Last updated: 2019-04-17

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Knit directory: apaQTL/analysis/

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Rmd 057dc1a brimittleman 2019-04-17 add bam 2 pas analysis

In the previous analysis I used a snakefile to process the fastq files to bam files. The goal of this analysis is to call peaks in the data and refine these to the PAS I will use for the differences between fractions and for the QTL analysis. This relies on the use of 2 snake pipelines. One will call the peaks and the second will filter the peaks to the PAS set. I use the same config file for all 3 pipelines.

Call peaks in data

I use an in house peak caller to call regions of the genome that have reads in all of the libraries merged.

First I need to load the environment.

module load Anaconda3
source activate three-prime-env

I will run the call peak file.

sbatch code/snakemakePAS.batch

This will call the submit-snakemakePAS.sh script to run each rule in the SnakefilePAS pipeline.

The log files go to the same log directory as the files from the fastq to bam pas code/log.

Filter peaks to PAS

This anaylsis filters the peaks first on individual read count per individual and the number of individuals with non zero counts in the peak. I then keep peaks that are used at an average of at least 5% in the total and nuclear fractions. Finally, I convert to PAS by flipping the strand and keeping the most 3’ end of the bin as the pas.

sbatch code/snakemakefiltPAS.batch

These log files are also in the code/log directory.

This will call the submit-snakemakefiltPAS.sh script to run each rule in the SnakefilefiltPAS pipeline.



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_1.2.0 Rcpp_1.0.0      digest_0.6.18   rprojroot_1.3-2
 [5] backports_1.1.3 git2r_0.24.0    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.3.1   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11 
[13] tools_3.5.1     stringr_1.4.0   glue_1.3.0      xfun_0.5       
[17] yaml_2.2.0      compiler_3.5.1  htmltools_0.3.6 knitr_1.21