Last updated: 2019-02-16

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Knit directory: threeprimeseq/analysis/

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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/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:   code/Snakefile

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File Version Author Date Message
Rmd b3d5773 Briana Mittleman 2019-02-16 add n sig genes
html ab3722b Briana Mittleman 2019-02-15 Build site.
Rmd a38fd8c Briana Mittleman 2019-02-15 add QTL analysis
html 4f17cca Briana Mittleman 2019-02-15 Build site.
Rmd 606e562 Briana Mittleman 2019-02-15 repub
html 03c4f95 Briana Mittleman 2019-02-14 Build site.
Rmd 41a2537 Briana Mittleman 2019-02-14 add map stat plots
html b8cfd6f Briana Mittleman 2019-02-07 Build site.
Rmd 3fea644 Briana Mittleman 2019-02-07 add accountmapbias 
library(tidyverse)
── Attaching packages ──────────────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──
✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
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✔ readr   1.1.1     ✔ forcats 0.3.0
Warning: package 'stringr' was built under R version 3.5.2
── Conflicts ─────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()
library(cowplot)

Attaching package: 'cowplot'
The following object is masked from 'package:ggplot2':

    ggsave

We are worried there amy be false positives in the QTL analysis if the QTL is in the read and the snp leads to a mapping bias for the data. I can account for this using WASP.

I have an example script from Yang:

/project2/yangili1/yangili/TCGA_pipe/script_process.sh

STAR2.6  --genomeDir /project2/yangili1/RNAseq_pipeline/index/GRCh37/STAR_hg19 --readFilesIn $inFile\_1.fastq $inFile\_2.fastq --outSAMstrandField intronMotif --outFileNamePrefix $outFile. --outSAMtype BAM Unsorted --varVCFfile $vcfFile --waspOutputMode SAMtag --outSAMattributes vA vG

First I need to find my star indexed genome:

*/project2/gilad/briana/genome_anotation_data/star_genome

Next I need my VCF file:

  • /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf.gz

runStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=runStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=runStarwWASP.out
#SBATCH --error=runStarwWASP.err
#SBATCH --partition=bigmem2
#SBATCH --mem=100G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


in=$1 
out=$2
STAR --runThreadN 4 --genomeDir /project2/gilad/briana/genome_anotation_data/star_genome --readFilesIn $1  --outSAMstrandField intronMotif --outFileNamePrefix /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP/$2.combined.STARwWASP.bam --outSAMtype BAM Unsorted --varVCFfile /project2/gilad/briana/YRI_geno_hg19/allChrom.dose.filt.vcf   --waspOutputMode SAMtag --outSAMattributes vA vG

test_runStartwWASP.sh

#!/bin/bash


#SBATCH --job-name=test_runStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=test_runStarwWASP.out
#SBATCH --error=test_runStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  


i=/project2/gilad/briana/threeprimeseq/data/fastq/YL-SP-19239-T-combined.fastq  
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/combined.fastq//")
sbatch runStarwWASP.sh $i $describer

Wraper:
wrap_runStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=wrap_runStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_runStarwWASP.out
#SBATCH --error=wrap_runStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env  

for i in $(ls /project2/gilad/briana/threeprimeseq/data/fastq/*);do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/combined.fastq//")
sbatch runStarwWASP.sh $i $describer
done

Quota reached at 19193N for jobs- create a wrap2
wrap_runStarwWASP2.sh

#!/bin/bash


#SBATCH --job-name=wrap_runStarwWASP2
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_runStarwWASP2.out
#SBATCH --error=wrap_runStarwWASP2.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 


for i in $(ls /project2/gilad/briana/threeprimeseq/data/fastq/YL-SP-192*); do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/combined.fastq//")
sbatch runStarwWASP.sh $i $describer
done

Sort and index these files.

SortIndexStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=SortIndexStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=SortIndexStarwWASP.out
#SBATCH --error=SortIndexStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

describer=$1

samtools sort /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP/${describer}combined.STARwWASP.bamAligned.out.bam > /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/${describer}combined.STARwWASP.bamAligned.sort.bam
samtools index /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/${describer}combined.STARwWASP.bamAligned.sort.bam

wrap_SortIndexStarwWASP.sh

#!/bin/bash


#SBATCH --job-name=wrap_SortIndexStarwWASP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_SortIndexStarwWASP.out
#SBATCH --error=wrap_SortIndexStarwWASP.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 



for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP/*STARwWASP.bamAligned.out.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP\///' | sed -e "s/combined.STARwWASP.bamAligned.out.bam//")
sbatch SortIndexStarwWASP.sh $describer
done

