ChIP-Seq Workflow Template
Author: Daniela Cassol (danielac@ucr.edu) and Thomas Girke (thomas.girke@ucr.edu)
Last update: 14 October, 2020
Source:vignettes/systemPipeChIPseq.Rmd
systemPipeChIPseq.RmdIntroduction
Users want to provide here background information about the design of their ChIP-Seq project.
Workflow environment
Load packages
The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016). The package allows users to run the entire analysis workflow interactively or with a single command while also generating the corresponding analysis report. For details see systemPipeR's main vignette.
library(systemPipeR)
Generate workflow environment
systemPipeRdata package is a helper package to generate a fully populated systemPipeR workflow environment in the current working directory with a single command. All the instruction for generating the workflow template are provide in the systemPipeRdata vignette here.
After building and loading the workflow environment generated by genWorkenvir from systemPipeRdata all data inputs are stored in a data/ directory and all analysis results will be written to a separate results/ directory, while the systemPipeChIPseq.Rmd script and the targets file are expected to be located in the parent directory. The R session is expected to run from this parent directory. Additional parameter files are stored under param/.
To work with real data, users want to organize their own data similarly and substitute all test data for their own data. To rerun an established workflow on new data, the initial targets file along with the corresponding FASTQ files are usually the only inputs the user needs to provide.
For more details, please consult the documentation here. More information about the targets files from systemPipeR can be found here.
Run workflow
Now open the R markdown script systemPipeRIBOseq.Rmdin your R IDE (e.g. vim-r or RStudio) and run the workflow as outlined below.
Here pair-end workflow example is provided. Please refer to the main vignette systemPipeR.Rmd for running the workflow with single-end data.
If you are running on a single machine, use following code as an example to check if some tools used in this workflow are in your environment PATH. No warning message should be shown if all tools are installed.
Read preprocessing
Experiment definition provided by targets file
The targets file defines all FASTQ files and sample comparisons of the analysis workflow.
targetspath <- system.file("extdata", "targetsPE_chip.txt", package = "systemPipeR") targets <- read.delim(targetspath, comment.char = "#") targets[1:4, -c(5, 6)]
## FileName1 FileName2
## 1 ./data/SRR446027_1.fastq.gz ./data/SRR446027_2.fastq.gz
## 2 ./data/SRR446028_1.fastq.gz ./data/SRR446028_2.fastq.gz
## 3 ./data/SRR446029_1.fastq.gz ./data/SRR446029_2.fastq.gz
## 4 ./data/SRR446030_1.fastq.gz ./data/SRR446030_2.fastq.gz
## SampleName Factor Date SampleReference
## 1 M1A M1 23-Mar-2012
## 2 M1B M1 23-Mar-2012
## 3 A1A A1 23-Mar-2012 M1A
## 4 A1B A1 23-Mar-2012 M1BRead quality filtering and trimming
The following example shows how one can design a custom read preprocessing function using utilities provided by the ShortRead package, and then apply it with preprocessReads in batch mode to all FASTQ samples referenced in the corresponding SYSargs2 instance (trim object below). More detailed information on read preprocessing is provided in systemPipeR's main vignette.
First, we construct SYSargs2 object from cwl and yml param and targets files.
dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", package = "systemPipeR") trim <- loadWF(targets = targetspath, wf_file = "trim-pe.cwl", input_file = "trim-pe.yml", dir_path = dir_path) trim <- renderWF(trim, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_")) trim output(trim)[1:2]
Next, we execute the code for trimming all the raw data.
filterFct <- function(fq, cutoff = 20, Nexceptions = 0) { qcount <- rowSums(as(quality(fq), "matrix") <= cutoff, na.rm = TRUE) fq[qcount <= Nexceptions] # Retains reads where Phred scores are >= cutoff with N # exceptions } preprocessReads(args = trim, Fct = "filterFct(fq, cutoff=20, Nexceptions=0)", batchsize = 1e+05) writeTargetsout(x = trim, file = "targets_chip_trimPE.txt", step = 1, new_col = c("FileName1", "FileName2"), new_col_output_index = c(1, 2), overwrite = TRUE)
FASTQ quality report
The following seeFastq and seeFastqPlot functions generate and plot a series of useful quality statistics for a set of FASTQ files including per cycle quality box plots, base proportions, base-level quality trends, relative k-mer diversity, length and occurrence distribution of reads, number of reads above quality cutoffs and mean quality distribution. The results are written to a PDF file named fastqReport.pdf. Parallelization of FASTQ quality report via scheduler (e.g. Slurm) across several compute nodes.
