Approach to better assign genes to peaks

New Approach Idea

Lin et al: An in-depth map of polyadenylation sites in cancer 2012: -Mapped locations to annotated locations in UCUS browser: “The mapped locations were annotated using the UCSC genome browser tables ( 26 ). When a locus could be attributed to multiple possible annotations, the locus was assigned with a single annotation in the following priority order: 3′ UTRs (sense), coding sequences (CDS, sense), 5′ UTRs (sense), intron (sense), non-coding RNAs (ncRNAs, sense), 5′ UTR antisense, CDS antisense, 3′ UTR antisense, intron antisense, promoter antisense, ncRNA antisense and intergenic”

I want to download this annotation and try this. I am using the ncbi_refseq annotations. I will download regions of the genome seperatly and then merge the files.

• 5’ UTR

• Coding Exon

• Intron

• 3’ UTR

• (downstream 5000)-downstream proximal region

I also want a dictionary with the transcripts and the gene names for the annotation. This information will come from the Transcript2GeneName file. In this file the transcript ID is in column1 and the gene name column 13.

I have downloaded all of the these to data/RefSeq_annotations. I will concatinate all of these for a full annotation dataset, I will then sort this file. The file is ncbiRefSeq_allAnnotation.sort.bed

Using this I can create an annotation in a bed file I can use for the overlap with my peaks. This will include getting the transcript to gene annotations. I will transfer the files to midway in my genome annotation directory and work with them there.

Format full refseq annotation:

TXN2Gene_file="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms"

gene_dic={}

for ln in open(TXN2Gene_file,"r"):
   txn=ln.split()[1]
   gene=ln.split()[12]
   gene_dic[txn]=gene

outF=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed","w")

inFile="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_allAnnotation.sort.bed"  


for ln in open(inFile, "r"):
   chrom, start, end, name, score, strand = ln.split()
   chrom_fix=chrom[3:]
   txn=name.split("_")[:2]
   txnF="_".join(txn)
   gene=gene_dic[txnF]
   type=name.split("_")[2]
   id=type + ":" + gene
   outF.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chrom_fix, start, end, id, score, strand))

outF.close()

Map peaks with new annotation

I want to create a file with my peaks mapped to these regions. I will include a structure for when there is a tie and put intergenic if it is not found. I need to do an intersect that gives me all of the IDs. After this I can use python to parse the hiarchy.

I can use bedtools map for this. I want all of the data to come back.

-c 4 -o distinct
-S opposite strand

I will do this on the peaks before I looked at usage.

mapnoMPPeaks2GenomeLoc.sh

#!/bin/bash

#SBATCH --job-name=mapnoMPPeaks2GenomeLoc
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=mapnoMPPeaks2GenomeLoc.out
#SBATCH --error=mapnoMPPeaks2GenomeLoc.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env

#annotation: /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed

#peaks:  /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed


bedtools map -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_filtered/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR.bed -b /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_FormatedallAnnotation.sort.bed -c 4 -S -o distinct > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed

Look at how many get no annotation. I can do this interactively in python.

num=0
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed", "r"):
    location=ln.split()[7]
    if location==".":
        num +=1

print(num)

This shows me that 85% of the peaks fall into one of these annotations.

Now I need to sort out the peaks with multiple annotations.

4. 5’ UTR

5. Coding Exon

6. Intron

7. 3’ UTR

8. (downstream 5000)-downstream proximal region

I can write this out as the SAF I need. GeneID (peak1:1:14404:14484:-:OR4F16) Chr Start End Strand. For this I cannot include the peaks with no gene association. I can go back to this if i need to in the future.

processGenLocPeakAnno2SAF.py

inFile="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLoc.bed"
outFile=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.SAF" , "w")

outFile.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open(inFile, "r"):
    chrom, start, end, peak, cov, strand, score, anno = ln.split()
    if anno==".": 
        continue  
    anno_lst=anno.split(",")
    if len(anno_lst)==1:
        gene=anno_lst[0].split(":")[1]
        print("1 gene")
        peak_i=int(peak)
        start_i=int(start)
        end_i=int(end)
        ID="peak%d:%s:%d:%d:%s:%s"%(peak_i, chrom, start_i, end_i, strand, gene)
        outFile.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
    else:
        type_dic={}
        for each in anno_lst:
            type_dic[each.split(":")[0]]=each.split(":")[1]
        if "utr3" in type_dic.keys():
            gene=type_dic["utr3"]
            peak_i=int(peak)
            start_i=int(start)
            end_i=int(end)
            ID="peak%d:%s:%d:%d:%s:%s"%(peak_i, chrom, start_i, end_i, strand, gene)
            outFile.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
        elif "end" in type_dic.keys():
            gene=type_dic["end"]
            peak_i=int(peak)
            start_i=int(start)
            end_i=int(end)
            ID="peak%d:%s:%d:%d:%s:%s"%(peak_i, chrom, start_i, end_i, strand, gene)
            outFile.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
        elif "cds" in type_dic.keys():
            gene=type_dic["cds"] 
            peak_i=int(peak)
            start_i=int(start)
            end_i=int(end)
            ID="peak%d:%s:%d:%d:%s:%s"%(peak_i, chrom, start_i, end_i, strand, gene)
            outFile.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
        elif "utr5" in type_dic.keys():
            gene=type_dic["utr5"]
            peak_i=int(peak)
            start_i=int(start)
            end_i=int(end)
            ID="peak%d:%s:%d:%d:%s:%s"%(peak_i, chrom, start_i, end_i, strand, gene)
            outFile.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
        elif "intron" in type_dic.keys():
            gene=type_dic["intron"]
            peak_i=int(peak)
            start_i=int(start)
            end_i=int(end)
            ID="peak%d:%s:%d:%d:%s:%s"%(peak_i, chrom, start_i, end_i, strand, gene)
            outFile.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))

