esATAC-Introduction
Zheng Wei and Wei Zhang
Fri Nov 3 21:15:59 2017
Introduction
The esATAC package provides a dataflow graphs organized pipeline for quantifying and annotating ATAC-seq and DNase-seq Reads in R, which integrate the functionality of several R packages (such as Rsamtools, ChIPpeakAnno and so on) and external softwares (e.g. AdapterRemoval[1], bowtie2[2], through the Rowtie2 package and Fseq[3]). Users can call the preset pipeline functions or rebuild the workflow with elements process of esATAC for sequence data (FASTQ, SAM, BAM, BED file) complete/partial processing and analyzing easily and flexibly in a single R script. That will be convenient to migrate, share and reproduce all details such as parameters settings, intermediate result and so on. Besides, a quality control report file in HTML, which is able to be viewed in web browser, will be created in preset pipelines.
esATAC can be easily installed on various operator system platforms (Windows, Linux, Mac OS). All functions in package consume up to 16G memory. Most function only consume less than 8G. So the package is available for not only servers but also most of PC. esATAC supports analysis of both single end reads and paired-end ATAC-seq and DNase-seq sequencing reads data. We have successfully apply the pipeline on raw datasets (FASTQ files) from GEO. The related sequencing platform are Illumina. All valid standard format intermediate result files (FASTQ, SAM, BAM, BED file) generated by other program (such as BAM BED files from ENCODE) are also tested by rebuilt sub-pipeline.
Flowchart and overview
If you do not know where to start with ATAC-seq or DNase-seq data, you can print flowchart like this:
library(esATAC)
printMap()Following the flowchart, related functions could be found in manual.
The workflow start with “UnzipAndMerge” function atacUnzipAndMerge. It unzips and merges the replicates into one FASTQ file(two for paired end file). Names of reads will be renamed as numbers: 1,2,3,… by calling “Renamer” function atacRenamer. The file will be smaller for further analysis. Adapter of reads may be found and removed by “RemoveAdapter” function atacRemoveAdapter. Then reads are ready for mapping to reference genome. “Bowtie2Mapping” mapping function atacBowtie2Mapping can do this job. “SamToBam”, “Rsortbam”,“BamToBed”,“SamToBed” and “BedUtils” provide general processing methods for SAM file including converting format into BAM or BED file, sorting according to chromosome/start site/end site, reads conditional filtering, reads shifting and so on. The ready-use reads in BED file may call peak by “PeakCallingFseq” function atacPeakCallingFseq. The pipeline also provide quality control elements (e.g. “FastQC”, “LibComplexQC”, “TSSQC”) and some general genome function analysis elements (e.g. “RMotifScan”,“RPeakAnno”). For more detail, you can see the manual or the examples in following sections.
Contact
Contact
This package is developed and maintained by members of Xiaowo Wang Lab
MOE Key Laboratory of Bioinformatics and Bioinformatics Division,
TNLIST / Department of Automation, Tsinghua University
email:{wei-z14,w-zhang16}(at)mails.tsinghua.edu.cn
Preparation
Package Installation
To install the latest version of esATAC, you will need to be using the latest version of R. esATAC is part of Bioconductor project, so you can install esATAC and its dependencies like this:
source(“http://www.bioconductor.org/biocLite.R”)
biocLite(“esATAC”)
Loading
Just like other R package, you need to load esATAC like this each time before using the package.
library(esATAC)If you need to use fseq, we recommend to set max memory size for java (8G, 8000M in the example). Or rJava will use the default parameter for fseq.
options(java.parameters = "-Xmx8000m")Loading Recommend Package
The BSgenome package, TxDb known gene package and OrgDb annotation package for some functions are required. We recommend to install and load the specific species related packages before using the packages.
library(magrittr)
library(BSgenome.Hsapiens.UCSC.hg19)
library(TxDb.Hsapiens.UCSC.hg19.knownGene)
library(org.Hs.eg.db)
library(R.utils)
library(magrittr)Configure Other Parameters
These configurations are also optional. “tmpdir” is the path to save all of the temporary data and the default result storage path. If it is configured, current work directory will be set as “tmpdir”. “threads” is the maximum threads allowed to be created for data processing. The default value is 1.
