# 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

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.

systemPipeRdata::genWorkenvir(workflow = "rnaseq", mydirname = "rnaseq")
setwd("rnaseq")


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 workflow and the targets file are expected to be located in the parent directory.

The chosen data set used by this report 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. thaliana 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.

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.

### 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 = "#")
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
## 1        M1A     M1 23-Mar-2012
## 2        M1B     M1 23-Mar-2012
## 3        A1A     A1 23-Mar-2012
## 4        A1B     A1 23-Mar-2012


To work with custom data, users need to generate a targets file containing the paths to their own FASTQ files.

# Workflow environment

systemPipeR workflows can be designed and built from start to finish with a single command, importing from an R Markdown file or stepwise in interactive mode from the R console.

This tutorial will demonstrate how to build the workflow in an interactive mode, appending each step. The workflow is constructed by connecting each step via appendStep method. Each SYSargsList instance contains instructions for processing a set of input files with a specific command-line or R software and the paths to the corresponding outfiles generated by a particular command-line software/step.

To create a workflow within systemPipeR, we can start by defining an empty container and checking the directory structure:

library(systemPipeR)
sal <- SPRproject()
sal


## Required packages and resources

The systemPipeR package needs to be loaded (H Backman and Girke 2016).

appendStep(sal) <- LineWise(code = {
library(systemPipeR)
}, step_name = "load_SPR")


## Read preprocessing

### Preprocessing with preprocessReads function

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargsList container, such as quality filtering or adapter trimming routines. Internally, preprocessReads uses the FastqStreamer function from the ShortRead package to stream through large FASTQ files in a memory-efficient manner. The following example performs adapter trimming with the trimLRPatterns function from the Biostrings package.

Here, we are appending this step to the SYSargsList object created previously. All the parameters are defined on the preprocessReads/preprocessReads-pe.yml file.

appendStep(sal) <- SYSargsList(step_name = "preprocessing", targets = "targetsPE.txt",
dir = TRUE, wf_file = "preprocessReads/preprocessReads-pe.cwl",
input_file = "preprocessReads/preprocessReads-pe.yml", dir_path = system.file("extdata/cwl",
package = "systemPipeR"), inputvars = c(FileName1 = "_FASTQ_PATH1_",
FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"),
dependency = c("load_SPR"))


After the preprocessing step, the outfiles files can be used to generate the new targets files containing the paths to the trimmed FASTQ files. The new targets information can be used for the next workflow step instance, e.g. running the NGS alignments with the trimmed FASTQ files. The appendStep function is automatically handling this connectivity between steps. Please check the next step for more details.

The following example shows how one can design a custom read ‘preprocessReads’ function using utilities provided by the ShortRead package, and then run it in batch mode with the ‘preprocessReads’ function. Here, it is possible to replace the function used on the preprocessing step and modify the sal object. Because it is a custom function, it is necessary to save the part in the R object, and internally the preprocessReads.doc.R is loading the custom function. If the R object is saved with a different name (here "param/customFCT.RData"), please replace that accordingly in the preprocessReads.doc.R.

Please, note that this step is not added to the workflow, here just for demonstration.

First, we defined the custom function in the workflow:

appendStep(sal) <- LineWise(code = {
filterFct <- function(fq, cutoff = 20, Nexceptions = 0) {
qcount <- rowSums(as(quality(fq), "matrix") <= cutoff,
na.rm = TRUE)
# Retains reads where Phred scores are >= cutoff
# with N exceptions
fq[qcount <= Nexceptions]
}
save(list = ls(), file = "param/customFCT.RData")
}, step_name = "custom_preprocessing_function", dependency = "preprocessing")


After, we can edit the input parameter:

yamlinput(sal, "preprocessing")$Fct yamlinput(sal, "preprocessing", "Fct") <- "'filterFct(fq, cutoff=20, Nexceptions=0)'" yamlinput(sal, "preprocessing")$Fct  ## check the new function
cmdlist(sal, "preprocessing", targets = 1)  ## check if the command line was updated with success


### Read trimming with Trimmomatic

Trimmomatic software (Bolger, Lohse, and Usadel 2014) performs a variety of useful trimming tasks for Illumina paired-end and single ended data. Here, an example of how to perform this task using parameters template files for trimming FASTQ files.

