1 Introduction

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

2 Samples and environment settings

2.1 Environment settings and input data

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 are provide in the systemPipeRdata vignette here.

systemPipeRdata::genWorkenvir(workflow = "rnaseq", mydirname = "rnaseq")

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 workflow and the targets file are expected to be located in the parent directory.

The chosen data set used by this report 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. thaliana genome. The corresponding reference genome sequence (FASTA) and its GFF annotation files 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.

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.

For more details, please consult the documentation here. More information about the targets files from systemPipeR can be found here.

2.1.1 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, -c(5, 6)]
##                     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        Date
## 1        M1A     M1 23-Mar-2012
## 2        M1B     M1 23-Mar-2012
## 3        A1A     A1 23-Mar-2012
## 4        A1B     A1 23-Mar-2012

To work with custom data, users need to generate a targets file containing the paths to their own FASTQ files.

3 Workflow environment

systemPipeR workflows can be designed and built from start to finish with a single command, importing from an R Markdown file or stepwise in interactive mode from the R console.

This tutorial will demonstrate how to build the workflow in an interactive mode, appending each step. The workflow is constructed by connecting each step via appendStep method. Each SYSargsList instance contains instructions for processing a set of input files with a specific command-line or R software and the paths to the corresponding outfiles generated by a particular command-line software/step.

To create a workflow within systemPipeR, we can start by defining an empty container and checking the directory structure:

sal <- SPRproject()

3.1 Required packages and resources

The systemPipeR package needs to be loaded (H Backman and Girke 2016).

appendStep(sal) <- LineWise(code = {
}, step_name = "load_SPR")

3.2 Read preprocessing

3.2.1 Preprocessing with preprocessReads function

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargsList container, such as quality filtering or adapter trimming routines. Internally, preprocessReads uses the FastqStreamer function from the ShortRead package to stream through large FASTQ files in a memory-efficient manner. The following example performs adapter trimming with the trimLRPatterns function from the Biostrings package.

Here, we are appending this step to the SYSargsList object created previously. All the parameters are defined on the preprocessReads/preprocessReads-pe.yml file.

appendStep(sal) <- SYSargsList(step_name = "preprocessing", targets = "targetsPE.txt",
    dir = TRUE, wf_file = "preprocessReads/preprocessReads-pe.cwl",
    input_file = "preprocessReads/preprocessReads-pe.yml", dir_path = system.file("extdata/cwl",
        package = "systemPipeR"), inputvars = c(FileName1 = "_FASTQ_PATH1_",
        FileName2 = "_FASTQ_PATH2_", SampleName = "_SampleName_"),
    dependency = c("load_SPR"))

After the preprocessing step, the outfiles files can be used to generate the new targets files containing the paths to the trimmed FASTQ files. The new targets information can be used for the next workflow step instance, e.g. running the NGS alignments with the trimmed FASTQ files. The appendStep function is automatically handling this connectivity between steps. Please check the next step for more details.

The following example shows how one can design a custom read ‘preprocessReads’ function using utilities provided by the ShortRead package, and then run it in batch mode with the ‘preprocessReads’ function. Here, it is possible to replace the function used on the preprocessing step and modify the sal object. Because it is a custom function, it is necessary to save the part in the R object, and internally the preprocessReads.doc.R is loading the custom function. If the R object is saved with a different name (here "param/customFCT.RData"), please replace that accordingly in the preprocessReads.doc.R.

Please, note that this step is not added to the workflow, here just for demonstration.

First, we defined the custom function in the workflow:

appendStep(sal) <- LineWise(code = {
    filterFct <- function(fq, cutoff = 20, Nexceptions = 0) {
        qcount <- rowSums(as(quality(fq), "matrix") <= cutoff,
            na.rm = TRUE)
        # Retains reads where Phred scores are >= cutoff
        # with N exceptions
        fq[qcount <= Nexceptions]
    save(list = ls(), file = "param/customFCT.RData")
}, step_name = "custom_preprocessing_function", dependency = "preprocessing")

After, we can edit the input parameter:

yamlinput(sal, "preprocessing")$Fct
yamlinput(sal, "preprocessing", "Fct") <- "'filterFct(fq, cutoff=20, Nexceptions=0)'"
yamlinput(sal, "preprocessing")$Fct  ## check the new function
cmdlist(sal, "preprocessing", targets = 1)  ## check if the command line was updated with success

3.2.2 Read trimming with Trimmomatic

Trimmomatic software (Bolger, Lohse, and Usadel 2014) performs a variety of useful trimming tasks for Illumina paired-end and single ended data. Here, an example of how to perform this task using parameters template files for trimming FASTQ files.

This step is optional.

appendStep(sal) <- SYSargsList(step_name = "trimming", targets = "targetsPE.txt",
    wf_file = "trimmomatic/trimmomatic-pe.cwl", input_file = "trimmomatic/trimmomatic-pe.yml",
    dir_path = system.file("extdata/cwl", package = "systemPipeR"),
    inputvars = c(FileName1 = "_FASTQ_PATH1_", FileName2 = "_FASTQ_PATH2_",
        SampleName = "_SampleName_"), dependency = "load_SPR",
    run_step = "optional")

3.2.3 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 png file named fastqReport.png.

appendStep(sal) <- LineWise(code = {
    fastq <- getColumn(sal, step = "preprocessing", "targetsWF",
        column = 1)
    fqlist <- seeFastq(fastq = fastq, batchsize = 10000, klength = 8)
    png("./results/fastqReport.png", height = 162, width = 288 *
}, step_name = "fastq_report", dependency = "preprocessing")