RIBO-Seq Workflow Template
Last update: 28 September, 2020
Package
systemPipeR 1.22.0
1 Introduction
Ribo-Seq and polyRibo-Seq are a specific form of RNA-Seq gene expression experiments utilizing mRNA subpopulations directly bound to ribosomes. Compared to standard RNA-Seq, their readout of gene expression provides a better approximation of downstream protein abundance profiles due to their close association with translational processes. The most important difference among the two is that polyRibo-Seq utilizes polyribosomal RNA for sequencing, whereas Ribo-Seq is a footprinting approach restricted to sequencing RNA fragments protected by ribosomes (Ingolia et al. 2009; Aspden et al. 2014; Juntawong et al. 2015).
The workflow presented in this vignette contains most of the data analysis steps described by (Juntawong et al. 2014) including functionalities useful for processing both polyRibo-Seq and Ribo-Seq experiments. To improve re-usability and adapt to recent changes of software versions (e.g. R, Bioconductor and short read aligners), the code has been optimized accordingly. Thus, the results obtained with the updated workflow are expected to be similar but not necessarily identical with the published results described in the original paper.
Relevant analysis steps of this workflow include read preprocessing, read
alignments against a reference genome, counting of reads overlapping with a
wide range of genomic features (e.g. CDSs, UTRs, uORFs, rRNAs, etc.),
differential gene expression and differential ribosome binding analyses, as
well as a variety of genome-wide summary plots for visualizing RNA expression
trends. Functions are provided for evaluating the quality of Ribo-seq data,
for identifying novel expressed regions in the genomes, and for gaining
insights into gene regulation at the post-transcriptional and translational
levels. For example, the functions genFeatures and
featuretypeCounts can be used to quantify the expression output for
all feature types included in a genome annotation (e.g. genes,
introns, exons, miRNAs, intergenic regions, etc.). To determine the approximate
read length of ribosome footprints in Ribo-Seq experiments, these feature type
counts can be obtained and plotted for specific read lengths separately.
Typically, the most abundant read length obtained for translated features
corresponds to the approximate footprint length occupied by the ribosomes of a
given organism group. Based on the results from several Ribo-Seq studies, these
ribosome footprints are typically ~30 nucleotides long
(Ingolia, Lareau, and Weissman 2011; Ingolia et al. 2009; Juntawong et al. 2014). However, their
length can vary by several nucleotides depending upon the optimization of the
RNA digestion step and various factors associated with translational
regulation. For quality control purposes of Ribo-Seq experiments it is also
useful to monitor the abundance of reads mapping to rRNA genes due to the high
rRNA content of ribosomes. This information can be generated with the
featuretypeCounts function described above.
Coverage trends along transcripts summarized for any number of transcripts can
be obtained and plotted with the functions featureCoverage and
plotfeatureCoverage, respectively. Their results allow monitoring
of the phasing of ribosome movements along triplets of coding sequences.
Commonly, high quality data will display here for the first nucleotide of each
codon the highest depth of coverage computed for the 5’ ends of the aligned
reads.
Ribo-seq data can also be used to evaluate various aspects of translational
control due to ribosome occupancy in upstream open reading frames (uORFs). The
latter are frequently present in (or near) 5’ UTRs of transcripts. For this,
the function predORFs can be used to identify ORFs in the
nucleotide sequences of transcripts or their subcomponents such as UTR regions.
After scaling the resulting ORF coordinates back to the corresponding genome
locations using scaleRanges, one can use these novel features
(e.g. uORFs) for expression analysis routines similar to those
employed for pre-existing annotations, such as the exonic regions of genes. For
instance, in Ribo-Seq experiments one can use this approach to systematically identify all transcripts occupied by ribosomes in their uORF regions. The binding of
ribosomes to uORF regions may indicate a regulatory role in the translation of
the downstream main ORFs and/or translation of the uORFs into functionally
relevant peptides.
1.1 Experimental design
Typically, users want to specify here all information relevant for the analysis of their NGS study. This includes detailed descriptions of FASTQ files, experimental design, reference genome, gene annotations, etc.