Now I want to filter out reads with mapping problems at place we see a variant. I want to keep reads with the vW:i:1 tag. ( I will resort and index these files after this step)

I can use pysam to do this. Then I can move the final sorted duplicate files.

filterBamBasedonWasp.py

def main(Bamin, out):
    okRead={}
    #pysam to read in bam allignments
    bamfile = pysam.AlignmentFile(Bamin, "rb")
    finalBam =  pysam.AlignmentFile(out, "wb", template=bamfile)
    n=0
    k=0
    #read name is the first col in each bam file
    for read in bamfile.fetch():
        #last piece is always the right piece  
        #vw=read.split(\t)[-1]
        if read.has_tag('vW'):
            x= read.get_tag('vW')
            print(x)
            if x == 1:
                k+=1
                finalBam.write(read)
            else:
                n+=1
                continue
        else:
          finalBam.write(read)
  
    print("with wv" + n)
    print("pass filter" + k)
    bamfile.close()
    finalBam.close()
    
    
    
if __name__ == "__main__":
    import sys, pysam
    describer = sys.argv[1]
    inBam="/project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/" + describer + "combined.STARwWASP.bamAligned.sort.bam"
    outBam="/project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered/" + describer + "combined.STARwWASP.bamAligned.filtered.out.bam"
    main(inBam, outBam)

Run this on all individuals:

run_filterBamBasedonWasp.sh

#!/bin/bash


#SBATCH --job-name=run_filterBamBasedonWasp
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_filterBamBasedonWasp.out
#SBATCH --error=run_filterBamBasedonWasp.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_sort\///' | sed -e "s/combined.STARwWASP.bamAligned.sort.bam//")
python filterBamBasedonWasp.py $describer
done

Sort and index these:

SortIndexStarwWASP_filtered.sh

#!/bin/bash


#SBATCH --job-name=SortIndexStarwWASP_filtered
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=SortIndexStarwWASP_filtered.out
#SBATCH --error=SortIndexStarwWASP_filtered.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

describer=$1

samtools sort /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered/${describer}combined.STARwWASP.bamAligned.filtered.out.bam > /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/${describer}combined.STARwWASP.bamAligned.filtered.sort.bam
samtools index /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/${describer}combined.STARwWASP.bamAligned.filtered.sort.bam

wrap_SortIndexStarwWASP_filtered.sh

#!/bin/bash


#SBATCH --job-name=wrap_SortIndexStarwWASP_filtered
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=wrap_SortIndexStarwWASP_filtered.out
#SBATCH --error=wrap_SortIndexStarwWASP_filtered.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 



for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered/*STARwWASP.bamAligned.filtered.out.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_filtered\///' | sed -e "s/combined.STARwWASP.bamAligned.filtered.out.bam//")
sbatch SortIndexStarwWASP_filtered.sh $describer
done

Now I need to make these into a bed format. I also will move the old files and but these in the sort/ bed/ dirs. This way I can use the same pipeline from the Pipeline for 55 indivduals analysis.

At this point I will move the old bam and bed files to different directories

  • /project2/gilad/briana/threeprimeseq/data/sort_oldmapp/

  • /project2/gilad/briana/threeprimeseq/data/bed_sort_oldMap

  • /project2/gilad/briana/threeprimeseq/data/bed_oldMap

Run bam to bed:

bam2BedandSort.waspmap.sh

#!/bin/bash


#SBATCH --job-name=bam2BedandSort.waspmap
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=bam2BedandSort.waspmap.out
#SBATCH --error=bam2BedandSort.waspmap.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 



for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_filtered_sort\///' | sed -e "s/.STARwWASP.bamAligned.filtered.sort.bam//")
bedtools bamtobed -i $i > /project2/gilad/briana/threeprimeseq/data/bed/YL-SP-$describer.combined.bed
sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/bed/YL-SP-$describer.combined.bed > /project2/gilad/briana/threeprimeseq/data/bed_sort/YL-SP-$describer.combined.sort.bed
done

Move duplicate files and rename:

problem: these are called combined.combined (fix this)


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//")
mv $i /project2/gilad/briana/threeprimeseq/data/bed_10up/YL-SP-$describer-sort10up.bed
done

Also move the bam files to the sort dir from /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/

for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_filtered_sort/*.bam)
do 
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_filtered_sort\///' | sed -e "s/.STARwWASP.bamAligned.filtered.sort.bam//")
mv $i /project2/gilad/briana/threeprimeseq/data/sort/YL-SP-$describer-sort.bam
done

