Last updated: 2018-08-14
workflowr checks: (Click a bullet for more information) ✔ R Markdown file: up-to-date
Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.
✔ Environment: empty
Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.
✔ Seed:
set.seed(12345)
The command set.seed(12345)
was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.
✔ Session information: recorded
Great job! Recording the operating system, R version, and package versions is critical for reproducibility.
✔ Repository version: ba9a74f
wflow_publish
or wflow_git_commit
). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated:
Ignored files:
Ignored: .DS_Store
Ignored: .Rhistory
Ignored: .Rproj.user/
Ignored: output/.DS_Store
Untracked files:
Untracked: analysis/ncbiRefSeq_sm.sort.mRNA.bed
Untracked: analysis/snake.config.notes.Rmd
Untracked: data/18486.genecov.txt
Untracked: data/APApeaksYL.total.inbrain.bed
Untracked: data/Totalpeaks_filtered_clean.bed
Untracked: data/YL-SP-18486-T-combined-genecov.txt
Untracked: data/YL-SP-18486-T_S9_R1_001-genecov.txt
Untracked: data/bedgraph_peaks/
Untracked: data/bin200.5.T.nuccov.bed
Untracked: data/bin200.Anuccov.bed
Untracked: data/bin200.nuccov.bed
Untracked: data/clean_peaks/
Untracked: data/comb_map_stats.csv
Untracked: data/comb_map_stats.xlsx
Untracked: data/combined_reads_mapped_three_prime_seq.csv
Untracked: data/gencov.test.csv
Untracked: data/gencov.test.txt
Untracked: data/gencov_zero.test.csv
Untracked: data/gencov_zero.test.txt
Untracked: data/gene_cov/
Untracked: data/joined
Untracked: data/leafcutter/
Untracked: data/merged_combined_YL-SP-threeprimeseq.bg
Untracked: data/nuc6up/
Untracked: data/reads_mapped_three_prime_seq.csv
Untracked: data/smash.cov.results.bed
Untracked: data/smash.cov.results.csv
Untracked: data/smash.cov.results.txt
Untracked: data/smash_testregion/
Untracked: data/ssFC200.cov.bed
Untracked: data/temp.file1
Untracked: data/temp.file2
Untracked: data/temp.gencov.test.txt
Untracked: data/temp.gencov_zero.test.txt
Untracked: output/picard/
Untracked: output/plots/
Untracked: output/qual.fig2.pdf
Unstaged changes:
Modified: analysis/28ind.peak.explore.Rmd
Modified: analysis/cleanupdtseq.internalpriming.Rmd
Modified: analysis/dif.iso.usage.leafcutter.Rmd
Modified: analysis/explore.filters.Rmd
Modified: analysis/peak.cov.pipeline.Rmd
Modified: analysis/test.max2.Rmd
Modified: code/Snakefile
Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | ba9a74f | brimittleman | 2018-08-14 | add phenotype leafcutter analysis |
Like I did on the first 16 individuals, I want to prepare a phenotype file for leafcutter. I will use this to start calling QTLs. I am using the filtered peaks called with Yang’s script. I need a file that has the peak and the coverage per individual. The phenotype per peak per individual is coverage at peak/coverage for all peaks in the same gene. First step is to map the peaks to a gene. I am going to use the refseq genes because they look like that have better annotated UTRs. I am going to subset to only the NM tagged mRNAs.
/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm.sort.bed
awk '$4 ~ /NM/ {print}' ncbiRefSeq_sm.sort.bed > ncbiRefSeq_sm.sort.mRNA.bed
I will use bedtools intersect and have it write peak and the gene that it intersects with. A is the peaks and B is the genes. I want to write out A with -wa and -wb because I want all of the info. I can then subset the parts I care about after. I want to force strandedness with -s. I say it is sorted with -sorted
#!/bin/bash
#SBATCH --job-name=intGenes_combfilterPeaks
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=intGenes_combfilterPeaks.out
#SBATCH --error=intGenes_combfilterPeaks.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
bedtools intersect -wa -wb -sorted -s -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.bed
The result of this file has both files. I want to keep columns 1-6 and 10. This will be the peaks and the gene that overlaped it.
awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $10}' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes_sm.bed
Now I can run feature counts on this file. In need to make the file into a saf file. This file has GeneID, Chr, Start, End, Strand. I want the ID to be peak#:chr1:start:end:strand:gene
from misc_helper import *
fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes_sm.bed"):
chrom, start, end, name, score, strand, gene = ln.split()
name_i=int(name)
start_i=int(start)
end_i=int(end)
ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, chrom, start_i, end_i, strand, gene)
fout.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
fout.close()
ref_gene_peak_fc.sh
#!/bin/bash
#SBATCH --job-name=ref_gene_peak_fc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=ref_gene_peak_fc.out
#SBATCH --error=ref_gene_peak_fc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END
module load Anaconda3
source activate three-prime-env
featureCounts -a /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant.fc /project2/gilad/briana/threeprimeseq/data/sort/*-sort.bam -s 1
The header of this file will need to be changed. I can do this by writing it out in python.
sessionInfo()
R version 3.5.1 (2018-07-02)
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/3.5/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_0.12.18 digest_0.6.15
[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_2.6.0 rmarkdown_1.10 tools_3.5.1
[16] stringr_1.3.1 yaml_2.1.19 compiler_3.5.1
[19] htmltools_0.3.6 knitr_1.20
This reproducible R Markdown analysis was created with workflowr 1.1.1