visualize_peaks
Brian M. Schilder
Most recent update:
2021-01-28
# root.dir <- "/rds/general/project/neurogenomics-lab/live/GitRepos/CUT_n_TAG"
# root.dir <- "/Volumes/RDS/project/neurogenomics-lab/live/GitRepos/CUT_n_TAG"
root.dir <- "/Volumes/bms20/projects/neurogenomics-lab/live/GitRepos/CUT_n_TAG"
CT.dir <- file.path(root.dir,"processed_data/HK5M2BBXY_merged")
CT.peaks_dir <- file.path(CT.dir,"bwa/mergedLibrary/macs/narrowPeak")
CT.bw_dir <- file.path(CT.dir,"bwa/mergedLibrary/bigwig")
source("functions.R")
try({setwd(root.dir)})
knitr::opts_chunk$set(echo = T, root.dir = root.dir)
knitr::opts_knit$set(root.dir = root.dir)
library(dplyr)
# library(echolocatoR) # devtools::install_github("RajLabMSSM/echolocatoR")
library(rtracklayer) # BiocManager::install("rtracklayer")
library(ggbio) # BiocManager::install("ggbio")
library(rGADEM) # BiocManager::install("rGADEM")
library(BSgenome.Hsapiens.UCSC.hg19) #BiocManager::install("BSgenome.Hsapiens.UCSC.hg19")
library(ChIPseeker) #BiocManager::install("ChIPseeker")
library(ggupset) # install.packages("ggupset")
library(ggimage) # install.packages("ggimage")
library(clusterProfiler)
library(ReactomePA) # BiocManager::install("ReactomePA")
library(rGADEM)
library(BSgenome.Hsapiens.UCSC.hg19)
library(GenomicRanges)
# IMPORTANT! Otherwise can have issues with rtracklayer::import()
# base::closeAllConnections()The following scripts primarily follows these tutorials: - genomation
- ChIPseeker
Data descriptions
ENCODE
All ENCODE peak/bigwig/bam/fastq.gz files can be found here on UCSC.
For general info on file formats (BED, narrowPeak, broadPeak) see UCSC Genome Browser documentation. Specifically, there are 3 ENCODE histone datasets:
- wgEncode Broad Histone
- broadPeak: Yes
- narrowPeak: No
- H3k27ac in K562 cell-line: Yes.
- H3k27me3 in K562 cell-line: Yes.
- broadPeak: Yes
- wgEncode Uw Histone
- broadPeak: Yes
- narrowPeak: Yes
- H3k27ac in K562 cell-line: No.
- H3k27me3 in K562 cell-line: Yes.
- broadPeak: Yes
- wgEncode Sydh Histone
- broadPeak: No
- narrowPeak: Yes
- broadPeak: No
Peak files are also available for all three datasets in .bb format on UCSC. - The latest version of rtracklayer (>=1.5) has import.bb function, but can’t seem to install it…
TF-specific ChIP-seq ENCODE (narrow) peak files can again be found here.
Several other datasets of potential interest for comparisons to CUT&TAG (though they only contain bigWig files, not peak files):
- wgEncodeRegMarkH3k4me1/
- wgEncodeRegMarkH3k4me3/
- wgEncodeRegMarkH3k27ac/
ENCODE metadata
# contains links and metadata for all ENCODE files, but the format is not very readable...
meta <- data.table::fread("http://ftp.ebi.ac.uk/pub/databases/ensembl/encode/integration_data_jan2011/files.txt", sep=";", sep2 = " ")
links <- meta$V1 # links to .bb files?Create query data
Import a GRanges object from the echolocatoR Fine-mapping Portal to use for querying a small subset of the CUT&TAG data.
file_names <- echolocatoR::GITHUB.list_files(creator = "RajLabMSSM",
repo = "Fine_Mapping_Shiny",
query = "*Nalls23andMe_2019.*BST1.UKB.multi_finemap.csv.gz")
gr.dat <- GenomicRanges::makeGRangesFromDataFrame(data.table::fread(file_names),
keep.extra.columns = T,
seqnames.field = "CHR",
start.field = "POS",
end.field = "POS")
# ! IMPORTANT !: Needs to be in same chromosome format as bigwig in order to query!
suppressWarnings(GenomeInfoDb::seqlevelsStyle(gr.dat) <- "UCSC")ENCODE
.broadPeak
- Naming conventions for broadPeak come from UCSC.
