Peak To Gene Assignment

Prepare referece

#!/bin/bash

#SBATCH --job-name=intGenes_combfilterPeaksOppStrand
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=intGenes_combfilterPeaksOppStrand.out
#SBATCH --error=intGenes_combfilterPeaksOppStrand.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.OppStrand.bed

This results in peaks being mapped to multiple genes. I want to use a method where I look for the closest end of transcript to each peak then use that gene for the assignment. This would mean each peak is assigned to one gene.

Create a python script to process the NCBI file. I want protien coding transcript ends with the associated gene names. Original file: ncbiRefSeq.txt

• Column 2 transcript name
• Column 13 gene name
• NM is protein coding

EndOfProCodTrans.py

def main(inF, outF):
  infile= open(inF, "r")
  fout = open(outF,'w')
  for line in infile:
      linelist=line.split()
      transcript=linelist[1]
      transcript_id=transcript.split("_")[0]
      if transcript_id=="NM":
          chr=linelist[2][3:]
          strand=linelist[3] 
          gene= linelist[12]
          if strand == "+" :
              end = int(linelist[7])
              end2= end - 1
              fout.write("%s\t%d\t%d\t%s:%s\t.\t%s\n"%(chr, end2, end, transcript,gene, strand))
          if strand == "-":
              end= int(linelist[4])
              end2= end + 1
              fout.write("%s\t%d\t%d\t%s:%s\t.\t%s\n"%(chr, end, end2, transcript,gene, strand))


if __name__ == "__main__":
    inF = "/project2/gilad/briana/genome_anotation_data/ncbiRefSeq.txt"
    outF= "/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes.txt"
    main(inF, outF)

Find closest gene to each peak

bedtools closest

-A peaks /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -B transcript file /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt -S (opposite strand) -D b (give distance wrt to gene strand)

#!/bin/bash

#SBATCH --job-name=TransClosest2End
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=TransClosest2End.out
#SBATCH --error=TransClosest2End.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3 

source activate three-prime-env


bedtools closest -S -D b -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b  /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_endProtCodGenes_sort.txt > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed

I will take a look at this file in R then I will process the file in python.

names=c("PeakChr", "PeakStart", "PeakEnd", "PeakName","PeakScore", "PeakStrand", "GeneChr", "GeneStart", "GeneEnd", "Transcript", "GeneScore", "GeneStrand", "Distance" )
peak2transDist=read.table("../data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.bed", col.names = names, stringsAsFactors = F, header=F)

ggplot(peak2transDist, aes(x=abs(Distance)))+ geom_density() + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 4362 rows containing non-finite values (stat_density).

peak2transDist0=peak2transDist %>% filter(Distance==0)
nrow(peak2transDist0)

[1] 4362

peak2transDist200=peak2transDist %>% filter(abs(Distance)<200)
nrow(peak2transDist200)

[1] 23778

summary(peak2transDist$Distance)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-5523243   -57698   -12830   -23711     3373  5592124 

try adding the no ties flag -t first.

peak2transDist_noties=read.table("../data/filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed", col.names = names, stringsAsFactors = F, header=F)
ggplot(peak2transDist_noties, aes(x=abs(Distance)))+ geom_density() + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 2044 rows containing non-finite values (stat_density).

peak2transDist0_noT=peak2transDist_noties%>% filter(Distance==0)
nrow(peak2transDist0_noT)

[1] 2044

peak2transDist200_noT=peak2transDist_noties %>% filter(abs(Distance)<200)
nrow(peak2transDist200_noT)

[1] 10488

summary(peak2transDist$Distance)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
-5523243   -57698   -12830   -23711     3373  5592124 

ggplot(peak2transDist_noties, aes(x=abs(Distance)))+ geom_histogram(binwidth = .5) + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 2044 rows containing non-finite values (stat_bin).

Looking at this visually suggests that we have way too many peaks. I want to compare the peak score which is related to the coverage to the abs(distace)

ggplot(peak2transDist_noties, aes(y=PeakScore, x=abs(Distance + 1))) + geom_point() + scale_x_log10() + scale_y_log10() + geom_density2d(na.rm = TRUE, size = 1, colour = 'red') 

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Transformation introduced infinite values in continuous x-axis

Alternatively let me try to remove low peak score values.