library(BiocParallel) library(batchtools) f <- function(x) { library(systemPipeR) targets <- system.file("extdata", "targetsPE_chip.txt", package = "systemPipeR") dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", package = "systemPipeR") trim <- loadWorkflow(targets = targets, wf_file = "trim-pe.cwl", input_file = "trim-pe.yml", dir_path = dir_path) trim <- renderWF(trim, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_")) seeFastq(fastq = infile1(trim)[x], batchsize = 1e+05, klength = 8) } resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024) param <- BatchtoolsParam(workers = 4, cluster = "slurm", template = "batchtools.slurm.tmpl", resources = resources) fqlist <- bplapply(seq(along = trim), f, BPPARAM = param) pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist)) seeFastqPlot(unlist(fqlist, recursive = FALSE)) dev.off()
Alignments
Read mapping with Bowtie2
The NGS reads of this project will be aligned with Bowtie2 against the reference genome sequence (Langmead and Salzberg 2012). The parameter settings of the aligner are defined in the bowtie2-index.cwl and bowtie2-index.yml files. In ChIP-Seq experiments it is usually more appropriate to eliminate reads mapping to multiple locations. To achieve this, users want to remove the argument setting -k 50 non-deterministic in the configuration files.
Building the index:
dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-idx", package = "systemPipeR") idx <- loadWorkflow(targets = NULL, wf_file = "bowtie2-index.cwl", input_file = "bowtie2-index.yml", dir_path = dir_path) idx <- renderWF(idx) idx cmdlist(idx) ## Run in single machine runCommandline(idx, make_bam = FALSE)
The following submits 18 alignment jobs via a scheduler to a computer cluster.
targets <- system.file("extdata", "targetsPE_chip.txt", package = "systemPipeR") dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-pe", package = "systemPipeR") args <- loadWF(targets = targets, wf_file = "bowtie2-mapping-pe.cwl", input_file = "bowtie2-mapping-pe.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_")) args cmdlist(args)[1:2] output(args)[1:2]
moduleload(modules(args)) # Skip if a module system is not used resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024) reg <- clusterRun(args, FUN = runCommandline, more.args = list(args = args, dir = FALSE), conffile = ".batchtools.conf.R", template = "batchtools.slurm.tmpl", Njobs = 18, runid = "01", resourceList = resources) getStatus(reg = reg) waitForJobs(reg = reg) args <- output_update(args, dir = FALSE, replace = TRUE, extension = c(".sam", ".bam")) ## Updates the output(args) to the right location in the subfolders output(args)
Alternatively, one can run the alignments sequentially on a single system.
args <- runCommandline(args, force = F)
Check whether all BAM files have been created and write out the new targets file.
writeTargetsout(x = args, file = "targets_bam.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE) outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) file.exists(outpaths)
Read and alignment stats
The following provides an overview of the number of reads in each sample and how many of them aligned to the reference.
read_statsDF <- alignStats(args = args) write.table(read_statsDF, "results/alignStats.xls", row.names = FALSE, quote = FALSE, sep = "\t") read.delim("results/alignStats.xls")
Create symbolic links for viewing BAM files in IGV
The symLink2bam function creates symbolic links to view the BAM alignment files in a genome browser such as IGV without moving these large files to a local system. The corresponding URLs are written to a file with a path specified under urlfile, here IGVurl.txt. Please replace the directory and the user name.
symLink2bam(sysargs = args, htmldir = c("~/.html/", "somedir/"), urlbase = "http://cluster.hpcc.ucr.edu/~tgirke/", urlfile = "./results/IGVurl.txt")
Utilities for coverage data
The following introduces several utilities useful for ChIP-Seq data. They are not part of the actual workflow.
