Introduction

Users want to provide here background information about the design of their RNA-Seq project.

Samples and environment settings

Environment settings and input data

Typically, the user wants to record here the sources and versions of the reference genome sequence along with the corresponding annotations. In the provided sample data set all data inputs are stored in a data subdirectory and all results will be written to a separate results directory, while the systemPipeRNAseq.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.

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 are provide in the systemPipeRdata vignette here.

The mini sample FASTQ files used by this report as well as the associated reference genome files can be loaded via the systemPipeRdata package. The chosen data set SRP010938 contains 18 paired-end (PE) read sets from Arabidposis thaliana (Howard et al. 2013). To minimize processing time during testing, each FASTQ file has been subsetted to 90,000-100,000 randomly sampled PE reads that map to the first 100,000 nucleotides of each chromosome of the A. thalina genome. The corresponding reference genome sequence (FASTA) and its GFF annotation files have been truncated accordingly. This way the entire test sample data set is less than 200MB in storage space. A PE read set has been chosen for this test data set for flexibility, because it can be used for testing both types of analysis routines requiring either SE (single end) reads or PE reads.

Required packages and resources

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016).

To apply workflows to custom data, the user needs to modify the targets file and if necessary update the corresponding parameter (.cwl and .yml) files. A collection of pre-generated .cwl and .yml files are provided in the param/cwl subdirectory of each workflow template. They are also viewable in the GitHub repository of systemPipeRdata (see here). For more information of the structure of the targets file, please consult the documentation here. More details about the new parameter files from systemPipeR can be found here.

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.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")[, 1:4]
targets
##                      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
## 5  ./data/SRR446031_1.fastq.gz ./data/SRR446031_2.fastq.gz
## 6  ./data/SRR446032_1.fastq.gz ./data/SRR446032_2.fastq.gz
## 7  ./data/SRR446033_1.fastq.gz ./data/SRR446033_2.fastq.gz
## 8  ./data/SRR446034_1.fastq.gz ./data/SRR446034_2.fastq.gz
## 9  ./data/SRR446035_1.fastq.gz ./data/SRR446035_2.fastq.gz
## 10 ./data/SRR446036_1.fastq.gz ./data/SRR446036_2.fastq.gz
## 11 ./data/SRR446037_1.fastq.gz ./data/SRR446037_2.fastq.gz
## 12 ./data/SRR446038_1.fastq.gz ./data/SRR446038_2.fastq.gz
## 13 ./data/SRR446039_1.fastq.gz ./data/SRR446039_2.fastq.gz
## 14 ./data/SRR446040_1.fastq.gz ./data/SRR446040_2.fastq.gz
## 15 ./data/SRR446041_1.fastq.gz ./data/SRR446041_2.fastq.gz
## 16 ./data/SRR446042_1.fastq.gz ./data/SRR446042_2.fastq.gz
## 17 ./data/SRR446043_1.fastq.gz ./data/SRR446043_2.fastq.gz
## 18 ./data/SRR446044_1.fastq.gz ./data/SRR446044_2.fastq.gz
##    SampleName Factor
## 1         M1A     M1
## 2         M1B     M1
## 3         A1A     A1
## 4         A1B     A1
## 5         V1A     V1
## 6         V1B     V1
## 7         M6A     M6
## 8         M6B     M6
## 9         A6A     A6
## 10        A6B     A6
## 11        V6A     V6
## 12        V6B     V6
## 13       M12A    M12
## 14       M12B    M12
## 15       A12A    A12
## 16       A12B    A12
## 17       V12A    V12
## 18       V12B    V12

Read preprocessing

Read quality filtering and trimming

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargs2 container, such as quality filtering or adapter trimming routines. The paths to the resulting output FASTQ files are stored in the output slot of the SYSargs2 object. The following example performs adapter trimming with the trimLRPatterns function from the Biostrings package. After the trimming step a new targets file is generated (here targets_trim.txt) containing the paths to the trimmed FASTQ files. The new targets file can be used for the next workflow step with an updated SYSargs2 instance, e.g. running the NGS alignments using the trimmed FASTQ files.