Index all of these files:

reIndex.sh

#!/bin/bash
#SBATCH --job-name=reIndex
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=reIndex.out
#SBATCH --error=reIndex.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

for i in $(ls /project2/gilad/briana/threeprimeseq/data/sort/)
samtools index /project2/gilad/briana/threeprimeseq/data/sort/$i 
done

  • Get 10 basepairs upstream: wrap_Upstream10Bases.sh

  • Find sequence for these regions: Nuc10BasesUp.sh

Fixed names (ok now)

for i in $(ls /project2/gilad/briana/threeprimeseq/data/bed_sort/)
do
describer=$(echo ${i} | sed -e 's/.*YL-SP-//' | sed -e "s/.combined.sort.bed//")  
cp $i  /project2/gilad/briana/threeprimeseq/data/bed_sort/YL-SP-$describer-sort.bed
done
  • find which are bad run_filterMissprimingInNuc10.sh
  • filter bed file run_filterSortBedbyCleanedBed.sh
  • sort clean bed file sort_filterSortBedbyCleanedBed.sh

  • filter bam files wrap_filterBamforMP.pysam2.sh

  • sort and index clean bam SortIndexBam_noMP.sh
  • merge clean bam files mergeBamFiles_noMP.sh and mergeBamFiles_byfrac_noMP.sh

  • sort and index merged SortIndexMergedBam_noMP.sh and SortIndex_mergeBamFiles_byfrac_noMP.sh

  • create BW mergedBam2Bedgraph.sh
  • make it a coverage file run_bgtocov_noMP.sh
  • call peaks run_callPeaksYL_noMP.sh
  • filter peaks
    • cat /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/*.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP/APApeaks_merged_allchrom_noMP.bed
    • make SAF file bed2saf_noMP.py
    • run feature counts peak_fc_noMP.sh
    • filter peaks run_filter_peaks_noMP.sh

  • name peaks

170824 = wc -l /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.bed



seq 1 170824 > peak.num.txt

sort -k1,1 -k2,2n Filtered_APApeaks_merged_allchrom_noMP.bed > Filtered_APApeaks_merged_allchrom_noMP.sort.bed

paste /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.bed peak.num.txt | column -s $'\t' -t > temp
awk '{print $1 "\t" $2 "\t" $3 "\t" $7  "\t"  $4 "\t"  $5 "\t" $6}' temp >   /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.bed

#cut the chr  

sed 's/^chr//' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed

  • Gene assignments mapnoMPPeaks2GenomeLoc.sh

  • make SAF processGenLocPeakAnno2SAF.py
  • feature counts GeneLocAnno_fc_TN_noMP.sh
  • fix header fix_head_fc_geneLoc_tot_noMP.py
  • fix header fix_head_fc_geneLoc_nuc_noMP.py
  • create_fileid_geneLocAnno_total.py (remove top line)
  • create_fileid_geneLocAnno_nuclear.py (remove top line)
  • make phenotype run_makePhen_sep_GeneLocAnno_noMP.sh
  • pheno2CountOnly_genelocAnno.R
  • counts to numeric convertCount2Numeric_noMP_GeneLocAnno.py
  • run_filter_5percUsagePeaks.sh

  • filterPheno_bothFraction_GeneLocAnno_5perc.py

In /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/

#zip file 
gzip filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc
gzip filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc


module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz

python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz




#source activate three-prime-env
 sh filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz_prepare.sh
sh filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz_prepare.sh


#keep only 2 PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs

  • makeSampleList_newGeneAnno.py

  • APAqtl_nominal_GeneLocAnno_noMP_5percUsage.sh

  • APAqtl_perm_GeneLocAnno_noMP_5percUsage.sh

  • run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs.sh

totQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)

Sig_TotQTLs= totQTLs %>% filter(-log10(bh)>=1)
nrow(Sig_TotQTLs)
[1] 291

How many genes are tested:

totQTLs %>% separate(pid, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_") %>% group_by(gene) %>% select(gene) %>% tally() %>% nrow()
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [886,
887, 888].
[1] 11183
sigQTLTGenes=Sig_TotQTLs %>%  separate(pid, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_") %>%  group_by(gene) %>% tally() %>% nrow()
sigQTLTGenes
[1] 235
nucQTLs=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)

