Prepare leafcutter pheno combined peak

Last updated: 2018-08-15

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Unstaged changes:
    Modified:   analysis/28ind.peak.explore.Rmd
    Modified:   analysis/cleanupdtseq.internalpriming.Rmd
    Modified:   analysis/dif.iso.usage.leafcutter.Rmd
    Modified:   analysis/explore.filters.Rmd
    Modified:   analysis/peak.cov.pipeline.Rmd
    Modified:   analysis/test.max2.Rmd
    Modified:   code/Snakefile

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File	Version	Author	Date	Message
Rmd	7ccb243	brimittleman	2018-08-15	code for pheno file sep total and nuc
html	1ca6084	brimittleman	2018-08-15	Build site.
Rmd	7393002	brimittleman	2018-08-15	code for pheno file
html	e0eefe6	brimittleman	2018-08-14	Build site.
Rmd	edbdbcc	brimittleman	2018-08-14	attempt at x/y pheno format
html	c67380e	brimittleman	2018-08-14	Build site.
Rmd	ba9a74f	brimittleman	2018-08-14	add phenotype leafcutter analysis

Like I did on the first 16 individuals, I want to prepare a phenotype file for leafcutter. I will use this to start calling QTLs. I am using the filtered peaks called with Yang’s script. I need a file that has the peak and the coverage per individual. The phenotype per peak per individual is coverage at peak/coverage for all peaks in the same gene. First step is to map the peaks to a gene. I am going to use the refseq genes because they look like that have better annotated UTRs. I am going to subset to only the NM tagged mRNAs.

/project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm.sort.bed

awk '$4 ~ /NM/ {print}' ncbiRefSeq_sm.sort.bed > ncbiRefSeq_sm.sort.mRNA.bed

I will use bedtools intersect and have it write peak and the gene that it intersects with. A is the peaks and B is the genes. I want to write out A with -wa and -wb because I want all of the info. I can then subset the parts I care about after. I want to force strandedness with -s. I say it is sorted with -sorted

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

bedtools intersect -wa -wb -sorted -s -a /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed -b /project2/gilad/briana/genome_anotation_data/ncbiRefSeq_sm_noChr.sort.mRNA.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.bed

The result of this file has both files. I want to keep columns 1-6 and 10. This will be the peaks and the gene that overlaped it.

awk '{print $1 "\t" $2 "\t" $3 "\t" $4 "\t" $5 "\t" $6 "\t" $10}' /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes.bed > /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes_sm.bed

Now I can run feature counts on this file. In need to make the file into a saf file. This file has GeneID, Chr, Start, End, Strand. I want the ID to be peak#:chr1:start:end:strand:gene

from misc_helper import *

fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom_refseqGenes_sm.bed"):
    chrom, start, end, name, score, strand, gene = ln.split()
    name_i=int(name)
    start_i=int(start)
    end_i=int(end)
    ID = "peak%d:%s:%d:%d:%s:%s"%(name_i, chrom, start_i, end_i, strand, gene)
    fout.write("%s\t%s\t%d\t%d\t%s\n"%(ID, chrom, start_i, end_i, strand))
fout.close()

ref_gene_peak_fc.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env


featureCounts -a /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant.fc /project2/gilad/briana/threeprimeseq/data/sort/*-sort.bam -s 1

The header of this file will need to be changed. I can do this by writing it out in python. fix_head_fc.py


infile= open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_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()

The next step is looking by gene and make the x/y form. x is the number that already appears and y is the sum for all peaks in a specific gene.

The final file will just have the GeneID column and a column for each individual.

To work on this step I am going to make a smaller version of this file that I can easily work with interactively in python.

head -n 4 filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_fixed.fc > small.test.txt

#example from yangs dir /project2/yangili1/CMC/reformat_counts,py


from misc_helper import *
import gzip

dic_IND = {}
dic_BAM = {}
header = [x for x in gzip.open("DGN_perind.counts.gz").readline().split()]
                                                                                                                                           
for ln in open("file_id_mapping.txt"):
    bam, IND = ln.split()
    if bam not in header: continue
    IND = IND.strip()
    dic_IND[bam] = IND
    if IND not in dic_BAM:
        dic_BAM[IND] = []
    dic_BAM[IND].append(bam)