Plot coverage for defined region
library(ggbio) myloc <- c("Chr1", 1, 1e+05) ga <- readGAlignments(outpaths[1], use.names = TRUE, param = ScanBamParam(which = GRanges(myloc[1], IRanges(as.numeric(myloc[2]), as.numeric(myloc[3]))))) autoplot(ga, aes(color = strand, fill = strand), facets = strand ~ seqnames, stat = "coverage")
Peak calling with MACS2
Merge BAM files of replicates prior to peak calling
Merging BAM files of technical and/or biological replicates can improve the sensitivity of the peak calling by increasing the depth of read coverage. The mergeBamByFactor function merges BAM files based on grouping information specified by a factor, here the Factor column of the imported targets file. It also returns an updated SYSargs2 object containing the paths to the merged BAM files as well as to any unmerged files without replicates. This step can be skipped if merging of BAM files is not desired.
dir_path <- system.file("extdata/cwl/mergeBamByFactor", package = "systemPipeR") args <- loadWF(targets = "targets_bam.txt", wf_file = "merge-bam.cwl", input_file = "merge-bam.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName = "_BAM_PATH_", SampleName = "_SampleName_")) args_merge <- mergeBamByFactor(args = args, overwrite = TRUE) writeTargetsout(x = args_merge, file = "targets_mergeBamByFactor.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
Peak calling without input/reference sample
MACS2 can perform peak calling on ChIP-Seq data with and without input samples (Zhang et al. 2008). The following performs peak calling without input on all samples specified in the corresponding args object. Note, due to the small size of the sample data, MACS2 needs to be run here with the nomodel setting. For real data sets, users want to remove this parameter in the corresponding *.param file(s).
dir_path <- system.file("extdata/cwl/MACS2/MACS2-noinput/", package = "systemPipeR") args <- loadWF(targets = "targets_mergeBamByFactor.txt", wf_file = "macs2.cwl", input_file = "macs2.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) runCommandline(args, make_bam = FALSE, force = T) outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) file.exists(outpaths) writeTargetsout(x = args, file = "targets_macs.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
Peak calling with input/reference sample
To perform peak calling with input samples, they can be most conveniently specified in the SampleReference column of the initial targets file. The writeTargetsRef function uses this information to create a targets file intermediate for running MACS2 with the corresponding input samples.
writeTargetsRef(infile = "targets_mergeBamByFactor.txt", outfile = "targets_bam_ref.txt", silent = FALSE, overwrite = TRUE) dir_path <- system.file("extdata/cwl/MACS2/MACS2-input/", package = "systemPipeR") args_input <- loadWF(targets = "targets_bam_ref.txt", wf_file = "macs2-input.cwl", input_file = "macs2.yml", dir_path = dir_path) args_input <- renderWF(args_input, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_")) cmdlist(args_input)[1] ## Run args_input <- runCommandline(args_input, make_bam = FALSE, force = T) outpaths_input <- subsetWF(args_input, slot = "output", subset = 1, index = 1) file.exists(outpaths_input) writeTargetsout(x = args_input, file = "targets_macs_input.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
The peak calling results from MACS2 are written for each sample to separate files in the results directory. They are named after the corresponding files with extensions used by MACS2.
Identify consensus peaks
The following example shows how one can identify consensus preaks among two peak sets sharing either a minimum absolute overlap and/or minimum relative overlap using the subsetByOverlaps or olRanges functions, respectively. Note, the latter is a custom function imported below by sourcing it.
# source('http://faculty.ucr.edu/~tgirke/Documents/R_BioCond/My_R_Scripts/rangeoverlapper.R') outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) ## escolher um dos outputs index peak_M1A <- outpaths["M1A"] peak_M1A <- as(read.delim(peak_M1A, comment = "#")[, 1:3], "GRanges") peak_A1A <- outpaths["A1A"] peak_A1A <- as(read.delim(peak_A1A, comment = "#")[, 1:3], "GRanges") (myol1 <- subsetByOverlaps(peak_M1A, peak_A1A, minoverlap = 1)) # Returns any overlap myol2 <- olRanges(query = peak_M1A, subject = peak_A1A, output = "gr") # Returns any overlap with OL length information myol2[values(myol2)["OLpercQ"][, 1] >= 50] # Returns only query peaks with a minimum overlap of 50%
Annotate peaks with genomic context
Annotation with ChIPpeakAnno package
The following annotates the identified peaks with genomic context information using the ChIPpeakAnno and ChIPseeker packages, respectively (Zhu et al. 2010; Yu, Wang, and He 2015).