Sig_NucQTLs= nucQTLs %>% filter(-log10(bh)>=1)
nrow(Sig_NucQTLs)
[1] 615

How many genes:

nucQTLs %>% separate(pid, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_") %>% group_by(gene) %>% select(gene) %>% tally() %>% nrow()
Warning: Expected 3 pieces. Additional pieces discarded in 3 rows [1056,
1057, 1058].
[1] 11499
sigQTLNGenes=Sig_NucQTLs %>%  separate(pid, into=c("chr", "start", "end", "id"), sep=":") %>% separate(id, into=c("gene", "strand", "peak"), sep="_") %>%  group_by(gene) %>% tally() %>% nrow()
sigQTLNGenes
[1] 496

Write QTLs to get locations:

totQTLs_sig=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)

write.table(totQTLs_sig,"../data/ApaQTLs/TotalapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)

nucQTLs_sig=read.table("../data/perm_QTL_GeneLocAnno_noMP_5percov/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", stringsAsFactors = F, header=T)%>% filter(-log10(bh)>=1)

write.table(nucQTLs_sig,"../data/ApaQTLs/NuclearapaQTLs.GeneLocAnno.noMP.5perc.10FDR.txt", row.names = F, col.names = F, quote = F)

getDistPeakEnd2QTL.py

TotDist=read.table("../data/ApaQTLs/Distance2EndPeak.Total.apaQTLs.txt", header=T) %>% mutate(Fraction="Total") %>% select(Fraction, Distance)
NucDist=read.table("../data/ApaQTLs/Distance2EndPeak.Nuclear.apaQTLs.txt", header=T)%>% mutate(Fraction="Nuclear") %>% select(Fraction, Distance)

BothDist=data.frame(rbind(TotDist, NucDist))

ggplot(BothDist, aes(x=Distance, by=Fraction, fill=Fraction))+geom_histogram(bins=70, alpha=.5) + scale_fill_manual(values=c("deepskyblue3","darkviolet")) + labs(title="Distance From apaQTL to End of Peak" )

Version Author Date
ab3722b Briana Mittleman 2019-02-15

Where are the QTLs:

QTLfile2Bed.py “Total” QTLfile2Bed.py “Nuclear”

I will need to sort the output

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Total.apaQTLs.sort.bed

sort -k1,1 -k2,2n /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.bed > /project2/gilad/briana/threeprimeseq/data/ApaQTLs/Nuclear.apaQTLs.sort.bed

mapQTLs2GenomeLoc.sh

QC reruns:

  • processGenLocPeakAnno2SAF_withAnno.py
  • filternamePeaks5percCov_GeneLocAnno_withAnno.py
peakswAnno=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov_withAnno.SAF", header=T) %>% separate(GeneID, into=c("Peak", "chrom", "start", "end", "strand", "gene", "loc"),sep=":") %>% select(Peak, loc) %>% group_by(loc) %>% summarise(Num=n())
locationOfPeaks=ggplot(peakswAnno, aes(x=loc, y=Num)) + geom_bar(stat="identity", fill="blue") + labs(x="Gene Location", y="Number of Peaks", title="Location distribution for all PAS with 5% Usage")
locationOfPeaks

Version Author Date
ab3722b Briana Mittleman 2019-02-15
03c4f95 Briana Mittleman 2019-02-14 
ggsave(locationOfPeaks, file="../output/plots/PeakLocationByAnnotation.png")
Saving 7 x 5 in image
  • GetDistTXNend2Peak.py

convert /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF to bed file

peaksGeneLocAnno_5percSAF2Bed.py

distTXN2Peak=read.table("../data/DistTXN2Peak_genelocAnno/distPeak2EndTXN.txt", col.names = c("Peak", "name2", "Distance", "Gene_Strand"),stringsAsFactors = F)
txnanno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", header=T,stringsAsFactors = F) %>% mutate(length=abs(txEnd-txStart)) %>% semi_join(distTXN2Peak, by="name2")
distTXN2Peak =distTXN2Peak %>% mutate(AbsDist=abs(Distance))
mean(txnanno$length)
[1] 60808.79
distTXN2PeakPlot=ggplot(distTXN2Peak, aes(x=AbsDist + 1)) + geom_density() + scale_x_log10() + labs(x="Absolute Distance between end of Transcription and center of Peak", title="Distribution of transcription to peak absolute distance") +  geom_vline(xintercept=mean(txnanno$length), col="red") + annotate("text", x=1000000, y=.4, label="Average transcript length \n for genes in peaks", col='red')

distTXN2PeakPlot

Version Author Date
ab3722b Briana Mittleman 2019-02-15 
ggsave(distTXN2PeakPlot, file="../output/plots/DistanceBetweenPeakandTES.png")
Saving 7 x 5 in image

