1 Introduction

Users want to provide here background information about the design of their VAR-Seq project.

1.1 Background and objectives

This report describes the analysis of a VAR-Seq project studying the genetic differences among several strains … from organism ….

1.2 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 Workflow environment

2.1 Generate workflow environment

Load workflow environment with sample data into your current working directory. The sample data are described here.

genWorkenvir(workflow = "varseq")

Alternatively, this can be done from the command-line as follows:

Rscript -e "systemPipeRdata::genWorkenvir(workflow='varseq')"

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 systemPipeVARseq.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 Run workflow

Now open the R markdown script systemPipeVARseq.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.

2.2.1 Run R session on computer node

In a computer cluster enviornment. Typically, after opening the Rmd file of this workflow in Vim and attaching a connected R session via the F2 ( vim-r plugin installed) key, following command sequence can be used to run your R session on a computer node.

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/3.6.0

Now check your R session running environment.

system("hostname")  # should return the computer name or cluster name
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).


If applicable users can load custom functions not provided by systemPipeR. Skip this step if this is not the case.


If you are running on a single machine, use following code as an exmaple 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.

3 Read preprocessing

3.1 Experiment definition provided by targets file

The 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 = "#")
targets[1:4, 1:4]
##                     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
##   SampleName Factor
## 1        M1A     M1
## 2        M1B     M1
## 3        A1A     A1
## 4        A1B     A1

3.2 Read quality filtering and trimming

The following removes reads with low quality base calls (here a certain pattern) from all FASTQ files.

targetsPE <- system.file("extdata", "targetsPE.txt", package = "systemPipeR")
dir_path <- system.file("extdata/cwl/preprocessReads/trim-pe", 
    package = "systemPipeR")
trim <- loadWorkflow(targets = targetsPE, 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_"))

preprocessReads(args = trim, Fct = "trimLRPatterns(Rpattern='GCCCGGGTAA', subject=fq)", 
    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)

3.3 FASTQ quality report

The following seeFastq and seeFastqPlot functions generate and plot a series of useful quality statistics for a set of FASTQ files including per cycle quality box plots, base proportions, base-level quality trends, relative k-mer diversity, length and occurrence distribution of reads, number of reads above quality cutoffs and mean quality distribution. The results are written to a PDF file named fastqReport.pdf. Use the output from previous step (fastq trimming) as the demonstration here to generate fastq report.

fqlist <- seeFastq(fastq = infile1(trim), batchsize = 1e+05, 
    klength = 8)
pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist))