ApproachForGeneAssignment.Rmd

---
title: "Approach to better assign genes to peaks"
author: "Briana Mittleman"
date: "1/28/2019"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```


##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: 
```{bash,eval=F}
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
```{bash,eval=F}
#!/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.  

```{bash,eval=F}
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   

3. Coding Exon  

5. Intron  

1. 3' UTR  

2. (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
```{bash,eval=F}
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 
 
```{bash,eval=F}
#!/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  
 
```{bash,eval=F}
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  
 
```{bash,eval=F}
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

```{bash,eval=F}
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:  

```{bash,eval=F}
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
```{bash,eval=F}
#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
```{bash,eval=F}
#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
```{bash,eval=F}
#!/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
```{r,eval=F}
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

```{bash,eval=F}
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.  

```{r}
library(workflowr)
library(tidyverse)
library(reshape2)
library(cowplot)
library(ggpubr)
```


```{r}
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"))
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"))

```

```{r}
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)
```

```{r}
#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  

```{r}
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  


```{r}
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:  

```{r}
ggsave(peakUsage5perc, file="../output/plots/QC_plots/peakUsage5perc_noMP_geneLocAnno.png")
```

Genes covered with these annoations. 

```{r}
#nuclear
nrow(NuclearPeakUSMean_filt) 
#total
nrow(TotalPeakUSMean_filt) 
```

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

Look at number of peaks in each set.  

```{r}
#nuclear  
NuclearPeakUSMean %>% filter(nuclearPeakUs_CountNum_mean>=.05) %>% nrow()
#total
TotalPeakUSMean %>% filter(totalPeakUs_CountNum_mean>=.05) %>% nrow()
```
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  

```{r}
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  

```{bash,eval=F}
#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
```{bash,eval=F}
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
```{bash,eval=F}
#!/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

```{bash,eval=F}
#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   

```{bash,eval=F}
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  

```{bash,eval=F}
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  

```{bash,eval=F}
#!/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

```{bash,eval=F}
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  

```{bash,eval=F}
#!/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
```

```{bash,eval=F}
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  

```{r}
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  

```{r}
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))
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:  

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

Files to make: 
-bed file with all 5% peaks
- bed file with refseq anno