The growth in the volume and complexity of genomic data resources over
the past few decades poses both opportunities and challenges for data
reuse. Presently, reuse of data often involves similar preprocessing
steps in different research projects. Lack of a standardized
annotation strategy can lead to difficult-to-find and even duplicated
datasets, resulting in substantial inefficiencies and wasted computing
resources, especially for research collaborations and bioinformatics
core facilities. Tools such as GoGetData and AnnotationHub have
been developed to mitigate common problems in managing and accessing
curated genomic datasets. However, their use can be limited due to
software requirements (e.g., Conda https://conda.io), forms of data
representation or scope of data resources.
To respond to the FAIR (findability, accessibility, interoperability,
and reusability) data principles that are being widely adopted and
organizational requirements for Data Management Plans (DMPs), here, we
introduce ReUseData, an R/Bioconductor software tool to provide a
systematic and versatile approach for standardized and reproducible
data management. ReUseData facilitates transformation of shell or
other ad hoc scripts for data preprocessing into workflow-based data
recipes. Evaluation of data recipes generate curated data files in
their generic formats (e.g., VCF, bed) with full annotations for
subsequent reuse.
This package focuses on the management of genomic data resources and uses classes and functions from existing Bioconductor packages. So we think it should be a good fit for the Bioconductor.
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("ReUseData")Use the development version:
BiocManager::install("ReUseData", version = "devel")suppressPackageStartupMessages(library(Rcwl))
library(ReUseData)ReUseData recipe landing pagesThe project website https://rcwl.org/dataRecipes/ contains all prebuilt data recipes for public data downloading and curation. They are available for direct use with convenient webpage searching. Each data recipe has a landing page including recipe description (inputs, outputs, etc.) and user instructions. Make sure to check the instructions of eligible input parameter values before recipe evaluation. These prebuilt data recipes demonstrate the use of software and can be taken as templates for users to create their own recipes for protected datasets.
There are many other R resources available on this main website
https://rcwl.org/, including package vignettes for Rcwl
andRcwlPipelines, Rcwl tutorial e-book, case studies of using
RcwlPipelines in preprocessing single-cell RNA-seq data, etc.
ReUseData recipe scriptsThe prebuilt data recipe scripts are included in the package, and are
physically residing in a dedicated GitHub
repository, which
demonstrates the recipe construction for different situations. The
most common case is that a data recipe can manage multiple data
resources with different input parameters (species, versions,
etc.). For example, the gencode_transcripts recipe download from
GENCODE, unzip and index the transcript fasta file for human or mouse
with different versions. A simple data downloading (using wget) for
a specific file can be written as a data recipe without any input
parameter. For example, the data recipe
gcp_broad_gatk_hg38_1000G_omni2.5) downloads the
1000G_omni2.5.hg38.vcf.gz and the tbi index files from Google
Cloud Platform bucket for Broad reference data GATK hg38.
If the data curation gets more complicated, say, multiple command-line
tools are to be involved, and conda can be used to install required
packages, or some secondary files are to be generated and collected,
the raw way of building a ReUseData recipe using Rcwl functions is
recommended, which gives more flexibility and power to accommodate
different situations. An example recipe is the reference_genome
which downloads, formats, and index reference genome data using tools
of samtools, picard and bwa, and manages multiple secondary
files besides the main fasta file for later reuse.
ReUseData core functionsHere we show the usage of 4 core functions recipeMake,
recipeUpdate, recipeSearch, recipeLoad for constructing,
updating, searching and loading ReUseData recipes in R.
One can construct a data recipe from scratch or convert existing
shell scripts for data processing into data recipes, by specifying
input parameters, and output globbing patterns using recipeMake
function. Then the data recipe is represented in R as an S4 class
cwlProcess. Upon assigning values to the input parameters, the
recipe is ready to be evaluated to generate data of interest. Here
are two examples:
script <- '
input=$1
outfile=$2
echo "Print the input: $input" > $outfile.txt
'Equivalently, we can load the shell script directly:
script <- system.file("extdata", "echo_out.sh", package = "ReUseData")rcp <- recipeMake(shscript = script,
paramID = c("input", "outfile"),
paramType = c("string", "string"),
outputID = "echoout",
outputGlob = "*.txt")
inputs(rcp)
#> inputs:
#> input (string):
#> outfile (string):
outputs(rcp)
#> outputs:
#> echoout:
#> type: File[]
#> outputBinding:
#> glob: '*.txt'Evaluation of the data recipes are internally submitted as CWL
workflow tasks, which requires the latest version of cwltool. Here
we have used basilisk to initiate a conda environment and install
the cwltool in that environment if it is not available (or only
older versions are available) in the computer system.