Construct SYSargs2 object from cwl and yml param and targets files.

dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", 
    package = "systemPipeR")
trim <- loadWorkflow(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]
preprocessReads(args = trim, Fct = "trimLRPatterns(Rpattern='GCCCGGGTAA', 
                subject=fq)", 
    batchsize = 1e+05, overwrite = TRUE, compress = TRUE)
writeTargetsout(x = trim, file = "targets_trim.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.

fqlist <- seeFastq(fastq = infile1(trim), batchsize = 10000, 
    klength = 8)
pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist))
seeFastqPlot(fqlist)
dev.off()
Figure 1: FASTQ quality report for 18 samples

Alignments

Read mapping with HISAT2

The following steps will demonstrate how to use the short read aligner Hisat2 (Kim, Langmead, and Salzberg 2015) in both interactive job submissions and batch submissions to queuing systems of clusters using the systemPipeR's new CWL command-line interface.

Build Hisat2 index.

dir_path <- system.file("extdata/cwl/hisat2/hisat2-idx", package = "systemPipeR")
idx <- loadWorkflow(targets = NULL, wf_file = "hisat2-index.cwl", 
    input_file = "hisat2-index.yml", dir_path = dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)

## Run
runCommandline(idx, make_bam = FALSE)

The parameter settings of the aligner are defined in the hisat2-mapping-se.cwl and hisat2-mapping-se.yml files. The following shows how to construct the corresponding SYSargs2 object, here args.

dir_path <- system.file("extdata/cwl/hisat2/hisat2-pe", package = "systemPipeR")
args <- loadWorkflow(targets = targetspath, wf_file = "hisat2-mapping-pe.cwl", 
    input_file = "hisat2-mapping-pe.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName1 = "_FASTQ_PATH1_", 
    FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"))
args
## Instance of 'SYSargs2':
##    Slot names/accessors: 
##       targets: 18 (M1A...V12B), targetsheader: 4 (lines)
##       modules: 1
##       wf: 0, clt: 1, yamlinput: 8 (components)
##       input: 18, output: 18
##       cmdlist: 18
##    WF Steps:
##       1. hisat2-mapping-pe (rendered: TRUE)
cmdlist(args)[1:2]
## $M1A
## $M1A$`hisat2-mapping-pe`
## [1] "hisat2 -S ./results/M1A.sam  -x ./data/tair10.fasta  -k 1  --min-intronlen 30  --max-intronlen 3000  -1 ./data/SRR446027_1.fastq.gz -2 ./data/SRR446027_2.fastq.gz --threads 4"
## 
## 
## $M1B
## $M1B$`hisat2-mapping-pe`
## [1] "hisat2 -S ./results/M1B.sam  -x ./data/tair10.fasta  -k 1  --min-intronlen 30  --max-intronlen 3000  -1 ./data/SRR446028_1.fastq.gz -2 ./data/SRR446028_2.fastq.gz --threads 4"
output(args)[1:2]
## $M1A
## $M1A$`hisat2-mapping-pe`
## [1] "./results/M1A.sam"
## 
## 
## $M1B
## $M1B$`hisat2-mapping-pe`
## [1] "./results/M1B.sam"

Interactive job submissions in a single machine

To simplify the short read alignment execution for the user, the command-line can be run with the runCommandline function. The execution will be on a single machine without submitting to a queuing system of a computer cluster. This way, the input FASTQ files will be processed sequentially. By default runCommandline auto detects SAM file outputs and converts them to sorted and indexed BAM files, using internally the Rsamtools package (Morgan et al. 2019). Besides, runCommandline allows the user to create a dedicated results folder for each workflow and a sub-folder for each sample defined in the targets file. This includes all the output and log files for each step. When these options are used, the output location will be updated by default and can be assigned to the same object.

## Run single Machine
args <- runCommandline(args)

Parallelization on clusters

Alternatively, the computation can be greatly accelerated by processing many files in parallel using several compute nodes of a cluster, where a scheduling/queuing system is used for load balancing. For this the clusterRun function submits the computing requests to the scheduler using the run specifications defined by runCommandline.

To avoid over-subscription of CPU cores on the compute nodes, the value from yamlinput(args)['thread'] is passed on to the submission command, here ncpus in the resources list object. The number of independent parallel cluster processes is defined under the Njobs argument. The following example will run 18 processes in parallel using for each 4 CPU cores. If the resources available on a cluster allow running all 18 processes at the same time then the shown sample submission will utilize in total 72 CPU cores. Note, clusterRun can be used with most queueing systems as it is based on utilities from the batchtools package which supports the use of template files (*.tmpl) for defining the run parameters of different schedulers. To run the following code, one needs to have both a conf file (see .batchtools.conf.R samples here) and a template file (see *.tmpl samples here) for the queueing available on a system. The following example uses the sample conf and template files for the Slurm scheduler provided by this package.

library(batchtools)
resources <- list(walltime = 120, ntasks = 1, ncpus = 4, memory = 1024)
reg <- clusterRun(args, FUN = runCommandline, more.args = list(args = args, 
    make_bam = TRUE, 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)

Check whether all BAM files have been created.