Look at number of reads lost due to WASP filter

getWASPfiltStats.py

def main(Bamin,out,desc):
    okRead={}
    #pysam to read in bam allignments
    outF=open(out, "w")
    bamfile = pysam.AlignmentFile(Bamin, "rb")
    n=0
    k=0
    #read name is the first col in each bam file
    for read in bamfile.fetch():
        #last piece is always the right piece  
        #vw=read.split(\t)[-1]
        if read.has_tag('vW'):
            x= read.get_tag('vW')
            #print(x)
            if x == 1:
                k+=1
                #finalBam.write(read)
            else:
                n+=1
                continue
        else:
          continue
          #finalBam.write(read)
    outF.write("%s\t%d\n"%(desc, n))
    bamfile.close()
    outF.clos()
    
    
if __name__ == "__main__":
    import sys, pysam
    describer = sys.argv[1]
    describer2=describer[:-1]
    inBam="/project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/" + describer + "combined.STARwWASP.bamAligned.sort.bam"
    outFile="/project2/gilad/briana/threeprimeseq/data/WASP_filt_stat/WASPFilt" + describer2 + ".txt"
    main(inBam,outFile, describer2)

run_getWASPfiltStats.sh

#!/bin/bash

#SBATCH --job-name=run_getWASPfiltStats
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_getWASPfiltStats.out
#SBATCH --error=run_getWASPfiltStats.err
#SBATCH --partition=broadwl
#SBATCH --mem=36G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

for i in $(ls /project2/gilad/briana/threeprimeseq/data/STAR_bam_WASP_sort/*.bam)
do
describer=$(echo ${i} | sed -e 's/.*STAR_bam_WASP_sort\///' | sed -e "s/combined.STARwWASP.bamAligned.sort.bam//")
python getWASPfiltStats.py $describer
done

Cat all of the files together and move the duplicates to replicate folder

waspStat=read.table("../data/WASP_STAT/WASP_Filt_AllLineStats.txt",stringsAsFactors = F, col.names = c("Sample", "FilteredReads")) %>% separate(Sample, into=c("Line", "Fraction"), sep="-")

Plot

ggplot(waspStat, aes(x=Line, fill=Fraction, y=FilteredReads, by=Fraction)) + geom_bar(stat="identity", position="dodge")

Version Author Date
ab3722b Briana Mittleman 2019-02-15
03c4f95 Briana Mittleman 2019-02-14

make boxplot

ggplot(waspStat, aes(x=Fraction, y=log10(FilteredReads), fill=Fraction)) + geom_boxplot()

Version Author Date
ab3722b Briana Mittleman 2019-02-15
03c4f95 Briana Mittleman 2019-02-14

Plto barplots by fractions with error bar

waspStat_sem= waspStat %>% group_by(Fraction) %>% summarise(mean=mean(FilteredReads), sd=sd(FilteredReads))

ggplot(waspStat_sem, aes(x=Fraction, y=mean, fill=Fraction)) + geom_bar(stat='identity')  + geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2,) + labs(title="Reads filtered out due to WASP filter", y='Reads') +scale_fill_manual(values=c("deepskyblue3","darkviolet"))

Version Author Date
ab3722b Briana Mittleman 2019-02-15
03c4f95 Briana Mittleman 2019-02-14

Map stat plots:

mapStats_wasp=read.table("../data/threePrimeSeqMetaData55Ind_noDup_WASPMAP.txt", stringsAsFactors = F, header = T)

Plot mappeded reads no MP by fractions:

mapStats_wasp_noMP=mapStats_wasp %>% group_by(fraction) %>% summarise(mean=mean(Mapped_noMP), sd=sd(Mapped_noMP))
mapreads_plot=ggplot(mapStats_wasp_noMP, aes(x=fraction, y=mean, fill=fraction)) + geom_bar(stat='identity')+  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2)+ scale_fill_manual(values=c("deepskyblue3","darkviolet")) +  labs(title="Number of reads\n mapping and passing missprime filter", y="Number of sequence reads")
mapStats_wasp_propnoMP=mapStats_wasp %>% group_by(fraction) %>% summarise(mean=mean(prop_MappedwithoutMP), sd=sd(prop_MappedwithoutMP))
propmap_plot=ggplot(mapStats_wasp_propnoMP, aes(x=fraction, y=mean, fill=fraction)) + geom_bar(stat='identity')+  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2)+ scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Proportion of reads\n mapping and passing missprime filter", y="Proportion of sequence reads")
mapStats_wasp_reads=mapStats_wasp %>% group_by(fraction) %>% summarise(mean=mean(reads), sd=sd(reads))
seqread_plot=ggplot(mapStats_wasp_reads, aes(x=fraction, y=mean, fill=fraction)) + geom_bar(stat='identity')+  geom_errorbar(aes(ymin=mean-sd, ymax=mean+sd), width=.2)+ scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Sequenced Reads", y="Number of sequence reads")