This step is optional.

appendStep(sal) <- SYSargsList(step_name = "trimming", targets = "targetsPE.txt",
wf_file = "trimmomatic/trimmomatic-pe.cwl", input_file = "trimmomatic/trimmomatic-pe.yml",
dir_path = system.file("extdata/cwl", package = "systemPipeR"),
inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_",
SampleName = "_SampleName_"), dependency = "load_SPR",
run_step = "optional")


### 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.

appendStep(sal) <- LineWise(code = {
fastq <- getColumn(sal, step = "preprocessing", "targetsWF",
column = 1)
fqlist <- seeFastq(fastq = fastq, batchsize = 10000, klength = 8)
pdf("./results/fastqReport.pdf", height = 18, width = 4 *
length(fqlist))
seeFastqPlot(fqlist)
dev.off()
}, step_name = "fastq_report", dependency = "preprocessing")


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). First, the Hisat2 index needs to be created.

appendStep(sal) <- SYSargsList(step_name = "hisat2_index", dir = FALSE,
targets = NULL, wf_file = "hisat2/hisat2-index.cwl", input_file = "hisat2/hisat2-index.yml",
dir_path = "param/cwl", dependency = "load_SPR")


### HISAT2 mapping

The parameter settings of the aligner are defined in the workflow_hisat2-pe.cwl and workflow_hisat2-pe.yml files. The following shows how to construct the corresponding SYSargsList object.

appendStep(sal) <- SYSargsList(step_name = "hisat2_mapping",
dir = TRUE, targets = "preprocessing", wf_file = "workflow-hisat2/workflow_hisat2-pe.cwl",
input_file = "workflow-hisat2/workflow_hisat2-pe.yml", dir_path = "param/cwl",
inputvars = c(preprocessReads_1 = "_FASTQ_PATH1_", preprocessReads_2 = "_FASTQ_PATH2_",
SampleName = "_SampleName_"), rm_targets_col = c("FileName1",
"FileName2"), dependency = c("preprocessing", "hisat2_index"))


To double-check the command line for each sample, please use the following:

cmdlist(sal, step = "hisat2_mapping", targets = 1)


### 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.

appendStep(sal) <- LineWise(code = {
fqpaths <- getColumn(sal, step = "preprocessing", "targetsWF",
column = "FileName1")
bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles",
column = "samtools_sort_bam")
read_statsDF <- alignStats(args = bampaths, fqpaths = fqpaths,
pairEnd = TRUE)
write.table(read_statsDF, "results/alignStats.xls", row.names = FALSE,
quote = FALSE, sep = "\t")
}, step_name = "align_stats", dependency = "hisat2_mapping")


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.

appendStep(sal) <- LineWise(code = {
bampaths <- getColumn(sal, step = "hisat2_mapping", "outfiles",
column = "samtools_sort_bam")
symLink2bam(sysargs = bampaths, htmldir = c("~/.html/", "somedir/"),
urlbase = "http://cluster.hpcc.ucr.edu/~tgirke/", urlfile = "./results/IGVurl.txt")
}, step_name = "bam_IGV", dependency = "hisat2_mapping", run_step = "optional")


## Read quantification

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 exon 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 4 CPU cores.

