Last updated: 2019-03-01

Checks: 6 0

Knit directory: threeprimeseq/analysis/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: f812272

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use 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:    data/perm_QTL_trans_noMP_5percov/
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  KalistoAbundance18486.txt
    Untracked:  analysis/4suDataIGV.Rmd
    Untracked:  analysis/DirectionapaQTL.Rmd
    Untracked:  analysis/EvaleQTLs.Rmd
    Untracked:  analysis/YL_QTL_test.Rmd
    Untracked:  analysis/groSeqAnalysis.Rmd
    Untracked:  analysis/ncbiRefSeq_sm.sort.mRNA.bed
    Untracked:  analysis/snake.config.notes.Rmd
    Untracked:  analysis/verifyBAM.Rmd
    Untracked:  analysis/verifybam_dubs.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/AllPeak_counts/
    Untracked:  data/ApaQTLs/
    Untracked:  data/ApaQTLs_otherPhen/
    Untracked:  data/ChromHmmOverlap/
    Untracked:  data/DistTXN2Peak_genelocAnno/
    Untracked:  data/GM12878.chromHMM.bed
    Untracked:  data/GM12878.chromHMM.txt
    Untracked:  data/LianoglouLCL/
    Untracked:  data/LocusZoom/
    Untracked:  data/LocusZoom_Unexp/
    Untracked:  data/LocusZoom_proc/
    Untracked:  data/MatchedSnps/
    Untracked:  data/NuclearApaQTLs.txt
    Untracked:  data/PeakCounts/
    Untracked:  data/PeakCounts_noMP_5perc/
    Untracked:  data/PeakCounts_noMP_genelocanno/
    Untracked:  data/PeakUsage/
    Untracked:  data/PeakUsage_noMP/
    Untracked:  data/PeakUsage_noMP_GeneLocAnno/
    Untracked:  data/PeaksUsed/
    Untracked:  data/PeaksUsed_noMP_5percCov/
    Untracked:  data/QTL_overlap/
    Untracked:  data/RNAkalisto/
    Untracked:  data/RefSeq_annotations/
    Untracked:  data/Replicates_usage/
    Untracked:  data/TotalApaQTLs.txt
    Untracked:  data/Totalpeaks_filtered_clean.bed
    Untracked:  data/UnderstandPeaksQC/
    Untracked:  data/WASP_STAT/
    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/apaExamp_proc/
    Untracked:  data/apaQTL_examp_noMP/
    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_GeneLocAnno/
    Untracked:  data/diff_iso_proc/
    Untracked:  data/diff_iso_trans/
    Untracked:  data/eQTLs_Lietal/
    Untracked:  data/ensemble_to_genename.txt
    Untracked:  data/example_gene_peakQuant/
    Untracked:  data/explainProtVar/
    Untracked:  data/filtPeakOppstrand_cov_noMP_GeneLocAnno_5perc/
    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/molPheno_noMP/
    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/nuc_10up/
    Untracked:  data/other_qtls/
    Untracked:  data/pQTL_otherphen/
    Untracked:  data/pacbio_cov/
    Untracked:  data/peakPerRefSeqGene/
    Untracked:  data/peaks4DT/
    Untracked:  data/perm_QTL/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov/
    Untracked:  data/perm_QTL_GeneLocAnno_noMP_5percov_3UTR/
    Untracked:  data/perm_QTL_diffWindow/
    Untracked:  data/perm_QTL_opp/
    Untracked:  data/perm_QTL_trans/
    Untracked:  data/perm_QTL_trans_filt/
    Untracked:  data/protAndAPAAndExplmRes.Rda
    Untracked:  data/protAndAPAlmRes.Rda
    Untracked:  data/protAndExpressionlmRes.Rda
    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:  data/threePrimeSeqMetaData55Ind.txt
    Untracked:  data/threePrimeSeqMetaData55Ind.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup.xlsx
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt
    Untracked:  data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.xlsx
    Untracked:  output/LZ/
    Untracked:  output/deeptools_plots/
    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/NewPeakPostMP.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/fixBWChromNames.Rmd
    Modified:   analysis/flash2mash.Rmd
    Modified:   analysis/mispriming_approach.Rmd
    Modified:   analysis/overlapMolQTL.Rmd
    Modified:   analysis/overlapMolQTL.opposite.Rmd
    Modified:   analysis/overlap_qtls.Rmd
    Modified:   analysis/peakOverlap_oppstrand.Rmd
    Modified:   analysis/peakQCPPlots.Rmd
    Modified:   analysis/pheno.leaf.comb.Rmd
    Modified:   analysis/pipeline_55Ind.Rmd
    Modified:   analysis/swarmPlots_QTLs.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   analysis/test.smash.Rmd
    Modified:   analysis/understandPeaks.Rmd
    Modified:   analysis/unexplainedeQTL_analysis.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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File Version Author Date Message
Rmd f812272 Briana Mittleman 2019-03-01 add erna and rep element analysis