We can install cwltool first to make sure a cwl-runner is available.
invisible(Rcwl::install_cwltool())rcp$input <- "Hello World!"
rcp$outfile <- "outfile"
outdir <- file.path(tempdir(), "SharedData")
res <- getData(rcp,
outdir = outdir,
notes = c("echo", "hello", "world", "txt"))
#> }[1;30mINFO[0m Final process status is successLet’s take a look at the output file, which is successfully generated
in user-specified directory and grabbed through the outputGlob
argument. For more details of the getData function for recipe
evaluation, check the other vignette for reusable data management.
res$out
#> [1] "/tmp/RtmpgEiA85/SharedData/outfile.txt"
readLines(res$out)
#> [1] "Print the input: Hello World!"Here we show a more complex example where the shell script has
required command line tools. When specific tools are needed for the
data processing, users just need to add their names in the
requireTools argument in recipeMake function, and then add conda = TRUE when evaluating the recipe with getData function. Then these
tools will be automatically installed by initiating a conda
environment and the script can be successfully run in that
environment.
This function promotes data reproducibility across different computing platforms, and removes barrier of using sophisticated bioinformatics tools by less experienced users.
The following code chunk is not evaluated for time-limit of package building but can be evaluated by users.
shfile <- system.file("extdata", "gencode_transcripts.sh",
package = "ReUseData")
readLines(shfile)
rcp <- recipeMake(shscript = shfile,
paramID = c("species", "version"),
paramType = c("string", "string"),
outputID = "transcripts",
outputGlob = "*.transcripts.fa*",
requireTools = c("wget", "gzip", "samtools")
)
rcp$species <- "human"
rcp$version <- "42"
res <- getData(rcp,
outdir = outdir,
notes = c("gencode", "transcripts", "human", "42"),
conda = TRUE)
res$outputrecipeUpdate() creates a local cache for data recipes that are saved
in specified GitHub repository (if first time use), syncs and updates
data recipes from the GitHub repo to local caching system, so any
newly added recipes can be readily accessed and loaded directly in
R.
NOTE:
cachePath argument need to match between recipeUpdate,
recipeLoad and recipeSearch functions.force=TRUE when any old recipes that are previously cached are
updated.remote = TRUEto sync with remote GitHub repositories. By
default, it syncs with ReUseDataRecipe GitHub
repository](https://github.com/rworkflow/ReUseDataRecipe) for
public, prebuilt data recipes. repo can also be a private GitHub
repository.## First time use
recipeUpdate(cachePath = "ReUseDataRecipe",
force = TRUE)
#> NOTE: existing caches will be removed and regenerated!
#> Updating recipes...
#> STAR_index.R added
#> bowtie2_index.R added
#> echo_out.R added
#> ensembl_liftover.R added
#> gcp_broad_gatk_hg19.R added
#> gcp_broad_gatk_hg38.R added
#> gcp_gatk_mutect2_b37.R added
#> gcp_gatk_mutect2_hg38.R added
#> gencode_annotation.R added
#> gencode_genome_grch38.R added
#> gencode_transcripts.R added
#> hisat2_index.R added
#> reference_genome.R added
#> salmon_index.R added
#> ucsc_database.R added
#>
#> recipeHub with 15 records
#> cache path: /tmp/RtmpgEiA85/cache/ReUseDataRecipe
#> # recipeSearch() to query specific recipes using multipe keywords
#> # recipeUpdate() to update the local recipe cache
#>
#> name
#> BFC31 | STAR_index
#> BFC32 | bowtie2_index
#> BFC33 | echo_out
#> BFC34 | ensembl_liftover
#> BFC35 | gcp_broad_gatk_hg19
#> ... ...