- IMPORTANT!: Note that “pValue” and “qValue” are actually the -log() of these metrics. Additionally, -1 means that this metric is not available (instead of NA?….)
ENCODE.broadPeaks <- rtracklayer::import("http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/wgEncodeBroadHistoneK562H3k27acStdPk.broadPeak.gz") .bigWig
- ENCODE bigWig and peaks data is hosted on the UCSC Genome Browser here.
- Specifcally, we’ll be querying the H3K27ac ChIP-seq data from K562 cell-lines, to best match up with the CUT&TAG assay being used here.
ENCODE.bw_filt <- import.bw.parallel(bw.file = "http://hgdownload.soe.ucsc.edu/goldenPath/hg19/encodeDCC/wgEncodeBroadHistone/wgEncodeBroadHistoneK562H3k27acStdSig.bigWig",
gr.dat = ENCODE.broadPeaks,
bw.file_format = "UCSC")
head(ENCODE.broadPeaks)CUT&TAG
When I ran the nf-core/atacseq pipeline for the first time on this CUT&TAG data, I accidentally mis-specified the design matrix such that the H3k27ac and H3k27ame3 assays were merged into one (in the mergedReplicates subfolder).
However, we can still recover the independent assays from the mergedLibrary since each assay only had one sample. In this case, use these keys:
- control_R1 = H3k27ac
- control_R2 = H3k27ame3
- control_R1 = H3k27ac
File types:
The nf-core/atacseq pipeline produces of number of peaks-related files.
- bigwig
- summits
- annotatePeaks
- narrowPeaks and/or broadPeaks
.bigWig
- Import a subset of a bigWig file based on the coordinates in the
GRangesobject (ENCODE broadPeaks).
CT.bw_filt <- import.bw.parallel(bw.file = file.path(CT.bw_dir,
"control_R1.mLb.clN.bigWig"),
gr.dat = ENCODE.broadPeaks,
bw.file_format = "NCBI")
head(CT.bw_filt).summits
Summits are especially the peaks of the peaks (1bp/peak).
CT.summits <- rtracklayer::import(file.path(CT.peaks_dir,
"control_R1.mLb.clN_summits.bed"))
CT.summits$width <- GenomicRanges::width(CT.summits)
summary(CT.summits$width)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 1 1 1 1 1.annotatePeaks
This previously annotated file contains additional information like which gene each peak is closest to.
# Narrow peaks
CT.annotatePeaks <- data.table::fread(file.path(CT.peaks_dir,"control_R1.mLb.clN_peaks.annotatePeaks.txt")) %>%
dplyr::mutate(peak_score=`Peak Score`) %>%
GenomicRanges::makeGRangesFromDataFrame(seqnames.field = "Chr",
start.field = "Start", end.field = "End",strand.field = "Strand",
keep.extra.columns = T)
CT.annotatePeaks$width <- GenomicRanges::width(CT.annotatePeaks)
summary(CT.annotatePeaks$width) ## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 192.0 220.0 266.0 306.1 354.0 1381.0.narrowPeak
Column names derived from UCSC documentation.
Interestingly, these peaks are slightly different lengths than those in .annotatePeaks.
NOTE!:
rtracklayer::importwill get confused by a narrowPeak file that is missing the extra stats columns, which are missing here because it’s not a mergedReplicates file. Instead, just read it in as a regular bed file for now.
CT.narrowPeaks <- rtracklayer::import.bed(file.path(CT.peaks_dir,
"control_R1.mLb.clN_peaks.narrowPeak"))
CT.narrowPeaks$width <- GenomicRanges::width(CT.narrowPeaks)
summary(CT.narrowPeaks$width)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 192.0 220.0 266.0 306.1 354.0 1381.0ChIPseeker::covplot(peak = CT.narrowPeaks,
weightCol = "score",#"qValue",
title = "CUT&TAG peaks")
CUT&TAG vs. ENCODE
Prepare peaks lists
suppressWarnings(GenomeInfoDb::seqlevelsStyle(CT.narrowPeaks) <- "UCSC")
peaks_list <- list("ICL CUT&TAG"=CT.narrowPeaks,
"ENCODE ChIP-seq"=ENCODE.broadPeaks)
tagMatrix_list <- prepare_tagMatrix(peaks_list=peaks_list)
annotatePeak_list <- prepare_annotatePeak(peaks_list = peaks_list)Compare overlap
compare_peak_overlap(gr.query = CT.narrowPeaks,
gr.subject = ENCODE.broadPeaks)## [1] "4465 / 5052 (88.38%) of query peaks overlap with subject peaks."