library(ChIPpeakAnno) library(GenomicFeatures) dir_path <- system.file("extdata/cwl/annotate_peaks", package = "systemPipeR") args <- loadWF(targets = "targets_macs.txt", wf_file = "annotate-peaks.cwl", input_file = "annotate-peaks.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff", dataSource = "TAIR", organism = "Arabidopsis thaliana") ge <- genes(txdb, columns = c("tx_name", "gene_id", "tx_type")) for (i in seq(along = args)) { peaksGR <- as(read.delim(infile1(args)[i], comment = "#"), "GRanges") annotatedPeak <- annotatePeakInBatch(peaksGR, AnnotationData = genes(txdb)) df <- data.frame(as.data.frame(annotatedPeak), as.data.frame(values(ge[values(annotatedPeak)$feature, ]))) outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) write.table(df, outpaths[i], quote = FALSE, row.names = FALSE, sep = "\t") } writeTargetsout(x = args, file = "targets_peakanno.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
The peak annotation results are written for each peak set to separate files in the results directory. They are named after the corresponding peak files with extensions specified in the annotate_peaks.param file, here *.peaks.annotated.xls.
Annotation with ChIPseeker package
Same as in previous step but using the ChIPseeker package for annotating the peaks.
library(ChIPseeker) for (i in seq(along = args)) { peakAnno <- annotatePeak(infile1(args)[i], TxDb = txdb, verbose = FALSE) df <- as.data.frame(peakAnno) outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) write.table(df, outpaths[i], quote = FALSE, row.names = FALSE, sep = "\t") } writeTargetsout(x = args, file = "targets_peakanno.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
Summary plots provided by the ChIPseeker package. Here applied only to one sample for demonstration purposes.
peak <- readPeakFile(infile1(args)[1]) covplot(peak, weightCol = "X.log10.pvalue.") outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1) peakHeatmap(outpaths[1], TxDb = txdb, upstream = 1000, downstream = 1000, color = "red") plotAvgProf2(outpaths[1], TxDb = txdb, upstream = 1000, downstream = 1000, xlab = "Genomic Region (5'->3')", ylab = "Read Count Frequency")
Count reads overlapping peaks
The countRangeset function is a convenience wrapper to perform read counting iteratively over serveral range sets, here peak range sets. Internally, the read counting is performed with the summarizeOverlaps function from the GenomicAlignments package. The resulting count tables are directly saved to files, one for each peak set.
library(GenomicRanges) dir_path <- system.file("extdata/cwl/count_rangesets", package = "systemPipeR") args <- loadWF(targets = "targets_macs.txt", wf_file = "count_rangesets.cwl", input_file = "count_rangesets.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) ## Bam Files targets <- system.file("extdata", "targetsPE_chip.txt", package = "systemPipeR") dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-pe", package = "systemPipeR") args_bam <- loadWF(targets = targets, wf_file = "bowtie2-mapping-pe.cwl", input_file = "bowtie2-mapping-pe.yml", dir_path = dir_path) args_bam <- renderWF(args_bam, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) args_bam <- output_update(args_bam, dir = FALSE, replace = TRUE, extension = c(".sam", ".bam")) outpaths <- subsetWF(args_bam, slot = "output", subset = 1, index = 1) bfl <- BamFileList(outpaths, yieldSize = 50000, index = character()) countDFnames <- countRangeset(bfl, args, mode = "Union", ignore.strand = TRUE) writeTargetsout(x = args, file = "targets_countDF.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
Differential binding analysis
The runDiff function performs differential binding analysis in batch mode for several count tables using edgeR or DESeq2 (Robinson, McCarthy, and Smyth 2010; Love, Huber, and Anders 2014). Internally, it calls the functions run_edgeR and run_DESeq2. It also returns the filtering results and plots from the downstream filterDEGs function using the fold change and FDR cutoffs provided under the dbrfilter argument.