# we use temp directiory here
# change it to your directiory because the intermediate file may be huge
td<-tempdir()
options(atacConf=setConfigure("tmpdir",td))
options(atacConf=setConfigure("threads",8))Reference Data Installation and Loading
We strongly recommend to install reference data first before using the package although it is optional. “refdir” is the folder that will save all of the reference data. “genome” is the genome name like hg19, hg38, mm10, mm9 and so on. The program will detect the elements that have not been installed and install them. Some resources need to be downloaded from internet. So don’t forget to connect internet during installation. Or the installation will be failed. If all of the reference data was installed, these two lines still need to be called for configuring the reference data path and genome.
#uncomment and modify to run:
#options(atacConf=setConfigure("refdir","path/to/refdatafolder"))
#options(atacConf=setConfigure("genome","hg19"))NOTE: The installation will consume several hours for data download, building bowtie2 index depending on computer performance and network bandwidth.
NOTE: The installation is network based. Please keep your network connection. But you don need to worry about disconnect. We will continue to check finished part and only build unfinish part.
Genome hg19 and mm10 are supported currently. More genomes will be supported in the future.
If the reference data is not configured, the related argument of functions has to be set manually during using.
Datasets
Most of the test datasets in this package are generated from GEO: SRR891271 (from GSE47753)[7]. The data is ATAC-seq paired end sequencing for GM12878 cell line. We random sampling 20000 mapped fragments from chr20 and rebuild raw paired-end FASTQ files(file names with chr20 prefix). We also subsample the reads in SAM file and peak calling BED result files. Besides, the files in “uzmg”, “adrm” and “bt2” are the test files from AdapterRemoval and Bowtie2. For detail, you can read the subsequent sections.
Preset Pipeline
Note: The users have to configure step by step in last section to call preset pipeline. All of the reference, temporary data storage path, max memory for rJava and threads allowed to be created will according to the configurations.
Case Study
For single-end and paired-end case study, fastq file(s) of sequencing data are required. Adapter is also required for single-end data. The preset pipeline can be called like this:
td<-tempdir()
options(atacConf=setConfigure("threads",4))
dir.create(file.path(td,"ref"))
# Change the refdir to your installed refdir path
# or it will be time comsuming to build bowtie2 index.
options(atacConf=setConfigure("refdir",file.path(td,"ref")))
options(atacConf=setConfigure("genome","hg19"))
bedbzfile11 <- c(
system.file(package="esATAC", "extdata", "chr20_1.1.fq.gz"),
system.file(package="esATAC", "extdata", "chr20_1.2.fq.bz2")
)
bedbzfile12 <- c(
system.file(package="esATAC", "extdata", "chr20_2.1.fq.gz"),
system.file(package="esATAC", "extdata", "chr20_2.2.fq.bz2")
)
# for single end
dir.create(file.path(td,"single"))
options(atacConf=setConfigure("tmpdir",file.path(td,"single")))
conclusion1<-atacPipe(fastqInput1 = bedbzfile11,adapter1 = "CTGTCTCTTATACACATCTCCGAGCCCACGAGACTGAAG")
# for paired end
dir.create(file.path(td,"paired"))
options(atacConf=setConfigure("tmpdir",file.path(td,"paired")))
conclusion2<-atacPipe(fastqInput1 = bedbzfile11,fastqInput2 = bedbzfile12)First of all, you need to configure the max threads that may be created, the reference directory (folder path) and the genome. If you haven’t installed the reference, you can set path for reference storage that can be used next time, rather than using the example temp path in example. If you have installed the reference, set the path last time you have installed. The package will only check the reference and install the deprecated files rather than all of the files.
Secondly, prepare your raw sequencing data FASTQ files of single-end or paired-end
Finally, you will receive an conclusion list (conclusion1 and conclusion2 in example code). A HTML report of quality control and analysis will be created for paired-end data.