outFile.close()

Map reads to new annotated peaks

This has all of the peaks with at least one gene annotation. (104555) I can run feature counts on this to start getting the usage.

GeneLocAnno_fc_TN_noMP.sh

Because I mapped the genes opposite. The reads are now on in the same direction as the peaks

#!/bin/bash

#SBATCH --job-name=GeneLocAnno_fc_TN_noMP
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=GeneLocAnno_fc_TN_noMP.out
#SBATCH --error=GeneLocAnno_fc_TN_noMP.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_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*T-combined-sort.noMP.sort.bam -s 1

featureCounts -O -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*N-combined-sort.noMP.sort.bam -s 1

Around 4 mill mapping in nuclear and 7 mill in total.

fix_head_fc_geneLoc_tot_noMP.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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_geneLoc_nuc_noMP.py

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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()

I can use the fileIDs

/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_nuclear_Transcript.txt and /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/file_id_mapping_total_Transcript.txt

Make phenotypes

I need to make a file with the gene start and ends because I use these in the phenotypes for QTL mapping. I can do this with the project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms file. I want the file to look like chr,start,end,txn:gene,.,strand I will sue the start and end of the coding seq

getGeneEnds.py

TXN2Gene_file=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms","r")

outFile=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_endAllGenes.bed", "w")

for i, ln in enumerate(TXN2Gene_file):
    if i >0 :
        chrom=ln.split()[2]
        chromf=chrom[3:]
        start=int(ln.split()[6])
        end=int(ln.split()[7])
        txn=ln.split()[1]
        genename=ln.split()[12]
        id=txn + ":" + genename
        strand=ln.split()[3]
        score="."
        outFile.write("%s\t%s\t%s\t%s\t%s\t%s\n"%(chromf, start, end, id, score, strand))

outFile.close()

Sort the bed file:

sort -k1,1 -k2,2n /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_endAllGenes.bed > /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_endAllGenes.sort.bed

makePhenoRefSeqPeaks_GeneLoc_Total_noMP.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/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  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_endAllGenes.sort.bed"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
#    if "-" in gene:
 #       gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.fc","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_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        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_GeneLoc_Nuclear_noMP.py

#PYTHON 3

dic_IND = {}
dic_BAM = {}

for ln in open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP/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  

#gene start and end dictionaries: 
dic_geneS = {}
dic_geneE = {}
for ln in open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/ncbiRefSeq_endAllGenes.sort.bed"):
    chrom, start, end, geneID, score, strand = ln.split('\t')
    gene= geneID.split(":")[1]
    #if "-" in gene:
     #   gene=gene.split("-")[0]
    if gene not in dic_geneS:
        dic_geneS[gene]=int(start)
        dic_geneE[gene]=int(end)
        


#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.fc","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_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_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=dic_geneS[id_list[5]]
        end=dic_geneE[id_list[5]]
        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()

Run make pheno files:

run_makePhen_sep_GeneLocAnno_noMP.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_GeneLoc_Total_noMP.py  

python makePhenoRefSeqPeaks_GeneLoc_Nuclear_noMP.py  

Convert to Usage

pheno2CountOnly_genelocAnno.R

library(reshape2)
library(tidyverse)


totalPeakUs=read.table("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))
nuclearPeakUs=read.table("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))



write.table(totalPeakUs[,7:dim(totalPeakUs)[2]], file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.CountsOnly",quote=FALSE, col.names = F, row.names = F)

write.table(nuclearPeakUs[,7:dim(nuclearPeakUs)[2]], file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.CountsOnly",quote=FALSE, col.names = F, row.names = F)

convertCount2Numeric_noMP_GeneLocAnno.py

def convert(infile, outfile):
  final=open(outfile, "w")
  for ln in open(infile, "r"):
    line_list=ln.split()
    new_list=[]
    for i in line_list:
      num, dem = i.split("/")
      if dem == "0":
        perc = "0.00"
      else:
        perc = int(num)/int(dem)
        perc=round(perc,2)
        perc= str(perc)
      new_list.append(perc)
    final.write("\t".join(new_list)+ '\n')
  final.close()
  
convert("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.CountsOnly","/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.CountsOnlyNumeric.txt")


convert("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.CountsOnly","/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.CountsOnlyNumeric.txt")

