The GA4GH Categorical Variation Representation Specification: A Unified Computational Framework for Reasoning over Genomic Variant Categories
Puthawala, D.; Reardon, B.; Babb, L.; Kuzma, K.; Stevenson, J. S.; Goar, W. A.; Dolin, R. H.; Freimuth, R. R.; Procknow, C.; Pitel, B.; Kundu, P.; Rampersad, A.; Van Allen, E. M.; Wagner, A. H.
Show abstract
Categorical variants, or sets of genomic alterations constrained by shared properties, are pervasive across clinical, regulatory, and research domains in the biomedical ecosystem, yet their inconsistent and non-computable representation hinders data interoperability and clinical interpretation. We surveyed genomic knowledgebases spanning regulatory approvals and the biomedical literature and found that categorical variants underpin a substantial proportion of clinical genomics knowledge, but are largely described using incompatible bespoke models. To address this, we developed the GA4GH Categorical Variation Representation Specification (Cat-VRS), a constraint-based framework that provides a unified computable representation for both precise and intentionally broad categories across molecular and systemic variant domains. Cat-VRS enables harmonization of genomic knowledgebases, computable category-based search, and automated matching between assayed variants and categorical entities in clinical and research contexts. By providing a principled, extensible model for categorical variation, Cat-VRS enables computable reasoning over genomic variant categories and establishes a foundation for the standardized representation and exchange of genomic knowledge.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- Sharing Data from the Human Tumor Atlas Network through Standards, Infrastructure, and Community Engagement 94%
- Genomics 2 Proteins portal: A resource and discovery tool for linking genetic screening outputs to protein sequences and structures 93%
- Haplotype-aware variant calling enables high accuracy in nanopore long-reads using deep neural networks 93%
Similar papers in this journal
- Echtvar: Compressed variant representation for rapid annotation and filtering of SNPs and indels 94%
- SVCROWS: A User-Defined Tool for Interpreting Significant Structural Variants in Heterogeneous Datasets 93%
- Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers 93%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.