All plots:

library(cowplot)
allmapstatplots=plot_grid(seqread_plot,mapreads_plot,propmap_plot,ncol = 3)
allmapstatplots

Version Author Date
ab3722b Briana Mittleman 2019-02-15 
ggsave(allmapstatplots, file="../output/plots/MapStatBarPlots.png",width=15)
Saving 15 x 5 in image

Boxplot:

seqread_plotbar=ggplot(mapStats_wasp, aes(x=fraction, y=log10(reads), fill=fraction)) + geom_boxplot()+scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Sequenced Reads", y="log10(Number of sequence reads)") 
seqread_plotbar

Version Author Date
ab3722b Briana Mittleman 2019-02-15 
mapreads_plotbar=ggplot(mapStats_wasp, aes(x=fraction, y=log10(Mapped_noMP), fill=fraction)) + geom_boxplot()+scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Mapped Reads\n filtered for misspriming", y="log10(Mapped Reads)") 
mapreads_plotbar

Version Author Date
ab3722b Briana Mittleman 2019-02-15 
maprop_plotbar=ggplot(mapStats_wasp, aes(x=fraction, y=prop_MappedwithoutMP, fill=fraction)) + geom_boxplot()+scale_fill_manual(values=c("deepskyblue3","darkviolet"))+ labs(title="Proportion Mapped Reads\n and filtered for misspriming", y="Proportion mapped post misspriming") 
maprop_plotbar

Version Author Date
ab3722b Briana Mittleman 2019-02-15 
allmapstatboxplots=plot_grid(seqread_plotbar,mapreads_plotbar,maprop_plotbar,ncol = 3)
allmapstatboxplots

Version Author Date
ab3722b Briana Mittleman 2019-02-15 
ggsave(allmapstatboxplots, file="../output/plots/MapStatBoxPlots.png",width=15)
Saving 15 x 5 in image

Peak length:

peaks=read.table("../data/PeaksUsed_noMP_5percCov/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed",col.names=c("chr", 'start','end', 'peak', 'score', 'strand')) %>% mutate(length=end-start)
ggplot(peaks,aes(x=length)) + geom_histogram(bins=300) + labs(title="Peak Size", x="number of basepairs") + geom_vline(xintercept =mean(peaks$length),col="red")

Version Author Date
ab3722b Briana Mittleman 2019-02-15 


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     

other attached packages:
 [1] bindrcpp_0.2.2  cowplot_0.9.3   forcats_0.3.0   stringr_1.4.0  
 [5] dplyr_0.7.6     purrr_0.2.5     readr_1.1.1     tidyr_0.8.1    
 [9] tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.19     cellranger_1.1.0 plyr_1.8.4       compiler_3.5.1  
 [5] pillar_1.3.0     git2r_0.24.0     workflowr_1.2.0  bindr_0.1.1     
 [9] tools_3.5.1      digest_0.6.17    lubridate_1.7.4  jsonlite_1.6    
[13] evaluate_0.13    nlme_3.1-137     gtable_0.2.0     lattice_0.20-35 
[17] pkgconfig_2.0.2  rlang_0.2.2      cli_1.0.1        rstudioapi_0.9.0
[21] yaml_2.2.0       haven_1.1.2      withr_2.1.2      xml2_1.2.0      
[25] httr_1.3.1       knitr_1.20       hms_0.4.2        fs_1.2.6        
[29] rprojroot_1.3-2  grid_3.5.1       tidyselect_0.2.4 glue_1.3.0      
[33] R6_2.3.0         readxl_1.1.0     rmarkdown_1.11   modelr_0.1.2    
[37] magrittr_1.5     whisker_0.3-2    backports_1.1.2  scales_1.0.0    
[41] htmltools_0.3.6  rvest_0.3.2      assertthat_0.2.0 colorspace_1.3-2
[45] labeling_0.3     stringi_1.2.4    lazyeval_0.2.1   munsell_0.5.0   
[49] broom_0.5.0      crayon_1.3.4