Pull these into R here so I can filter peaks with 5% and understand how many per gene.

library(workflowr)

This is workflowr version 1.1.1
Run ?workflowr for help getting started

library(tidyverse)

── Attaching packages ────────────────────────────────────────────────────────────────── tidyverse 1.2.1 ──

✔ ggplot2 3.0.0     ✔ purrr   0.2.5
✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.3.1
✔ readr   1.1.1     ✔ forcats 0.3.0

── Conflicts ───────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag()    masks stats::lag()

library(reshape2)

Attaching package: 'reshape2'

The following object is masked from 'package:tidyr':

    smiths

library(cowplot)

Attaching package: 'cowplot'

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

    ggsave

library(ggpubr)

Loading required package: magrittr

Attaching package: 'magrittr'

The following object is masked from 'package:purrr':

    set_names

The following object is masked from 'package:tidyr':

    extract

Attaching package: 'ggpubr'

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

    get_legend

totalPeakUs=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))

Warning: Expected 3 pieces. Additional pieces discarded in 4 rows [13402,
13403, 13404, 101569].

nuclearPeakUs=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.fc", header = T, stringsAsFactors = F) %>% separate(chrom, sep = ":", into = c("chr", "start", "end", "id")) %>% separate(id, sep="_", into=c("gene", "strand", "peak"))

Warning: Expected 3 pieces. Additional pieces discarded in 4 rows [13402,
13403, 13404, 101569].

ind=colnames(totalPeakUs)[7:dim(totalPeakUs)[2]]
totalPeakUs_CountNum=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.CountsOnlyNumeric.txt", col.names = ind)

nuclearPeakUs_CountNum=read.table("../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.CountsOnlyNumeric.txt", col.names = ind)

#numeric with anno
totalPeak=as.data.frame(cbind(totalPeakUs[,1:6], totalPeakUs_CountNum))
nuclearPeak=as.data.frame(cbind(nuclearPeakUs[,1:6], nuclearPeakUs_CountNum))

#mean
totalPeakUs_CountNum_mean=rowMeans(totalPeakUs_CountNum)
nuclearPeakUs_CountNum_mean=rowMeans(nuclearPeakUs_CountNum)


#append mean to anno
TotalPeakUSMean=as.data.frame(cbind(totalPeakUs[,1:6],totalPeakUs_CountNum_mean))
NuclearPeakUSMean=as.data.frame(cbind(nuclearPeakUs[,1:6],nuclearPeakUs_CountNum_mean))

Filter on the mean

TotalPeakUSMean_filt=TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% group_by(gene) %>% summarise(Npeaks=n())
totalPeaksPerGene=TotalPeakUSMean_filt %>% group_by(Npeaks) %>% summarise(GenesWithNPeaks=n())


NuclearPeakUSMean_filt=NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% group_by(gene) %>% summarise(Npeaks=n())
nuclearPeaksPerGene=NuclearPeakUSMean_filt %>% group_by(Npeaks) %>% summarise(GenesWithNPeaks=n())
nuclearPeaksPerGene$GenesWithNPeaks=as.integer(nuclearPeaksPerGene$GenesWithNPeaks)

Peak num level

nPeaksBoth=totalPeaksPerGene %>% full_join(nuclearPeaksPerGene, by="Npeaks")
colnames(nPeaksBoth)= c("Peaks", "Total", "Nuclear")
nPeaksBoth$Total= nPeaksBoth$Total %>% replace_na(0)

#melt nPeaksBoth
nPeaksBoth_melt=melt(nPeaksBoth, id.var="Peaks")
colnames(nPeaksBoth_melt)= c("Peaks", "Fraction", "Genes")

#plot
peakUsage5perc=ggplot(nPeaksBoth_melt, aes(x=Peaks, y=Genes, fill=Fraction)) + geom_bar(stat="identity", position = "dodge") + labs(title="Number of Genes with >5% Peak Usage \n cleaned for mispriming") + theme(axis.text.y = element_text(size=12),axis.title.y=element_text(size=10,face="bold"), axis.title.x=element_text(size=12,face="bold"))+ scale_fill_manual(values=c("darkviolet","deepskyblue3"))  + facet_grid(~Fraction)

peakUsage5perc

This is a similar distribution to the other annotations.

Save this plot:

ggsave(peakUsage5perc, file="../output/plots/QC_plots/peakUsage5perc_noMP_geneLocAnno.png")

Saving 7 x 5 in image

Genes covered with these annoations.

