RNA-Seq Workflow Template
Last update: 07 May, 2019
Package
systemPipeR 1.18.0
1 Introduction
Users want to provide here background information about the design of their RNA-Seq project.
2 Samples and environment settings
2.1 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.
To run this sample report, mini sample FASTQ and reference genome files can be downloaded from here. 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 annotion files (provided in the same download) 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.
The following loads one of the available NGS workflow templates (here RNA-Seq) into the user’s current working directory. At the moment, the package includes workflow templates for RNA-Seq, ChIP-Seq, VAR-Seq and Ribo-Seq. Templates for additional NGS applications will be provided in the future.
library(systemPipeRdata)
genWorkenvir(workflow = "rnaseq")
setwd("rnaseq")Alternatively, this can be done from the command-line as follows:
Rscript -e "systemPipeRdata::genWorkenvir(workflow='rnaseq')"Now open the R markdown script systemPipeRNAseq.Rmdin your R IDE (e.g.
vim-r or RStudio) and run the workflow as outlined below. If you work under
Vim-R-Tmux, the following command sequence will connect the user in an
interactive session with a node on the cluster. The code of the Rmd
script can then be sent from Vim on the login (head) node to an open R session running
on the corresponding computer node. This is important since Tmux sessions
should not be run on the computer nodes.
q("no") # closes R session on head nodesrun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 2:00:00 --pty bash -l
module load R/3.4.2
RNow check whether your R session is running on a computer node of the cluster and not on a head node.
system("hostname") # should return name of a compute node starting with i or c
getwd() # checks current working directory of R session
dir() # returns content of current working directory2.2 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).
library(systemPipeR)If applicable load custom functions not provided by systemPipeR package.
source("systemPipeRNAseq_Fct.R")2.3 Experiment definition provided by targets file
The targets file defines all FASTQ files and sample
comparisons of the analysis workflow.
targetspath <- system.file("extdata", "targets.txt", package = "systemPipeR")
targets <- read.delim(targetspath, comment.char = "#")[, 1:4]
targets## FileName SampleName Factor SampleLong
## 1 ./data/SRR446027_1.fastq.gz M1A M1 Mock.1h.A
## 2 ./data/SRR446028_1.fastq.gz M1B M1 Mock.1h.B
## 3 ./data/SRR446029_1.fastq.gz A1A A1 Avr.1h.A
## 4 ./data/SRR446030_1.fastq.gz A1B A1 Avr.1h.B
## 5 ./data/SRR446031_1.fastq.gz V1A V1 Vir.1h.A
## 6 ./data/SRR446032_1.fastq.gz V1B V1 Vir.1h.B
## 7 ./data/SRR446033_1.fastq.gz M6A M6 Mock.6h.A
## 8 ./data/SRR446034_1.fastq.gz M6B M6 Mock.6h.B
## 9 ./data/SRR446035_1.fastq.gz A6A A6 Avr.6h.A
## 10 ./data/SRR446036_1.fastq.gz A6B A6 Avr.6h.B
## 11 ./data/SRR446037_1.fastq.gz V6A V6 Vir.6h.A
## 12 ./data/SRR446038_1.fastq.gz V6B V6 Vir.6h.B
## 13 ./data/SRR446039_1.fastq.gz M12A M12 Mock.12h.A
## 14 ./data/SRR446040_1.fastq.gz M12B M12 Mock.12h.B
## 15 ./data/SRR446041_1.fastq.gz A12A A12 Avr.12h.A
## 16 ./data/SRR446042_1.fastq.gz A12B A12 Avr.12h.B
## 17 ./data/SRR446043_1.fastq.gz V12A V12 Vir.12h.A
## 18 ./data/SRR446044_1.fastq.gz V12B V12 Vir.12h.B3 Read preprocessing
3.1 Read quality filtering and trimming
The function preprocessReads allows to apply predefined or custom
read preprocessing functions to all FASTQ files referenced in a
SYSargs container, such as quality filtering or adaptor trimming
routines. The following example performs adaptor 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
SYSargs instance, e.g. running the NGS alignments using the
trimmed FASTQ files.
