Introduction

ReUseData is 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). Both recipes and data are cached using database infrastructure for easy data management and reuse. Prebuilt data recipes are available through ReUseData portal ("https://rcwl.org/dataRecipes/") with full annotation and user instructions. Pregenerated data are available through ReUseData cloud bucket that is directly downloadable through "getCloudData()".

This quick start shows the basic use of package functions in 2 major categories for managing:

• Data recipes
• Reusable data

Details for each section can be found in the other vignettes ReUseData_recipe.html and ReUseData_data.html.

Package installation

BiocManager::install(c("ReUseData", "Rcwl"))
library(ReUseData)

Data recipes

All pre-built data recipes are included in the package and can be easily updated (recipeUpdate), searched (recipeSearch) and loaded (recipeLoad). Details about data recipes can be found in the vignette ReUseData_recipe.html.

Search and load a data recipe

recipeUpdate(cachePath = "ReUseDataRecipe", force = TRUE)
recipeSearch("echo")
recipeLoad("echo_out", return = TRUE)

Evaluate a data recipe

A data recipe can be evaluated by assigning values to the recipe parameters. getData runs the recipe as a CWL scripts internally, and generates the data of interest with annotation files for future reuse.

Rcwl::inputs(echo_out)
echo_out$input <- "Hello World!"
echo_out$outfile <- "outfile"
outdir <- file.path(tempdir(), "SharedData")
res <- getData(echo_out,
               outdir = outdir,
               notes = c("echo", "hello", "world", "txt"))
res$out
readLines(res$out)

Create your own data recipes

One can create a data recipe from scratch or by converting an existing shell script for data processing, by specifying input parameters, output globbing patterns using recipeMake function.

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")
Rcwl::inputs(rcp)
Rcwl::outputs(rcp)

Reusable data

The data that are generated from evaluating data recipes are automatically annotated and tracked with user-specified keywords and time/date tags. It uses a similar cache system as for recipes for users to easily update (dataUpdate), search (dataSearch) and use (toList).

Pre-generated data files from existing data recipes are saved in Google Cloud Bucket, that are ready to be queried (dataSearch(cloud=TRUE)) and downloaded (getCloudData) to local cache system with annotations.

Update data files that are generated using ReUseData

dh <- dataUpdate(dir = outdir)
dataSearch(c("echo", "hello"))
dataNames(dh)
dataParams(dh)
dataNotes(dh)

Export data into workflow-ready files

toList(dh, format="json", file = file.path(outdir, "data.json"))

Download pregenerated data from Google Cloud

dh <- dataUpdate(dir = outdir, cloud = TRUE)
getCloudData(dh[2], outdir = outdir)