RcwlPipelines is a
Bioconductor package that manages a collection of commonly used
bioinformatics tools and pipeline based on Rcwl. These
pre-built and pre-tested tools and pipelines are highly modularized with
easy customization to meet different bioinformatics data analysis
needs.
Rcwl and RcwlPipelines together forms a
Bioconductor toolchain for use and development of reproducible
bioinformatics pipelines in Common Workflow Language (CWL). The project
also aims to develop a community-driven platform for open source, open
development, and open review of best-practice CWL bioinformatics
pipelines.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("RcwlPipelines")The development version is also available to download from GitHub.
The project website https://rcwl.org/ serves as a central hub for all related resources. It provides guidance for new users and tutorials for both users and developers. Specific resources are listed below.
The R scripts to build the CWL tools and pipelines are now
residing in a dedicated GitHub repository,
which is intended to be a community effort to collect and contribute
Bioinformatics tools and pipelines using Rcwl and CWL.
The tutorial book provides
detailed instructions for developing Rcwl tools/pipelines,
and also includes examples of some commonly-used tools and pipelines
that covers a wide range of Bioinformatics data analysis needs.
RcwlPipelines core functionsHere we show the usage of 3 core functions: cwlUpdate,
cwlSearch and cwlLoad for updating, searching,
and loading the needed tools or pipelines in R.
cwlUpdateThe cwlUpdate function syncs the current
Rcwl recipes and returns a cwlHub object which
contains the most updated Rcwl recipes. The
mcols() function returns all related information about each
available tool or pipeline.
The recipes will be locally cached, so users don’t need to call
cwlUpdate every time unless they want to use a
tool/pipeline that is newly added to RcwlPipelines. Here we
are using the recipes from Bioconductor devel version.
atls <- cwlUpdate(branch = "dev") ## sync the tools/pipelines.
atls
#> cwlHub with 196 records
#> cache path: /tmp/RtmpuHewrT/Rcwl
#> # last modified date: 2022-02-11
#> # cwlSearch() to query scripts
#> # cwlLoad('title') to load the script
#> # additional mcols(): rid, rpath, Type, Container, mtime, ...
#>
#> title
#> BFC1 | pl_AnnPhaseVcf
#> BFC2 | pl_BaseRecal
#> BFC3 | pl_BwaAlign
#> BFC4 | pl_COMPSRA_rn
#> BFC5 | pl_CombineGenotypeGVCFs
#> ... ...
#> BFC192 | tl_vcf_expression_annotator
#> BFC193 | tl_vcf_readcount_annotator
#> BFC194 | tl_vep
#> BFC195 | tl_vep_plugin
#> BFC196 | tl_vt_decompose
#> Command
#> BFC1 VCFvep+dVCFcoverage+rVCFcoverage+VCFexpression+PhaseVcf
#> BFC2 BaseRecalibrator+ApplyBQSR+samtools_index+samtools_flagstat+samt...
#> BFC3 bwa+sam2bam+sortBam+idxBam
#> BFC4 copy+compsra
#> BFC5 CombineGVCFs+GenotypeGVCFs
#> ... ...
#> BFC192 vcf-expression-annotator
#> BFC193 vcf-readcount-annotator
#> BFC194 vep
#> BFC195 vep
#> BFC196 vt decompose
table(mcols(atls)$Type)
#>
#> pipeline tool
#> 43 153Currently, we have integrated 153 command line tools and 43 pipelines.
cwlSearchWe can use (multiple) keywords to search for specific tools/pipelines
of interest, which internally search the mcols of “rname”,
“rpath”, “fpath”, “Command” and “Containers”. Here we show how to search
the alignment tool bwa mem.
t1 <- cwlSearch(c("bwa", "mem"))
t1
#> cwlHub with 1 records
#> cache path: /tmp/RtmpuHewrT/Rcwl
#> # last modified date: 2021-10-25
#> # cwlSearch() to query scripts
#> # cwlLoad('title') to load the script
#> # additional mcols(): rid, rpath, Type, Container, mtime, ...