args <- systemArgs(sysma = "param/trim.param", mytargets = "targets.txt")
preprocessReads(args = args, Fct = "trimLRPatterns(Rpattern='GCCCGGGTAA', subject=fq)",
batchsize = 1e+05, overwrite = TRUE, compress = TRUE)
writeTargetsout(x = args, file = "targets_trim.txt", overwrite = TRUE)3.2 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.
args <- systemArgs(sysma = "param/tophat.param", mytargets = "targets.txt")
fqlist <- seeFastq(fastq = infile1(args), batchsize = 1e+05,
klength = 8)
pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist))
seeFastqPlot(fqlist)
dev.off()
4 Alignments
4.1 Read mapping with Bowtie2/Tophat2
The NGS reads of this project will be aligned against the reference
genome sequence using Bowtie2/TopHat2 (Kim et al. 2013; Langmead and Salzberg 2012). The parameter
settings of the aligner are defined in the tophat.param file.
args <- systemArgs(sysma = "param/tophat.param", mytargets = "targets.txt")
sysargs(args)[1] # Command-line parameters for first FASTQ fileSubmission of alignment jobs to compute cluster, here using 72 CPU cores
(18 qsub processes each with 4 CPU cores).
moduleload(modules(args))
system("bowtie2-build ./data/tair10.fasta ./data/tair10.fasta")
resources <- list(walltime = 120, ntasks = 1, ncpus = cores(args),
memory = 1024)
reg <- clusterRun(args, conffile = ".batchtools.conf.R", Njobs = 18,
template = "batchtools.slurm.tmpl", runid = "01", resourceList = resources)
getStatus(reg = reg)
waitForJobs(reg = reg)4.2 Read mapping with HISAT2
args <- systemArgs(sysma = "param/hisat2.param", mytargets = "targets.txt")
sysargs(args)[1] # Command-line parameters for first FASTQ file
moduleload(modules(args))
system("hisat2-build ./data/tair10.fasta ./data/tair10.fasta")
resources <- list(walltime = 120, ntasks = 1, ncpus = cores(args),
memory = 1024)
reg <- clusterRun(args, conffile = ".batchtools.conf.R", Njobs = 18,
template = "batchtools.slurm.tmpl", runid = "01", resourceList = resources)
getStatus(reg = reg)
waitForJobs(reg = reg)Check whether all BAM files have been created.
file.exists(outpaths(args))4.3 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 78.244574.4 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. The corresponding URLs are written to a file
with a path specified under urlfile in the results directory.
symLink2bam(sysargs = args, htmldir = c("~/.html/", "somedir/"),
urlbase = "http://biocluster.ucr.edu/~tgirke/", urlfile = "./results/IGVurl.txt")5 Read quantification
5.1 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 correspoding
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")
(align <- readGAlignments(outpaths(args)[1])) # Demonstrates how to read bam file into R
eByg <- exonsBy(txdb, by = c("gene"))
bfl <- BamFileList(outpaths(args), 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]])$counts)
rownames(countDFeByg) <- names(rowRanges(counteByg[[1]]))
colnames(countDFeByg) <- names(bfl)
rpkmDFeByg <- apply(countDFeByg, 2, function(x) returnRPKM(counts = x,
ranges = eByg))
write.table(countDFeByg, "results/countDFeByg.xls", col.names = NA,
quote = FALSE, sep = "\t")
write.table(rpkmDFeByg, "results/rpkmDFeByg.xls", col.names = NA,
quote = FALSE, sep = "\t")Sample of data slice of count table
read.delim("results/countDFeByg.xls", row.names = 1, check.names = FALSE)[1:4,
1:5]Sample of data slice of RPKM table
read.delim("results/rpkmDFeByg.xls", row.names = 1, check.names = FALSE)[1:4,
1:4]Note, for most statistical differential expression or abundance analysis
methods, such as edgeR or DESeq2, the raw count values should be used as input. The
usage of RPKM values should be restricted to specialty applications
required by some users, e.g. manually comparing the expression levels
among different genes or features.
