Data Processing Figures

Peaks per gene

I want to create a figure similar to the one I created in https://brimittleman.github.io/comparative_threeprime/characterize.ortho.peaks.html. I will use the count distinct function from bedtools map. For this I am using the RefSeq mRNA annotations.

#!/bin/bash

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



bedtools map -c 4 -s -o count_distinct -a /project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.bed -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed  > /project2/gilad/briana/threeprimeseq/data/peakPerRefseqGene/filtered_APApeaks_perRefseqGene.txt 


#try opp strand 
bedtools map -c 4 -S -o count_distinct -a /project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.bed -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed  > /project2/gilad/briana/threeprimeseq/data/peakPerRefseqGene/filtered_APApeaks_perRefseqGene_oppStrand.txt 

library(tidyverse)

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

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✔ tibble  1.4.2     ✔ dplyr   0.7.6
✔ tidyr   0.8.1     ✔ stringr 1.4.0
✔ readr   1.1.1     ✔ forcats 0.3.0

Warning: package 'stringr' was built under R version 3.5.2

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

library(workflowr)

This is workflowr version 1.2.0
Run ?workflowr for help getting started

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

names=c("Chr", "Start", "End", "Name", "Score", "Strand", "numPeaks")
peakpergene=read.table("../data/peakPerRefSeqGene/filtered_APApeaks_perRefseqGene.txt", stringsAsFactors = F, header = F, col.names = names) %>% mutate(onePeak=ifelse(numPeaks==1, 1, 0 )) %>%  mutate(multPeaks=ifelse(numPeaks > 1, 1, 0 ))

genes1peak=sum(peakpergene$onePeak)/nrow(peakpergene) 
genesMultpeak=sum(peakpergene$multPeaks)/nrow(peakpergene)
genes0peak= 1- genes1peak - genesMultpeak

perPeak= c(round(genes0peak,digits = 3), round(genes1peak,digits = 3),round(genesMultpeak, digits = 3))
Category=c("Zero", "One", "Multiple")
perPeakdf=as.data.frame(cbind(Category,as.numeric(perPeak)))

Plot these proportions:

lab1=paste("Genes =", genes0peak*nrow(peakpergene), sep=" ")
lab2=paste("Genes =", sum(peakpergene$onePeak), sep=" ")
lab3=paste("Genes =", sum(peakpergene$multPeaks), sep=" ")

genepeakplot=ggplot(perPeakdf, aes(x="", y=perPeak, fill=Category)) + geom_bar(stat="identity")+ labs(title="Characterize genes by number of PAS", y="Proportion of Protein Coding gene", x="")+ scale_fill_brewer(palette="Paired") + coord_cartesian(ylim=c(0,1)) + annotate("text", x="", y= .35, label=lab1) + annotate("text", x="", y= .78, label=lab2) + annotate("text", x="", y= .92, label=lab3)
genepeakplot

Past versions of unnamed-chunk-5-1.png

This includes for than 1 isoform for different genes. I am going to go back to the original refseq file and resegment it. Column 13 is the gene name. Column 2 needs to start with NM because that is mRNA.

grep  "NM" ncbiRefSeq.txt | awk '{print $3 "\t" $5 "\t" $6 "\t" $2 "\t" $13 "\t" $4}' > ncbiRefSeq.mRNA.named.bed

I can write a script that writes only the longest isoform for each gene.

outfile=open("refseq.ProteinCoding.bed", "w")




infile=open("ncbiRefSeq.mRNA.named.bed", "r")

lines=infile.readlines()
lot_lines=len(lines)
for n,ln in enumerate(lines):
    chrom, start, end, mRNA, gene, strand = ln.split()
    #if first line
    if n == 0:
        #first line condition
        SE_list=[]
        cur_gene=gene
        SE_list.append(int(start))   
        SE_list.append(int(end)) 
    elif n == lot_lines-1:
        #last line condition
        if gene == cur_gene:
            SE_list.append(int(start))   
            SE_list.append(int(end))
            SE_list.sort()
            outfile.write("%s\t%d\t%d\t%s\t.\t%s\n"%(chrom, SE_list[0], SE_list[-1], gene, strand))
        else: 
           outfile.write("%s\t%d\t%d\t%s\t.\t%s\n"%(chrom, int(start), int(end), gene, strand))
    elif gene == cur_gene:
        SE_list.append(int(start))   
        SE_list.append(int(end))
    elif gene != cur_gene:
        #write out the last line but with the start end from the SE list
        prevline=lines[n-1]
        chrom2, start2, end2, mRNA2, gene2, strand2 = prevline.split()
        outfile.write("%s\t%d\t%d\t%s\t.\t%s\n"%(chrom2, SE_list[0], SE_list[-1], gene2, strand2))
        cur_gene=gene
        SE_list=[int(start), int(end)]


outfile.close()

