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

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.

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.

The mini sample FASTQ files used by this report as well as the associated reference genome files can be loaded via the systemPipeRdata package. 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 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.

2.2 Required packages and resources

The systemPipeR package needs to be loaded to perform the analysis steps shown in this report (H Backman and Girke 2016).

library(systemPipeR)

To apply workflows to custom data, the user needs to modify the targets file and if necessary update the corresponding parameter (.cwl and .yml) files. A collection of pre-generated .cwl and .yml files are provided in the param/cwl subdirectory of each workflow template. They are also viewable in the GitHub repository of systemPipeRdata (see here). For more information of the structure of the targets file, please consult the documentation here. More details about the new parameter files from systemPipeR can be found here.

2.3 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 = "#")[, 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

3 Read preprocessing

3.1 Read quality filtering and trimming

The function preprocessReads allows to apply predefined or custom read preprocessing functions to all FASTQ files referenced in a SYSargs2 container, such as quality filtering or adapter trimming routines. The paths to the resulting output FASTQ files are stored in the output slot of the SYSargs2 object. The following example performs adapter 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 SYSargs2 instance, e.g. running the NGS alignments using the trimmed FASTQ files.

Construct SYSargs2 object from cwl and yml param and targets files.

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]
preprocessReads(args = trim, Fct = "trimLRPatterns(Rpattern='GCCCGGGTAA', 
                subject=fq)",
    batchsize = 1e+05, overwrite = TRUE, compress = TRUE)
writeTargetsout(x = trim, file = "targets_trim.txt", step = 1,
    new_col = c("FileName1", "FileName2"), new_col_output_index = c(1,
        2), overwrite = TRUE)

3.2 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.

fqlist <- seeFastq(fastq = infile1(trim), batchsize = 10000,
    klength = 8)
pdf("./results/fastqReport.pdf", height = 18, width = 4 * length(fqlist))
seeFastqPlot(fqlist)
dev.off()