2 Load workflow template
The following loads one of the available NGS workflow templates (here RIBO-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. The sample data are described here.
library(systemPipeRdata)
genWorkenvir(workflow = "riboseq", bam = TRUE)
setwd("riboseq")2.1 Load packages and sample data
The systemPipeR package needs to be loaded to perform the analysis
steps shown in this report (Girke 2014). The package allows users
to run the entire analysis workflow interactively or with a single command
while also generating the corresponding analysis report. For details
see systemPipeR's main vignette.
library(systemPipeR)In the workflow environments generated by genWorkenvir all data
inputs are stored in a data/ directory and all analysis results will
be written to a separate results/ directory, while the
systemPipeRIBOseq.Rmd script and the targets file are
expected to be located in the parent directory. The R session is expected to
run from this parent directory. Additional parameter files are stored under
param/.
To work with real data, users want to organize their own data similarly and
substitute all test data for their own data. To rerun an established workflow
on new data, the initial targets file along with the corresponding
FASTQ files are usually the only inputs the user needs to provide.
2.2 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 Quality filtering and adaptor trimming
The following custom function trims adaptors hierarchically from the longest to
the shortest match of the right end of the reads. If internalmatch=TRUE then internal matches will trigger the same behavior. The argument minpatternlength defines the shortest adaptor match to consider in this iterative process. In addition, the function removes reads containing Ns or homopolymer regions. More detailed information on read preprocessing is provided in systemPipeR's main vignette.
First, we construct SYSargs2 object from cwl and yml param and targets files.
dir_path <- system.file("extdata/cwl/preprocessReads/trim-se",
package = "systemPipeR")
trim <- loadWorkflow(targets = targetspath, wf_file = "trim-se.cwl",
input_file = "trim-se.yml", dir_path = dir_path)
trim <- renderWF(trim, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
trim
output(trim)[1:2]Next, we execute the code for trimming all the raw data.
fctpath <- system.file("extdata", "custom_Fct.R", package = "systemPipeR")
source(fctpath)
iterTrim <- ".iterTrimbatch1(fq, pattern='ACACGTCT', internalmatch=FALSE, minpatternlength=6, Nnumber=1, polyhomo=50, minreadlength=16, maxreadlength=101)"
preprocessReads(args = trim, Fct = iterTrim, batchsize = 1e+05,
overwrite = TRUE, compress = TRUE)
writeTargetsout(x = trim, file = "targets_trim.txt", step = 1,
new_col = "FileName", new_col_output_index = 1, 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.png.
fqlist <- seeFastq(fastq = infile1(trim), batchsize = 10000,
klength = 8)
png("./results/fastqReport.png", height = 18, width = 4 * length(fqlist),
units = "in", res = 72)
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) in both
interactive job submissions and batch submissions to queuing systems of clusters
using the systemPipeR's new CWL command-line interface.
Build Bowtie2 index.
dir_path <- system.file("extdata/cwl/bowtie2/bowtie2-idx", package = "systemPipeR")
idx <- loadWorkflow(targets = NULL, wf_file = "bowtie2-index.cwl",
input_file = "bowtie2-index.yml", dir_path = dir_path)
idx <- renderWF(idx)
idx
cmdlist(idx)
## Run in single machine
runCommandline(idx, make_bam = FALSE)The parameter settings of the aligner are defined in the tophat2-mapping-pe.cwl
and tophat2-mapping-pe.yml files. The following shows how to construct the
corresponding SYSargs2 object, here args.
dir_path <- system.file("extdata/cwl/tophat2/tophat2-se", package = "systemPipeR")
args <- loadWorkflow(targets = targetspath, wf_file = "tophat2-mapping-se.cwl",
input_file = "tophat2-mapping-se.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
args
cmdlist(args)[1:2]
output(args)[1:2]
## Run in single machine
args <- runCommandline(args, make_bam = TRUE)Submission of alignment jobs to compute cluster, here using 72 CPU cores
(18 qsub processes each with 4 CPU cores).
## Run on the cluster
moduleload(modules(args))
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)
waitForJobs(reg = reg)4.2 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-se", package = "systemPipeR")
args <- loadWorkflow(targets = targetspath, wf_file = "hisat2-mapping-se.cwl",
input_file = "hisat2-mapping-se.yml", dir_path = dir_path)
args <- renderWF(args, inputvars = c(FileName = "_FASTQ_PATH1_",
SampleName = "_SampleName_"))
args
cmdlist(args)[1:2]
output(args)[1:2]
## Run
args <- runCommandline(args)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)Check whether all BAM files have been created.
