Last updated: 2019-01-11
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: ec05274 
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:    data/.DS_Store
    Ignored:    output/.DS_Store
Untracked files:
    Untracked:  KalistoAbundance18486.txt
    Untracked:  analysis/DirectionapaQTL.Rmd
    Untracked:  analysis/PreAshExplore.Rmd
    Untracked:  analysis/YL_QTL_test.Rmd
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/snake.config.notes.Rmd
    Untracked:  analysis/verifyBAM.Rmd
    Untracked:  code/PeaksToCoverPerReads.py
    Untracked:  code/strober_pc_pve_heatmap_func.R
    Untracked:  data/18486.genecov.txt
    Untracked:  data/APApeaksYL.total.inbrain.bed
    Untracked:  data/ChromHmmOverlap/
    Untracked:  data/GM12878.chromHMM.bed
    Untracked:  data/GM12878.chromHMM.txt
    Untracked:  data/LianoglouLCL/
    Untracked:  data/LocusZoom/
    Untracked:  data/NuclearApaQTLs.txt
    Untracked:  data/PeakCounts/
    Untracked:  data/PeaksUsed/
    Untracked:  data/RNAkalisto/
    Untracked:  data/TotalApaQTLs.txt
    Untracked:  data/Totalpeaks_filtered_clean.bed
    Untracked:  data/UnderstandPeaksQC/
    Untracked:  data/YL-SP-18486-T-combined-genecov.txt
    Untracked:  data/YL-SP-18486-T_S9_R1_001-genecov.txt
    Untracked:  data/YL_QTL_test/
    Untracked:  data/apaExamp/
    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/comb_map_stats_39ind.csv
    Untracked:  data/combined_reads_mapped_three_prime_seq.csv
    Untracked:  data/diff_iso_trans/
    Untracked:  data/ensemble_to_genename.txt
    Untracked:  data/example_gene_peakQuant/
    Untracked:  data/explainProtVar/
    Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed
    Untracked:  data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed
    Untracked:  data/first50lines_closest.txt
    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/mol_overlap/
    Untracked:  data/mol_pheno/
    Untracked:  data/nom_QTL/
    Untracked:  data/nom_QTL_opp/
    Untracked:  data/nom_QTL_trans/
    Untracked:  data/nuc6up/
    Untracked:  data/other_qtls/
    Untracked:  data/pQTL_otherphen/
    Untracked:  data/peakPerRefSeqGene/
    Untracked:  data/perm_QTL/
    Untracked:  data/perm_QTL_opp/
    Untracked:  data/perm_QTL_trans/
    Untracked:  data/perm_QTL_trans_filt/
    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:  data/threePrimeSeqMetaData.csv
    Untracked:  output/picard/
    Untracked:  output/plots/
    Untracked:  output/qual.fig2.pdf
Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/CompareLianoglouData.Rmd
    Modified:   analysis/apaQTLoverlapGWAS.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/coloc_apaQTLs_protQTLs.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/diff_iso_pipeline.Rmd
    Modified:   analysis/explainpQTLs.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   code/Snakefile
In this analysis I am gonig to explore the ways to handle mispriming in the 3’ seq data. Some people call this internal priming. This is when the polyDt primer attached to an RNA molecule that has a long stretch of A’s rather than to the tail. You can identify when this is happening because polyA tails are not in the genome but mispriming As are. In my data I need to look for Ts upstream of the read. This is because our reads are on the opposite strand.
Sheppard et al. cited 2 other papers, Beaudoing et al 2000 and Tian et al 2005. Thet excluded reads with 6 consequitive upstream As or those with 7 in a 10nt window. They did this at the read level.
I started thinking about this in https://brimittleman.github.io/threeprimeseq/filter_As.html. I did not have it mapped out correctly because I was looking for A’s on one strand and T’s on the other.
I will assess the problem then will create a blacklist to get rid of the reads. I should do this in the snakefile before we create BW for the peak calling.