## [1] "4465 / 58937 (7.58%) of subject peaks overlap with query peaks."Compare peaks
Compare peaks and their annotations across datasets using tools like ChIPseeker.
tagHeatmap
ChIPseeker::tagHeatmap(tagMatrix_list,
xlim=c(-3000, 3000))
plotAvgProf
ChIPseeker::plotAvgProf(tagMatrix_list,
conf = 0.95, resample = 100,
xlim=c(-3000, 3000),
facet="row")## >> Running bootstrapping for tag matrix... 2021-01-28 00:57:19
## >> Running bootstrapping for tag matrix... 2021-01-28 00:57:44
plotAnnoBar
ChIPseeker::plotAnnoBar(annotatePeak_list)
plotDistToTSS
ChIPseeker::plotDistToTSS(annotatePeak_list)
upsetplot
# peakAnno <- annotatePeak_list$CT
# ChIPseeker::plotAnnoPie(peakAnno)
# ChIPseeker::plotAnnoBar(peakAnno)
# ChIPseeker::vennpie(peakAnno)
lapply(annotatePeak_list, function(x){ChIPseeker::upsetplot(x, vennpie=T)})## Warning: Removed 2 rows containing non-finite values (stat_count).## $`ICL CUT&TAG`
##
## $`ENCODE ChIP-seq`
## Gene enrichment
There are different strategies for assigning each peak to a gene. ChIPseeker provides two main alternatives:
1. annotatePeak(): Default method, used by prepare_annotatePeak(). 2. seq2gene(): Alternative method, can be used with use_seq2gene=T argument where applicable.
clusterProfiler
Use clusterProfiler to compare functional profiles across datasets.
res_clusterProfiler <- enrich_clusterProfiler(annotatePeak_list,
fun="enrichKEGG",
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
use_seq2gene = F,
show_plot = T)
ReactomePA enrichment
res_ReactomePA <- enrich_ReactomePA(annotatePeak_list,
pvalueCutoff = 0.05,
pAdjustMethod = "BH",
use_seq2gene = T)## [1] "ICL CUT&TAG"## Loading required package: org.Hs.eg.db## 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## 
## [1] "ENCODE ChIP-seq"
Gene overlap
vp_res <- gene_vennplot(annotatePeak_list = annotatePeak_list,
use_seq2gene = F)
## NULLvp_res.seq2gene <- gene_vennplot(annotatePeak_list = annotatePeak_list,
use_seq2gene = T)
## NULLStatistical testing of peak overlap
- Calculate whether overlap is statistically significant between two datasets of peaks.
- In this case, we’re comparing CUT&TAG peaks to ENCODE ChiP-seq peaks.
Convert to UCSC format
CT.UCSC_path <- file.path(CT.peaks_dir,"control_R1.mLb.clN_peaks.narrowPeak")
rtracklayer::export.bed(CT.narrowPeaks,
con = CT.UCSC_path)enrichPeakOverlapiterates enrichment tests over many perturbations (shuffle), parallizing acrossdetectCores() - 1cores.- Parameter
targetPeakcan be the folder, e.g. hg19, that containing bed files.
txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene::TxDb.Hsapiens.UCSC.hg19.knownGene
peak_enrich <- ChIPseeker::enrichPeakOverlap(queryPeak = CT.UCSC_path,
targetPeak = file.path(root.dir,"raw_data/ENCODE","wgEncodeBroadHistoneK562H3k27acStdPk.broadPeak.gz"),
TxDb = txdb,
pAdjustMethod = "BH",
nShuffle = 100,
chainFile = NULL,
verbose = T)## >> permutation test of peak overlap... 2021-01-28 00:58:44
##
|
| | 0%print(peak_enrich)## qSample
## 1 control_R1.mLb.clN_peaks.narrowPeak
## tSample qLen tLen N_OL pvalue
## 1 wgEncodeBroadHistoneK562H3k27acStdPk.broadPeak.gz 5052 58937 3801 0.00990099
## p.adjust
## 1 0.00990099Discover TFBS motifs
under construction
- From the genomatic documentation.
