Explore, Diagnose and Filter Genomic Data • radr

radr explores, diagnoses, visualizes, and filters individual genomic data. It works primarily with GDS files and objects created by genometranslator.

The two packages have deliberately different responsibilities:

genometranslator reads, standardizes, and writes genomic formats;
radr investigates data quality and applies explicit filters.

explore_genomes() offers a guided first exploration. It is not a universal filtering recipe: established analyses should use selected detect_*() and filter_*() functions in an order justified for the dataset.

Installation

Starting from a basic R installation, install the CRAN installer and required Bioconductor foundation first:

install.packages(c("BiocManager", "remotes"))

BiocManager::install(c(
  "gdsfmt",
  "Rsamtools",
  "SeqArray"
))

remotes::install_github("thierrygosselin/tgbase")
remotes::install_github("thierrygosselin/genometranslator")
remotes::install_github("thierrygosselin/radr")

The Remotes field in radr’s DESCRIPTION records its GitHub dependencies, but the explicit sequence above makes a clean installation easier to diagnose.

Check the installation without changing it:

radr::radr_dependencies()

The returned table distinguishes required components from optional components and states which workflow uses each optional dependency.

Optional R packages

Install only what is needed for the planned analysis:

# LD, linkage pruning, and IBS calculations on GDS
BiocManager::install("SNPRelate")

# Tidy-data distances and fast IBM PNG rendering
install.packages(c("amap", "ragg"))

Function documentation identifies its additional dependencies. An optional package is not required merely to install or load radr.

Optional command-line tools with Conda

Some VCF-level filters use bcftools, while run_bayescan() uses the BayeScan executable. These are programs, not R packages. A shared Conda environment can provide both:

conda create --name genomics --channel conda-forge --channel bioconda bcftools bayescan=2.1
conda activate genomics
bcftools --version
bayescan --help

For an existing environment:

conda activate genomics
conda install --channel conda-forge --channel bioconda bcftools bayescan=2.1

Start R or RStudio from the activated environment, then verify visibility:

Sys.which(c("bcftools", "bayescan"))
radr::radr_dependencies()
radr::check_bayescan()

A minimal workflow

Import and standardize a genomic file with genometranslator, then diagnose and filter the resulting GDS with radr:

genome <- genometranslator::read_genome(
  data = "individuals.vcf.gz",
  strata = "strata.tsv"
)

# Preserve the original sample and marker order for the first missingness view
ibm <- radr::detect_ibm(
  data = genome,
  filename = "initial_missingness.png"
)

# Guided exploration for a new dataset
screened <- radr::explore_genomes(data = genome)

Filtering order should follow what is known about the project rather than a fixed recipe. Filtering individuals first changes marker statistics, while filtering markers first changes individual statistics.

The getting-started vignette develops two contrasting examples: a marker-first workflow for a noisy callset and a sample-first workflow for known sequencing failures. It also explains how to return to guided exploration after correcting a known problem and how to compare alternative filtering orders reproducibly.

Citation

citation("radr")
packageVersion("radr")

Until a dedicated publication or DOI is available, cite the version and, for a development build, record the Git commit and access date:

Gosselin, T. (2026). radr: Explore, diagnose and filter genomic data. R package version 0.0.0.9000. https://github.com/thierrygosselin/radr. Accessed 2026-09-02.

Website and support

Documentation and articles are available at https://thierrygosselin.github.io/radr/. Report problems or request features through the GitHub issue tracker.