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")

The following shows the alignment statistics for a sample file provided by the systemPipeR package.

read.table(system.file("extdata", "alignStats.xls", package = "systemPipeR"), 
    header = TRUE)[1:4, ]
##   FileName Nreads2x Nalign Perc_Aligned Nalign_Primary
## 1      M1A   192918 177961     92.24697         177961
## 2      M1B   197484 159378     80.70426         159378
## 3      A1A   189870 176055     92.72397         176055
## 4      A1B   188854 147768     78.24457         147768
##   Perc_Aligned_Primary
## 1             92.24697
## 2             80.70426
## 3             92.72397
## 4             78.24457

Read quantification

Read counting with summarizeOverlaps in parallel mode using multiple cores

Reads overlapping with annotation ranges of interest are counted for each sample using the summarizeOverlaps function (Lawrence et al. 2013). The read counting is preformed for exonic gene regions in a non-strand-specific manner while ignoring overlaps among different genes. Subsequently, the expression count values are normalized by reads per kp per million mapped reads (RPKM). The raw read count table (countDFeByg.xls) and the corresponding RPKM table (rpkmDFeByg.xls) are written to separate files in the directory of this project. Parallelization is achieved with the BiocParallel package, here using 8 CPU cores.

library("GenomicFeatures")
library(BiocParallel)
txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff", 
    dataSource = "TAIR", organism = "Arabidopsis thaliana")
saveDb(txdb, file = "./data/tair10.sqlite")
txdb <- loadDb("./data/tair10.sqlite")
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
(align <- readGAlignments(outpaths[1]))  # Demonstrates how to read bam file into R
eByg <- exonsBy(txdb, by = c("gene"))
bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
multicoreParam <- MulticoreParam(workers = 2)
register(multicoreParam)
registered()
counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg, 
    x, mode = "Union", ignore.strand = TRUE, inter.feature = FALSE, 
    singleEnd = TRUE))
countDFeByg <- sapply(seq(along = counteByg), function(x) assays(counteByg[[x]])$counts)
rownames(countDFeByg) <- names(rowRanges(counteByg[[1]]))
colnames(countDFeByg) <- names(bfl)
rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts = x, 
    ranges = eByg))
write.table(countDFeByg, "results/countDFeByg.xls", col.names = NA, 
    quote = FALSE, sep = "\t")
write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names = NA, 
    quote = FALSE, sep = "\t")

Sample of data slice of count table

read.delim("results/countDFeByg.xls", row.names = 1, check.names = FALSE)[1:4, 
    1:5]

Sample of data slice of RPKM table

read.delim("results/rpkmDFeByg.xls", row.names = 1, check.names = FALSE)[1:4, 
    1:4]

Note, for most statistical differential expression or abundance analysis methods, such as edgeR or DESeq2, the raw count values should be used as input. The usage of RPKM values should be restricted to specialty applications required by some users, e.g. manually comparing the expression levels among different genes or features.

Sample-wise correlation analysis

The following computes the sample-wise Spearman correlation coefficients from the rlog transformed expression values generated with the DESeq2 package. After transformation to a distance matrix, hierarchical clustering is performed with the hclust function and the result is plotted as a dendrogram (also see file sample_tree.pdf).

library(DESeq2, quietly = TRUE)
library(ape, warn.conflicts = FALSE)
countDF <- as.matrix(read.table("./results/countDFeByg.xls"))
colData <- data.frame(row.names = targets.as.df(targets(args))$SampleName, 
    condition = targets.as.df(targets(args))$Factor)
dds <- DESeqDataSetFromMatrix(countData = countDF, colData = colData, 
    design = ~condition)
d <- cor(assay(rlog(dds)), method = "spearman")
hc <- hclust(dist(1 - d))
pdf("results/sample_tree.pdf")
plot.phylo(as.phylo(hc), type = "p", edge.col = "blue", edge.width = 2, 
    show.node.label = TRUE, no.margin = TRUE)
dev.off()
Figure 2: Correlation dendrogram of samples

Analysis of DEGs

The analysis of differentially expressed genes (DEGs) is performed with the glm method of the edgeR package (Robinson, McCarthy, and Smyth 2010). The sample comparisons used by this analysis are defined in the header lines of the targets.txt file starting with <CMP>.

