CACSV: a computational web-sever that provides classification for cancer somatic genetic variants from different tissues

1Citations
Citations of this article
4Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Background: Understanding the role and function of genetic variants is extremely important when analyzing and interpreting a myriad of human disease processes. For cancer in general, cell-specific genetic variants are ubiquitous and distinct tissues have significantly heterogenic genetic profiles. In clinical practice, only a few genetic variants have identifiable clinical utility. Finding clinically relevant genetic variants constitute a challenging process. In addition, there had been no reference protocol to provide guidance for cancer somatic genetic variants classification and interpretation. In 2017, the first version of a reference protocol was published by the Association for Molecular Pathology, the American Society of Clinical Oncology, and the College of American Pathologists. Previously, we incorporated the reference protocol into a computational method to expedite the process of identification of clinically relevant genetic variants. In this work, we developed a computational web-server to increase the accessibility and availability of clinically relevant genetic variants. Results: Our work provides the clinical classification for ~ 3 million cancer genetic variants that are now publicly available in a shareable database on GitHub. We have developed a graphical user interface for the database to enhance the accessibility and ease-of-use. Conclusion: CACSV provides an open-source for about 3 million cancer tissue-specific genetic variants with their assigned clinical annotations.

Cite

CITATION STYLE

APA

AlKurabi, N., AlGahtani, A., & Sobahy, T. M. (2023). CACSV: a computational web-sever that provides classification for cancer somatic genetic variants from different tissues. BMC Bioinformatics, 24(1). https://doi.org/10.1186/s12859-023-05207-1

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free