5.2 Sample-wise correlation analysis
The following computes the sample-wise Spearman correlation coefficients from
the rlog transformed expression values generated with the DESeq2 package. After
transformation to a distance matrix, hierarchical clustering is performed with
the hclust function and the result is plotted as a dendrogram
(also see file sample_tree.pdf).
library(DESeq2, quietly = TRUE)
library(ape, warn.conflicts = FALSE)
countDF <- as.matrix(read.table("./results/countDFeByg.xls"))
colData <- data.frame(row.names = targetsin(args)$SampleName,
condition = targetsin(args)$Factor)
dds <- DESeq2::DESeqDataSetFromMatrix(countData = countDF, colData = colData,
design = ~condition)
d <- cor(assay(DESeq2::rlog(dds)), method = "spearman")
hc <- hclust(dist(1 - d))
png("results/sample_tree.pdf")
ape::plot.phylo(ape::as.phylo(hc), type = "p", edge.col = "blue",
edge.width = 2, show.node.label = TRUE, no.margin = TRUE)
dev.off()
6 Analysis of DEGs
The analysis of differentially expressed genes (DEGs) is performed with
the glm method of the edgeR package (Robinson, McCarthy, and Smyth 2010). The sample
comparisons used by this analysis are defined in the header lines of the
targets.txt file starting with <CMP>.
6.1 Run edgeR
library(edgeR)
countDF <- read.delim("results/countDFeByg.xls", row.names = 1,
check.names = FALSE)
targets <- read.delim("targets.txt", comment = "#")
cmp <- readComp(file = "targets.txt", format = "matrix", delim = "-")
edgeDF <- run_edgeR(countDF = countDF, targets = targets, cmp = cmp[[1]],
independent = FALSE, mdsplot = "")Add gene descriptions
library("biomaRt")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
desc <- getBM(attributes = c("tair_locus", "description"), mart = m)
desc <- desc[!duplicated(desc[, 1]), ]
descv <- as.character(desc[, 2])
names(descv) <- as.character(desc[, 1])
edgeDF <- data.frame(edgeDF, Desc = descv[rownames(edgeDF)],
check.names = FALSE)
write.table(edgeDF, "./results/edgeRglm_allcomp.xls", quote = FALSE,
sep = "\t", col.names = NA)6.2 Plot DEG results
Filter and plot DEG results for up and down regulated genes. The
definition of up and down is given in the corresponding help
file. To open it, type ?filterDEGs in the R console.
edgeDF <- read.delim("results/edgeRglm_allcomp.xls", row.names = 1,
check.names = FALSE)
pdf("results/DEGcounts.pdf")
DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2, FDR = 20))
dev.off()
write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote = FALSE,
sep = "\t", row.names = FALSE)
6.3 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 possiblity 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()
7 GO term enrichment analysis
7.1 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")7.2 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$Down
names(down) <- paste(names(down), "_down", sep = "")
DEGlist <- c(up_down, up, down)
DEGlist <- DEGlist[sapply(DEGlist, length) > 0]
BatchResult <- GOCluster_Report(catdb = catdb, setlist = DEGlist,
method = "all", id_type = "gene", CLSZ = 2, cutoff = 0.9,
gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)
library("biomaRt")
m <- useMart("plants_mart", dataset = "athaliana_eg_gene", host = "plants.ensembl.org")
goslimvec <- as.character(getBM(attributes = c("goslim_goa_accession"),
mart = m)[, 1])
BatchResultslim <- GOCluster_Report(catdb = catdb, setlist = DEGlist,
method = "slim", id_type = "gene", myslimv = goslimvec, CLSZ = 10,
cutoff = 0.01, gocats = c("MF", "BP", "CC"), recordSpecGO = NULL)7.3 Plot batch GO term results
The data.frame generated by GOCluster can be plotted with the goBarplot function. Because of the
variable size of the sample sets, it may not always be desirable to show
the results from different DEG sets in the same bar plot. Plotting
single sample sets is achieved by subsetting the input data frame as
shown in the first line of the following example.