- Motif discovery step uses
rGADEMR package. - MotifbreakR could also be used, but is more designed for individual SNPs rather than genomic ranges (to know knowledge). See
echolocatoR::MOTIFBREAKR()for a convenience wrapper of this package.
# order the peaks by qvalue, and take top 250 peaks
CT.narrowPeaks_top = CT.narrowPeaks[order(CT.narrowPeaks$pValue)]
CT.narrowPeaks_top = head(CT.narrowPeaks_top, n = 500)
# CT.narrowPeaks_top_stored <- CT.narrowPeaks_top
# merge nearby peaks
CT.narrowPeaks_top = GenomicRanges::reduce(CT.narrowPeaks_top, )
# Create a region of +/-50 bp around the center of the peaks
CT.narrowPeaks_top = GenomicRanges::resize(CT.narrowPeaks_top,
width = 50, fix='center')
suppressWarnings(GenomeInfoDb::seqlevelsStyle(CT.narrowPeaks_top) <- "UCSC")
CT.narrowPeaks_top$score <- GenomicRanges::score(CT.narrowPeaks_top)
CT.narrowPeaks_top$width <- GenomicRanges::width(CT.narrowPeaks_top)
## Example data
# pwd<-"" #INPUT FILES- BedFiles, FASTA, etc.
# path<- system.file("extdata/Test_100.bed",package="rGADEM")
# BedFile<-paste(pwd,path,sep="")
# Sequences <- rtracklayer::import(BedFile)
# Sequences <- rtracklayer::import(BedFile)
gadem <- GADEM(Sequences=CT.narrowPeaks_top,
verbose=1,
genome=Hsapiens)Session Info
sessioninfo::session_info()## ─ Session info ───────────────────────────────────────────────────────────────
## setting value
## version R version 3.6.3 (2020-02-29)
## os macOS 10.16
## system x86_64, darwin15.6.0
## ui X11
## language (EN)
## collate en_GB.UTF-8
## ctype en_GB.UTF-8
## tz Europe/London
## date 2021-01-28
##
## ─ Packages ───────────────────────────────────────────────────────────────────
## package * version date lib
## AnnotationDbi * 1.48.0 2019-10-29 [1]
## AnnotationFilter 1.10.0 2019-10-29 [1]
## askpass 1.1 2019-01-13 [1]
## assertthat 0.2.1 2019-03-21 [1]
## backports 1.2.1 2020-12-09 [1]
## base64enc 0.1-3 2015-07-28 [1]
## Biobase * 2.46.0 2019-10-29 [1]
## BiocFileCache 1.10.2 2019-11-08 [1]
## BiocGenerics * 0.32.0 2019-10-29 [1]
## BiocManager 1.30.10 2019-11-16 [1]
## BiocParallel 1.20.1 2019-12-21 [1]
## biomaRt 2.46.2 2021-01-27 [1]
## Biostrings * 2.54.0 2019-10-29 [1]
## biovizBase 1.34.1 2019-12-04 [1]
## bit 4.0.4 2020-08-04 [1]
## bit64 4.0.5 2020-08-30 [1]
## bitops 1.0-6 2013-08-17 [1]
## blob 1.2.1 2020-01-20 [1]
## boot 1.3-26 2021-01-25 [1]
## BSgenome * 1.54.0 2019-10-29 [1]
## BSgenome.Hsapiens.UCSC.hg19 * 1.4.0 2021-01-20 [1]
## cachem 1.0.1 2021-01-21 [1]
## caTools 1.18.1 2021-01-11 [1]
## checkmate 2.0.0 2020-02-06 [1]
## ChIPseeker * 1.22.1 2019-12-23 [1]
## cli 2.2.0 2020-11-20 [1]
## cluster 2.1.0 2019-06-19 [1]
## clusterProfiler * 3.14.3 