I can start by updating 6up_bed.sh. To make a new script that grabs the upstream 10 bases. I will look for7 of 10 T’s in this region. I am going to do this in python because it is more straight forward to read then an awk script. I can also wrap it easier this way. I can also account for negative values and values larger than the chromosome this way.
Upstream10Bases.py
#python  
def main(Fin, Fout):
  outBed=open(Fout, "w")
  chrom_lengths=open("/project2/gilad/briana/genome_anotation_data/chrom_lengths2.sort.bed","r")
  #make a dictionary with chrom lengths
  length_dic={}
  for i in chrom_lengths:
    chrom, start, end = i.split()
    length_dic[str(chrom)]=int(end)  
#write file 
  for ln in open(Fin):
    chrom, start, end, name, score, strand = ln.split()
    chrom=str(chrom)
    if strand=="+":
      start_new=int(start)-10
      if start_new <= 1:
        start_new = 0 
      end_new= int(start)
      if end_new == 0:
        end_new=1
      outBed.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_new, end_new, name, score, strand))
    if strand == "-":
      start_new=int(end)
      end_new=int(end) + 10
      if end_new >= length_dic[chrom]:
        end_new = length_dic[chrom]
        start_new=end_new-1
      outBed.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_new, end_new, name, score, strand))
  outBed.close()  
if __name__ == "__main__":
    import sys
    inFile = sys.argv[1]
    fileNoPath=inFile.split("/")[-1]
    fileshort=fileNoPath[:-4]
    outFile="/project2/gilad/briana/threeprimeseq/data/bed_10up/" + fileshort + "10up.bed"
    main(inFile, outFile)
I can wrap this for all of the files.
wrap_Upstream10Bases.sh
#!/bin/bash
#SBATCH --job-name=w_Upstream10Bases
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=w_Upstream10Bases.out
#SBATCH --error=w_Upstream10Bases.err
#SBATCH --partition=broadwl
#SBATCH --mem=8G
#SBATCH --mail-type=END
module load Anaconda3  
source activate three-prime-env
for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_sort/*-combined-sort.bed); do
            python  Upstream10Bases.py  $i 
        done
I need to sort the files:
Next step is running the nuc function to get the sequences of the positions I just put in the bed files.
bedtools nuc
-fi (fasta file) /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa
-bed results from 10up stream
-s strand specific
-seq print exracted sequence
output
Nuc10BasesUp.sh
#!/bin/bash
#SBATCH --job-name=Nuc10BasesUp
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=Nuc10BasesUp.out
#SBATCH --error=Nuc10BasesUp.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END
for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_10up/*);do
   describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-combined-sort10up.bed$//")
   bedtools nuc -s -seq -fi /project2/gilad/briana/genome_anotation_data/genome/Homo_sapiens.GRCh37.75.dna_sm.all.fa -bed $i > /project2/gilad/briana/threeprimeseq/data/nuc_10up/TenBaseUP.${describer}.txt   
donesort_10upbedFile.sh
#!/bin/bash
#SBATCH --job-name=sort_10upbedFile
#SBATCH --account=pi-yangili1
#SBATCH --time=8:00:00
#SBATCH --output=sort_10upbedFile.out
#SBATCH --error=sort_10upbedFile.err
#SBATCH --partition=broadwl
#SBATCH --mem=8G
#SBATCH --mail-type=END
for i in $( ls /project2/gilad/briana/threeprimeseq/data/bed_10up/*);do
  describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/-combined-sort10up.bed$//")
  sort -k 1,1 -k2,2n $i > /project2/gilad/briana/threeprimeseq/data/bed_10up_sort/YL-SP-${describer}-combined-sort10up.sort.bed
  donesessionInfo()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_1.1.1   Rcpp_0.12.19      digest_0.6.17    
 [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_2.7.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20       
This reproducible R Markdown analysis was created with workflowr 1.1.1