#nuclear
nrow(NuclearPeakUSMean_filt) 

[1] 14851

#total
nrow(TotalPeakUSMean_filt) 

[1] 14852

There are more genes in this. We expect this because i have included LINCs.

Look at number of peaks in each set.

#nuclear  
NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% nrow()

[1] 37370

#total
TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% nrow()

[1] 33002

There are a few less peaks. This is expected give we cut 15% of the peaks because they are not within 5kb of an annotated gene.

Write out these peaks

NuclearPeakUSMean_5perc=NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05)
write.table(NuclearPeakUSMean_5perc,file="../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt", row.names=F, col.names=F, quote = F)


TotalPeakUSMean_5per= TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) 
write.table(TotalPeakUSMean_5per,file="../data/PeakUsage_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt", row.names=F, col.names=F, quote = F)

I want to filter these peaks so I can rerun the leafcutter analysis and look at some of the results. I transfered these to /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/.

filterPheno_bothFraction_GeneLocAnno_5perc.py

#python  

totalokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt"

totalokPeaks5perc={}
for ln in open(totalokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    totalokPeaks5perc[peakname]=""


nuclearokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt"
nuclearokPeaks5perc={}
for ln in open(nuclearokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    nuclearokPeaks5perc[peakname]=""
    
    
totalPhenoBefore=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno.fc","r")
totalPhenoAfter=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc", "w")
for num, ln in enumerate(totalPhenoBefore):
    if num ==0:
        totalPhenoAfter.write(ln)
    else:  
        id=ln.split()[0].split(":")[3].split("_")[2]
        if id in totalokPeaks5perc.keys():
            totalPhenoAfter.write(ln)
totalPhenoAfter.close()  

nuclearPhenoBefore=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno.fc","r")
nuclearPhenoAfter=open("/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc", "w")
for num, ln in enumerate(nuclearPhenoBefore):
    if num ==0:
        nuclearPhenoAfter.write(ln)
    else:  
        id=ln.split()[0].split(":")[3].split("_")[2]
        if id in nuclearokPeaks5perc.keys():
            nuclearPhenoAfter.write(ln)
nuclearPhenoAfter.close() 

Diff Iso in leafcutter

Now I can use these peaks to get counts for leafcutter.

I want a file that will have the peaks from total or nuclear.

I am starting here with the SAF file

filternamePeaks5percCov_GeneLocAnno.py

assignedPeaks=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.SAF","r")
outFile=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF", "w")

totalokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total_fixed.pheno.5percPeaks.txt"
nuclearokPeaks5perc_file="/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear_fixed.pheno.5percPeaks.txt"

allPeakOk={}
for ln in open(nuclearokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    peaknum=peakname[4:]
    allPeakOk[peaknum]=""
for ln in open(totalokPeaks5perc_file,"r"):
    peakname=ln.split()[5]
    peaknum=peakname[4:]
    if peaknum not in allPeakOk.keys():
        allPeakOk[peaknum]=""
        
for i, ln in enumerate(assignedPeaks): 
    if i == 0:
        outFile.write(ln)
    else:
        ID=ln.split()[0]
        peak=ID.split(":")[0]
        peak_num=peak[4:]
        if peak_num in allPeakOk.keys():
             outFile.write(ln)
outFile.close()
    

Now I will run feature counts, remembering that i want to look at the same strand as the peaks.

bothFrac_processed_GeneLocAnno_FC.sh

#!/bin/bash

#SBATCH --job-name=bothFrac_processed_GeneLocAnno_FC
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=bothFrac_processed_GeneLocAnno_FC.out
#SBATCH --error=bothFrac_processed_GeneLocAnno_FC.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_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed.fc /project2/gilad/briana/threeprimeseq/data/bam_NoMP_sort/*sort.bam -s 1

Fix header for this:

fix_head_fc_procBothFrac_GeneLocAnno.py

#python 

infile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed.fc", "r")
fout = open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc",'w')
for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries = i_list[:6]
        print(libraries)
        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()

fc2leafphen_processed_GeneLocAnno.py

inFile= open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_fixed.fc", "r")
outFile= open("/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_forLC.fc", "w")

for num, ln in enumerate(inFile):
        if num == 1:
            lines=ln.split()[6:]
            outFile.write(" ".join(lines)+'\n')
        if num > 1:
            ID=ln.split()[0]
            peak=ID.split(":")[0]
            chrom=ID.split(":")[1]
            start=ID.split(":")[2]
            start=int(start)
            end=ID.split(":")[3]
            end=int(end)
            strand=ID.split(":")[4]
            gene=ID.split(":")[5]
            new_ID="chr%s:%d:%d:%s"%(chrom, start, end, gene)
            pheno=ln.split()[6:]
            pheno.insert(0, new_ID)
            outFile.write(" ".join(pheno)+'\n')
            