2020-01-08 [1]
## colorspace 2.0-0 2020-11-11 [1]
## cowplot 1.1.1 2020-12-30 [1]
## crayon 1.3.4 2017-09-16 [1]
## curl 4.3 2019-12-02 [1]
## data.table 1.13.6 2020-12-30 [1]
## DBI 1.1.1 2021-01-15 [1]
## dbplyr 2.0.0 2020-11-03 [1]
## DelayedArray 0.12.3 2020-04-09 [1]
## dichromat 2.0-0 2013-01-24 [1]
## digest 0.6.27 2020-10-24 [1]
## DO.db 2.9 2021-01-20 [1]
## DOSE 3.12.0 2019-10-29 [1]
## dplyr * 1.0.3 2021-01-15 [1]
## ellipsis 0.3.1 2020-05-15 [1]
## enrichplot 1.6.1 2019-12-16 [1]
## ensembldb 2.10.2 2019-11-20 [1]
## europepmc 0.4 2020-05-31 [1]
## evaluate 0.14 2019-05-28 [1]
## fansi 0.4.2 2021-01-15 [1]
## farver 2.0.3 2020-01-16 [1]
## fastmap 1.1.0 2021-01-25 [1]
## fastmatch 1.1-0 2017-01-28 [1]
## fgsea 1.12.0 2019-10-29 [1]
## foreign 0.8-75 2020-01-20 [1]
## Formula 1.2-4 2020-10-16 [1]
## generics 0.1.0 2020-10-31 [1]
## GenomeInfoDb * 1.22.1 2020-03-27 [1]
## GenomeInfoDbData 1.2.2 2020-11-16 [1]
## GenomicAlignments 1.22.1 2019-11-12 [1]
## GenomicFeatures 1.38.2 2020-02-15 [1]
## GenomicRanges * 1.38.0 2019-10-29 [1]
## GGally 2.1.0 2021-01-06 [1]
## ggbio * 1.34.0 2020-11-23 [1]
## ggforce 0.3.2 2020-06-23 [1]
## ggimage * 0.2.8 2020-04-02 [1]
## ggplot2 * 3.3.3 2020-12-30 [1]
## ggplotify 0.0.5 2020-03-12 [1]
## ggraph 2.0.4 2020-11-16 [1]
## ggrepel 0.9.1 2021-01-15 [1]
## ggridges 0.5.3 2021-01-08 [1]
## ggupset * 0.3.0 2020-05-05 [1]
## glue 1.4.2 2020-08-27 [1]
## GO.db 3.10.0 2021-01-13 [1]
## GOSemSim 2.12.1 2020-03-19 [1]
## gplots 3.1.1 2020-11-28 [1]
## graph 1.64.0 2019-10-29 [1]
## graphite 1.32.0 2019-10-29 [1]
## graphlayouts 0.7.1 2020-10-26 [1]
## gridExtra 2.3 2017-09-09 [1]
## gridGraphics 0.5-1 2020-12-13 [1]
## gtable 0.3.0 2019-03-25 [1]
## gtools 3.8.2 2020-03-31 [1]
## Hmisc 4.4-2 2020-11-29 [1]
## hms 1.0.0 2021-01-13 [1]
## htmlTable 2.1.0 2020-09-16 [1]
## htmltools 0.5.1.1 2021-01-22 [1]
## htmlwidgets 1.5.3 2020-12-10 [1]
## httr 1.4.2 2020-07-20 [1]
## igraph 1.2.6 2020-10-06 [1]
## IRanges * 2.20.2 2020-01-13 [1]
## jpeg 0.1-8.1 2019-10-24 [1]
## jsonlite 1.7.2 2020-12-09 [1]
## KernSmooth 2.23-18 2020-10-29 [1]
## knitr 1.30 2020-09-22 [1]
## labeling 0.4.2 2020-10-20 [1]
## lattice 0.20-41 2020-04-02 [1]
## latticeExtra 0.6-29 2019-12-19 [1]
## lazyeval 0.2.2 2019-03-15 [1]
## lifecycle 0.2.0 2020-03-06 [1]
## magick 2.6.0 2021-01-13 [1]
## magrittr 2.0.1 2020-11-17 [1]
## MASS 7.3-53 2020-09-09 [1]
## Matrix 1.3-2 2021-01-06 [1]
## matrixStats 0.57.0 2020-09-25 [1]
## memoise 2.0.0 2021-01-26 [1]
## munsell 0.5.0 2018-06-12 [1]
## nnet 7.3-15 2021-01-24 [1]
## openssl 1.4.3 2020-09-18 [1]
## org.Hs.eg.db * 3.10.0 2021-01-13 [1]
## OrganismDbi 1.28.0 