I can check this by maknig sure there is 1 line for all of the unique names in the in file.

awk '{print $5}' ncbiRefSeq.mRNA.named.bed | sort | uniq | wc -l
#19243
wc -l refseq.ProteinCoding.bed 
#20298
sed 's/^chr//' refseq.ProteinCoding.bed > refseq.ProteinCoding.noCHR.bed

There is still a problem with the script. The problem is when the same gene name is on extra haplotypes. I could remove all of the lines in the file that have _ in the first column. These are on contigs or specfic haplotypes. They will not map to our peaks anyway. I also need to remove the chr.

This still seems lower than previos APA estimates. I had used gencode estimates before. I am gonig to run this analysis again with those gene.

#!/bin/bash

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



bedtools map -c 4 -s -o count_distinct -a /project2/gilad/briana/genome_anotation_data/gencode.v19.annotation.proteincodinggene.sort.bed   -b /project2/gilad/briana/threeprimeseq/data/mergedPeaks_comb/filtered_APApeaks_merged_allchrom.named.fixed.bed  >

Gpeakpergene=read.table("../data/peakPerRefSeqGene/filtered_APApeaks_perGencodeGene.txt", stringsAsFactors = F, header = F, col.names = names) %>% mutate(onePeak=ifelse(numPeaks==1, 1, 0 )) %>%  mutate(multPeaks=ifelse(numPeaks > 1, 1, 0 ))

Ggenes1peak=sum(Gpeakpergene$onePeak)/nrow(Gpeakpergene) 
GgenesMultpeak=sum(Gpeakpergene$multPeaks)/nrow(Gpeakpergene)
Ggenes0peak= 1- Ggenes1peak - GgenesMultpeak

GperPeak= c(round(Ggenes0peak,digits = 3), round(Ggenes1peak,digits = 3),round(GgenesMultpeak, digits = 3))

GperPeakdf=as.data.frame(cbind(Category,as.numeric(GperPeak)))

Plot these proportions:

Glab1=paste("Genes =", Ggenes0peak*nrow(Gpeakpergene), sep=" ")
Glab2=paste("Genes =", sum(Gpeakpergene$onePeak), sep=" ")
Glab3=paste("Genes =", sum(Gpeakpergene$multPeaks), sep=" ")

Ggenepeakplot=ggplot(GperPeakdf, aes(x="", y=perPeak, fill=Category)) + geom_bar(stat="identity")+ labs(title="Characterize Gencode genes by number of PAS", y="Proportion of Protein Coding gene", x="")+ scale_fill_brewer(palette="Paired") + coord_cartesian(ylim=c(0,1)) + annotate("text", x="", y= .35, label=Glab1) + annotate("text", x="", y= .78, label=Glab2) + annotate("text", x="", y= .92, label=Glab3)
Ggenepeakplot

Past versions of unnamed-chunk-12-1.png

These results are still lower than expected. This is because all of my previous analysis mapped the genes to the peaks as which were closest in the upstream direction. Here I am saying the peak must overlap the gene.

I should again look at some of the genes with the top counts in RNA seq and the 0 peaks.

I am going to run feaureCounts on 18486 guevardis with the refseq annotation with the named genes. I need to make this a SAF file.

from misc_helper import *

fout = file("/project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.SAF",'w')
fout.write("GeneID\tChr\tStart\tEnd\tStrand\n")
for ln in open("/project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.bed"):
    chrom, start, end, gene, score, strand = ln.split()
    start_i=int(start)
    end_i=int(end)
    fout.write("%s\t%s\t%d\t%d\t%s\n"%(gene, chrom, start_i, end_i, strand))
fout.close()

#!/bin/bash

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


module load Anaconda3
source activate three-prime-env


# outdir: /project2/gilad/briana/comparitive_threeprime/data/PeakwGene_quant

featureCounts -a /project2/gilad/briana/genome_anotation_data/refseq.ProteinCoding.noCHR.SAF -F SAF -o /project2/gilad/briana/threeprimeseq/data/peakPerRefseqGene/refseq18486exp.quant /project2/yangili1/LCL/RNAseqGeuvadisBams/RNAseqGeuvadis_STAR_18486.final.bam -s 1

Now I can upload the results and compare them to the peak counts in these genes.

