Abstract

Germline 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. We 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. The source code can be consulted in the GitHub repository (https://github.com/emunte/vaRHC) Additionally, it will be submitted to CRAN soon.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.