### Create a database for gene annotation

appendStep(sal) <- LineWise(code = {
library(GenomicFeatures)
txdb <- suppressWarnings(makeTxDbFromGFF(file = "data/tair10.gff",
format = "gff", dataSource = "TAIR", organism = "Arabidopsis thaliana"))
saveDb(txdb, file = "./data/tair10.sqlite")
}, step_name = "create_db", dependency = "hisat2_mapping")


### Read counting with summarizeOverlaps in parallel mode using multiple cores

appendStep(sal) <- LineWise(code = {
library(GenomicFeatures)
library(BiocParallel)
txdb <- loadDb("./data/tair10.sqlite")
outpaths <- getColumn(sal, step = "hisat2_mapping", "outfiles",
column = "samtools_sort_bam")
eByg <- exonsBy(txdb, by = c("gene"))
bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
multicoreParam <- MulticoreParam(workers = 4)
register(multicoreParam)
registered()
counteByg <- bplapply(bfl, function(x) summarizeOverlaps(eByg,
x, mode = "Union", ignore.strand = TRUE, inter.feature = FALSE,
singleEnd = FALSE, BPPARAM = multicoreParam))
countDFeByg <- sapply(seq(along = counteByg), function(x) assays(counteByg[[x]])$counts) rownames(countDFeByg) <- names(rowRanges(counteByg[[1]])) colnames(countDFeByg) <- names(bfl) rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts = x, ranges = eByg)) write.table(countDFeByg, "results/countDFeByg.xls", col.names = NA, quote = FALSE, sep = "\t") write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names = NA, quote = FALSE, sep = "\t") ## Creating a SummarizedExperiment object colData <- data.frame(row.names = SampleName(sal, "hisat2_mapping"), condition = getColumn(sal, "hisat2_mapping", position = "targetsWF", column = "Factor")) colData$condition <- factor(colData$condition) countDF_se <- SummarizedExperiment::SummarizedExperiment(assays = countDFeByg, colData = colData) ## Add results as SummarizedExperiment to the workflow ## object SE(sal, "read_counting") <- countDF_se }, step_name = "read_counting", dependency = "create_db")  When providing a BamFileList as in the example above, summarizeOverlaps methods use by default bplapply and use the register interface from BiocParallel package. If the number of workers is not set, MulticoreParam will use the number of cores returned by parallel::detectCores(). For more information, please check help("summarizeOverlaps") documentation. 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). appendStep(sal) <- LineWise(code = { library(DESeq2, quietly = TRUE) library(ape, warn.conflicts = FALSE) ## Extracting SummarizedExperiment object se <- SE(sal, "read_counting") dds <- DESeqDataSet(se, 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() }, step_name = "sample_tree", dependency = "read_counting")  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 appendStep(sal) <- LineWise(code = { library(edgeR) countDF <- read.delim("results/countDFeByg.xls", row.names = 1, check.names = FALSE) cmp <- readComp(stepsWF(sal)[["hisat2_mapping"]], format = "matrix", delim = "-") edgeDF <- run_edgeR(countDF = countDF, targets = targetsWF(sal)[["hisat2_mapping"]], cmp = cmp[[1]], independent = FALSE, mdsplot = "") }, step_name = "run_edger", dependency = "read_counting")  ### Add gene descriptions appendStep(sal) <- LineWise(code = { library("biomaRt") m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "https://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) }, step_name = "custom_annot", dependency = "run_edger")  ### 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. appendStep(sal) <- LineWise(code = { 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)
}, step_name = "filter_degs", dependency = "custom_annot")


### 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).

appendStep(sal) <- LineWise(code = {
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()
}, step_name = "venn_diagram", dependency = "filter_degs")


## 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.

appendStep(sal) <- LineWise(code = {
library("biomaRt")
# listMarts() # To choose BioMart database
# listMarts(host='plants.ensembl.org')
m <- useMart("plants_mart", host = "https://plants.ensembl.org")
# listDatasets(m)
m <- useMart("plants_mart", dataset = "athaliana_eg_gene",
host = "https://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, ]
if (!dir.exists("./data/GO"))
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")
}, step_name = "get_go_annot", dependency = "filter_degs")