outFile.close()

subset_diffisopheno_processed_GeneLocAnno.py

def main(inFile, outFile, target):
    ifile=open(inFile, "r")
    ofile=open(outFile, "w")
    target=int(target)
    for num, ln in enumerate(ifile):
        if num == 0:
            ofile.write(ln)
        else:
            ID=ln.split()[0]
            chrom=ID.split(":")[0][3:]
            print(chrom)
            chrom=int(chrom)
            if chrom == target:
                ofile.write(ln)
            
if __name__ == "__main__":
    import sys

    target = sys.argv[1]
    inFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_forLC.fc"
    outFile = "/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_forLC_%s.txt"%(target)
    main(inFile, outFile, target)

run_subset_diffisopheno_processed_GeneLocAnno.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

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
python subset_diffisopheno_processed_GeneLocAnno.py  $i 
done

makeLCSampleList_processed_GeneLocAnno.py

outfile=open("/project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed_GeneLocAnno/sample_groups.txt", "w")
infile=open("/project2/gilad/briana/threeprimeseq/data/filtPeakOppstrand_cov_processed_GeneLocAnno_bothFrac/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed.fc", "r")

for line, i in enumerate(infile):
    if line == 1:
        i_list=i.split()
        libraries=[]
        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)
        for l in libraries:
            if l[-1] == "T":
                outfile.write("%s\tTotal\n"%(l))
            else:
                outfile.write("%s\tNuclear\n"%(l))
    else:
          next
                
outfile.close()

run_leafcutter_ds_bychrom_processed_GeneLocAnno.sh

#!/bin/bash

#SBATCH --job-name=run_leafcutter_ds_bychrom_processed_GeneLocAnno
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_leafcutter_ds_bychrom_processed_GeneLocAnno.out
#SBATCH --error=run_leafcutter_ds_bychrom_processed_GeneLocAnno.err
#SBATCH --partition=bigmem2
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load R

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
Rscript /project2/gilad/briana/davidaknowles-leafcutter-c3d9474/scripts/leafcutter_ds.R --num_threads 4  /project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed_GeneLocAnno/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_sm_quant_processed_forLC_${i}.txt /project2/gilad/briana/threeprimeseq/data/pheno_DiffIso_processed_GeneLocAnno/sample_groups.txt -o /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_chr${i}.txt 
done

awk '{if(NR>1)print}' /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_chr*.txt_effect_sizes.txt > /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_effect_sizes.txt

awk '{if(NR>1)print}' /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_chr*cluster_significance.txt > /project2/gilad/briana/threeprimeseq/data/diff_iso_processed_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_cluster_significance.txt

Look at the significant peaks

diffIso=read.table("../data/diff_iso_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_cluster_significance.txt", header = F,col.names = c("status",   "loglr",    "df",   "p",    "cluster",  "p.adjust"),stringsAsFactors = F,sep="\t") %>% filter(status == "Success")


diffIso$p.adjust=as.numeric(as.character(diffIso$p.adjust))


qqplot(-log10(runif(nrow(diffIso))), -log10(diffIso$p.adjust),ylab="-log10 Total Adjusted Leafcutter pvalue", xlab="-log 10 Uniform expectation", main="Leafcutter differencial isoform analysis between fractions")
abline(0,1)

Look at the effect sizes and delta PSI values

effectsize=read.table("../data/diff_iso_GeneLocAnno/TN_diff_isoform_GeneLocAnno_AllChrom.txt_effect_sizes.txt", stringsAsFactors = F, col.names=c('intron',  'logef' ,'Nuclear', 'Total','deltapsi'))

effectsize$deltapsi=as.numeric(as.character(effectsize$deltapsi))

Warning: NAs introduced by coercion

plot(sort(effectsize$deltapsi),main="Leafcutter delta PSI", ylab="Delta PSI", xlab="Peak Index")

I will look at the top PSI values to check these assignments:

filterHighPSI=effectsize %>% filter(abs(deltapsi)>.4) %>% arrange(deltapsi)
slice(filterHighPSI, 1:10)

                              intron              logef           Nuclear
1  chr20:58647920:58648008:C20orf197  -1.30029968847689  0.75961199274038
2       chr16:81123538:81123627:GCSH  -1.74065267433486 0.670469380269928
3         chr6:84007319:84007404:ME1  -1.78018772633575 0.606503443795698
4        chr3:60357325:60357413:FHIT  -1.26667104228337 0.610069267936541
5  chr19:36806427:36806515:LINC00665  -1.07992220081782 0.777431269442217
6      chr5:158459560:158459648:EBF1  -1.42831987542981 0.748019907501062
7      chr14:51976014:51976057:FRMD6  -1.78704520971336 0.547050860607198
8   chr7:147596894:147596932:CNTNAP2 -0.774209465545986 0.705908150746242
9      chr9:86581185:86581267:MIR7-1  -1.06118532400755   0.8197624679828
10  chr9:131123419:131123501:SLC27A4 -0.785767406518831  0.78532017459677
                Total   deltapsi
1   0.189994498257158 -0.5696175
2   0.137387545557619 -0.5330818
3   0.094302459218906 -0.5122010
4   0.110490691620035 -0.4995786
5   0.287187087245474 -0.4902442
6   0.266472225627269 -0.4815477
7  0.0746333216691989 -0.4724175
8   0.238177555374367 -0.4677306
9   0.352599645113899 -0.4671628
10  0.327502618698554 -0.4578176