#>
#> title Command
#> BFC118 | tl_bwa bwa mem
mcols(t1)
#> DataFrame with 1 row and 14 columns
#> rid rname create_time access_time
#> <character> <character> <character> <character>
#> 1 BFC118 tl_bwa 2025-11-16 07:31:20 2025-11-16 07:31:20
#> rpath rtype fpath last_modified_time
#> <character> <character> <character> <numeric>
#> 1 /tmp/RtmpuHewrT/Rcwl.. local /tmp/RtmpuHewrT/Rcwl.. NA
#> etag expires Type Command Container
#> <character> <numeric> <character> <character> <character>
#> 1 NA NA tool bwa mem biocontainers/bwa:v0..
#> mtime
#> <character>
#> 1 2021-10-25 12:58:45cwlLoadThe last core function cwlLoad loads the
Rcwl tool/pipeline into the R working environment.
The code below loads the tool with a user-defined name bwa
to do the read alignment.
bwa <- cwlLoad(title(t1)[1]) ## "tl_bwa"
bwa <- cwlLoad(mcols(t1)$fpath[1]) ## equivalent to the above.
bwa
#> class: cwlProcess
#> cwlClass: CommandLineTool
#> cwlVersion: v1.0
#> baseCommand: bwa mem
#> requirements:
#> - class: DockerRequirement
#> dockerPull: biocontainers/bwa:v0.7.17-3-deb_cv1
#> inputs:
#> threads (int): -t
#> RG (string?): -R
#> Ref (File):
#> FQ1 (File):
#> FQ2 (File?):
#> outputs:
#> sam:
#> type: File
#> outputBinding:
#> glob: '*.sam'
#> stdout: bwaOutput.samNow the R tool of bwa is ready to use.
To fit users’ specific needs，the existing tool or pipline can be
easily customized. Here we use the rnaseq_Sf pipeline to
demonstrate how to access and change the arguments of a specific tool
inside a pipeline. This pipeline covers RNA-seq reads quality summary by
fastQC, alignment by STAR, quantification by
featureCounts and quality control by
RSeQC.
rnaseq_Sf <- cwlLoad("pl_rnaseq_Sf")
#> fastqc loaded
#> STAR loaded
#> sortBam loaded
#> samtools_index loaded
#> samtools_flagstat loaded
#> featureCounts loaded
#> gtfToGenePred loaded
#> genePredToBed loaded
#> read_distribution loaded
#> geneBody_coverage loaded
#> STAR loaded
#> gCoverage loaded
plotCWL(rnaseq_Sf)There are many default arguments defined for the tool of
STAR inside the pipeline. Users might want to change some
of them. For example, we can change the value for
--outFilterMismatchNmax argument from 2 to 5 for longer
reads.
arguments(rnaseq_Sf, "STAR")[5:6]
#> [[1]]
#> [1] "--readFilesCommand"
#>
#> [[2]]
#> [1] "zcat"
arguments(rnaseq_Sf, "STAR")[[6]] <- 5
arguments(rnaseq_Sf, "STAR")[5:6]
#> [[1]]
#> [1] "--readFilesCommand"
#>
#> [[2]]
#> [1] "5"We can also change the docker image for a specific tool (e.g., to a
specific version). First, we search for all available docker images for
STAR in biocontainers repository. The Source server could
be quay or dockerhub.
searchContainer("STAR", repo = "biocontainers", source = "quay")
#> DataFrame with 0 rows and 0 columnsThen, we can change the STAR version into 2.7.8a (tag
name: 2.7.8a–0).
requirements(rnaseq_Sf, "STAR")[[1]]
#> $class
#> [1] "DockerRequirement"
#>
#> $dockerPull
#> [1] "quay.io/biocontainers/star:2.7.9a--h9ee0642_0"
requirements(rnaseq_Sf, "STAR")[[1]] <- requireDocker(
docker = "quay.io/biocontainers/star:2.7.8a--0")
requirements(rnaseq_Sf, "STAR")[[1]]
#> $class
#> [1] "DockerRequirement"
#>
#> $dockerPull
#> [1] "quay.io/biocontainers/star:2.7.8a--0"Once the tool or pipeline is ready, we only need to assign values for
each of the input parameters, and then submit using one of the
functions: runCWL, runCWLBatch and
cwlShiny. More detailed Usage and examples can be refer to
the Rcwl vignette.