Run edgeR

library(edgeR)
countDF <- read.delim("results/countDFeByg.xls", row.names = 1, 
    check.names = FALSE)
targets <- read.delim("targetsPE.txt", comment = "#")
cmp <- readComp(file = "targetsPE.txt", format = "matrix", delim = "-")
edgeDF <- run_edgeR(countDF = countDF, targets = targets, cmp = cmp[[1]], 
    independent = FALSE, mdsplot = "")

Add gene descriptions

library("biomaRt")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
desc <- getBM(attributes = c("tair_locus", "description"), mart = m)
desc <- desc[!duplicated(desc[, 1]), ]
descv <- as.character(desc[, 2])
names(descv) <- as.character(desc[, 1])
edgeDF <- data.frame(edgeDF, Desc = descv[rownames(edgeDF)], 
    check.names = FALSE)
write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote = FALSE, 
    sep = "\t", col.names = NA)

Plot DEG results

Filter and plot DEG results for up and down regulated genes. The definition of up and down is given in the corresponding help file. To open it, type ?filterDEGs in the R console.

edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names = 1, 
    check.names = FALSE)
pdf("results/DEGcounts.pdf")
DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2, FDR = 20))
dev.off()
write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote = FALSE, 
    sep = "\t", row.names = FALSE)
Figure 3: Up and down regulated DEGs with FDR of 1%

Venn diagrams of DEG sets

The overLapper function can compute Venn intersects for large numbers of sample sets (up to 20 or more) and plots 2-5 way Venn diagrams. A useful feature is the possibility to combine the counts from several Venn comparisons with the same number of sample sets in a single Venn diagram (here for 4 up and down DEG sets).

vennsetup <- overLapper(DEG_list$Up[6:9], type = "vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type = "vennsets")
pdf("results/vennplot.pdf")
vennPlot(list(vennsetup, vennsetdown), mymain = "", mysub = "", 
    colmode = 2, ccol = c("blue", "red"))
dev.off()
Figure 4: Venn Diagram for 4 Up and Down DEG Sets

GO term enrichment analysis

Obtain gene-to-GO mappings

The following shows how to obtain gene-to-GO mappings from biomaRt (here for A. thaliana) and how to organize them for the downstream GO term enrichment analysis. Alternatively, the gene-to-GO mappings can be obtained for many organisms from Bioconductor’s *.db genome annotation packages or GO annotation files provided by various genome databases. For each annotation this relatively slow preprocessing step needs to be performed only once. Subsequently, the preprocessed data can be loaded with the load function as shown in the next subsection.

library("biomaRt")
listMarts()  # To choose BioMart database
listMarts(host = "plants.ensembl.org")
m <- useMart("plants_mart", host = "plants.ensembl.org")
listDatasets(m)
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
listAttributes(m)  # Choose data types you want to download
go <- getBM(attributes = c("go_id", "tair_locus", "namespace_1003"), 
    mart = m)
go <- go[go[, 3] != "", ]
go[, 3] <- as.character(go[, 3])
go[go[, 3] == "molecular_function", 3] <- "F"
go[go[, 3] == "biological_process", 3] <- "P"
go[go[, 3] == "cellular_component", 3] <- "C"
go[1:4, ]
dir.create("./data/GO")
write.table(go, "data/GO/GOannotationsBiomart_mod.txt", quote = FALSE, 
    row.names = FALSE, col.names = FALSE, sep = "\t")
catdb <- makeCATdb(myfile = "data/GO/GOannotationsBiomart_mod.txt", 
    lib = NULL, org = "", colno = c(1, 2, 3), idconv = NULL)
save(catdb, file = "data/GO/catdb.RData")

Batch GO term enrichment analysis

Apply the enrichment analysis to the DEG sets obtained the above differential expression analysis. Note, in the following example the FDR filter is set here to an unreasonably high value, simply because of the small size of the toy data set used in this vignette. Batch enrichment analysis of many gene sets is performed with the function. When method=all, it returns all GO terms passing the p-value cutoff specified under the cutoff arguments. When method=slim, it returns only the GO terms specified under the myslimv argument. The given example shows how a GO slim vector for a specific organism can be obtained from BioMart.