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
file.exists(outpaths)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")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/", "projects/tests/"),
urlbase = "http://biocluster.ucr.edu/~tgirke/", urlfile = "./results/IGVurl.txt")5 Read distribution across genomic features
The genFeatures function generates a variety of feature types from
TxDb objects using utilities provided by the GenomicFeatures package.
5.1 Obtain feature types
The first step is the generation of the feature type ranges based on
annotations provided by a GFF file that can be transformed into a
TxDb object. This includes ranges for mRNAs, exons, introns, UTRs,
CDSs, miRNAs, rRNAs, tRNAs, promoter and intergenic regions. In addition, any
number of custom annotations can be included in this routine.
library(GenomicFeatures)
txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff3",
organism = "Arabidopsis")
feat <- genFeatures(txdb, featuretype = "all", reduce_ranges = TRUE,
upstream = 1000, downstream = 0, verbose = TRUE)5.2 Count and plot reads of any length
The featuretypeCounts function counts how many reads in short read
alignment files (BAM format) overlap with entire annotation categories. This
utility is useful for analyzing the distribution of the read mappings across
feature types, e.g. coding versus non-coding genes. By default the
read counts are reported for the sense and antisense strand of each feature
type separately. To minimize memory consumption, the BAM files are processed in
a stream using utilities from the Rsamtools and
GenomicAlignment packages. The counts can be reported for each read
length separately or as a single value for reads of any length. Subsequently,
the counting results can be plotted with the associated
plotfeaturetypeCounts function.
The following generates and plots feature counts for any read length.
library(ggplot2)
library(grid)
outpaths <- subsetWF(args, slot = "output", subset = 1, index = 1)
fc <- featuretypeCounts(bfl = BamFileList(outpaths, yieldSize = 50000),
grl = feat, singleEnd = TRUE, readlength = NULL, type = "data.frame")
p <- plotfeaturetypeCounts(x = fc, graphicsfile = "results/featureCounts.png",
graphicsformat = "png", scales = "fixed", anyreadlength = TRUE,
scale_length_val = NULL)
5.3 Count and plot reads of specific lengths
To determine the approximate read length of ribosome footprints in Ribo-Seq experiments, one can generate and plot the feature counts for specific read lengths separately. Typically, the most abundant read length obtained for translated features corresponds to the approximate footprint length occupied by the ribosomes.
fc2 <- featuretypeCounts(bfl = BamFileList(outpaths, yieldSize = 50000),
grl = feat, singleEnd = TRUE, readlength = c(74:76, 99:102),
type = "data.frame")
p2 <- plotfeaturetypeCounts(x = fc2, graphicsfile = "results/featureCounts2.png",
graphicsformat = "png", scales = "fixed", anyreadlength = FALSE,
scale_length_val = NULL)
6 Adding custom features to workflow
6.1 Predicting uORFs in 5’ UTR regions
The function predORF can be used to identify open reading frames (ORFs) and coding sequences (CDSs) in DNA sequences provided as DNAString or DNAStringSet objects. The setting mode='ORF' returns continuous reading frames that begin with a start codon and end with a stop codon, while mode='CDS' returns continuous reading frames that do not need to begin or end with start or stop codons, respectively. Non-canonical start and stop condons are supported by allowing the user to provide any custom set of triplets under the startcodon and stopcodon arguments (i.e. non-ATG start codons). The argument n defines the maximum number of ORFs to return for each input sequence (e.g. n=1 returns only the longest ORF). It also supports the identification of overlapping and nested ORFs. Alternatively, one can return all non-overlapping ORFs including the longest ORF for each input sequence with n="all" and longest_disjoint=TRUE.