Files to make: -bed file with all 5% peaks (named with gene)

Convert Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF to a bed file

Chrom, start, end, name, score, strand

peaksGeneLocAnno_5percSAF2Bed.py

inFile="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.SAF"
outFile=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed", "w")

for i, ln in enumerate(inFile):
  if i >0:
     ID, chrom, start, end, strand = ln.split()
     gene=ID.split(":")[5]
     peaknum=ID.split(:)[0]
     newID=gene + ":"+ peaknum
     start_i=int(start)
     end_i=int(end)
     score="."
     outFile.write("%s\t%d\t%d\t%s\t%s\t%s\n"%(chrom, start_i, end_i, newID, score, strand))
outFile.close()     

filtermidPSI=effectsize %>% filter(abs(deltapsi)>.1,  abs(deltapsi)<.2 ) %>% arrange(desc(deltapsi))
slice(filtermidPSI, 1:15)

                               intron             logef            Nuclear
1  chr12:8397823:8397909:LOC101927905 0.448461429949208  0.250725451684143
2    chr1:219385083:219385168:LYPLAL1 0.954954667285417   0.47522506522198
3       chr21:16333556:16333644:NRIP1 0.836443235134216  0.351219846182051
4    chr15:37102357:37102448:C15orf41  1.47799526856201 0.0446234385277765
5      chr3:57611182:57611269:DENND6A  1.06294476937985  0.647880789005199
6     chr13:41493162:41493319:TPTE2P5  1.02758163936723  0.120932187018109
7        chr16:70514471:70514557:COG4 0.966111688983429   0.71445630320153
8        chr16:58741035:58741118:GOT2  1.18588583796077  0.773862280159172
9      chr6:10756725:10757026:TMEM14B 0.602233504297137  0.500765966416333
10      chr11:94153032:94153131:MRE11 0.459564470994093  0.329029417246997
11      chr20:42275772:42275861:IFT52  1.06151089956002  0.765140743199829
12        chr7:75162621:75162703:HIP1 0.740637787039692  0.659230309804744
13     chr20:18364013:18364097:DZANK1 0.821963077144347  0.502350766672134
14      chr4:185308878:185308968:IRF2 0.618692449311092  0.244938382583888
15     chr1:200520630:200520714:KIF14 0.679726537109473  0.559096573104719
               Total  deltapsi
1   0.45070463537782 0.1999792
2  0.675186030796747 0.1999610
3  0.551128323720684 0.1999085
4  0.244503990743873 0.1998806
5  0.847659137175468 0.1997783
6  0.320706102127373 0.1997739
7  0.914220776425143 0.1997645
8  0.973455289793462 0.1995930
9  0.700276572781718 0.1995106
10 0.528529300729028 0.1994999
11 0.964569032134389 0.1994283
12 0.858643155030094 0.1994128
13 0.701691505947061 0.1993407
14 0.444264508262277 0.1993261
15 0.758376508386949 0.1992799

Run QTLs with these annotations

/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

Use same sample list:

“/project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt”

#!/bin/bash


#SBATCH --job-name=APAqtl_nominal_GeneLocAnno_noMP_5percUsage
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_nominal_GeneLocAnno_noMP_5percUsage.out
#SBATCH --error=APAqtl_nominal_GeneLocAnno_noMP_5percUsage.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/nominal_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.nominal.out --chunk 1 1  --window 5e5 --include-samples /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript/SAMPLE.txt
done

#!/bin/bash


#SBATCH --job-name=APAqtl_perm_GeneLocAnno_noMP_5percUsage
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=APAqtl_perm_GeneLocAnno_noMP_5percUsage.out
#SBATCH --error=APAqtl_perm_GeneLocAnno_noMP_5percUsage.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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Nuclear_fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --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$i.dose.filt.vcf.gz --cov /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.2PCs --bed /project2/gilad/briana/threeprimeseq/data/phenotypes_filtPeakTranscript_noMP_GeneLocAnno_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.gz --out /project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno_NoMP_sm_quant.Total.fixed.pheno_5perc.fc.gz.qqnorm_chr$i.perm.out --chunk 1 1  --window 5e5 --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_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_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
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_total_APAperm_GeneLocAnno_noMP_5percCov.png") 
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\n Gene Loc Anno")
abline(0,1)
dev.off()

