systemPipeR 1.18.0
Users want to provide here background information about the design of their RNA-Seq project.
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.Rmd
in 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 node
srun --x11 --partition=short --mem=2gb --cpus-per-task 4 --ntasks 1 --time 2:00:00 --pty bash -l
module load R/3.4.2
R
Now 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 directory
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")
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.B
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)
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()