systemPipeR 1.20.0
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.
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.
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")In case there is a newer version of this tutorial, download its systemPipeRIBOseq.Rmd source and open it in your R IDE (e.g. vim-r or RStudio).
download.file("https://raw.githubusercontent.com/tgirke/systemPipeRdata/master/vignettes/systemPipeRIBOseq.Rmd",
"systemPipeRIBOseq.Rmd")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.
If applicable users can load custom functions not provided by
systemPipeR. Skip this step if this is not the case.
source("systemPipeRIBOseq_Fct.R")targets fileThe 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.BThe 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)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()