INDs = dic_BAM.keys()
print INDs, len(INDs)
fout = gzip.open("DGNmerged_perind.counts.gz",'w')

fout.write(" ".join(['chrom']+INDs)+'\n')


for dic in stream_table(gzip.open("DGN_perind.counts.gz"),' '):
    buf = [dic['chrom']]
    for ind in INDs:
        T,B = 0, 0
        for bam in dic_BAM[ind]:
            t,b =dic[bam].split('/')
            T+=int(t)
            B+=int(b)
            #print ind, t,b
        buf.append("%d/%d"%(T,B))
    fout.write(" ".join(buf)+'\n')

fout.close()

Re-write this for my test file. I need a file with the bams and the individuals. I can make this using the header from the previous file.

create_fileid.py

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


<!-- from misc_helper import * -->


<!-- dic_IND = {} -->
<!-- dic_BAM = {} -->

<!-- for ln in open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/file_id_mapping.txt"): -->
<!--     bam, IND = ln.split() -->
<!--     IND = IND.strip() -->
<!--     dic_IND[bam] = IND -->
<!--     if IND not in dic_BAM: -->
<!--         dic_BAM[IND] = [] -->
<!--     dic_BAM[IND].append(bam) -->


<!-- INDs = dic_BAM.keys() -->
<!-- print INDs, len(INDs) -->
<!-- fout = open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/small.test.counts.txt",'w') -->

<!-- fout.write(" ".join(['chrom']+INDs)+'\n') -->


<!-- for dic in stream_table(open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/small.test.txt"),' '): -->
<!--     print(dic) -->
<!--     buf = [dic['chrom']] -->
<!--     for ind in INDs: -->
<!--         T,B = 0, 0 -->
<!--         for bam in dic_BAM[ind]: -->
<!--             t,b =dic[bam].split('/') -->
<!--             T+=int(t) -->
<!--             B+=int(b) -->
<!--             print ind, t,b -->
<!--         buf.append("%d/%d"%(T,B)) -->
<!--     fout.write(" ".join(buf)+'\n') -->

<!-- fout.close() -->

I dont think this is the right script. It looks like the DGN_perind.counts.gz file is already in the x/y format. I need to look more into this script or write my own. I will def need dictionaries for the genes and for the individuals.

makePhenoRefSeqPeaks.py


#PYTHON 3

dic_IND = {}
dic_BAM = {}

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


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

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_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/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_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/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno.txt","w")
peak=["PeakID"]
fout.write(" ".join(peak + inds) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_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]
        buff=[id]
        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_makePhen.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks.py

Seperate total and nuclear columns. I can run FC seperatly then fix the headers and rerun the makephen file.

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env


featureCounts -a /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_Total.fc /project2/gilad/briana/threeprimeseq/data/sort/*-T-*-sort.bam -s 1

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env


featureCounts -a /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_Nuclear.fc /project2/gilad/briana/threeprimeseq/data/sort/*-N-*-sort.bam -s 1

Fix the headers: * fix_head_fc_tot.py


infile= open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_sm_quant_Total.fc", "r")
fout = file("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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_nuc.py


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

Create file IDs:

create_fileid_total.py

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

create_fileid_nuc.py

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

Make pheno:

makePhenoRefSeqPeaks_Total.py


#PYTHON 3

dic_IND = {}
dic_BAM = {}

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


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

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Total.txt","w")
peak=["PeakID"]
fout.write(" ".join(peak + inds) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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]
        buff=[id]
        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_Nuclear.py


#PYTHON 3

dic_IND = {}
dic_BAM = {}

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


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

#list of genes   

count_file=open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_pheno_Nuclear.txt","w")
peak=["PeakID"]
fout.write(" ".join(peak + inds) + '\n' )
count_file=open("/project2/gilad/briana/threeprimeseq/data/filt_peak_refGene_cov/filtered_APApeaks_merged_allchrom_refseqGenes_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]
        buff=[id]
        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 the pheno.py scripts.

run_makePhen_sep.sh

#!/bin/bash

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

module load Anaconda3
source activate three-prime-env

python makePhenoRefSeqPeaks_Total.py 

python makePhenoRefSeqPeaks_Nuclear.py

Session information

sessionInfo()

R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_0.12.18      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.23.0      magrittr_1.5      evaluate_0.11    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.6.0     rmarkdown_1.10    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.1.19       compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1

Prepare leafcutter pheno combined peak

Briana Mittleman

8/14/2018

Session information