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.
BiocManager::install("rworkflow/RcwlPipelines")library(RcwlPipelines)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.
## For vignette use only. users don't need to do this step.
Sys.setenv(cachePath = tempdir()) atls <- cwlUpdate(branch = "dev") ## sync the tools/pipelines.
atls
#> cwlHub with 196 records
#> cache path: /tmp/RtmpUlz6zd/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)$Type)
#>
#> pipeline tool
#> 43 153Currently, we have integrated 153 command line tools and 43 pipelines.
cwlSearchWe can use (multiple) keywords to search for specific tools/pipelines
of interest, which internally search the mcols of “rname”, “rpath”,
“fpath”, “Command” and “Containers”. Here we show how to search the
alignment tool bwa mem.
t1 <- cwlSearch(c("bwa", "mem"))
t1
#> cwlHub with 1 records
#> cache path: /tmp/RtmpUlz6zd/Rcwl
#> # last modified date: 2021-10-25
#> # cwlSearch() to query scripts
#> # cwlLoad('title') to load the script
#> # additional mcols(): rid, rpath, Type, Container, mtime, ...
#>
#> title Command
#> BFC118 | tl_bwa bwa mem
mcols(t1)
#> DataFrame with 1 row and 14 columns
#> rid rname create_time access_time
#> <character> <character> <character> <character>
#> 1 BFC118 tl_bwa 2024-10-29 23:04:23 2024-10-29 23:04:23
#> rpath rtype fpath last_modified_time
#> <character> <character> <character> <numeric>
#> 1 /tmp/RtmpUlz6zd/Rcwl.. local /tmp/RtmpUlz6zd/Rcwl.. NA
#> etag expires Type Command Container
#> <character> <numeric> <character> <character> <character>
#> 1 NA NA tool bwa mem biocontainers/bwa:v0..
#> mtime
#> <character>
#> 1 2021-10-25 12:58:45cwlLoadThe last core function cwlLoad loads the Rcwl tool/pipeline into
the R working environment. The code below loads the tool with a
user-defined name bwa to do the read alignment.
bwa <- cwlLoad(title(t1)[1]) ## "tl_bwa"
bwa <- cwlLoad(mcols(t1)$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 Under development (unstable) (2024-10-21 r87258)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.1 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB 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: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] RcwlPipelines_1.23.0 BiocFileCache_2.15.0 dbplyr_2.5.0
#> [4] Rcwl_1.23.0 S4Vectors_0.45.0 BiocGenerics_0.53.0
#> [7] yaml_2.3.10 BiocStyle_2.35.0
#>
#> 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_2.12.3 fastmap_1.2.0
#> [7] promises_1.3.0 digest_0.6.37 base64url_1.4
#> [10] mime_0.12 lifecycle_1.0.4 RSQLite_2.3.7
#> [13] magrittr_2.0.3 compiler_4.5.0 rlang_1.1.4
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#> [19] igraph_2.1.1 utf8_1.2.4 data.table_1.16.2
#> [22] knitr_1.48 prettyunits_1.2.0 brew_1.0-10
#> [25] htmlwidgets_1.6.4 bit_4.5.0 curl_5.2.3
#> [28] reticulate_1.39.0 RColorBrewer_1.1-3 batchtools_0.9.17
#> [31] BiocParallel_1.41.0 withr_3.0.2 purrr_1.0.2
#> [34] R.oo_1.26.0 grid_4.5.0 fansi_1.0.6
#> [37] git2r_0.35.0 xtable_1.8-4 debugme_1.2.0
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#> [43] crayon_1.5.3 generics_0.1.3 rstudioapi_0.17.1
#> [46] httr_1.4.7 visNetwork_2.1.2 DBI_1.2.3
#> [49] cachem_1.1.0 stringr_1.5.1 parallel_4.5.0
#> [52] BiocManager_1.30.25 basilisk_1.19.0 vctrs_0.6.5
#> [55] Matrix_1.7-1 jsonlite_1.8.9 dir.expiry_1.15.0
#> [58] bookdown_0.41 hms_1.1.3 bit64_4.5.2
#> [61] jquerylib_0.1.4 glue_1.8.0 codetools_0.2-20
#> [64] stringi_1.8.4 later_1.3.2 tibble_3.2.1
#> [67] pillar_1.9.0 basilisk.utils_1.19.0 rappdirs_0.3.3
#> [70] htmltools_0.5.8.1 R6_2.5.1 evaluate_1.0.1
#> [73] shiny_1.9.1 lattice_0.22-6 R.methodsS3_1.8.2
#> [76] png_0.1-8 backports_1.5.0 memoise_2.0.1
#> [79] httpuv_1.6.15 bslib_0.8.0 Rcpp_1.0.13
#> [82] checkmate_2.3.2 xfun_0.48 pkgconfig_2.0.3