Repetitive elements:

Could this be do to repetitive elements

Process: /project2/gilad/briana/genome_anotation_data/RepeatMask.dms

I just need to cut the chr to make the chroms the same as mine

sed 's/^chr//'  /project2/gilad/briana/genome_anotation_data/RepeatMask.dms   > /project2/gilad/briana/genome_anotation_data/RepeatMask.bed

BothFracDTPlotRepeats_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPlotRepeats_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=BothFracDTPlotRepeats_noMPFilt.out
#SBATCH --error=BothFracDTPlotRepeats_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/genome_anotation_data/RepeatMask.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_Repetitive_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_Repetitive_Nompfilt.gz --refPointLabel "Repetitive Regions" --plotTitle "Combined Reads at Repetitive Regions" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_Repetitive_Nompfilt.png

eRNA

download LCL eRNAs from phantom

http://enhancer.binf.ku.dk/presets/

process this file

/project2/gilad/briana/genome_anotation_data/0000945_lymphocyte_of_B_lineage_differentially_expressed_enhancers.bed

interactively in python

inFile=open("/project2/gilad/briana/genome_anotation_data/CL:0000945_lymphocyte_of_B_lineage_differentially_expressed_enhancers.bed", "r")
outBed=open("/project2/gilad/briana/genome_anotation_data/LCLenhancerRNA.bed","w")

for ln in inFile:
    chrom=ln.split()[0]
    chromnoch=chrom[3:]
    start=int(ln.split()[1])
    end=int(ln.split()[2])  
    outBed.write("%s\t%d\t%d\n"%(chromnoch, start,end))
outBed.close()

Look at this in total and nuclear three prime seq BW

BothFracDTPloteRNA_noMPFilt.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPloteRNA_noMPFilt
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=BothFracDTPloteRNA_noMPFilt.out
#SBATCH --error=BothFracDTPloteRNA_noMPFilt.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix reference-point -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/genome_anotation_data/LCLenhancerRNA.bed -b 1000 -a 1000  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_eRNA_Nompfilt.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_eRNA_Nompfilt.gz --refPointLabel "eRNA Regions" --plotTitle "Combined Reads at eRNA" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_eRNA_Nompfilt.png

Do this as region rather than reference point
BothFracDTPloteRNA_noMPFilt_region.sh

#!/bin/bash

#SBATCH --job-name=BothFracDTPloteRNA_noMPFilt_region
#SBATCH --account=pi-yangili1
#SBATCH --time=36:00:00
#SBATCH --output=BothFracDTPloteRNA_noMPFilt_region.out
#SBATCH --error=BothFracDTPloteRNA_noMPFilt_region.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


computeMatrix scale-regions -S /project2/gilad/briana/threeprimeseq/data/mergedBW/Total_MergedBamCoverage.bw /project2/gilad/briana/threeprimeseq/data/mergedBW/Nuclear_MergedBamCoverage.bw -R /project2/gilad/briana/genome_anotation_data/LCLenhancerRNA.bed -b 500 -a 500  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_eRNA_Nompfilt_regions.gz

plotHeatmap --sortRegions descend -m /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_eRNA_Nompfilt_regions.gz --refPointLabel "eRNA Regions" --plotTitle "Combined Reads at eRNA" --heatmapHeight 7 --colorMap YlGnBu  -out /project2/gilad/briana/threeprimeseq/data/LianoglouDeepTools/BothFrac_eRNA_Nompfilt_regions.png

overlap potential drivers of extra peaks

Does not look like there are a strong driver. I will see if any of these overlap with our peaks.I will need to look at the opposite strand overlap or use the fixed strand peaks. I will ask how many of these eRNAs or rep elements overlap a peak.

