ArticleBioinformatics (Oxford, England)2023
vaRHC: an R package for semi-automation of variant classification in hereditary cancer genes according to ACMG/AMP and gene-specific ClinGen guidelines.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed, 13 citations in OpenAlex.
- Evaluation of BoostDM, a somatic variant prediction tool, for the interpretation of germline variants in hereditary cancer genes.European journal of human genetics : EJHG · 2026Article
- aiDIVA - hybrid AI for rare disease diagnostics using evidence-based, machine learning and language models.NPJ genomic medicine · 2026Article
- Comprehensive evaluation of ACMG/AMP-based variant classification tools.Bioinformatics (Oxford, England) · 2026Article
- The promises and pitfalls of automated variant interpretation: a comprehensive review.Briefings in bioinformatics · 2025Review
- Interplay of physical activity, self-rated health, and life satisfaction among large-bodied adolescents in Europe and North America: analysis using clustered binary mixed effects logit modelling.BMC public health · 2025Article
- HerediVar and HerediClassify: tools for streamlining genetic variant classification in hereditary breast and ovarian cancer.Human genomics · 2025Article
- Tumor analysis of MMR genes in Lynch-like syndrome: Challenges associated with results interpretation.Cancer medicine · 2024Article
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Authors and funding
12 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationGermline variant classification allows accurate genetic diagnosis and risk assessment. However, it is a tedious iterative process integrating information from several sources and types of evidence. It should follow gene-specific (if available) or general updated international guidelines. Thus, it is the main burden of the incorporation of next-generation sequencing into the clinical setting.
resultsWe created the vaRiants in HC (vaRHC) R package to assist the process of variant classification in hereditary cancer by: (i) collecting information from diverse databases; (ii) assigning or denying different types of evidence according to updated American College of Molecular Genetics and Genomics/Association of Molecular Pathologist gene-specific criteria for ATM, CDH1, CHEK2, MLH1, MSH2, MSH6, PMS2, PTEN, and TP53 and general criteria for other genes; (iii) providing an automated classification of variants using a Bayesian metastructure and considering CanVIG-UK recommendations; and (iv) optionally printing the output to an .xlsx file. A validation using 659 classified variants demonstrated the robustness of vaRHC, presenting a better criteria assignment than Cancer SIGVAR, an available similar tool. AVAILABILITY AND IMPLEMENTATION: The source code can be consulted in the GitHub repository (https://github.com/emunte/vaRHC) Additionally, it will be submitted to CRAN soon.
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