Last updated: 2020-03-16
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Knit directory: Comparative_APA/analysis/
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File | Version | Author | Date | Message |
---|---|---|---|---|
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