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
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
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.
systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016). 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.
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 template are provide in the systemPipeRdata vignette here.
After building and loading the workflow environment generated by
genWorkenvir from systemPipeRdata 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
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.
Now open the R markdown script
systemPipeRIBOseq.Rmdin your R IDE (e.g. vim-r or RStudio) and run the workflow as outlined below.
Here pair-end workflow example is provided. Please refer to the main vignette
systemPipeR.Rmd for running the workflow with single-end data.
If you are running on a single machine, use following code as an example to check if some tools used in this workflow are in your environment PATH. No warning message should be shown if all tools are installed.
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
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
yml param and
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]
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_trimPE.txt", step = 1, new_col = c("FileName1", "FileName2"), new_col_output_index = c(1, 2), overwrite = TRUE)
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
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.
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.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 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.
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")
## 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
genFeatures function generates a variety of feature types from
TxDb objects using utilities provided by the
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.
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
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
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)
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)
predORF can be used to identify open reading frames (ORFs) and coding sequences (CDSs) in DNA sequences provided as
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
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
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.
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
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)))
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
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)
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)
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()
fcov <- featureCoverage(bfl = BamFileList(outpaths[1:2]), grl = grl, resizereads = NULL, readlengthrange = NULL, Nbins = NULL, method = mean, fixedmatrix = FALSE, resizefeatures = TRUE, upstream = 20, downstream = 20, outfile = NULL)
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[])) 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
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.
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
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()
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
targetsPE.txt file starting with
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[], independent = FALSE, mdsplot = "")
Add functional gene descriptions, here from
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
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$Summary, "./results/DEGcounts.xls", quote = FALSE, sep = "\t", row.names = FALSE)
overLapper can compute Venn intersects for large numbers of sample sets (up to 20 or more) and
vennPlot can plot 2-5 way Venn diagrams. A useful feature is the possiblity to combine the counts from several Venn comparisons with the same number of sample sets in a single Venn diagram (here for 4 up and down DEG sets).
vennsetup <- overLapper(DEG_list$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()
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")
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)
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.
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 (
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]) coldata
Differences 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
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')
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.
## R Under development (unstable) (2020-08-07 r78979) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Ubuntu 20.04.1 LTS ## ## Matrix products: default ## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 ## LAPACK: /home/dcassol/src/R-devel/lib/libRlapack.so ## ## locale: ##  LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C ##  LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 ##  LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8 ##  LC_PAPER=en_US.UTF-8 LC_NAME=C ##  LC_ADDRESS=C LC_TELEPHONE=C ##  LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C ## ## attached base packages: ##  stats4 parallel stats graphics grDevices ##  utils datasets methods base ## ## other attached packages: ##  systemPipeR_1.23.4 ShortRead_1.47.2 ##  GenomicAlignments_1.25.3 SummarizedExperiment_1.19.9 ##  Biobase_2.49.1 MatrixGenerics_1.1.5 ##  matrixStats_0.57.0 BiocParallel_1.23.2 ##  Rsamtools_2.5.3 Biostrings_2.57.2 ##  XVector_0.29.3 GenomicRanges_1.41.6 ##  GenomeInfoDb_1.25.11 IRanges_2.23.10 ##  S4Vectors_0.27.14 BiocGenerics_0.35.4 ##  BiocStyle_2.17.1 ## ## loaded via a namespace (and not attached): ##  colorspace_1.4-1 rjson_0.2.20 ##  hwriter_1.3.2 ellipsis_0.3.1 ##  rprojroot_1.3-2 fs_1.5.0 ##  rstudioapi_0.11 bit64_4.0.5 ##  AnnotationDbi_1.51.3 xml2_1.3.2 ##  codetools_0.2-16 splines_4.1.0 ##  knitr_1.30 jsonlite_1.7.1 ##  annotate_1.67.1 GO.db_3.11.4 ##  dbplyr_1.4.4 png_0.1-7 ##  pheatmap_1.0.12 graph_1.67.1 ##  BiocManager_1.30.10 compiler_4.1.0 ##  httr_1.4.2 GOstats_2.55.0 ##  backports_1.1.10 assertthat_0.2.1 ##  Matrix_1.2-18 limma_3.45.14 ##  formatR_1.7 htmltools_0.5.0 ##  prettyunits_1.1.1 tools_4.1.0 ##  gtable_0.3.0 glue_1.4.2 ##  GenomeInfoDbData_1.2.4 Category_2.55.0 ##  dplyr_1.0.2 rsvg_2.1 ##  batchtools_0.9.13 rappdirs_0.3.1 ##  V8_3.2.0 Rcpp_1.0.5 ##  pkgdown_1.6.1 vctrs_0.3.4 ##  rtracklayer_1.49.5 xfun_0.18 ##  stringr_1.4.0 lifecycle_0.2.0 ##  XML_3.99-0.5 edgeR_3.31.4 ##  zlibbioc_1.35.0 scales_1.1.1 ##  BSgenome_1.57.7 VariantAnnotation_1.35.3 ##  ragg_0.4.0 hms_0.5.3 ##  RBGL_1.65.0 RColorBrewer_1.1-2 ##  yaml_2.2.1 curl_4.3 ##  memoise_1.1.0 ggplot2_3.3.2 ##  biomaRt_2.45.7 latticeExtra_0.6-29 ##  stringi_1.5.3 RSQLite_2.2.1 ##  genefilter_1.71.0 desc_1.2.0 ##  checkmate_2.0.0 GenomicFeatures_1.41.3 ##  DOT_0.1 rlang_0.4.8 ##  pkgconfig_2.0.3 systemfonts_0.3.2 ##  bitops_1.0-6 evaluate_0.14 ##  lattice_0.20-41 purrr_0.3.4 ##  bit_4.0.4 tidyselect_1.1.0 ##  GSEABase_1.51.1 AnnotationForge_1.31.3 ##  magrittr_1.5 bookdown_0.21 ##  R6_2.4.1 generics_0.0.2 ##  base64url_1.4 DelayedArray_0.15.16 ##  DBI_1.1.0 withr_2.3.0 ##  pillar_1.4.6 survival_3.2-7 ##  RCurl_1.98-1.2 tibble_3.0.4 ##  crayon_1.3.4 BiocFileCache_1.13.1 ##  rmarkdown_2.4 jpeg_0.1-8.1 ##  progress_1.2.2 locfit_1.5-9.4 ##  grid_4.1.0 data.table_1.13.0 ##  Rgraphviz_2.33.0 blob_1.2.1 ##  digest_0.6.25 xtable_1.8-4 ##  brew_1.0-6 textshaping_0.1.2 ##  openssl_1.4.3 munsell_0.5.0 ##  askpass_1.1
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.
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: e03528. https://doi.org/10.7554/eLife.03528.
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.
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.
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.