2019-10-29 [1]
## pillar 1.4.7 2020-11-20 [1]
## pkgconfig 2.0.3 2019-09-22 [1]
## plotrix 3.8-1 2021-01-21 [1]
## plyr 1.8.6 2020-03-03 [1]
## png 0.1-7 2013-12-03 [1]
## polyclip 1.10-0 2019-03-14 [1]
## prettyunits 1.1.1 2020-01-24 [1]
## progress 1.2.2 2019-05-16 [1]
## ProtGenerics 1.18.0 2019-10-29 [1]
## purrr 0.3.4 2020-04-17 [1]
## qvalue 2.18.0 2019-10-29 [1]
## R6 2.5.0 2020-10-28 [1]
## rappdirs 0.3.1 2016-03-28 [1]
## RBGL 1.62.1 2019-10-30 [1]
## RColorBrewer 1.1-2 2014-12-07 [1]
## Rcpp 1.0.6 2021-01-15 [1]
## RCurl 1.98-1.2 2020-04-18 [1]
## reactome.db 1.70.0 2021-01-20 [1]
## ReactomePA * 1.30.0 2019-10-29 [1]
## reshape 0.8.8 2018-10-23 [1]
## reshape2 1.4.4 2020-04-09 [1]
## rGADEM * 2.34.1 2019-12-16 [1]
## rlang 0.4.10 2020-12-30 [1]
## rmarkdown 2.6 2020-12-14 [1]
## rpart 4.1-15 2019-04-12 [1]
## Rsamtools 2.2.3 2020-02-23 [1]
## RSQLite 2.2.3 2021-01-24 [1]
## rstudioapi 0.13 2020-11-12 [1]
## rtracklayer * 1.46.0 2019-10-29 [1]
## rvcheck 0.1.8 2020-03-01 [1]
## S4Vectors * 0.24.4 2020-04-09 [1]
## scales 1.1.1 2020-05-11 [1]
## seqLogo * 1.52.0 2019-10-29 [1]
## sessioninfo 1.1.1 2018-11-05 [1]
## stringi 1.5.3 2020-09-09 [1]
## stringr 1.4.0 2019-02-10 [1]
## SummarizedExperiment 1.16.1 2019-12-19 [1]
## survival 3.2-7 2020-09-28 [1]
## tibble 3.0.5 2021-01-15 [1]
## tidygraph 1.2.0 2020-05-12 [1]
## tidyr 1.1.2 2020-08-27 [1]
## tidyselect 1.1.0 2020-05-11 [1]
## triebeard 0.3.0 2016-08-04 [1]
## tweenr 1.0.1 2018-12-14 [1]
## TxDb.Hsapiens.UCSC.hg19.knownGene 3.2.2 2021-01-14 [1]
## urltools 1.7.3 2019-04-14 [1]
## VariantAnnotation 1.32.0 2019-10-29 [1]
## vctrs 0.3.6 2020-12-17 [1]
## viridis 0.5.1 2018-03-29 [1]
## viridisLite 0.3.0 2018-02-01 [1]
## withr 2.4.1 2021-01-26 [1]
## xfun 0.20 2021-01-06 [1]
## XML 3.99-0.3 2020-01-20 [1]
## xml2 1.3.2 2020-04-23 [1]
## XVector * 0.26.0 2019-10-29 [1]
## yaml 2.2.1 2020-02-01 [1]
## zlibbioc 1.32.0 2019-10-29 [1]
## source
## Bioconductor
## Bioconductor
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 3.6.0)
## Bioconductor
## Github (grimbough/biomaRt@d7d0ad1)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.3)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## bioc_git2r (@15346db)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## Bioconductor
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.3)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.0)
## Bioconductor
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.0)
## CRAN (R 3.6.3)
## CRAN (R 3.6.2)
## CRAN (R 3.6.0)
## CRAN (R 3.6.2)
## Bioconductor
## CRAN (R 3.6.0)
## Bioconductor
##
## [1] /Library/Frameworks/R.framework/Versions/3.6/Resources/library