dir_path <- system.file("extdata/cwl/rundiff", package = "systemPipeR") args_diff <- loadWF(targets = "targets_countDF.txt", wf_file = "rundiff.cwl", input_file = "rundiff.yml", dir_path = dir_path) args_diff <- renderWF(args_diff, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) cmp <- readComp(file = args_bam, format = "matrix") dbrlist <- runDiff(args = args_diff, diffFct = run_edgeR, targets = targets.as.df(targets(args_bam)), cmp = cmp[[1]], independent = TRUE, dbrfilter = c(Fold = 2, FDR = 1)) writeTargetsout(x = args_diff, file = "targets_rundiff.txt", step = 1, new_col = "FileName", new_col_output_index = 1, overwrite = TRUE)
GO term enrichment analysis
The following performs GO term enrichment analysis for each annotated peak set.
dir_path <- system.file("extdata/cwl/annotate_peaks", package = "systemPipeR") args <- loadWF(targets = "targets_bam_ref.txt", wf_file = "annotate-peaks.cwl", input_file = "annotate-peaks.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_")) args_anno <- loadWF(targets = "targets_macs.txt", wf_file = "annotate-peaks.cwl", input_file = "annotate-peaks.yml", dir_path = dir_path) args_anno <- renderWF(args_anno, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) annofiles <- subsetWF(args_anno, slot = "output", subset = 1, index = 1) gene_ids <- sapply(names(annofiles), function(x) unique(as.character(read.delim(annofiles[x])[, "geneId"])), simplify = FALSE) load("data/GO/catdb.RData") BatchResult <- GOCluster_Report(catdb = catdb, setlist = gene_ids, method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9, gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)
Motif analysis
Parse DNA sequences of peak regions from genome
Enrichment analysis of known DNA binding motifs or de novo discovery of novel motifs requires the DNA sequences of the identified peak regions. To parse the corresponding sequences from the reference genome, the getSeq function from the Biostrings package can be used. The following example parses the sequences for each peak set and saves the results to separate FASTA files, one for each peak set. In addition, the sequences in the FASTA files are ranked (sorted) by increasing p-values as expected by some motif discovery tools, such as BCRANK.
library(Biostrings) library(seqLogo) library(BCRANK) dir_path <- system.file("extdata/cwl/annotate_peaks", package = "systemPipeR") args <- loadWF(targets = "targets_macs.txt", wf_file = "annotate-peaks.cwl", input_file = "annotate-peaks.yml", dir_path = dir_path) args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_", SampleName = "_SampleName_")) rangefiles <- infile1(args) for (i in seq(along = rangefiles)) { df <- read.delim(rangefiles[i], comment = "#") peaks <- as(df, "GRanges") names(peaks) <- paste0(as.character(seqnames(peaks)), "_", start(peaks), "-", end(peaks)) peaks <- peaks[order(values(peaks)$X.log10.pvalue., decreasing = TRUE)] pseq <- getSeq(FaFile("./data/tair10.fasta"), peaks) names(pseq) <- names(peaks) writeXStringSet(pseq, paste0(rangefiles[i], ".fasta")) }
Motif discovery with BCRANK
The Bioconductor package BCRANK is one of the many tools available for de novo discovery of DNA binding motifs in peak regions of ChIP-Seq experiments. The given example applies this method on the first peak sample set and plots the sequence logo of the highest ranking motif.
