedtools intersect to filter the bam files from these bed files. I will write the code then wrap it.

filterOnlyOKPrimeFromBam.sh

a is the bam, b is the clean bed , stranded, sorted, -wa

#!/bin/bash

#SBATCH --job-name=filterOnlyOKPrimeFromBam
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=filterOnlyOKPrimeFromBam.out
#SBATCH --error=filterOnlyOKPrimeFromBam.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


$1 = describer

bedtools intersect -wa -sorted -s -abam /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-${desciber}-combined-sort.bam -b /project2/gilad/briana/threeprimeseq/data/bed_sort_CleanedMP/YL-SP-${descirber}-combined-sort.clean.bed > /project2/gilad/briana/threeprimeseq/data/bam_NoMP/YL-SP-${desciber}-combined-sort.noMP.bam

I will sort and index these:

/project2/gilad/briana/threeprimeseq/data/sort_NoMP/

Session information

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.19      digest_0.6.17    
 [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

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New Peaks After Misprime Approach

Briana Mittleman

1/14/2019

Session information