To successfully run the tool or pipeline, users either need to have
all required command line tools pre-installed locally, or using the
docker/singularity runtime by specifying docker = TRUE or
docker = "singularity" argument inside runCWL
or runCWLBatch function. Since the Bioconductor
building machine doesn’t have all the tools installed, nor does it
support the docker runtime, here we use some pseudo-code to demonstrate
the tool/pipeline execution.
inputs(rnaseq_Sf)
rnaseq_Sf$in_seqfiles <- list("sample_R1.fq.gz",
"sample_R2.fq.gz")
rnaseq_Sf$in_prefix <- "sample"
rnaseq_Sf$in_genomeDir <- "genome_STAR_index_Dir"
rnaseq_Sf$in_GTFfile <- "GENCODE_version.gtf"
runCWL(rnaseq_Sf, outdir = "output/sample", docker = TRUE)Users can also submit parallel jobs to HPC for multiple samples using
runCWLBatch function. Different cluster job managers, such
as “multicore”, “sge” and “slurm”, are supported using the
BiocParallel::BatchtoolsParam.
library(BioParallel)
bpparam <- BatchtoolsParam(workers = 2, cluster = "sge",
template = batchtoolsTemplate("sge"))
inputList <- list(in_seqfiles = list(sample1 = list("sample1_R1.fq.gz",
"sample1_R2.fq.gz"),
sample2 = list("sample2_R1.fq.gz",
"sample2_R2.fq.gz")),
in_prefix = list(sample1 = "sample1",
sample2 = "sample2"))
paramList <- list(in_genomeDir = "genome_STAR_index_Dir",
in_GTFfile = "GENCODE_version.gtf",
in_runThreadN = 16)
runCWLBatch(rnaseq_Sf, outdir = "output",
inputList, paramList,
BPPARAM = bpparam)sessionInfo()
#> R version 4.5.2 (2025-10-31)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] RcwlPipelines_1.26.0 BiocFileCache_3.0.0 dbplyr_2.5.1
#> [4] Rcwl_1.26.0 S4Vectors_0.48.0 BiocGenerics_0.56.0
#> [7] generics_0.1.4 yaml_2.3.10 BiocStyle_2.38.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 dplyr_1.1.4 blob_1.2.4
#> [4] filelock_1.0.3 R.utils_2.13.0 fastmap_1.2.0
#> [7] promises_1.5.0 digest_0.6.38 base64url_1.4
#> [10] mime_0.13 lifecycle_1.0.4 RSQLite_2.4.4
#> [13] magrittr_2.0.4 compiler_4.5.2 rlang_1.1.6
#> [16] sass_0.4.10 progress_1.2.3 tools_4.5.2
#> [19] igraph_2.2.1 data.table_1.17.8 knitr_1.50
#> [22] prettyunits_1.2.0 brew_1.0-10 htmlwidgets_1.6.4
#> [25] bit_4.6.0 curl_7.0.0 reticulate_1.44.1
#> [28] RColorBrewer_1.1-3 batchtools_0.9.18 BiocParallel_1.44.0
#> [31] withr_3.0.2 purrr_1.2.0 sys_3.4.3
#> [34] R.oo_1.27.1 grid_4.5.2 git2r_0.36.2
#> [37] xtable_1.8-4 cli_3.6.5 rmarkdown_2.30
#> [40] DiagrammeR_1.0.11 crayon_1.5.3 otel_0.2.0
#> [43] rstudioapi_0.17.1 httr_1.4.7 visNetwork_2.1.4
#> [46] DBI_1.2.3 cachem_1.1.0 stringr_1.6.0
#> [49] parallel_4.5.2 BiocManager_1.30.27 basilisk_1.22.0
#> [52] vctrs_0.6.5 Matrix_1.7-4 jsonlite_2.0.0
#> [55] dir.expiry_1.18.0 hms_1.1.4 bit64_4.6.0-1
#> [58] maketools_1.3.2 jquerylib_0.1.4 glue_1.8.0
#> [61] codetools_0.2-20 stringi_1.8.7 later_1.4.4
#> [64] tibble_3.3.0 pillar_1.11.1 rappdirs_0.3.3
#> [67] htmltools_0.5.8.1 R6_2.6.1 httr2_1.2.1
#> [70] evaluate_1.0.5 shiny_1.11.1 lattice_0.22-7
#> [73] R.methodsS3_1.8.2 png_0.1-8 backports_1.5.0
#> [76] memoise_2.0.1 httpuv_1.6.16 bslib_0.9.0
#> [79] Rcpp_1.1.0 checkmate_2.3.3 xfun_0.54
#> [82] buildtools_1.0.0 pkgconfig_2.0.3