set.seed(0) BCRANKout <- bcrank(paste0(rangefiles[1], ".fasta"), restarts = 25, use.P1 = TRUE, use.P2 = TRUE) toptable(BCRANKout) topMotif <- toptable(BCRANKout, 1) weightMatrix <- pwm(topMotif, normalize = FALSE) weightMatrixNormalized <- pwm(topMotif, normalize = TRUE) pdf("results/seqlogo.pdf") seqLogo(weightMatrixNormalized) dev.off()
BCRANK
Version Information
## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.1 LTS
##
## Matrix products: default
## BLAS: /usr/local/bin/R/lib/R/lib/libRblas.so
## LAPACK: /usr/local/bin/R/lib/R/lib/libRlapack.so
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## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices
## [6] utils datasets methods base
##
## other attached packages:
## [1] systemPipeR_1.22.0 ShortRead_1.46.0
## [3] GenomicAlignments_1.24.0 SummarizedExperiment_1.18.2
## [5] DelayedArray_0.14.1 matrixStats_0.56.0
## [7] Biobase_2.48.0 BiocParallel_1.22.0
## [9] Rsamtools_2.4.0 Biostrings_2.56.0
## [11] XVector_0.28.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [17] BiocStyle_2.16.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 rjson_0.2.20
## [3] hwriter_1.3.2 ellipsis_0.3.1
## [5] rprojroot_1.3-2 fs_1.5.0
## [7] rstudioapi_0.11 bit64_4.0.2
## [9] AnnotationDbi_1.50.3 codetools_0.2-16
## [11] splines_4.0.0 knitr_1.29
## [13] jsonlite_1.7.0 annotate_1.66.0
## [15] GO.db_3.11.4 dbplyr_1.4.4
## [17] png_0.1-7 pheatmap_1.0.12
## [19] graph_1.66.0 BiocManager_1.30.10
## [21] compiler_4.0.0 httr_1.4.2
## [23] GOstats_2.54.0 backports_1.1.9
## [25] assertthat_0.2.1 Matrix_1.2-18
## [27] limma_3.44.3 formatR_1.7
## [29] htmltools_0.5.0 prettyunits_1.1.1
## [31] tools_4.0.0 gtable_0.3.0
## [33] glue_1.4.1 GenomeInfoDbData_1.2.3
## [35] Category_2.54.0 dplyr_1.0.2
## [37] rsvg_2.1 batchtools_0.9.13
## [39] rappdirs_0.3.1 V8_3.2.0
## [41] Rcpp_1.0.5 pkgdown_1.5.1
## [43] vctrs_0.3.2 rtracklayer_1.48.0
## [45] xfun_0.16 stringr_1.4.0
## [47] lifecycle_0.2.0 XML_3.99-0.5
## [49] edgeR_3.30.3 zlibbioc_1.34.0
## [51] MASS_7.3-52 scales_1.1.1
## [53] BSgenome_1.56.0 VariantAnnotation_1.34.0
## [55] hms_0.5.3 RBGL_1.64.0
## [57] RColorBrewer_1.1-2 yaml_2.2.1
## [59] curl_4.3 memoise_1.1.0
## [61] ggplot2_3.3.2 biomaRt_2.44.1
## [63] latticeExtra_0.6-29 stringi_1.4.6
## [65] RSQLite_2.2.0 genefilter_1.70.0
## [67] desc_1.2.0 checkmate_2.0.0
## [69] GenomicFeatures_1.40.1 DOT_0.1
## [71] rlang_0.4.7 pkgconfig_2.0.3
## [73] bitops_1.0-6 evaluate_0.14
## [75] lattice_0.20-41 purrr_0.3.4
## [77] bit_4.0.4 tidyselect_1.1.0
## [79] GSEABase_1.50.1 AnnotationForge_1.30.1
## [81] magrittr_1.5 bookdown_0.20
## [83] R6_2.4.1 generics_0.0.2
## [85] base64url_1.4 DBI_1.1.0
## [87] withr_2.2.0 pillar_1.4.6
## [89] survival_3.2-3 RCurl_1.98-1.2
## [91] tibble_3.0.3 crayon_1.3.4
## [93] BiocFileCache_1.12.1 rmarkdown_2.3
## [95] jpeg_0.1-8.1 progress_1.2.2
## [97] locfit_1.5-9.4 grid_4.0.0
## [99] data.table_1.13.0 blob_1.2.1
## [101] Rgraphviz_2.32.0 digest_0.6.25
## [103] xtable_1.8-4 brew_1.0-6
## [105] openssl_1.4.2 munsell_0.5.0
## [107] askpass_1.1Funding
This project was supported by funds from the National Institutes of Health (NIH) and the National Science Foundation (NSF).
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