library(systemPipeRdata)
library(GenomicFeatures)
library(rtracklayer)
txdb <- makeTxDbFromGFF(file = "data/tair10.gff", format = "gff3",
organism = "Arabidopsis")
futr <- fiveUTRsByTranscript(txdb, use.names = TRUE)
dna <- extractTranscriptSeqs(FaFile("data/tair10.fasta"), futr)
uorf <- predORF(dna, n = "all", mode = "orf", longest_disjoint = TRUE,
strand = "sense")To use the predicted ORF ranges for expression analysis given genome alignments
as input, it is necessary to scale them to the corresponding genome
coordinates. The function scaleRanges does this by transforming the
mappings of spliced features (query ranges) to their corresponding genome
coordinates (subject ranges). The method accounts for introns in the subject
ranges that are absent in the query ranges. The above uORFs predicted in the
provided 5’ UTRs sequences using predORF are a typical use case
for this application. These query ranges are given relative to the 5’ UTR
sequences and scaleRanges will convert them to the corresponding
genome coordinates. The resulting GRangesList object (here grl_scaled)
can be directly used for read counting.
grl_scaled <- scaleRanges(subject = futr, query = uorf, type = "uORF",
verbose = TRUE)
export.gff3(unlist(grl_scaled), "results/uorf.gff")To confirm the correctness of the obtained uORF ranges, one can parse their
corresponding DNA sequences from the reference genome with the getSeq
function and then translate them with the translate function into
proteins. Typically, the returned protein sequences should start with a
M (corresponding to start codon) and end with * (corresponding to stop codon). The following example does this for a single uORF containing three exons.
translate(unlist(getSeq(FaFile("data/tair10.fasta"), grl_scaled[[7]])))6.2 Adding custom features to other feature types
If required custom feature ranges can be added to the standard features generated with the genFeatures function above. The following does this for the uORF ranges predicted with predORF.
feat <- genFeatures(txdb, featuretype = "all", reduce_ranges = FALSE)
feat <- c(feat, GRangesList(uORF = unlist(grl_scaled)))6.3 Predicting sORFs in intergenic regions
The following identifies continuous ORFs in intergenic regions. Note,
predORF can only identify continuous ORFs in query sequences. The
function does not identify and remove introns prior to the ORF prediction.
feat <- genFeatures(txdb, featuretype = "intergenic", reduce_ranges = TRUE)
intergenic <- feat$intergenic
strand(intergenic) <- "+"
dna <- getSeq(FaFile("data/tair10.fasta"), intergenic)
names(dna) <- mcols(intergenic)$feature_by
sorf <- predORF(dna, n = "all", mode = "orf", longest_disjoint = TRUE,
strand = "both")
sorf <- sorf[width(sorf) > 60] # Remove sORFs below length cutoff, here 60bp
intergenic <- split(intergenic, mcols(intergenic)$feature_by)
grl_scaled_intergenic <- scaleRanges(subject = intergenic, query = sorf,
type = "sORF", verbose = TRUE)
export.gff3(unlist(grl_scaled_intergenic), "sorf.gff")
translate(getSeq(FaFile("data/tair10.fasta"), unlist(grl_scaled_intergenic)))7 Genomic read coverage along transripts or CDSs
The featureCoverage function computes the read coverage along
single and multi component features based on genomic alignments. The coverage
segments of component features are spliced to continuous ranges, such as exons
to transcripts or CDSs to ORFs. The results can be obtained with single
nucleotide resolution (e.g. around start and stop codons) or as mean coverage
of relative bin sizes, such as 100 bins for each feature. The latter allows
comparisons of coverage trends among transcripts of variable length. Additionally,
the results can be obtained for single or many features (e.g. any number of
transcripts) at once. Visualization of the coverage results is facilitated by
the downstream plotfeatureCoverage function.