#write df with BH  

write.table(tot.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Total.fixed.pheno_5perc_permResBH.txt", col.names = T, row.names = F, quote = F)

##nuclear results  


nuc.perm= read.table("/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_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
png("/project2/gilad/briana/threeprimeseq/output/plots/qqplot_nuclear_APAperm_GeneLocAnno_noMP_5percCov.png") 
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 \n Gene Loc Anno")
abline(0,1)
dev.off()

# write df with BH
write.table(nuc.perm, file = "/project2/gilad/briana/threeprimeseq/data/perm_APAqtl_GeneLocAnno_noMP_5percUs/filtered_APApeaks_merged_allchrom_refseqGenes.GeneLocAnno.NoMP_sm_quant.Nuclear.fixed.pheno_5perc_permResBH.txt", col.names = T, row.names = F, quote = F)

#!/bin/bash


#SBATCH --job-name=run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs.out
#SBATCH --error=run_APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env


Rscript APAqtlpermCorrectQQplot_GeneLocAnno_noMP_5perUs.R 

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] 97

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] 196

Validatation for these assignments

I want to compute some QC metrics for these peaks. First, I will compute the distance from the center of each peak to the end of TXN for the gene. I can get this from the /project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms file. With this file I can make a dictionary with the gene and end of txn. I will use the longest version by updating the dictionary if a key comes up again. For + strand its the furthest end, for the - strand its the first start.

GeneTXNEnd.py

TXN2Gene_file="/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/Transcript2GeneName.dms"
fout=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/GeneTXNEnd.txt", "w")

        

for ln in open(TXN2Gene_file,"r"):
   gene=ln.split()[12]
   strand=ln.split()[3]
   txStart=ln.split()[4]
   txEnd=ln.split()[5]
   if strand == "+":
      val=txEnd
   else:
      val=txStart
   fout.write("%s\t%s\t%s\n"%(gene,strand, val))
fout.close()

Run uniq on this file because many genes had multiple transcripts with the same ends.

uniq GeneTXNEnd.txt > GeneTXNEndUniq.txt

Now I can get just the longest trx with a dictionary and connect them to my peaks. I want the final file to have the dist to the end. First I need to get the average of peak/center of the peak. I will always do End - Peakcenter. I will put the strand of the gene, this is opposite of the peak.

GetDistTXNend2Peak.py

endF=open("/project2/gilad/briana/genome_anotation_data/RefSeq_annotations/GeneTXNEndUniq.txt","r")

endDic={}

for i,ln in enumerate(endF):
    if i > 0:
        gene, strand, end = ln.split()
        if gene not in endDic.keys():
            endDic[gene]=int(end)
        else:
            if strand == "+":
                if endDic[gene] > int(end):
                    endDic[gene]=int(end)
                else:
                    continue
            else: 
                if endDic[gene]< int(end):
                    endDic[gene]=int(end)
                else:
                    continue

peakFile="/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/Filtered_APApeaks_merged_allchrom_noMP.sort.named.noCHR_geneLocParsed.5percCov.bed"

distPeak2EndTXN=open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_noMP_GeneLoc/distPeak2EndTXN.txt","w")

n=0
for ln in open(peakFile, "r"):
    peakS=ln.split()[5]
    if peakS == "+":
        strand = "-"
    else:
        strand = "+"
    start=ln.split()[1]
    start=int(start)
    end=ln.split()[2]
    end=int(end)
    mid=(start+end)/2
    gene=ln.split()[3].split(":")[0]
    peak=ln.split()[3].split(":")[1]
    if gene in endDic.keys():
        txnEnd=endDic[gene]
        distance=txnEnd-mid
        distPeak2EndTXN.write("%s\t%s\t%d\t%s\n"%(peak, gene, distance, strand))
    else:
        print("not in file")
        n+=1
print(n)
distPeak2EndTXN.close()
        

bring to my computer:

distTXN2Peak=read.table("../data/DistTXN2Peak_genelocAnno/distPeak2EndTXN.txt", col.names = c("Peak", "name2", "Distance", "Gene_Strand"),stringsAsFactors = F)

Plot the ditribution of the ditances.

summary(distTXN2Peak$Distance)

     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-43480518     -2617        22   -107542      2638   2049472 

ggplot(distTXN2Peak, aes(x=Distance)) + geom_density()

Look at furthest ones in igv.

distTXN2Peak %>% arrange(Distance) %>% slice(1:15)

         Peak    name2  Distance Gene_Strand
1   peak50119   KANSL1 -43480518           -
2   peak50114   KANSL1 -43468229           -
3   peak50108   KANSL1 -43436524           -
4   peak50104   KANSL1 -43394661           -
5   peak50103   KANSL1 -43381414           -
6   peak50099   KANSL1 -43351333           -
7   peak50098   KANSL1 -43346626           -
8   peak50139      NSF -43257341           +
9   peak50138      NSF -43255873           +
10  peak50136      NSF -43231499           +
11  peak50135      NSF -43222606           +
12  peak50132      NSF -43212672           +
13 peak100477 HLA-DPB2 -28772478           +
14 peak100490 MIR219A1 -28770854           +
15 peak100352 HLA-DQA2 -28767889           +