allPeakplot=ggplot(peak2transDist_noties, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Distance all peaks to gene end") + annotate("text", label=nrow(peak2transDist_noties), x=10, y=.4)

peak2transDist_score500=peak2transDist_noties%>% filter(PeakScore>500)
score500plot=ggplot(peak2transDist_score500, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 500") + annotate("text", label=nrow(peak2transDist_score500), x=10, y=.4)


peak2transDist_score200=peak2transDist_noties%>% filter(PeakScore>200)
score200plot=ggplot(peak2transDist_score200, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 200") + annotate("text", label=nrow(peak2transDist_score200), x=10, y=.4)


peak2transDist_score100=peak2transDist_noties%>% filter(PeakScore>100)
score100plot=ggplot(peak2transDist_score100, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 100") + annotate("text", label=nrow(peak2transDist_score100), x=10, y=.4)

peak2transDist_score50=peak2transDist_noties%>% filter(PeakScore>50)
score50plot=ggplot(peak2transDist_score50, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 50")+ annotate("text", label=nrow(peak2transDist_score50), x=10, y=.4)

peak2transDist_score20=peak2transDist_noties%>% filter(PeakScore>20)
score20plot=ggplot(peak2transDist_score20, aes(x=abs(Distance + 1)))+ geom_density() + scale_x_log10() + labs(title="Peak Score > 10")+ annotate("text", label=nrow(peak2transDist_score20), x=10, y=.4)

plot_grid(allPeakplot,score20plot,score50plot,score100plot,score200plot, score500plot)

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 662 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 431 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 327 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 234 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 150 rows containing non-finite values (stat_density).

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 78 rows containing non-finite values (stat_density).

Call QTLS with this assignment

I am gonig to use this assignment method to call QTLs. The bed file I will make the phenotypes from is

• Peak CHR
  
• Peak Start
  
• Peak End
  
• Peak Name
• Peak Score
  
• Gene strand
• Gene/transcript name

in the filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed file this is

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $12 "\t" $10}' filtered_APApeaks_merged_allchrom_refseqTrans.closest2End.noties.bed > filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.bed

less /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SA | tr ":" "-" > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.fixed.bed

Make this an SAF file with the correct peak ID. bed2saf_peaks2trans.py

from misc_helper import *

fout = file("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_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_refseqTrans.noties_sm.fixed.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()

Run feature counts:
ref_gene_peakTranscript_fc_TN.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 2

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqTrans.noties_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 2

Fix the headers:

• fix_head_fc_opp_transcript_tot.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

• fix_head_fc_opp_transcript_nuc.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=i_list[:6]
        for sample in i_list[6:]:
            full = sample.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            libraries.append(samp_st)
        first_line= "\t".join(libraries)
        fout.write(first_line + '\n')
    else :
        fout.write(i)
fout.close()

Create file IDS:

• create_fileid_opp_transcript_total.py

fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

• create_fileid_opp_transcript_nuc.py

fout = file("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt",'w')
infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(infile):
    if line == 0:
        i_list=i.split()
        files= i_list[10:-2]
        for each in files:
            full = each.split("/")[7]
            samp= full.split("-")[2:4]
            lim="_"
            samp_st=lim.join(samp)
            outLine= full[:-1] + "\t" + samp_st
            fout.write(outLine + "\n")
fout.close()

(remove top line)

awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript_head.txt >  /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.txt




awk '{if (NR!=1) {print}}' /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript_head.txt > /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt

Make Phenotypes:

• makePhenoRefSeqPeaks_Transcript_Total.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscriptfiltered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA) 
fout.write(" ".join(peak + inds_noL) + '\n' )


count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=int(id_list[2])
        end=int(id_list[3])
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

• makePhenoRefSeqPeaks_Transcript_Nuclear.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_nuclear_Transcript.txt"):
    bam, IND = ln.split("\t")
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


#now I have ind dic with keys as the bam and ind as the values
#I also have a bam dic with ind as the keys and bam as the values  
    
inds=list(dic_BAM.keys()) #list of ind libraries  

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
genes=[]
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        if gene not in genes:
            genes.append(gene)
            
#make the ind and gene dic  
dic_dub={}
for g in genes:
    dic_dub[g]={}
    for i in inds:
        dic_dub[g][i]=0


#populate the dictionary  
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line, i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        g= id_list[5]
        values=list(i_list[6:])
        list_list=[]
        for ind,val in zip(inds, values):
            list_list.append([ind, val])
        for num, name in enumerate(list_list):
            dic_dub[g][list_list[num][0]] += int(list_list[num][1])
        

#write the file by acessing the dictionary and putting values in the table ver the value in the dic 
        

fout=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt","w")
peak=["chrom"]
inds_noL=[]
for each in inds:
    indsNA= "NA" + each[:-2]
    inds_noL.append(indsNA)  
fout.write(" ".join(peak + inds_noL) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear_fixed.fc", "r")
for line , i in enumerate(count_file):
    if line > 1:
        i_list=i.split()
        id=i_list[0]
        id_list=id.split(":")
        gene=id_list[5]
        start=int(id_list[2])
        end=int(id_list[3])
        buff=[]
        buff.append("chr%s:%d:%d:%s_%s_%s"%(id_list[1], start, end, id_list[5], id_list[4], id_list[0]))
        for x,y in zip(i_list[6:], inds):
            b=int(dic_dub[gene][y])
            t=int(x)
            buff.append("%d/%d"%(t,b))
        fout.write(" ".join(buff)+ '\n')
        
fout.close()