### 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.

appendStep(sal) <- LineWise(code = {
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$Down names(down) <- paste(names(down), "_down", sep = "") DEGlist <- c(up_down, up, down) DEGlist <- DEGlist[sapply(DEGlist, length) > 0] BatchResult <- GOCluster_Report(catdb = catdb, setlist = DEGlist, method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9, gocats = c("MF", "BP", "CC"), recordSpecGO = NULL) m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "https://plants.ensembl.org") goslimvec <- as.character(getBM(attributes = c("goslim_goa_accession"), mart = m)[, 1]) BatchResultslim <- GOCluster_Report(catdb = catdb, setlist = DEGlist, method = "slim", id_type = "gene", myslimv = goslimvec, CLSZ = 10, cutoff = 0.01, gocats = c("MF", "BP", "CC"), recordSpecGO = NULL) write.table(BatchResultslim, "results/GOBatchSlim.xls", row.names = FALSE, quote = FALSE, sep = "\t") }, step_name = "go_enrich", dependency = "get_go_annot")  ### Plot batch GO term results The data.frame generated by GOCluster can be plotted with the goBarplot function. Because of the variable size of the sample sets, it may not always be desirable to show the results from different DEG sets in the same bar plot. Plotting single sample sets is achieved by subsetting the input data frame as shown in the first line of the following example. appendStep(sal) <- LineWise(code = { gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID),
]
gos <- BatchResultslim
pdf("results/GOslimbarplotMF.pdf", height = 8, width = 10)
goBarplot(gos, gocat = "MF")
goBarplot(gos, gocat = "BP")
goBarplot(gos, gocat = "CC")
dev.off()
}, step_name = "go_plot", dependency = "go_enrich")


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.

appendStep(sal) <- LineWise(code = {
library(pheatmap)
geneids <- unique(as.character(unlist(DEG_list[[1]])))
y <- assay(rlog(dds))[geneids, ]
pdf("results/heatmap1.pdf")
pheatmap(y, scale = "row", clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation")
dev.off()
}, step_name = "heatmap", dependency = "go_enrich")


Figure 6: Heat Map with Hierarchical Clustering Dendrograms of DEGs

## Version Information

appendStep(sal) <- LineWise(code = {
sessionInfo()
}, step_name = "sessionInfo", dependency = "heatmap")


# Running workflow

## Interactive job submissions in a single machine

For running the workflow, runWF function will execute all the steps store in the workflow container. The execution will be on a single machine without submitting to a queuing system of a computer cluster.

sal <- runWF(sal)


## 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.

The resources list object provides the number of independent parallel cluster processes defined under the Njobs element in the list. The following example will run 18 processes in parallel using 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 a total of 72 CPU cores.

Note, runWF 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 conffile (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 conffile and template files for the Slurm scheduler provided by this package.

The resources can be appended when the step is generated, or it is possible to add these resources later, as the following example using the addResources function:

resources <- list(conffile=".batchtools.conf.R",
template="batchtools.slurm.tmpl",
Njobs=18,
walltime=120, ## minutes
ntasks=1,
ncpus=4,
memory=1024, ## Mb
partition = "short"
)
sal <- addResources(sal, c("hisat2_mapping"), resources = resources)
sal <- runWF(sal)


## Visualize workflow

systemPipeR workflows instances can be visualized with the plotWF function.

plotWF(sal, rstudio = TRUE)


## Checking workflow status

To check the summary of the workflow, we can use:

sal
statusWF(sal)


## Accessing logs report

systemPipeR compiles all the workflow execution logs in one central location, making it easier to check any standard output (stdout) or standard error (stderr) for any command-line tools used on the workflow or the R code stdout.

sal <- renderLogs(sal)


# Funding

This project is funded by NSF award ABI-1661152.

# References

Bolger, Anthony M, Marc Lohse, and Bjoern Usadel. 2014. “Trimmomatic: A Flexible Trimmer for Illumina Sequence Data.” Bioinformatics 30 (15): 2114–20.

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.

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.