This process is time consuming. You can visit our website to see the result example:
Case-Control Study
For case-Control study, paired-end sequencing data are supported only. Two fastq files of sequencing data for case and control each are required. The preset pipeline can be called like this:
td<-tempdir()
options(atacConf=setConfigure("threads",4))
dir.create(file.path(td,"ref"))
# change the refdir to your installed refdir path
# or it will be time comsuming to build bowtie2 index
options(atacConf=setConfigure("refdir",file.path(td,"ref")))
options(atacConf=setConfigure("genome","hg19"))
case <- c(
system.file(package="esATAC", "extdata", "chr20_1.1.fq.gz"),
system.file(package="esATAC", "extdata", "chr20_2.1.fq.gz")
)
ctrl <- c(
system.file(package="esATAC", "extdata", "chr20_1.2.fq.bz2"),
system.file(package="esATAC", "extdata", "chr20_2.2.fq.bz2")
)
conclusion<-atacPipe2(case=list(fastqInput1 = case[1],fastqInput2 = case[2]),The processes are similar to atacPipe. You will also receive conclusion list and
a HTML report for quality control and analysis will be created. You may see:
Build User-defined Pipeline or Use Functions Indivially
All sub-processes are available for recombine new whole pipeline or sub-pipeline easily and flexibly. They are also able to be called individually. We just show some functions and their combinations from the package. For detail, the users can read the manual.
Preprocess
Users can use %>% to build a pipeline to obtain merged, renamed and adapter removed clean reads fastq file(s) that is ready for mapping.
# Identify adapters
prefix<-system.file(package="esATAC", "extdata", "uzmg")
(reads_1 <-file.path(prefix,"m1",dir(file.path(prefix,"m1"))))
(reads_2 <-file.path(prefix,"m2",dir(file.path(prefix,"m2"))))
reads_merged_1 <- file.path(td,"reads1.fastq")
reads_merged_2 <- file.path(td,"reads2.fastq")
atacproc <-
atacUnzipAndMerge(fastqInput1 = reads_1,fastqInput2 = reads_2) %>%
atacRenamer %>% atacRemoveAdapterIf you want to modify the parameters of AdapterRemoval, you have to refer to Rbowtie2 package:
library(Rbowtie2)
adapterremoval_usage()Mapping
If the reference has not been configured, the bowtie2 index should be built first. Then bowtie2 mapping functions could used to map reads to reference genome.
## Building a bowtie2 index
library("Rbowtie2")
refs <- dir(system.file(package="esATAC", "extdata", "bt2","refs"),
full=TRUE)
bowtie2_build(references=refs, bt2Index=file.path(td, "lambda_virus"),
"--threads 4 --quiet",overwrite=TRUE)
## Alignments
reads_1 <- system.file(package="esATAC", "extdata", "bt2", "reads",
"reads_1.fastq")
reads_2 <- system.file(package="esATAC", "extdata", "bt2", "reads",
"reads_2.fastq")
if(file.exists(file.path(td, "lambda_virus.1.bt2"))){
(bowtie2Mapping(bt2Idx = file.path(td, "lambda_virus"),
samOutput = file.path(td, "result.sam"),
fastqInput1=reads_1,fastqInput2=reads_2,threads=3))
head(readLines(file.path(td, "result.sam")))
}If you want to modify the parameters of bowtie2, you have to refer to Rbowtie2 package:
library(Rbowtie2)
bowtie2_usage()Convert SAM File to BED File
The mapping results are stored in a SAM file. SamToBed functions can covert it into BED file. During converting, the operation like sorting, shifting, filtering chromosome and so on can also be set to do in the meantime.
sambzfile <- system.file(package="esATAC", "extdata", "Example.sam.bz2")
samfile <- file.path(td,"Example.sam")
bunzip2(sambzfile,destname=samfile,overwrite=TRUE,remove=FALSE)
samToBed(samInput = samfile) Filtering Reads and Calling
Filter the nucleosome free reads(<100bp) for peak calling.
bedbzfile <- system.file(package="esATAC", "extdata", "chr20.50000.bed.bz2")
bedfile <- file.path(td,"chr20.50000.bed")
bunzip2(bedbzfile,destname=bedfile,overwrite=TRUE,remove=FALSE)
bedUtils(bedInput = bedfile,maxFragLen = 100, chrFilterList = NULL) %>%
atacPeakCallingATAC-seq Peak Annotation
ATAC-seq peak locate at open chromatin regions. Annotating these peak could find whether they locate at functional regions(such as promoter and enhancer).
Function “atacPeakAnno” and “peakanno” use function “annotatePeak” in package “ChIPseeker” to annotate ATAC-seq peak. for more information about package “ChIPseeker”, please click here[4].
Function “atacPeakAnno” and “peakanno” accept a bed file path as an input, users can change the parameters like “tssRegion”, “TxDb” according to their require. Now, bioconductor offers many species’ annotation database, click here to search more.
The following example is to exhibit how to annotate a UCSC bed file.
