cov.200bp.wind

Last updated: 2018-05-30

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I will use this analysis to bin the genome into 200bp windows and look at coverage for the 3’ seq libraries for each of these windows. I will use this data then in the leafcutter pipeline to look at differences between data from the total and nuclear fractions.

I performed a similar analysis for the net-seq data so some of the code will come from that. https://brimittleman.github.io/Net-seq/create_blacklist.html

The binned genome file is called: genome_200_wind_fix2.saf, it is in my genome annotation directory.

#!/bin/bash

#SBATCH --job-name=cov200
#SBATCH --time=8:00:00
#SBATCH --output=cov200.out
#SBATCH --error=cov200.err
#SBATCH --partition=broadwl
#SBATCH --mem=20G
#SBATCH --mail-type=END

module load Anaconda3  

source activate three-prime-env

#input is a bam 
sample=$1


describer=$(echo ${sample} | sed -e 's/.*\YL-SP-//' | sed -e "s/-sort.bam$//")



featureCounts -T 5 -s 1 -a /project2/gilad/briana/genome_anotation_data/genome_200_wind_fix2.saf -F 'SAF' -o /project2/gilad/briana/threeprimeseq/data/cov_200/${describer}_FC200.cov.bed $1

I will need to create a wrapper to run this for all of the files.

#!/bin/bash

#SBATCH --job-name=w_cov200
#SBATCH --time=8:00:00
#SBATCH --output=w_cov200.out
#SBATCH --error=w_cov200.err
#SBATCH --partition=broadwl
#SBATCH --mem=8G
#SBATCH --mail-type=END


for i in $(ls /project2/gilad/briana/threeprimeseq/data/sort/*.bam); do
            sbatch cov200.sh $i 
        done

Current analysis is not stand specific. I need to make windows for the negative strand.

sessionInfo()

R version 3.4.2 (2017-09-28)
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/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/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_1.0.1   Rcpp_0.12.15      digest_0.6.14    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.21.0      magrittr_1.5      evaluate_0.10.1  
[10] stringi_1.1.6     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.6.0     rmarkdown_1.8.5   tools_3.4.2      
[16] stringr_1.2.0     yaml_2.1.16       compiler_3.4.2   
[19] htmltools_0.3.6   knitr_1.18       

This reproducible R Markdown analysis was created with workflowr 1.0.1