I can run these with the following bash script:

• run_makePhen_sep_Transcript.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_Transcript_Total.py  

python makePhenoRefSeqPeaks_Transcript_Nuclear.py  

Prepare for FastQTL

I will do this in the /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/ directory.

module load samtools
#zip file 
gzip filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt

module load python
#leafcutter script
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz 

#source activate three-prime-env
sh filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz_prepare.sh

#run for nuclear as well 
gzip  filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt
#unload anaconda, load python
python /project2/gilad/briana/threeprimeseq/code/prepare_phenotype_table.py filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz
#load anaconda and env. 
sh filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz_prepare.sh





#keep only 2 PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs
head -n 3 filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.PCs > filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs

Make a sample list.

• makeSampleList_trascript.py

#make a sample list  

fout = open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt",'w')

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov/file_id_mapping_total_Transcript.txt", "r"):
    bam, sample = ln.split()
    line=sample[:-2]
    fout.write("NA"+line + "\n")
fout.close()

** Manually ** Remove 18500, 19092 and 19193, 18497

Run FastQTL

#!/bin/bash


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

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.OppStrand_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

#!/bin/bash


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

for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Nuclear.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done


for i in 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 
do
/home/brimittleman/software/bin/FastQTL/bin/fastQTL.static --permute 1000  --vcf /project2/gilad/briana/YRI_geno_hg19/chr$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes.Transcript_sm_quant.Total.pheno_fixed.txt.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e4 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

library(dplyr)


##total results
tot.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))

#BH correction
tot.perm$bh=p.adjust(tot.perm$bpval, method="fdr")

#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_transcript.pdf") 
qqplot_total= qqplot(-log10(runif(nrow(tot.perm))), -log10(tot.perm$bpval),ylab="-log10 Total permuted pvalue", xlab="Uniform expectation", main="Total permuted pvalues for all snps")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total_transcript_permResBH.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permRes.txt",head=F, stringsAsFactors=F, col.names = c("pid", "nvar", "shape1", "shape2", "dummy", "sid", "dist", "npval", "slope", "ppval", "bpval"))
nuc.perm$bh=p.adjust(nuc.perm$bpval, method="fdr")


#plot qqplot
pdf("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_transcript.pdf") 
qqplot(-log10(runif(nrow(nuc.perm))), -log10(nuc.perm$bpval),ylab="-log10 Nuclear permuted pvalue", xlab="Uniform expectation", main="Nuclear permuted pvalues for all snps")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_trans/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear_transcript_permResBH.txt", col.names = T, row.names = F, quote = F)

#!/bin/bash


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

module load Anaconda3
source activate three-prime-env


Rscript APAqtlpermCorrectQQplot_trans.R

Session information

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     

other attached packages:
 [1] bindrcpp_0.2.2  cowplot_0.9.3   workflowr_1.1.1 forcats_0.3.0  
 [5] stringr_1.3.1   dplyr_0.7.6     purrr_0.2.5     readr_1.1.1    
 [9] tidyr_0.8.1     tibble_1.4.2    ggplot2_3.0.0   tidyverse_1.2.1

loaded via a namespace (and not attached):
 [1] tidyselect_0.2.4  haven_1.1.2       lattice_0.20-35  
 [4] colorspace_1.3-2  htmltools_0.3.6   yaml_2.2.0       
 [7] rlang_0.2.2       R.oo_1.22.0       pillar_1.3.0     
[10] glue_1.3.0        withr_2.1.2       R.utils_2.7.0    
[13] modelr_0.1.2      readxl_1.1.0      bindr_0.1.1      
[16] plyr_1.8.4        munsell_0.5.0     gtable_0.2.0     
[19] cellranger_1.1.0  rvest_0.3.2       R.methodsS3_1.7.1
[22] evaluate_0.11     labeling_0.3      knitr_1.20       
[25] broom_0.5.0       Rcpp_0.12.18      scales_1.0.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.16     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.0         tools_3.5.1      
[37] magrittr_1.5      lazyeval_0.2.1    crayon_1.3.4     
[40] whisker_0.3-2     pkgconfig_2.0.2   MASS_7.3-50      
[43] xml2_1.2.0        lubridate_1.7.4   assertthat_0.2.0 
[46] rmarkdown_1.10    httr_1.3.1        rstudioapi_0.7   
[49] R6_2.2.2          nlme_3.1-137      git2r_0.23.0     
[52] compiler_3.5.1