namesRNA=c("Name", "Chr", "Start", "End", "Strand", "Length", "RNAseq")
RNAseqrefseq=read.table("../data/peakPerRefSeqGene/refseq18486exp.quant", header=T, stringsAsFactors = F, col.names = namesRNA)
RNAseqrefseq$Start=as.integer(RNAseqrefseq$Start)

Warning: NAs introduced by coercion

RNAseqrefseq$End=as.integer(RNAseqrefseq$End)

Warning: NAs introduced by coercion

Join the peakpergene dataframe with this dataframe.

refPeakandRNA=peakpergene %>% inner_join(RNAseqrefseq, by=c("Name", "Chr", "Start", "End", "Strand")) 

refPeakandRNA_noPeak=refPeakandRNA %>% filter(RNAseq!=0) %>% filter(numPeaks==0) %>% arrange(desc(RNAseq)) %>% select(Name, Start, End, Chr, RNAseq, numPeaks)

This doesnt make much sense. Seems like the peaks are on the opposite strand for the top genes. I am gonig to force opposite strandedness and see what happens.

Opeakpergene=read.table("../data/peakPerRefSeqGene/filtered_APApeaks_perRefseqGene_oppStrand.txt", stringsAsFactors = F, header = F, col.names = names) %>% mutate(onePeak=ifelse(numPeaks==1, 1, 0 )) %>%  mutate(multPeaks=ifelse(numPeaks > 1, 1, 0 ))

Ogenes1peak=sum(Opeakpergene$onePeak)/nrow(Opeakpergene) 
OgenesMultpeak=sum(Opeakpergene$multPeaks)/nrow(Opeakpergene)
Ogenes0peak= 1- Ogenes1peak - OgenesMultpeak


OperPeak= c(round(Ogenes0peak,digits = 3), round(Ogenes1peak,digits = 3),round(OgenesMultpeak, digits = 3))

OperPeakdf=as.data.frame(cbind(Category,OperPeak))

OperPeakdf$OperPeak=as.numeric(as.character(OperPeakdf$OperPeak))

Plot these proportions:

Olab1=paste("Genes =", Ogenes0peak*nrow(Opeakpergene), sep=" ")
Olab2=paste("Genes =", sum(Opeakpergene$onePeak), sep=" ")
Olab3=paste("Genes =", sum(Opeakpergene$multPeaks), sep=" ")

Ogenepeakplot=ggplot(OperPeakdf, aes(x="", y=OperPeak, by=Category, fill=Category)) + geom_bar(stat="identity")+ labs(title="Characterize Refseq genes by number of PAS- Oppstrand", y="Proportion of Protein Coding gene", x="")+ scale_fill_brewer(palette="Paired") + coord_cartesian(ylim=c(0,1)) + annotate("text", x="", y= .2, label=Olab1) + annotate("text", x="", y= .4, label=Olab2) + annotate("text", x="", y= .9, label=Olab3)
Ogenepeakplot

Past versions of unnamed-chunk-19-1.png

This makes more sense now.

refPeakandRNA_withO=Opeakpergene %>% inner_join(RNAseqrefseq, by=c("Name", "Chr", "Start", "End", "Strand")) 
refPeakandRNA_noPeakw_withO=refPeakandRNA_withO %>% filter(RNAseq!=0) %>% filter(numPeaks==0) %>% arrange(desc(RNAseq)) %>% select(Name, Start, End, Chr, RNAseq, numPeaks)

plot(sort(log10(refPeakandRNA_withO$RNAseq), decreasing = T), main="Distribution of RNA expression counts 18486", ylab="log10 Gene count", xlab="Refseq Gene")
points(sort(log10(refPeakandRNA_noPeakw_withO$RNAseq), decreasing = T), col="Red")
legend("topright", legend=c("All Gene", "Gene without Peak"), col=c("black", "red"),pch=19)

Past versions of unnamed-chunk-21-1.png

Run Kalisto on the this RNA seq line and look at this plot with the kalisto output expression TPM. I added Kallisto to the three-prime-env.

Kallisto step:

• make index: kallisto_index18486.sh

This needs to be based on a transcriptome. I will use the protein coding transcripts from https://www.gencodegenes.org/releases/28lift37.html.