KANSL1

Gene comes us more than once.
KANSL1 - 44107281 KANSL1 + 760700

NSF

Gene comes up more than once NSF + 44834830
NSF + 1577443

HLA-DPB2- Many transcripts around 4383650 but one around 33096890 HLA-DPB2 + 33096890
HLA-DPB2 + 4383650
HLA-DPB2 + 4540678
HLA-DPB2 + 4378196
HLA-DPB2 + 4421814
HLA-DPB2 + 4329060
HLA-DPB2 + 4554153
HLA-DPB2 + 4577055

MIR219A1: Many transcripts at one location and 1 at other
MIR219A1 + 33175721 MIR219A1 + 4619551 MIR219A1 + 4457028 MIR219A1 + 4649482 MIR219A1 + 4407948 MIR219A1 + 4633041

HLA-DQA2: Many transcripts at one location and 1 at other
HLA-DQA2 + 32714664 HLA-DQA2 + 4002942 HLA-DQA2 + 4160566 HLA-DQA2 + 3997229 HLA-DQA2 + 4051847 HLA-DQA2 + 3946758 HLA-DQA2 + 4171833 HLA-DQA2 + 4146332

Look at the other end of the Dist.

distTXN2Peak %>% arrange(desc(Distance)) %>% slice(1:15)

         Peak     name2 Distance Gene_Strand
1   peak71042   MACROD2  2049472           +
2   peak71043   MACROD2  1661637           +
3  peak101832       EYS  1576814           -
4   peak32698      GPC5  1409165           +
5   peak32700      GPC5  1389871           +
6   peak32701      GPC5  1377331           +
7   peak32702      GPC5  1367899           +
8   peak32703      GPC5  1347401           +
9  peak101837       EYS  1332157           -
10  peak80287     ROBO2  1321995           +
11  peak32704      GPC5  1320325           +
12  peak80288     ROBO2  1305390           +
13  peak62336 LOC730100  1284160           +
14 peak111684   CNTNAP2  1253090           +
15  peak80293     ROBO2  1213859           +

MACROD2 + 16033842

EYS - 64429875 EYS - 66039168 EYS - 66044805

Look at distribution of the absolute value:

distTXN2Peak =distTXN2Peak %>% mutate(AbsDist=abs(Distance))

summary(distTXN2Peak$AbsDist)

    Min.  1st Qu.   Median     Mean  3rd Qu.     Max. 
       0       84     2626   134050    20651 43480518 

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") 
distTXN2PeakPlot

Make sure this is not different by strand

ggplot(distTXN2Peak, aes(x=AbsDist, by=Gene_Strand, fill=Gene_Strand)) + geom_density(alpha=.5) + scale_x_log10()

Warning: Transformation introduced infinite values in continuous x-axis

Warning: Removed 4 rows containing non-finite values (stat_density).

I want to add to this the average gene length. I can get this from the txn start to txn end. Start is column 5 and end is column 6. I will filter for genes in the peaks.

txnanno=read.table("../data/RefSeq_annotations/Transcript2GeneName.dms", header=T,stringsAsFactors = F) %>% mutate(length=abs(txEnd-txStart)) %>% semi_join(distTXN2Peak, by="name2")

mean(txnanno$length)

[1] 60808.79

Now I want to add this value as a verticle line

distTXN2PeakPlot=distTXN2PeakPlot+  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

Session information

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  ggpubr_0.1.8    magrittr_1.5    cowplot_0.9.3  
 [5] reshape2_1.4.3  forcats_0.3.0   stringr_1.3.1   dplyr_0.7.6    
 [9] purrr_0.2.5     readr_1.1.1     tidyr_0.8.1     tibble_1.4.2   
[13] ggplot2_3.0.0   tidyverse_1.2.1 workflowr_1.1.1

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_0.12.19      scales_1.0.0     
[28] backports_1.1.2   jsonlite_1.5      hms_0.4.2        
[31] digest_0.6.17     stringi_1.2.4     grid_3.5.1       
[34] rprojroot_1.3-2   cli_1.0.1         tools_3.5.1      
[37] lazyeval_0.2.1    crayon_1.3.4      whisker_0.3-2    
[40] pkgconfig_2.0.2   xml2_1.2.0        lubridate_1.7.4  
[43] assertthat_0.2.0  rmarkdown_1.10    httr_1.3.1       
[46] rstudioapi_0.8    R6_2.3.0          nlme_3.1-137     
[49] git2r_0.23.0      compiler_3.5.1