library("biomaRt")
load("data/GO/catdb.RData")
DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2, FDR = 50), 
    plot = FALSE)
up_down <- DEG_list$UporDown
names(up_down) <- paste(names(up_down), "_up_down", sep = "")
up <- DEG_list$Up
names(up) <- paste(names(up), "_up", sep = "")
down <- DEG_list$Down
names(down) <- paste(names(down), "_down", sep = "")
DEGlist <- c(up_down, up, down)
DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
BatchResult <- GOCluster_Report(catdb = catdb, setlist = DEGlist, 
    method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9, 
    gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)
library("biomaRt")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
goslimvec <- as.character(getBM(attributes = c("goslim_goa_accession"), 
    mart = m)[, 1])
BatchResultslim <- GOCluster_Report(catdb = catdb, setlist = DEGlist, 
    method = "slim", id_type = "gene", myslimv = goslimvec, CLSZ = 10, 
    cutoff = 0.01, gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)

Plot batch GO term results

The data.frame generated by GOCluster can be plotted with the goBarplot function. Because of the variable size of the sample sets, it may not always be desirable to show the results from different DEG sets in the same bar plot. Plotting single sample sets is achieved by subsetting the input data frame as shown in the first line of the following example.

gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID), 
    ]
gos <- BatchResultslim
pdf("GOslimbarplotMF.pdf", height = 8, width = 10)
goBarplot(gos, gocat = "MF")
dev.off()
goBarplot(gos, gocat = "BP")
goBarplot(gos, gocat = "CC")
Figure 5: GO Slim Barplot for MF Ontology

Clustering and heat maps

The following example performs hierarchical clustering on the rlog transformed expression matrix subsetted by the DEGs identified in the above differential expression analysis. It uses a Pearson correlation-based distance measure and complete linkage for cluster joining.

library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
pdf("heatmap1.pdf")
pheatmap(y, scale = "row", clustering_distance_rows = "correlation", 
    clustering_distance_cols = "correlation")
dev.off()
Figure 6: Heat Map with Hierarchical Clustering Dendrograms of DEGs