## extract example peak file from package "esATAC"
p1bz <- system.file("extdata", "Example_peak1.bed.bz2", package="esATAC")
peak1_path <- as.vector(bunzip2(filename = p1bz,
destname = file.path(getwd(), "Example_peak1.bed"),
ext="bz2", FUN=bzfile, overwrite=TRUE, remove = FALSE))
## run peakanno to annotate peaks
AnnoInfo <- peakanno(peakInput = peak1_path, TxDb = TxDb.Hsapiens.UCSC.hg19.knownGene, annoDb = "org.Hs.eg.db")The output contains a pie chart in pdf format like below. It reports the percentage of peaks located in different functional regions.
The function also generate a file(with suffix .df) contains annotation for all peaks. It is converted from dataframe in R, and users could open it with text editor or excel. Below is a part of the output.
| chromatin | start | end | annotation | geneStart | geneEnd | geneId | distanceToTSS | SYMBOL |
|---|---|---|---|---|---|---|---|---|
| chr1 | 416606 | 416895 | Distal Intergenic | 367659 | 368597 | 729759 | 48947 | OR4F29 |
| chr1 | 2313275 | 2313587 | Intron (uc001ajb.1/79906, intron 6 of 13) | 2252696 | 2322993 | 79906 | 9406 | MORN1 |
| chr1 | 2516858 | 2518618 | Promoter | 2517899 | 2522908 | 127281 | 0 | FAM213B |
| chr1 | 2685755 | 2686581 | Intron (uc021oey.1/100287898, intron 4 of 6) | 2572807 | 2706230 | 100287898 | 19649 | TTC34 |
| chr1 | 3418588 | 3418822 | Intron (uc001akk.3/1953, intron 14 of 29) | 3404506 | 3448012 | 1953 | 29190 | MEGF6 |
Go Analysis
GO analysis is performing enrichment analysis on gene sets. It establishes the relationship between gene sets and functions, and report the most significant function to users.
Function “atacGOAnalysis” and “goanalysis” use function “enrichGO” in package “clusterProfiler” to do GO analysis. for more information about package “clusterProfiler”, please click here[5].
The function need gene Id set as input. User could choose different GO terms(molecular function, biological process and cellular component) according to different input of parameter “ont”.
The following example is to exhibit how to do GO analysis on a gene set.
## extract gene ID
library(clusterProfiler)
data(geneList)
geneId <- names(geneList)[1:100]
## do GO analysis
goAna <- goanalysis(gene = geneId, OrgDb = "org.Hs.eg.db", keytype = "ENTREZID", ont = "MF")The output file(suffix .df) contains the GO term sorted by p-value, below is a part of the output.
| ID | Description | GeneRatio | pvalue | qvalue |
|---|---|---|---|---|
| GO:0008017 | microtubule binding | 13/93 | 0.0e+00 | 0.00e+00 |
| GO:0015631 | tubulin binding | 13/93 | 0.0e+00 | 7.00e-07 |
| GO:0003777 | microtubule motor activity | 7/93 | 2.0e-07 | 1.54e-05 |
| GO:0050786 | RAGE receptor binding | 4/93 | 3.0e-07 | 1.54e-05 |
| GO:0045236 | CXCR chemokine receptor binding | 4/93 | 1.5e-06 | 6.63e-05 |
Motif Scan
This function search motif occurrence in the given regions.
Function “atacMotifScan” and “motifscan” use function “matchPWM” in package “Biostrings”, for more parameters and usage, click here[6].
Multi-motif is supported, and the output file is named by your input PWM list. for Multi-motif, we offer parallel computing method for accelerating. Users could specify the parameter “n.cores” to accelerate the program.
The input motif PWM matrix is stored in a list like below.