#> BFC41 | gencode_transcripts
#> BFC42 | hisat2_index
#> BFC43 | reference_genome
#> BFC44 | salmon_index
#> BFC45 | ucsc_databaseTo sync the local recipe cache with remote GitHub repository. Currently the remote data recipes on GitHub are the same as the recipes in package (so not evaluted here to avoid duplicate messages). We will do our best to keep current of the data recipes in package development version with the remote GitHub repository.
recipeUpdate(remote = TRUE,
repos = "rworkflow/ReUseDataRecipe") ## can be private reporecipeUpdate returns a recipeHub object with a list of all
available recipes. One can subset the list with [ and use getter
functions recipeNames() to get the recipe names which can then be
passed to the recipeSearch() or recipeLoad().
rh <- recipeUpdate()
#> Updating recipes...
#>
is(rh)
#> [1] "recipeHub" "cwlHub" "BiocFileCacheReadOnly"
#> [4] "BiocFileCacheBase"
rh[1]
#> recipeHub with 1 records
#> cache path: /tmp/RtmpgEiA85/cache/ReUseDataRecipe
#> # recipeSearch() to query specific recipes using multipe keywords
#> # recipeUpdate() to update the local recipe cache
#>
#> name
#> BFC31 | STAR_index
recipeNames(rh)
#> [1] "STAR_index" "bowtie2_index" "echo_out"
#> [4] "ensembl_liftover" "gcp_broad_gatk_hg19" "gcp_broad_gatk_hg38"
#> [7] "gcp_gatk_mutect2_b37" "gcp_gatk_mutect2_hg38" "gencode_annotation"
#> [10] "gencode_genome_grch38" "gencode_transcripts" "hisat2_index"
#> [13] "reference_genome" "salmon_index" "ucsc_database"Cached data recipes can be searched using multiple keywords to match
the recipe name. It returns a recipeHub object with a list of
recipes available.
recipeSearch()
#> recipeHub with 15 records
#> cache path: /tmp/RtmpgEiA85/cache/ReUseDataRecipe
#> # recipeSearch() to query specific recipes using multipe keywords
#> # recipeUpdate() to update the local recipe cache
#>
#> name
#> BFC31 | STAR_index
#> BFC32 | bowtie2_index
#> BFC33 | echo_out
#> BFC34 | ensembl_liftover
#> BFC35 | gcp_broad_gatk_hg19
#> ... ...
#> BFC41 | gencode_transcripts
#> BFC42 | hisat2_index
#> BFC43 | reference_genome
#> BFC44 | salmon_index
#> BFC45 | ucsc_database
recipeSearch("gencode")
#> recipeHub with 3 records
#> cache path: /tmp/RtmpgEiA85/cache/ReUseDataRecipe
#> # recipeSearch() to query specific recipes using multipe keywords
#> # recipeUpdate() to update the local recipe cache
#>
#> name
#> BFC39 | gencode_annotation
#> BFC40 | gencode_genome_grch38
#> BFC41 | gencode_transcripts
recipeSearch(c("STAR", "index"))
#> recipeHub with 1 records
#> cache path: /tmp/RtmpgEiA85/cache/ReUseDataRecipe
#> # recipeSearch() to query specific recipes using multipe keywords
#> # recipeUpdate() to update the local recipe cache
#>
#> name
#> BFC31 | STAR_indexRecipes can be directly loaded into R using recipeLoad function
with user assigned name or the original recipe name. Once the recipe
is successfully loaded, a message will be returned with recipe
instructions.
rcp <- recipeLoad("STAR_index")
#> Note: you need to assign a name for the recipe: rcpName <- recipeLoad('xx')
#> Data recipe loaded!
#> Use inputs() to check required input parameters before evaluation.
#> Check here: https://rcwl.org/dataRecipes/STAR_index.html
#> for user instructions (e.g., eligible input values, data source, etc.)NOTE Use return=FALSE if you want to keep the original recipe
name, or if multiple recipes are to be loaded.
recipeLoad("STAR_index", return = FALSE)
#> Data recipe loaded!
#> Use inputs(STAR_index) to check required input parameters before evaluation.
