Qploidy2 is an R package designed for large-scale copy number variation, ploidy and aneuploidy estimation using genotyping platforms data.
Qploidy2 builds upon the original Qploidy package. While Qploidy focused on signal standardization to reduce noise for ploidy estimation and relied heavily on visual inspection of diagnostic plots, Qploidy2 introduces a multipoint approach using a Hidden Markov Model (HMM). This advanced method combines information from standardized allele ratios and total depth/intensities to provide automated and robust copy number estimation.
The package offers flexible implementation: you can access Qploidy's original standardization methods within Qploidy2, or apply the HMM directly to your data without standardization if noise reduction isn't needed.
# Install the development version from GitHub
#install.packages("devtools")
devtools::install_github("Breeding-Insight/Qploidy2")- Prepare input files tutorial
- Aneuploid Detection with DArTag Markers in Alfalfa F1 Population - large-scale copy number estimation in alfalfa mapping population using DArTag sequencing data
- Qploidy2 and nQuack Integration Tutorial - Large-scale copy number estimation in alfalfa mapping population using nQuack EM-based BAF model selection
The resulted files from Qploidy2 write_hmm_CN function can be uploaded on GenoBrew Shiny interface.
The tab CNV profiles allow you to interactively inspect the BAF, z-score and CN calls plots,
re-run the HMM adjusting parameters for specific samples and update the results.
Visit GenoBrew GitHub repository
and tutorial
for more information.
Qploidy2 ships with two built-in skills for Claude Code that guide you interactively through the analysis workflow:
qploidy2-input-prep— preparingdata,genos, andgenos.posfrom VCF, Axiom, or Illumina files, including dosage calling withupdogorfitPoly.qploidy2-analysis— the full pipeline: batch-effect checking, standardization, BAF model selection, HMM copy number estimation, filtering, visualization, and export.
Run this in R to download the skill files into your working project:
Qploidy2::install_claude_skills()This creates a .claude/agents/ folder in your current working directory with both skill files. Open that directory in Claude Code and the skills are available automatically.
If you prefer the claude.ai web or desktop interface, you can import the skills directly from their raw GitHub URLs — open each link, copy the content, and paste it into your Claude skill manager:
Follow the qploidy2-input-prep skill to prepare my input files
Follow the qploidy2-analysis skill to run HMM on my samples
Contributions are welcome! If you'd like to contribute, please fork the repository and submit a pull request to the development branch. For major changes, open an issue first to discuss your ideas.
If you find a bug or want an enhancement, please submit an issue here.
Taniguti, C. H., Lau, J., Hochhaus, T., Arias, D. C. L., Hokanson, S. C., Zlesak, D. C., Byrne, D. H., Klein, P. E., & Riera-Lizarazu, O. (2025). Exploring chromosomal variations in garden roses: Insights from high-density SNP array data and a new tool, Qploidy. The Plant Genome, e70044. https://doi.org/10.1002/tpg2.70044
Manuscript in preparation. Please contact the author for more information.
Gaynor, M., Landis, J., O'Connor, T., Laport, R., Doyle, J., Soltis, D., Ponciano, J., & Soltis, P. (2024). "nQuack: An R package for predicting ploidal level from sequence data using site-based heterozygosity." Applications in Plant Sciences, 12(4), e11606. doi:10.1002/aps3.11606
This work is funded in part by the Robert E. Basye Endowment in Rose Genetics, Dept. of Horticultural Sciences, Texas A&M University, and USDA’s National Institute of Food and Agriculture (NIFA), Specialty Crop Research Initiative (SCRI) projects: ‘‘Tools for Genomics-Assisted Breeding in Polyploids: Development of a Community Resource’’ (Award No. 2020-51181-32156); and ‘‘Developing Sustainable Rose Landscapes via Rose Rosette Disease Education, Socioeconomic Assessments, and Breeding RRD-Resistant Roses with Stable Black Spot Resistance’’ (Award No. 2022-51181-38330).
Supported by Breeding Insight.