7.1 Binned CDS coverage to compare many transcripts
grl <- cdsBy(txdb, "tx", use.names = TRUE)
fcov <- featureCoverage(bfl = BamFileList(outpaths[1:2]), grl = grl[1:4],
resizereads = NULL, readlengthrange = NULL, Nbins = 20, method = mean,
fixedmatrix = FALSE, resizefeatures = TRUE, upstream = 20,
downstream = 20, outfile = "results/featureCoverage.xls",
overwrite = TRUE)7.2 Coverage upstream and downstream of start and stop codons
fcov <- featureCoverage(bfl = BamFileList(outpaths[1:4]), grl = grl[1:12],
resizereads = NULL, readlengthrange = NULL, Nbins = NULL,
method = mean, fixedmatrix = TRUE, resizefeatures = TRUE,
upstream = 20, downstream = 20, outfile = "results/featureCoverage.xls",
overwrite = TRUE)
plotfeatureCoverage(covMA = fcov, method = mean, scales = "fixed",
extendylim = 2, scale_count_val = 10^6)7.3 Combined coverage for both binned CDS and start/stop codons
library(ggplot2)
library(grid)
fcov <- featureCoverage(bfl = BamFileList(outpaths[1:4]), grl = grl[1:4],
resizereads = NULL, readlengthrange = NULL, Nbins = 20, method = mean,
fixedmatrix = TRUE, resizefeatures = TRUE, upstream = 20,
downstream = 20, outfile = "results/featureCoverage.xls",
overwrite = TRUE)
png("./results/featurePlot.png", height = 12, width = 24, units = "in",
res = 72)
plotfeatureCoverage(covMA = fcov, method = mean, scales = "fixed",
extendylim = 2, scale_count_val = 10^6)
dev.off()
7.4 Nucleotide level coverage along entire transcripts/CDSs
fcov <- featureCoverage(bfl = BamFileList(outpaths[1:2]), grl = grl[1],
resizereads = NULL, readlengthrange = NULL, Nbins = NULL,
method = mean, fixedmatrix = FALSE, resizefeatures = TRUE,
upstream = 20, downstream = 20, outfile = NULL)8 Read quantification per annotation range
8.1 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 (RPKM). The raw read count table (countDFeByg.xls) and the corresponding
RPKM table (rpkmDFeByg.xls) are written to
separate files in the results directory of this project.
Parallelization is achieved with the BiocParallel package, here
using 8 CPU cores.
library("GenomicFeatures")
library(BiocParallel)
txdb <- loadDb("./data/tair10.sqlite")
eByg <- exonsBy(txdb, by = c("gene"))
bfl <- BamFileList(outpaths, yieldSize = 50000, index = character())
multicoreParam <- MulticoreParam(workers = 8)
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.
8.2 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 and written to a file named sample_tree.png
in the results directory.
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 <- 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()
9 Analysis of differentially expressed genes with edgeR
The analysis of differentially expressed genes (DEGs) is performed with the glm
method from 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>.
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 functional gene descriptions, here from biomaRt.
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)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)
png("./results/DEGcounts.png", height = 10, width = 10, units = "in",
res = 72)
DEG_list <- filterDEGs(degDF = edgeDF, filter = c(Fold = 2, FDR = 20))
dev.off()
write.table(DEG_list$Summary, "./results/DEGcounts.xls", quote = FALSE,
sep = "\t", row.names = FALSE)
The function overLapper can compute Venn intersects for large
numbers of sample sets (up to 20 or more) and vennPlot can plot 2-5
way Venn diagrams. A useful feature is the possiblity to combine the counts
from several Venn comparisons with the same number of sample sets in a single
Venn diagram (here for 4 up and down DEG sets).
vennsetup <- overLapper(DEG_list$Up[6:9], type = "vennsets")
vennsetdown <- overLapper(DEG_list$Down[6:9], type = "vennsets")
png("results/vennplot.png")
vennPlot(list(vennsetup, vennsetdown), mymain = "", mysub = "",
colmode = 2, ccol = c("blue", "red"))
dev.off()
10 GO term enrichment analysis of DEGs
10.1 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")10.2 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")
library(BBmisc) # Defines suppressAll()
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)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$CLID),
]
gos <- BatchResultslim
png("./results/GOslimbarplotMF.png", height = 12, width = 12,
units = "in", res = 72)
goBarplot(gos, gocat = "MF")
dev.off()
goBarplot(gos, gocat = "BP")
goBarplot(gos, gocat = "CC")
11 Differential ribosome loading analysis (translational efficiency)
Combined with mRNA-Seq data, Ribo-Seq or polyRibo-Seq experiments can be used
to study changes in translational efficiencies of genes and/or transcripts for
different treatments. For test purposes the following generates a small test
data set from the sample data used in this vignette, where two types of RNA
samples (assays) are considered: polyribosomal mRNA (Ribo)
and total mRNA (mRNA). In addition, there are two treatments
(conditions): M1 and A1.