pwm <- readRDS(system.file("extdata", "motifPWM.rds", package="esATAC"))
pwm## $CTCF
## 1 2 3 4 5 6
## A 0.02960540 0.03743389 0.04368501 0.02430735 0.001023094 0.05537836
## C 0.04410295 0.03573708 0.02271820 0.05624947 0.057703951 0.00675723
## G 0.02797499 0.04833945 0.04931320 0.01254142 -0.069438277 0.02610485
## T 0.04957887 0.03879208 0.03478530 0.01903153 -0.015525860 0.03014738
## 7 8 9 10 11 12
## A 0.020271900 0.03209952 0.057005153 -0.00460166 0.045748514 0.023909107
## C 0.051259116 0.04889070 0.004838716 -0.06943828 -0.010700941 0.005881734
## G 0.045758368 0.02246973 0.017613999 0.05773064 0.052120515 0.050726179
## T 0.004838716 0.04540858 0.010683413 -0.01070094 0.002433989 0.046034249
## 13 14 15 16 17 18
## A 0.005881734 0.024346457 0.03179891 0.04710491 0.02895828 0.03323194
## C -0.069438277 0.001023094 0.05525243 0.00684137 0.05022974 0.04538490
## G 0.057569617 0.055907445 -0.00460166 0.05082564 0.04481264 0.02756395
## T 0.001023094 0.027215451 0.02651920 0.01005852 0.01942021 0.04786956
## 19
## A 0.04804997
## C 0.03846515
## G 0.04309319
## T 0.02501100
##
## $ATF3
## 1 2 3 4 5 6
## A 0.04367299 0.03800285 0.07142085 0.009661673 -0.02889619 0.07216227
## C 0.05222528 0.03098095 0.03725448 -0.028896186 -0.02889619 -0.02889619
## G 0.04093099 0.07155594 0.04294621 -0.028896186 0.07216227 -0.02889619
## T 0.06989254 0.03510088 0.01473202 0.072148910 -0.02889619 -0.02889619
## 7 8 9 10 11 12
## A 0.01301289 0.02882151 -0.02889619 0.02959694 0.07211734 0.02431670
## C 0.07214318 -0.02889619 -0.02889619 0.07195987 -0.02889619 0.04391574
## G -0.02889619 0.07206064 0.01651982 0.01907820 -0.02889619 0.04041291
## T -0.02889619 -0.02889619 0.07213457 0.02292425 0.02109342 0.07121981
## 13 14
## A 0.03937329 0.06758346
## C 0.07143835 0.04184396
## G 0.03039048 0.05912350
## T 0.03823980 0.04864436Using “motifscan” function to search motif in given genome regions, UCSC bed file is recommended.
sample.path <- system.file("extdata", "chr20_sample_peak.bed.bz2", package="esATAC")
sample.path <- as.vector(bunzip2(filename = sample.path,
destname = file.path(getwd(), "chr20_sample_peak.bed"),
ext="bz2", FUN=bzfile, overwrite=TRUE, remove = FALSE))
motif.data <- motifscan(peak = sample.path, genome = BSgenome.Hsapiens.UCSC.hg19,
motifPWM = pwm, prefix = "test")This function reports the exact motif position in the given genome like below(motif: CTCF).
| chromatin | start | end | strand | score | sequence |
|---|---|---|---|---|---|
| chr20 | 189774 | 189792 | + | 0.8794931 | ACTCCTCTAGAGGGTGCTC |
| chr20 | 239773 | 239791 | + | 0.9003697 | TTGCCACTGGGGGGAGACA |
| chr20 | 247783 | 247801 | - | 0.9337214 | CTGCCGGCAGATGGCGGTA |
| chr20 | 281074 | 281092 | - | 0.8511201 | TTGCCTGCAGGGGTGGGAA |
Plot Footprint
The interaction between TF and DNA would leave a “footprint” in motif position, but it is not evident in a single site, so integrated footprint is necessary. In addition, we only consider Tn5 cut site. This function is based on the motif scan.
First, collecting all cut site from the bed file(Note: every line in the bed file is a DNA fragment) and save them.
## extract cut site position from bed file
fra_path <- system.file("extdata", "chr20.50000.bed.bz2", package="esATAC")
frag <- as.vector(bunzip2(filename = fra_path,
destname = file.path(getwd(), "chr20.50000.bed"),
ext="bz2", FUN=bzfile, overwrite=TRUE, remove = FALSE))
cs.data <- extractcutsite(bedInput = frag, prefix = "ATAC")Next, plot footprint for different motifs.
In the motif scan, we get a variable named “motif.data”, is contains multi-motif information. In order to plot footprint of these motif in a single procedure, we will use the output of function motifscan, here is “motif.data”.
fp <- atacCutSiteCount(atacProcCutSite = cs.data, atacProcMotifScan = motif.data)The following is CTCF footprint using example data.
Note: we only using a small part of the chromatin 20 as example.