#!/bin/bash

#SBATCH --job-name=kallisto_index18486
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=kallisto_index18486.out
#SBATCH --error=kallisto_index18486.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 


kallisto index  --make-unique -i /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto/RNA18486_index /project2/gilad/briana/genome_anotation_data/gencode.v28lift37.pc_transcripts.fa

• quantify: kallisto_quant18467.sh

#!/bin/bash

#SBATCH --job-name=kallisto_quant18486
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=kallisto_quant18486.out
#SBATCH --error=kallisto_quant18486.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

kallisto quant -i /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto/RNA18486_index -o /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto/ /project2/yangili1/LCL/RNAseq/RNA.18486_1.fastq.gz /project2/yangili1/LCL/RNAseq/RNA.18486_2.fastq.gz

Convert to readable with TPM:

 kallisto h5dump abundance.h5 -o /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto

This is the gencode annotation. I want to do this with the refseq transcriptome. https://www.ncbi.nlm.nih.gov/projects/genome/guide/human/

kallisto_refseqindex18486.sh

#!/bin/bash

#SBATCH --job-name=kallisto_refseqindex18486
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=kallisto_refseqindex18486.out
#SBATCH --error=kallisto_refseqindex18486.err
#SBATCH --partition=broadwl
#SBATCH --mem=50G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 


kallisto index  --make-unique -i /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto/RNA18486_refseq_index /project2/gilad/briana/genome_anotation_data/GRCh37_latest_rna.fna

• quantify: kallisto_refseq_quant18467.sh

#!/bin/bash

#SBATCH --job-name=kallisto_refseq_quant18486
#SBATCH --account=pi-yangili1
#SBATCH --time=24:00:00
#SBATCH --output=kallisto_refseq_quant18486.out
#SBATCH --error=kallisto_refseq_quant18486.err
#SBATCH --partition=broadwl
#SBATCH --mem=12G
#SBATCH --mail-type=END

module load Anaconda3
source activate three-prime-env 

kallisto quant -i /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto/RNA18486_refseq_index -o /project2/gilad/briana/threeprimeseq/data/RNAseqKallisto/refseq/project2/yangili1/LCL/RNAseq/RNA.18486_1.fastq.gz /project2/yangili1/LCL/RNAseq/RNA.18486_2.fastq.gz

I will use tximport to convert from the transcripts that are quantified in kalisto.

#source("https://bioconductor.org/biocLite.R")
#biocLite("tximport")
#biocLite("TxDb.Hsapiens.UCSC.hg19.knownGene")
library(tximport)
library("TxDb.Hsapiens.UCSC.hg19.knownGene")

Loading required package: GenomicFeatures

Loading required package: BiocGenerics

Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
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    anyDuplicated, append, as.data.frame, basename, cbind,
    colMeans, colnames, colSums, dirname, do.call, duplicated,
    eval, evalq, Filter, Find, get, grep, grepl, intersect,
    is.unsorted, lapply, lengths, Map, mapply, match, mget, order,
    paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind,
    Reduce, rowMeans, rownames, rowSums, sapply, setdiff, sort,
    table, tapply, union, unique, unsplit, which, which.max,
    which.min

Loading required package: S4Vectors

Loading required package: stats4

Attaching package: 'S4Vectors'

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    first, rename

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    expand.grid

Loading required package: IRanges

Attaching package: 'IRanges'

The following objects are masked from 'package:dplyr':

    collapse, desc, slice

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

    reduce

Loading required package: GenomeInfoDb

Loading required package: GenomicRanges

Loading required package: AnnotationDbi

Loading required package: Biobase

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Attaching package: 'AnnotationDbi'

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

    select

Import Kalisto resutls:

#I need to make a gene to transcript ID with the transcript id and gene id columns
tx2gene=read.table("../data/RNAkalisto/ncbiRefSeq.txn2gene.txt" ,header= F, sep="\t", stringsAsFactors = F)

txi.kallisto.tsv <- tximport("../data/RNAkalisto/abundance.tsv", type = "kallisto", tx2gene = tx2gene)

Note: importing `abundance.h5` is typically faster than `abundance.tsv`

reading in files with read_tsv

1 
removing duplicated transcript rows from tx2gene
transcripts missing from tx2gene: 99
summarizing abundance
summarizing counts
summarizing length

txi.kallisto.tsv$abundance= as.data.frame(txi.kallisto.tsv$abundance) %>% rownames_to_column(var="Name")
colnames(txi.kallisto.tsv$abundance)= c("Name", "TPM")

Now I want to join this with the RNA seq data so I am looking at the expression tpm rather than counts.

refPeakandRNA_withO_TPM=refPeakandRNA_withO %>% inner_join(txi.kallisto.tsv$abundance, by="Name") %>% filter(TPM>0)


refPeakandRNA_noPeakw_withO_TPM=refPeakandRNA_noPeakw_withO %>% inner_join(txi.kallisto.tsv$abundance, by="Name") %>% filter(TPM >0)

I can now replot the genes without peaks by TPM for the RNA seq rather than count.

plot(sort(log10(refPeakandRNA_withO_TPM$TPM), decreasing = T), main="Distribution of RNA expression 18486", ylab="log10 TPM", xlab="Refseq Gene")
points(sort(log10(refPeakandRNA_noPeakw_withO_TPM$TPM), decreasing = T), col="Red")
legend("topright", legend=c("All Genes", "Genes without Peak"), col=c("black", "red"),pch=19)

Past versions of unnamed-chunk-30-1.png

I can use this to look at some of the highest expressed genes that we do not have peaks for.

