panGenomeBreedr
(panGB
) is conceptualized to be a unified, crop agnostic platform for pangenome-enabled breeding that follows standardized conventions for natural or casual variant analysis using pangenomes, marker design, and marker QC hypothesis testing (Figure 1). It seeks to simplify and enhance the use of pangenome resources in cultivar development.

In its current development version, panGB provides customizable R functions for variant discovery from snpEff-annotated VCF files, KASP marker design, and marker validation (Steps 1–3 in Fig. 1).
To expand accessibility, panGB
will include a user-friendly Shiny application, allowing non-R users to leverage its core features without requiring R programming experience.
The SNP Viewer tool by LGC Genomics is limited to Windows platforms and lacks standardized conventions for visualizing positive controls in marker validation, making it difficult for users to conclusively assess marker performance. In contrast, panGB
offers platform-independent tools for hypothesis testing, quality control (QC), and validation of KASP markers, addressing a key gap in existing visualization and validation workflows.
Submit bug reports and feature suggestions, or track changes on the issues page.
Recommended packages
Rtools: Needed for package development and installation from GitHub on Windows PCs.
UpSetR: Required for generating UpSet plots.
Installation
First, ensure all existing packages are up to date.
You can install the development version of panGenomeBreedr
from GitHub with:
# Install panGenomeBreedr
if (!require("devtools")) install.packages("devtools")
devtools::install_github("awkena/panGenomeBreedr")
Installing Bioconductor dependency packages
panGB
depends on a list of Bioconductor packages that may not be installed automatically alongside panGB
. To manually install these packages, use the code snippet below:
# Install and load required Bioconductor packages if not already installed
if (!require("BiocManager", quietly = TRUE)) install.packages("BiocManager")
# Define required Bioconductor packages
bioc_packages <- c("rtracklayer", "Rsamtools", "msa", "IRanges",
"GenomicRanges", "BSgenome", "Biostrings")
# Install any missing Bioconductor packages
for (pkg in bioc_packages) {
if (!requireNamespace(pkg, quietly = TRUE)) {
BiocManager::install(pkg, ask = FALSE, update = FALSE)
}
}
Current Functionality of panGB
panGB
currently provides functionality for the following key tasks:
Variant discovery
Identify variants within candidate genes or any user-defined genomic interval using snpEff-annotated VCF files.KASP marker design
Generate allele-specific markers targeting either causal variants or any variant of interest.Marker validation and QC visualization
Produce quality control plots and perform hypothesis-driven evaluations to assess marker reliability.Decision-support for trait introgression
Guide marker-assisted backcrossing by profiling foreground, background, and precision-introgression markers to support selection decisions.
👉 For a full tutorial and worked example, check out the panGenomeBreedr Workflow vignette.
Support and Feedback
For support and submission of feedback, email the maintainer Alexander Kena, PhD at alex.kena24@gmail.com