Query and Object Operations
Workflow Map
esATAC is organized in data flow graph. Except for referring manual, the user may print the map to know workflow order.
bedbzfile <- system.file(package="esATAC", "extdata", "chr20.50000.bed.bz2")
bedfile <- file.path(td,"chr20.50000.bed")
bunzip2(bedbzfile,destname=bedfile,overwrite=TRUE,remove=FALSE)
peakproc <-bedUtils(bedInput = bedfile,maxFragLen = 100, chrFilterList = NULL) %>%
atacPeakCalling
peakproc %>% printMapBy printing the map, it is easy to know what valid processes are available to call next and what preprocess has been done before.
Parameters
It is easy to query the parameters set for the process with ATACProc objects. You can query available parameters like this:
#query all of available parameters
getParamItems(peakproc)## [1] "bedInput" "bedFileList" "inBedDir" "bedOutput"
## [5] "outTmpDir" "fragmentSize" "fileformat" "verbose"The value of a specific parameter can be obtain like this:
#query a parameter value
getParam(peakproc,"fragmentSize")## [1] 0Report Value
Similarly, it is also easy to query the report value calculated by the process with ATACProc objects
sambzfile <- system.file(package="esATAC", "extdata", "Example.sam.bz2")
samfile <- file.path(td,"Example.sam")
bunzip2(sambzfile,destname=samfile,overwrite=TRUE,remove=FALSE)
samToBedProc<-samToBed(samInput = samfile)When the ATACProc objects are obtained, you can query all of available report items.
getReportItems(samToBedProc)## [1] "report" "total"
## [3] "save" "filted"
## [5] "extlen" "unique"
## [7] "multimap" "non-mitochondrial"
## [9] "non-mitochondrial-multimap"The value of specific report item can be get like this:
#query a parameter value
getReportVal(samToBedProc,"report")## Item Value
## 1 total 209
## 2 save 13
## 3 filted 150
## 4 extlen 0
## 5 unique 0
## 6 multimap 15Clear the Cache and Redo Process
If the user call a process function that was called last time and finished, the process function will not redo the process. So if users need to redo the process, they have to clear the cache like this:
clearProcCache(peakproc)
process(peakproc)Acknowledgement
We would like to thank Huan Fang for package testing and valuable suggestions,
and Kui Hua for providing package testing on Macbook.
Reference
[1] Langmead, B., & Salzberg, S. L. (2012). Fast gapped-read alignment with Bowtie 2. Nature methods, 9(4), 357-359.
[2] Schubert, Lindgreen, and Orlando (2016). AdapterRemoval v2: rapid adapter trimming, identification, and read merging. BMC Research Notes, 12;9(1):88.
[3] Boyle, A. P., Guinney, J., Crawford, G. E., & Furey, T. S. (2008). F-Seq: a feature density estimator for high-throughput sequence tags. Bioinformatics, 24(21), 2537-2538.
[4] Yu G, Wang L and He Q (2015). “ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization.” Bioinformatics, 31(14), pp. 2382-2383. doi: 10.1093/bioinformatics/btv145.
[5] Yu G, Wang L, Han Y and He Q (2012). “clusterProfiler: an R package for comparing biological themes among gene clusters.” OMICS: A Journal of Integrative Biology, 16(5), pp. 284-287. doi: 10.1089/omi.2011.0118.
[6] Pagès H, Aboyoun P, Gentleman R and DebRoy S (2017). Biostrings: String objects representing biological sequences, and matching algorithms. R package version 2.44.2.