• HIST2H2AA4: no coverage at location

• HIST1H2AC: no coverage at location

• BOP1: Not in the protein coding gene file. Are 2 peaks.

• GSTM1: no coverage at location

• NPIPA1: no coverage at location

• SLX1A: difficult to interpret due to overlapping genes in the region

• HIST1H2BJ: no coverage at location

• MTX1: peak in the original filtered peaks, not in the refseq gene - lost due to direction, the peak goes the same was as the gene. probably noise

• GALE - looks like there is a peak but we are not detecting it. May be too close to the next peak at the 3’ end of LYPLA2 gene.

• HGH1: no coverage at location

• MSMP: difficult to interpret due to overlapping genes in the region

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] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
 [2] GenomicFeatures_1.32.3                 
 [3] AnnotationDbi_1.42.1                   
 [4] Biobase_2.40.0                         
 [5] GenomicRanges_1.32.7                   
 [6] GenomeInfoDb_1.16.0                    
 [7] IRanges_2.14.12                        
 [8] S4Vectors_0.18.3                       
 [9] BiocGenerics_0.26.0                    
[10] tximport_1.8.0                         
[11] bindrcpp_0.2.2                         
[12] cowplot_0.9.3                          
[13] reshape2_1.4.3                         
[14] workflowr_1.2.0                        
[15] forcats_0.3.0                          
[16] stringr_1.4.0                          
[17] dplyr_0.7.6                            
[18] purrr_0.2.5                            
[19] readr_1.1.1                            
[20] tidyr_0.8.1                            
[21] tibble_1.4.2                           
[22] ggplot2_3.0.0                          
[23] tidyverse_1.2.1                        

loaded via a namespace (and not attached):
 [1] nlme_3.1-137                matrixStats_0.54.0         
 [3] bitops_1.0-6                fs_1.2.6                   
 [5] lubridate_1.7.4             bit64_0.9-7                
 [7] RColorBrewer_1.1-2          progress_1.2.0             
 [9] httr_1.3.1                  rprojroot_1.3-2            
[11] tools_3.5.1                 backports_1.1.2            
[13] R6_2.3.0                    DBI_1.0.0                  
[15] lazyeval_0.2.1              colorspace_1.3-2           
[17] withr_2.1.2                 tidyselect_0.2.4           
[19] prettyunits_1.0.2           bit_1.1-14                 
[21] compiler_3.5.1              git2r_0.24.0               
[23] cli_1.0.1                   rvest_0.3.2                
[25] xml2_1.2.0                  DelayedArray_0.6.6         
[27] rtracklayer_1.40.6          labeling_0.3               
[29] scales_1.0.0                digest_0.6.17              
[31] Rsamtools_1.32.3            rmarkdown_1.11             
[33] XVector_0.20.0              pkgconfig_2.0.2            
[35] htmltools_0.3.6             rlang_0.2.2                
[37] readxl_1.1.0                rstudioapi_0.9.0           
[39] RSQLite_2.1.1               bindr_0.1.1                
[41] jsonlite_1.6                BiocParallel_1.14.2        
[43] RCurl_1.95-4.11             magrittr_1.5               
[45] GenomeInfoDbData_1.1.0      Matrix_1.2-14              
[47] Rcpp_0.12.19                munsell_0.5.0              
[49] stringi_1.2.4               whisker_0.3-2              
[51] yaml_2.2.0                  SummarizedExperiment_1.10.1
[53] zlibbioc_1.26.0             plyr_1.8.4                 
[55] grid_3.5.1                  blob_1.1.1                 
[57] crayon_1.3.4                lattice_0.20-35            
[59] Biostrings_2.48.0           haven_1.1.2                
[61] hms_0.4.2                   knitr_1.20                 
[63] pillar_1.3.0                biomaRt_2.36.1             
[65] XML_3.98-1.16               glue_1.3.0                 
[67] evaluate_0.13               modelr_0.1.2               
[69] cellranger_1.1.0            gtable_0.2.0               
[71] assertthat_0.2.0            broom_0.5.0                
[73] GenomicAlignments_1.16.0    memoise_1.1.0