TriosCompass: A Snakemake Workflow for Integrated Detection of SNVs, Indels, STRs, and Structural De Novo Variants in Parent – Child Trios

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ID: 314607
2026
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Abstract
MOTIVATION: The accurate and sensitive identification of de novo variants, which are unique to an individual and not found in the parents' germlines, is critical for understanding the genetic basis of rare diseases, developmental disorders, and evolutionary processes. Existing de novo variant detection pipelines often lack the flexibility to handle multiple variant types, struggle with speed and reproducibility across computational environments, demand extensive manual configuration, or require bioinformatics expertise for downstream curation and analysis, limiting their scalability and usability for large genomic studies. Accordingly, there is a pressing need to better address these challenges. RESULTS: We introduce TriosCompass, an open-source Snakemake workflow that addresses these challenges by providing a modular, accelerated, and environmentally-configurable end-to-end solution for comprehensive de novo variant discovery. It integrates state-of-the-art tools into a reproducible framework, empowering researchers to discover novel genetic insights with greater efficiency and reliability. AVAILABILITY: TriosCompass is implemented as a Snakemake workflow and is freely available at https://github.com/NCI-CGR/TriosCompass_v2 or on Zenodo (10.5281/zenodo.17981062). SUPPLEMENTARY INFORMATION: Supplementary data is available on GitHub at https://github.com/NCI-CGR/TriosCompass_v2/tree/manuscript/report_dashboards. Supplementary methods on DeepTrio benchmark runs can be viewed at: https://github.com/NCI-CGR/TriosCompass_v2/blob/manuscript/TriosCompass_Supp_Methods_deeptrio_benchmark.md.
Reference Key
openalex_W7161981835 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Wei Zhu, Meredith Yeager, Eric T. Dawson, Komal Jain, Belynda Hicks, Michael Dean, Stephen J Chanock, W T Wong
Journal BMC Bioinformatics
Year 2026
DOI
10.1093/bioinformatics/btag319
URL
https://doi.org/10.1093/bioinformatics/btag319
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