#> Check here: https://rcwl.org/dataRecipes/STAR_index.html
#> for user instructions (e.g., eligible input values, data source, etc.)identical(rcp, STAR_index)
#> [1] TRUErecipeLoad(c("ensembl_liftover", "gencode_annotation"), return=FALSE)
#> Data recipe loaded!
#> Use inputs(ensembl_liftover) to check required input parameters before evaluation.
#> Check here: https://rcwl.org/dataRecipes/ensembl_liftover.html
#> for user instructions (e.g., eligible input values, data source, etc.)
#> Data recipe loaded!
#> Use inputs(gencode_annotation) to check required input parameters before evaluation.
#> Check here: https://rcwl.org/dataRecipes/gencode_annotation.html
#> for user instructions (e.g., eligible input values, data source, etc.)It’s important to check the required inputs() of the recipe and the
recipe landing page for eligible input parameter values before
evaluating the recipe to generate data of interest.
inputs(STAR_index)
#> inputs:
#> ref (reference genome) ( string|File ):
#> gtf (GTF) ( string|File ):
#> genomeDir (genomeDir) (string):
#> threads (threads) (int):
#> sjdb (sjdbOverhang) (int): 100
inputs(ensembl_liftover)
#> inputs:
#> species (species) (string):
#> from (from) (string):
#> to (to) (string):
inputs(gencode_annotation)
#> inputs:
#> species (species) (string):
#> version (version) (string):sessionInfo()
#> R version 4.3.2 Patched (2023-11-13 r85521)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 22.04.3 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.18-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
#>
#> 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] ReUseData_1.2.2 Rcwl_1.18.0 S4Vectors_0.40.2
#> [4] BiocGenerics_0.48.1 yaml_2.3.7 BiocStyle_2.30.0
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.0 dplyr_1.1.4 blob_1.2.4
#> [4] filelock_1.0.2 R.utils_2.12.3 fastmap_1.1.1
#> [7] BiocFileCache_2.10.1 promises_1.2.1 digest_0.6.33
#> [10] base64url_1.4 mime_0.12 lifecycle_1.0.4
#> [13] ellipsis_0.3.2 RSQLite_2.3.4 magrittr_2.0.3
#> [16] compiler_4.3.2 rlang_1.1.2 sass_0.4.8
#> [19] progress_1.2.3 tools_4.3.2 utf8_1.2.4
#> [22] data.table_1.14.10 knitr_1.45 prettyunits_1.2.0
#> [25] brew_1.0-8 htmlwidgets_1.6.4 bit_4.0.5
#> [28] curl_5.2.0 reticulate_1.34.0 RColorBrewer_1.1-3
#> [31] batchtools_0.9.17 BiocParallel_1.36.0 purrr_1.0.2
#> [34] withr_2.5.2 R.oo_1.25.0 grid_4.3.2
#> [37] fansi_1.0.6 git2r_0.33.0 xtable_1.8-4
#> [40] debugme_1.1.0 cli_3.6.1 rmarkdown_2.25
#> [43] DiagrammeR_1.0.10 crayon_1.5.2 generics_0.1.3
#> [46] httr_1.4.7 visNetwork_2.1.2 DBI_1.1.3
#> [49] cachem_1.0.8 parallel_4.3.2 BiocManager_1.30.22
#> [52] basilisk_1.14.1 vctrs_0.6.5 Matrix_1.6-4
#> [55] jsonlite_1.8.8 dir.expiry_1.10.0 bookdown_0.37
#> [58] hms_1.1.3 bit64_4.0.5 jquerylib_0.1.4
#> [61] RcwlPipelines_1.18.0 glue_1.6.2 codetools_0.2-19
#> [64] stringi_1.8.2 later_1.3.2 tibble_3.2.1
#> [67] pillar_1.9.0 basilisk.utils_1.14.1 rappdirs_0.3.3
#> [70] htmltools_0.5.7 R6_2.5.1 dbplyr_2.4.0
#> [73] evaluate_0.23 shiny_1.8.0 lattice_0.22-5
#> [76] R.methodsS3_1.8.2 png_0.1-8 backports_1.4.1
#> [79] memoise_2.0.1 httpuv_1.6.13 bslib_0.6.1
#> [82] Rcpp_1.0.11 checkmate_2.3.1 xfun_0.41
#> [85] pkgconfig_2.0.3