I want to run the overlap in all of the peaks as well as those that have been filtered 5%

/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed

/project2/gilad/briana/threeprimeseq/data/peaks4DT/Peaks_5percCov_fixedStrand.bed

fix strand for nonfiltered:

fixStrand4DTplots_allpeaks.py

peaksIn="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed"
PeakOut="/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_allPeaks_fixedStrand.bed"


def fix_strand(Fin,Fout):
    fout=open(Fout,"w")
    for ln in open(Fin, "r"):
        chrom, start, end, name, score, strand, score2, pos = ln.split()
        if strand=="+":
            nameF="peak" + name + ":" + pos
            fout.write("%s\t%s\t%s\t%s\t%s\t-\n"%(chrom,start,end,nameF,score))
        else:
            nameF="peak" + name + ":" + pos
            fout.write("%s\t%s\t%s\t%s\t%s\t+\n"%(chrom,start,end,nameF,score))
    fout.close()
    
    
fix_strand(peaksIn, PeakOut)

ernas: /project2/gilad/briana/genome_anotation_data/LCLenhancerRNA.bed repetitive elements: /project2/gilad/briana/genome_anotation_data/RepeatMask.bed

make a python script with pybedtools that will take any bed file and overlap it

overlapWFilteredPeaks.py

def main(infile, outfile):
    peak_file=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_5percCov_fixedStrand.bed","r")
    peak=pybedtools.BedTool(peak_file)
    elementFile=open(infile, "r")
    for i,ln in enumerate(elementFile):
       if i == 0:
           if len(ln.split()) > 3:
               strand= "yes"
           else:
               strand= "no"
       else:
          break
    print(strand)
    elements=pybedtools.BedTool(elementFile)
    if strand== "yes": 
        elemOverpeak=elements.intersect(peak, wa=True,wb=True, s=True)
    else:
        elemOverpeak=elements.intersect(peak, wa=True,wb=True)
    elemOverpeak.saveas(outfile)

if __name__ == "__main__":
    import sys
    import pybedtools
    infile=sys.argv[1]
    outfile=sys.argv[2]
    main(infile, outfile)

run:

python overlapWFilteredPeaks.py /project2/gilad/briana/genome_anotation_data/LCLenhancerRNA.bed  /project2/gilad/briana/threeprimeseq/data/FeatureoverlapPeaks/FilteredPeak_overeRNA.txt  

python overlapWFilteredPeaks.py /project2/gilad/briana/genome_anotation_data/RepeatMask.bed /project2/gilad/briana/threeprimeseq/data/FeatureoverlapPeaks/FilteredPeak_overRepElements.txt

overlapWAllPeaks.py

def main(infile, outfile):
    peak_file=open("/project2/gilad/briana/threeprimeseq/data/peaks4DT/APAPeaks_allPeaks_fixedStrand.bed","r")
    peak=pybedtools.BedTool(peak_file)
    elementFile=open(infile, "r")
    for i,ln in enumerate(elementFile):
       if i == 0:
           if len(ln.split()) > 3:
               strand= "yes"
           else:
               strand= "no"
       else:
          break
    print(strand)
    elements=pybedtools.BedTool(elementFile)
    if strand== "yes": 
        elemOverpeak=elements.intersect(peak, wa=True,wb=True, s=True)
    else:
        elemOverpeak=elements.intersect(peak, wa=True,wb=True)
    elemOverpeak.saveas(outfile)

if __name__ == "__main__":
    import sys
    import pybedtools
    infile=sys.argv[1]
    outfile=sys.argv[2]
    main(infile, outfile)

run:

python overlapWAllPeaks.py /project2/gilad/briana/genome_anotation_data/LCLenhancerRNA.bed  /project2/gilad/briana/threeprimeseq/data/FeatureoverlapPeaks/AllPeak_overeRNA.txt  

python overlapWAllPeaks.py /project2/gilad/briana/genome_anotation_data/RepeatMask.bed /project2/gilad/briana/threeprimeseq/data/FeatureoverlapPeaks/AllPeak_overRepElements.txt

How long are each of these

Full eRNA file: 1167 All peak eRNA: 128 Filt peak eRNA: 14

Full rep file: 5298130 All peak rep:52965 Filt peak rep: 9542



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_0.12.19    digest_0.6.17   rprojroot_1.3-2
 [5] backports_1.1.2 git2r_0.24.0    magrittr_1.5    evaluate_0.13  
 [9] stringi_1.2.4   fs_1.2.6        whisker_0.3-2   rmarkdown_1.11 
[13] tools_3.5.1     stringr_1.4.0   glue_1.3.0      yaml_2.2.0     
[17] compiler_3.5.1  htmltools_0.3.6 knitr_1.20