1 Introduction

The Rfastp package provides an interface to the all-in-one preprocessing for FastQ files toolkit fastp(Chen et al. 2018).

2 Installation

Use the BiocManager package to download and install the package from Bioconductor as follows:

if (!requireNamespace("BiocManager", quietly = TRUE))

If required, the latest development version of the package can also be installed from GitHub.


Once the package is installed, load it into your R session:


3 FastQ Quality Control with rfastp

The package contains three example fastq files, corresponding to a single-end fastq file, a pair of paired-end fastq files.

se_read1 <- system.file("extdata","Fox3_Std_small.fq.gz",package="Rfastp")
pe_read1 <- system.file("extdata","reads1.fastq.gz",package="Rfastp")
pe_read2 <- system.file("extdata","reads2.fastq.gz",package="Rfastp")
outputPrefix <- tempfile(tmpdir = tempdir())

3.1 a normal QC run for single-end fastq file.

Rfastp support multiple threads, set threads number by parameter thread.

se_json_report <- rfastp(read1 = se_read1, 
    outputFastq = paste0(outputPrefix, "_se"), thread = 4)

3.2 a normal QC run for paired-end fastq files.

pe_json_report <- rfastp(read1 = pe_read1, read2 = pe_read2,
    outputFastq = paste0(outputPrefix, "_pe"))

3.3 merge paired-end fastq files after QC.

pe_merge_json_report <- rfastp(read1 = pe_read1, read2 = pe_read2, merge = TRUE,
    outputFastq = paste0(outputPrefix, '_unpaired'),
    mergeOut = paste0(outputPrefix, "_merged.fastq.gz"))

3.4 UMI processing

3.4.1 a normal UMI processing for 10X Single-Cell library.

umi_json_report <- rfastp(read1 = pe_read1, read2 = pe_read2, 
    outputFastq = paste0(outputPrefix, '_umi1'), umi = TRUE, umiLoc = "read1",
    umiLength = 16)

3.4.2 Set a customized UMI prefix and location in sequence name.

the following example will add prefix string before the UMI sequence in the sequence name. An “_" will be added between the prefix string and UMI sequence. The UMI sequences will be inserted into the sequence name before the first space.

umi_json_report <- rfastp(read1 = pe_read1, read2 = pe_read2, 
    outputFastq = paste0(outputPrefix, '_umi2'), umi = TRUE, umiLoc = "read1",
    umiLength = 16, umiPrefix = "#", umiNoConnection = TRUE, 
    umiIgnoreSeqNameSpace = TRUE)

3.5 A QC example with customized cutoffs and adapter sequence.

Trim poor quality bases at 3’ end base by base with quality higher than 5; trim poor quality bases at 5’ end by a 29bp window with mean quality higher than 20; disable the polyG trimming, specify the adapter sequence for read1.

clipr_json_report <- rfastp(read1 = se_read1, 
    outputFastq = paste0(outputPrefix, '_clipr'),
    disableTrimPolyG = TRUE,
    cutLowQualFront = TRUE,
    cutFrontWindowSize = 29,
    cutFrontMeanQual = 20,
    cutLowQualTail = TRUE,
    cutTailWindowSize = 1,
    cutTailMeanQual = 5,
    minReadLength = 29,
    adapterSequenceRead1 = 'GTGTCAGTCACTTCCAGCGG'

3.6 multiple input files for read1/2 in a vector.

rfastq can accept multiple input files, and it will concatenate the input files into one and the run fastp.

pe001_read1 <- system.file("extdata","splited_001_R1.fastq.gz",
pe002_read1 <- system.file("extdata","splited_002_R1.fastq.gz",
pe003_read1 <- system.file("extdata","splited_003_R1.fastq.gz",
pe004_read1 <- system.file("extdata","splited_004_R1.fastq.gz",
inputfiles <- c(pe001_read1, pe002_read1, pe003_read1, pe004_read1)
cat_rjson_report <- rfastp(read1 = inputfiles, 
    outputFastq = paste0(outputPrefix, "_merged1"))

4 concatenate multiple fastq files.

4.1 catfastq concatenate all the input files into a new file.

pe001_read2 <- system.file("extdata","splited_001_R2.fastq.gz",
pe002_read2 <- system.file("extdata","splited_002_R2.fastq.gz",
pe003_read2 <- system.file("extdata","splited_003_R2.fastq.gz",
pe004_read2 <- system.file("extdata","splited_004_R2.fastq.gz",
inputR2files <- c(pe001_read2, pe002_read2, pe003_read2, pe004_read2)
catfastq(output = paste0(outputPrefix,"_merged2_R2.fastq.gz"), 
    inputFiles = inputR2files)

5 Generate report tables/plots

5.1 A data frame for the summary.

dfsummary <- qcSummary(pe_json_report)

5.2 a ggplot2 object of base quality plot.

p1 <- curvePlot(se_json_report)

5.3 a ggplot2 object of GC Content plot.

p2 <- curvePlot(se_json_report, curve="content_curves")