[7] Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y., & Greenleaf, W. J. (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nature methods, 10(12), 1213-1218. ## Session Infomation
sessionInfo()## R version 3.4.2 (2017-09-28)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.6-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.6-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=en_US.UTF-8
## [9] LC_ADDRESS=en_US.UTF-8 LC_TELEPHONE=en_US.UTF-8
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=en_US.UTF-8
##
## attached base packages:
## [1] stats4 parallel stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] clusterProfiler_3.6.0
## [2] DOSE_3.4.0
## [3] ChIPseeker_1.14.0
## [4] Rbowtie2_1.0.0
## [5] R.utils_2.5.0
## [6] R.oo_1.21.0
## [7] R.methodsS3_1.7.1
## [8] org.Hs.eg.db_3.4.2
## [9] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [10] GenomicFeatures_1.30.0
## [11] AnnotationDbi_1.40.0
## [12] Biobase_2.38.0
## [13] BSgenome.Hsapiens.UCSC.hg19_1.4.0
## [14] BSgenome_1.46.0
## [15] rtracklayer_1.38.0
## [16] magrittr_1.5
## [17] bindrcpp_0.2
## [18] esATAC_1.0.2
## [19] Rsamtools_1.30.0
## [20] Biostrings_2.46.0
## [21] XVector_0.18.0
## [22] QuasR_1.18.0
## [23] Rbowtie_1.18.0
## [24] GenomicRanges_1.30.0
## [25] GenomeInfoDb_1.14.0
## [26] IRanges_2.12.0
## [27] S4Vectors_0.16.0
## [28] BiocGenerics_0.24.0
##
## loaded via a namespace (and not attached):
## [1] backports_1.1.1 fastmatch_1.1-0
## [3] VGAM_1.0-4 plyr_1.8.4
## [5] igraph_1.1.2 lazyeval_0.2.1
## [7] splines_3.4.2 BiocParallel_1.12.0
## [9] ggplot2_2.2.1 gridBase_0.4-7
## [11] TFBSTools_1.16.0 digest_0.6.12
## [13] BiocInstaller_1.28.0 htmltools_0.3.6
## [15] GOSemSim_2.4.0 viridis_0.4.0
## [17] GO.db_3.4.2 gdata_2.18.0
## [19] memoise_1.1.0 JASPAR2016_1.6.0
## [21] readr_1.1.1 annotate_1.56.0
## [23] matrixStats_0.52.2 prettyunits_1.0.2
## [25] colorspace_1.3-2 blob_1.1.0
## [27] dplyr_0.7.4 RCurl_1.95-4.8
## [29] jsonlite_1.5 bindr_0.1
## [31] TFMPvalue_0.0.6 brew_1.0-6
## [33] VariantAnnotation_1.24.0 glue_1.2.0
## [35] gtable_0.2.0 zlibbioc_1.24.0
## [37] UpSetR_1.3.3 DelayedArray_0.4.1
## [39] Rook_1.1-1 scales_0.5.0
## [41] futile.options_1.0.0 DBI_0.7
## [43] Rcpp_0.12.13 plotrix_3.6-6
## [45] viridisLite_0.2.0 xtable_1.8-2
## [47] progress_1.1.2 bit_1.1-12
## [49] htmlwidgets_0.9 httr_1.3.1
## [51] DiagrammeR_0.9.2 fgsea_1.4.0
## [53] gplots_3.0.1 RColorBrewer_1.1-2
## [55] rJava_0.9-9 pkgconfig_2.0.1
## [57] XML_3.98-1.9 rlang_0.1.2
## [59] reshape2_1.4.2 munsell_0.4.3
## [61] tools_3.4.2 visNetwork_2.0.1
## [63] downloader_0.4 DirichletMultinomial_1.20.0
## [65] RSQLite_2.0 evaluate_0.10.1
## [67] stringr_1.2.0 yaml_2.1.14
## [69] knitr_1.17 bit64_0.9-7
## [71] caTools_1.17.1 purrr_0.2.4
## [73] KEGGREST_1.18.0 poweRlaw_0.70.1
## [75] DO.db_2.9 biomaRt_2.34.0
## [77] compiler_3.4.2 rstudioapi_0.7
## [79] rgexf_0.15.3 png_0.1-7
## [81] tibble_1.3.4 stringi_1.1.5
## [83] highr_0.6 futile.logger_1.4.3
## [85] GenomicFiles_1.14.0 lattice_0.20-35
## [87] CNEr_1.14.0 Matrix_1.2-11
## [89] data.table_1.10.4-3 bitops_1.0-6
## [91] qvalue_2.10.0 R6_2.2.2
## [93] latticeExtra_0.6-28 hwriter_1.3.2
## [95] RMySQL_0.10.13 ShortRead_1.36.0
## [97] KernSmooth_2.23-15 gridExtra_2.3
## [99] lambda.r_1.2 boot_1.3-20
## [101] gtools_3.5.0 assertthat_0.2.0
## [103] seqLogo_1.44.0 SummarizedExperiment_1.8.0
## [105] rprojroot_1.2 GenomicAlignments_1.14.0
## [107] GenomeInfoDbData_0.99.1 hms_0.3
## [109] influenceR_0.1.0 VennDiagram_1.6.17
## [111] grid_3.4.2 tidyr_0.7.2
## [113] rmarkdown_1.6 rvcheck_0.0.9