library(DESeq2)
countDFeBygpath <- system.file("extdata", "countDFeByg.xls",
package = "systemPipeR")
countDFeByg <- read.delim(countDFeBygpath, row.names = 1)
coldata <- DataFrame(assay = factor(rep(c("Ribo", "mRNA"), each = 4)),
condition = factor(rep(as.character(targets.as.df(targets(args))$Factor[1:4]),
2)), row.names = as.character(targets.as.df(targets(args))$SampleName)[1:8])
coldataDifferences in translational efficiencies can be calculated by ratios of ratios for the two conditions:
\[(Ribo\_A1 / mRNA\_A1) / (Ribo\_M1 / mRNA\_M1)\]
The latter can be modeled with the DESeq2 package using the design \(\sim assay + condition + assay:condition\), where the interaction term \(assay:condition\) represents the ratio of ratios. Using the likelihood ratio test of DESeq2, which removes the interaction term in the reduced model, one can test whether the translational efficiency (ribosome loading) is different in condition A1 than in M1.
dds <- DESeq2::DESeqDataSetFromMatrix(countData = as.matrix(countDFeByg[,
rownames(coldata)]), colData = coldata, design = ~assay +
condition + assay:condition)
# model.matrix(~ assay + condition + assay:condition,
# coldata) # Corresponding design matrix
dds <- DESeq2::DESeq(dds, test = "LRT", reduced = ~assay + condition)
res <- DESeq2::results(dds)
head(res[order(res$padj), ], 4)
# write.table(res, file='transleff.xls', quote=FALSE,
# col.names = NA, sep='\t')12 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, ]
png("heatmap1.png")
pheatmap(y, scale = "row", clustering_distance_rows = "correlation",
clustering_distance_cols = "correlation")
dev.off()
13 Render report in HTML and PDF format
rmarkdown::render("systemPipeRIBOseq.Rmd", "html_document")
rmarkdown::render("systemPipeRIBOseq.Rmd", "pdf_document")14 Version Information
sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-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_1.22.0 ShortRead_1.46.0
## [3] GenomicAlignments_1.24.0 SummarizedExperiment_1.18.2
## [5] DelayedArray_0.14.1 matrixStats_0.57.0
## [7] Biobase_2.48.0 BiocParallel_1.22.0
## [9] Rsamtools_2.4.0 Biostrings_2.56.0
## [11] XVector_0.28.0 GenomicRanges_1.40.0
## [13] GenomeInfoDb_1.24.2 IRanges_2.22.2
## [15] S4Vectors_0.26.1 BiocGenerics_0.34.0
## [17] BiocStyle_2.16.1
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 rjson_0.2.20
## [3] hwriter_1.3.2 ellipsis_0.3.1
## [5] bit64_4.0.5 AnnotationDbi_1.50.3
## [7] codetools_0.2-16 splines_4.0.2
## [9] knitr_1.30 jsonlite_1.7.1
## [11] annotate_1.66.0 GO.db_3.11.4
## [13] dbplyr_1.4.4 png_0.1-7
## [15] pheatmap_1.0.12 graph_1.66.0
## [17] BiocManager_1.30.10 compiler_4.0.2
## [19] httr_1.4.2 GOstats_2.54.0
## [21] backports_1.1.10 assertthat_0.2.1
## [23] Matrix_1.2-18 limma_3.44.3
## [25] formatR_1.7 htmltools_0.5.0
## [27] prettyunits_1.1.1 tools_4.0.2
## [29] gtable_0.3.0 glue_1.4.2
## [31] GenomeInfoDbData_1.2.3 Category_2.54.0
## [33] dplyr_1.0.2 rsvg_2.1
## [35] batchtools_0.9.13 rappdirs_0.3.1
## [37] V8_3.2.0 Rcpp_1.0.5
## [39] vctrs_0.3.4 debugme_1.1.0
## [41] rtracklayer_1.48.0 xfun_0.17
## [43] stringr_1.4.0 lifecycle_0.2.0
## [45] XML_3.99-0.5 edgeR_3.30.3
## [47] zlibbioc_1.34.0 scales_1.1.1
## [49] BSgenome_1.56.0 