Last updated: 2020-03-16

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Knit directory: Comparative_APA/analysis/

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Rmd 94211e8 brimittleman 2020-03-16 add change misprime analysis and dapa qtls

I am trying to figure out why the most used PAS distributions are different when the major PAS does not mach between species. It is not the number of Ns in the genome. I will now try to change the misprimming filters to see if that makes a difference.

Right now I use 70% T or 6 in a row. I will change the filters to the following as a test:

60% Ts

50% Ts

40% Ts

I will make new directories. I do not want to copy the files too many times. I will point to the original data by changing the fastq directory in the snakefile and then add the data dir to the configs.

mkdir ../../Misprime6
mkdir ../../Misprime5
mkdir ../../Misprime4

#add all of these 
mkdir data
mkdir code

mkdir Human
mkdir Human/data
mkdir Human/data/fastq

mkdir Chimp
mkdir Chimp/data
mkdir Chimp/data/fastq


#copy all code for snakemake, code is in Comparative_noNs

cp -r * ../../Misprime4/code/
cp -r * ../../Misprime5/code/
cp -r * ../../Misprime6/code/ 

#change the config files for the correct directory  for base

File to change is:

filterMissprimingInNuc10_gen.py

For Ns. I do change all Ns to Ts for sake of the proportion of Ts in the read.

Start with 4.

Call peaks: Code I need:

SnakefilePAS * bg_to_cov.py * callPeaksYL.py

SnakefilePASfilt
* bed2saf.py * filterpeaks.py * namePeaks.py * bed215upbed.py
* filterPASforMP.py (change the cutoff for this as well- still do 6 in a row from the snape but change the proportion) * cleanbed2saf.py


sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.4 (Nitrogen)

Matrix products: default
BLAS/LAPACK: /software/openblas-0.2.19-el7-x86_64/lib/libopenblas_haswellp-r0.2.19.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.6.0 Rcpp_1.0.2      digest_0.6.18   later_0.7.5    
 [5] rprojroot_1.3-2 R6_2.3.0        backports_1.1.2 git2r_0.26.1   
 [9] magrittr_1.5    evaluate_0.12   stringi_1.2.4   fs_1.3.1       
[13] promises_1.0.1  whisker_0.3-2   rmarkdown_1.10  tools_3.5.1    
[17] stringr_1.3.1   glue_1.3.0      httpuv_1.4.5    yaml_2.2.0     
[21] compiler_3.5.1  htmltools_0.3.6 knitr_1.20