Version Information

## R version 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
## LAPACK: /home/dcassol/src/R-4.0.3/lib/libRlapack.so
## 
## 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] batchtools_0.9.15           ape_5.4-1                  
##  [3] ggplot2_3.3.3               systemPipeR_1.25.6         
##  [5] ShortRead_1.48.0            GenomicAlignments_1.26.0   
##  [7] SummarizedExperiment_1.20.0 Biobase_2.50.0             
##  [9] MatrixGenerics_1.2.1        matrixStats_0.58.0         
## [11] BiocParallel_1.24.1         Rsamtools_2.6.0            
## [13] Biostrings_2.58.0           XVector_0.30.0             
## [15] GenomicRanges_1.42.0        GenomeInfoDb_1.26.2        
## [17] IRanges_2.24.1              S4Vectors_0.28.1           
## [19] BiocGenerics_0.36.0         BiocStyle_2.18.1           
## 
## loaded via a namespace (and not attached):
##   [1] colorspace_2.0-0         rjson_0.2.20            
##   [3] hwriter_1.3.2            ellipsis_0.3.1          
##   [5] rprojroot_2.0.2          fs_1.5.0                
##   [7] bit64_4.0.5              AnnotationDbi_1.52.0    
##   [9] xml2_1.3.2               codetools_0.2-18        
##  [11] splines_4.0.3            cachem_1.0.3            
##  [13] knitr_1.31               jsonlite_1.7.2          
##  [15] annotate_1.68.0          GO.db_3.12.1            
##  [17] dbplyr_2.1.0             png_0.1-7               
##  [19] pheatmap_1.0.12          graph_1.68.0            
##  [21] BiocManager_1.30.10      compiler_4.0.3          
##  [23] httr_1.4.2               backports_1.2.1         
##  [25] GOstats_2.56.0           assertthat_0.2.1        
##  [27] Matrix_1.3-2             fastmap_1.1.0           
##  [29] limma_3.46.0             formatR_1.7             
##  [31] htmltools_0.5.1.1        prettyunits_1.1.1       
##  [33] tools_4.0.3              gtable_0.3.0            
##  [35] glue_1.4.2               GenomeInfoDbData_1.2.4  
##  [37] Category_2.56.0          dplyr_1.0.4             
##  [39] rsvg_2.1                 rappdirs_0.3.3          
##  [41] V8_3.4.0                 Rcpp_1.0.6              
##  [43] pkgdown_1.6.1            vctrs_0.3.6             
##  [45] nlme_3.1-152             rtracklayer_1.50.0      
##  [47] xfun_0.21                stringr_1.4.0           
##  [49] lifecycle_1.0.0.9000     XML_3.99-0.5            
##  [51] edgeR_3.32.1             zlibbioc_1.36.0         
##  [53] scales_1.1.1             BSgenome_1.58.0         
##  [55] VariantAnnotation_1.36.0 ragg_0.4.1              
##  [57] hms_1.0.0                RBGL_1.66.0             
##  [59] RColorBrewer_1.1-2       yaml_2.2.1              
##  [61] curl_4.3                 memoise_2.0.0           
##  [63] biomaRt_2.46.3           latticeExtra_0.6-29     
##  [65] stringi_1.5.3            RSQLite_2.2.3           
##  [67] genefilter_1.72.1        desc_1.2.0              
##  [69] checkmate_2.0.0          GenomicFeatures_1.42.1  
##  [71] DOT_0.1                  rlang_0.4.10            
##  [73] pkgconfig_2.0.3          systemfonts_1.0.1       
##  [75] bitops_1.0-6             evaluate_0.14           
##  [77] lattice_0.20-41          purrr_0.3.4             
##  [79] bit_4.0.4                tidyselect_1.1.0        
##  [81] GSEABase_1.52.1          AnnotationForge_1.32.0  
##  [83] magrittr_2.0.1           bookdown_0.21           
##  [85] R6_2.5.0                 generics_0.1.0          
##  [87] base64url_1.4            DelayedArray_0.16.1     
##  [89] DBI_1.1.1                withr_2.4.1             
##  [91] pillar_1.4.7             survival_3.2-7          
##  [93] RCurl_1.98-1.2           tibble_3.0.6            
##  [95] crayon_1.4.1             BiocFileCache_1.14.0    
##  [97] rmarkdown_2.6            jpeg_0.1-8.1            
##  [99] progress_1.2.2           locfit_1.5-9.4          
## [101] grid_4.0.3               data.table_1.13.6       
## [103] blob_1.2.1               Rgraphviz_2.34.0        
## [105] digest_0.6.27            xtable_1.8-4            
## [107] brew_1.0-6               textshaping_0.3.0       
## [109] openssl_1.4.3            munsell_0.5.0           
## [111] askpass_1.1

Funding

This project was supported by funds from the National Institutes of Health (NIH).

References

H Backman, Tyler W, and Thomas Girke. 2016. systemPipeR: NGS workflow and report generation environment.” BMC Bioinformatics 17 (1): 388. https://doi.org/10.1186/s12859-016-1241-0.
Howard, Brian E, Qiwen Hu, Ahmet Can Babaoglu, Manan Chandra, Monica Borghi, Xiaoping Tan, Luyan He, et al. 2013. “High-Throughput RNA Sequencing of Pseudomonas-Infected Arabidopsis Reveals Hidden Transcriptome Complexity and Novel Splice Variants.” PLoS One 8 (10): e74183. https://doi.org/10.1371/journal.pone.0074183.
Kim, Daehwan, Ben Langmead, and Steven L Salzberg. 2015. HISAT: A Fast Spliced Aligner with Low Memory Requirements.” Nat. Methods 12 (4): 357–60.
Lawrence, Michael, Wolfgang Huber, Hervé Pagès, Patrick Aboyoun, Marc Carlson, Robert Gentleman, Martin T Morgan, and Vincent J Carey. 2013. “Software for Computing and Annotating Genomic Ranges.” PLoS Comput. Biol. 9 (8): e1003118. https://doi.org/10.1371/journal.pcbi.1003118.
Morgan, Martin, Hervé Pagès, Valerie Obenchain, and Nathaniel Hayden. 2019. Rsamtools: Binary Alignment (BAM), FASTA, Variant Call (BCF), and Tabix File Import. http://bioconductor.org/packages/Rsamtools.
Robinson, M D, D J McCarthy, and G K Smyth. 2010. “edgeR: A Bioconductor Package for Differential Expression Analysis of Digital Gene Expression Data.” Bioinformatics 26 (1): 139–40. https://doi.org/10.1093/bioinformatics/btp616.