VariantAnnotation_1.34.0
## [51] hms_0.5.3 RBGL_1.64.0
## [53] RColorBrewer_1.1-2 yaml_2.2.1
## [55] curl_4.3 memoise_1.1.0
## [57] ggplot2_3.3.2 biomaRt_2.44.1
## [59] latticeExtra_0.6-29 stringi_1.5.3
## [61] RSQLite_2.2.0 genefilter_1.70.0
## [63] checkmate_2.0.0 GenomicFeatures_1.40.1
## [65] DOT_0.1 rlang_0.4.7
## [67] pkgconfig_2.0.3 bitops_1.0-6
## [69] evaluate_0.14 lattice_0.20-41
## [71] purrr_0.3.4 bit_4.0.4
## [73] tidyselect_1.1.0 GSEABase_1.50.1
## [75] AnnotationForge_1.30.1 magrittr_1.5
## [77] bookdown_0.20 R6_2.4.1
## [79] generics_0.0.2 base64url_1.4
## [81] DBI_1.1.0 pillar_1.4.6
## [83] withr_2.3.0 survival_3.2-3
## [85] RCurl_1.98-1.2 tibble_3.0.3
## [87] crayon_1.3.4 BiocFileCache_1.12.1
## [89] rmarkdown_2.3 jpeg_0.1-8.1
## [91] progress_1.2.2 locfit_1.5-9.4
## [93] grid_4.0.2 data.table_1.13.0
## [95] blob_1.2.1 Rgraphviz_2.32.0
## [97] digest_0.6.25 xtable_1.8-4
## [99] brew_1.0-6 openssl_1.4.3
## [101] munsell_0.5.0 askpass_1.115 Funding
This research was funded by National Science Foundation Grants IOS-0750811 and MCB-1021969, and a Marie Curie European Economic Community Fellowship PIOF-GA-2012-327954.
References
Aspden, Julie L, Ying Chen Eyre-Walker, Rose J Phillips, Unum Amin, Muhammad Ali S Mumtaz, Michele Brocard, and Juan-Pablo Couso. 2014. “Extensive Translation of Small Open Reading Frames Revealed by Poly-Ribo-Seq.” Elife 3 (21~aug):e03528. https://doi.org/10.7554/eLife.03528.
Girke, Thomas. 2014. “systemPipeR: NGS Workflow and Report Generation Environment.” UC Riverside. https://github.com/tgirke/systemPipeR.
Ingolia, N T, S Ghaemmaghami, J R Newman, and J S Weissman. 2009. “Genome-Wide Analysis in Vivo of Translation with Nucleotide Resolution Using Ribosome Profiling.” Science 324 (5924):218–23. https://doi.org/10.1016/j.ymeth.2009.03.016.
Ingolia, N T, L F Lareau, and J S Weissman. 2011. “Ribosome Profiling of Mouse Embryonic Stem Cells Reveals the Complexity and Dynamics of Mammalian Proteomes.” Cell 147 (4):789–802. https://doi.org/10.1016/j.cell.2011.10.002.
Juntawong, Piyada, Thomas Girke, Jérémie Bazin, and Julia Bailey-Serres. 2014. “Translational Dynamics Revealed by Genome-Wide Profiling of Ribosome Footprints in Arabidopsis.” Proc. Natl. Acad. Sci. U. S. A. 111 (1):E203–12. https://doi.org/10.1073/pnas.1317811111.
Juntawong, Piyada, Maureen Hummel, Jeremie Bazin, and Julia Bailey-Serres. 2015. “Ribosome Profiling: A Tool for Quantitative Evaluation of Dynamics in mRNA Translation.” Methods Mol. Biol. 1284:139–73. https://doi.org/10.1007/978-1-4939-2444-8\_7.
Kim, Daehwan, Ben Langmead, and Steven L Salzberg. 2015. “HISAT: A Fast Spliced Aligner with Low Memory Requirements.” Nat. Methods 12 (4):357–60.
Kim, Daehwan, Geo Pertea, Cole Trapnell, Harold Pimentel, Ryan Kelley, and Steven L Salzberg. 2013. “TopHat2: Accurate Alignment of Transcriptomes in the Presence of Insertions, Deletions and Gene Fusions.” Genome Biol. 14 (4):R36. https://doi.org/10.1186/gb-2013-14-4-r36.
Langmead, Ben, and Steven L Salzberg. 2012. “Fast Gapped-Read Alignment with Bowtie 2.” Nat. Methods 9 (4). Nature Publishing Group:357–59. https://doi.org/10.1038/nmeth.1923.
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.