gos <- BatchResultslim[grep("M6-V6_up_down", BatchResultslim$CLID),
]
gos <- BatchResultslim
pdf("GOslimbarplotMF.pdf", height = 8, width = 10)
goBarplot(gos, gocat = "MF")
dev.off()
goBarplot(gos, gocat = "BP")
goBarplot(gos, gocat = "CC")
8 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(DESeq2::rlog(dds))[geneids, ]
pdf("heatmap1.pdf")
pheatmap(y, scale = "row", clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation")
dev.off()
9 Version Information
sessionInfo()## R version 3.6.0 (2019-04-26)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.2 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.9-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.9-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=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_1.18.0 ShortRead_1.42.0
## [3] GenomicAlignments_1.20.0 SummarizedExperiment_1.14.0
## [5] DelayedArray_0.10.0 matrixStats_0.54.0
## [7] Biobase_2.44.0 BiocParallel_1.18.0
## [9] Rsamtools_2.0.0 Biostrings_2.52.0
## [11] XVector_0.24.0 GenomicRanges_1.36.0
## [13] GenomeInfoDb_1.20.0 IRanges_2.18.0
## [15] S4Vectors_0.22.0 BiocGenerics_0.30.0
## [17] BiocStyle_2.12.0
##
## loaded via a namespace (and not attached):
## [1] Category_2.50.0 bitops_1.0-6
## [3] bit64_0.9-7 RColorBrewer_1.1-2
## [5] progress_1.2.0 httr_1.4.0
## [7] Rgraphviz_2.28.0 backports_1.1.4
## [9] tools_3.6.0 R6_2.4.0
## [11] DBI_1.0.0 lazyeval_0.2.2
## [13] colorspace_1.4-1 withr_2.1.2
## [15] tidyselect_0.2.5 prettyunits_1.0.2
## [17] bit_1.1-14 compiler_3.6.0
## [19] graph_1.62.0 formatR_1.6
## [21] rtracklayer_1.44.0 bookdown_0.9
## [23] scales_1.0.0 checkmate_1.9.3
## [25] genefilter_1.66.0 RBGL_1.60.0
## [27] rappdirs_0.3.1 stringr_1.4.0
## [29] digest_0.6.18 rmarkdown_1.12
## [31] AnnotationForge_1.26.0 pkgconfig_2.0.2
## [33] htmltools_0.3.6 BSgenome_1.52.0
## [35] limma_3.40.0 rlang_0.3.4
## [37] RSQLite_2.1.1 GOstats_2.50.0
## [39] hwriter_1.3.2 dplyr_0.8.0.1
## [41] VariantAnnotation_1.30.0 RCurl_1.95-4.12
## [43] magrittr_1.5 GO.db_3.8.2
## [45] GenomeInfoDbData_1.2.1 Matrix_1.2-17
## [47] Rcpp_1.0.1 munsell_0.5.0
## [49] stringi_1.4.3 yaml_2.2.0
## [51] edgeR_3.26.0 zlibbioc_1.30.0
## [53] plyr_1.8.4 grid_3.6.0
## [55] blob_1.1.1 crayon_1.3.4
## [57] lattice_0.20-38 splines_3.6.0
## [59] GenomicFeatures_1.36.0 annotate_1.62.0
## [61] hms_0.4.2 batchtools_0.9.11
## [63] locfit_1.5-9.1 knitr_1.22
## [65] pillar_1.3.1 rjson_0.2.20
## [67] base64url_1.4 codetools_0.2-16
## [69] biomaRt_2.40.0 XML_3.98-1.19
## [71] glue_1.3.1 evaluate_0.13
## [73] latticeExtra_0.6-28 data.table_1.12.2
## [75] BiocManager_1.30.4 gtable_0.3.0
## [77] purrr_0.3.2 assertthat_0.2.1
## [79] ggplot2_3.1.1 xfun_0.6
## [81] xtable_1.8-4 survival_2.44-1.1
## [83] tibble_2.1.1 pheatmap_1.0.12
## [85] AnnotationDbi_1.46.0 memoise_1.1.0
## [87] brew_1.0-6 GSEABase_1.46.010 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.
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