MutAnt: mutation annotation tool predicts deleteriousness of missense mutations and improves mutation calling from transcriptomics.

ABSTRACT: Many pathogenic variants implicated in Mendelian diseases impair normal protein function, often through loss-of-function effects, while loss-of-function mutations in tumor suppressor genes commonly contribute to tumorigenesis. However, many disease-causing variants act through gain-of-function or other mechanisms that do not strictly disrupt the protein. Interpreting rare and novel variants remains a major challenge in clinical genomics, highlighting the need for computational tools informed by large, well-curated clinical datasets to reliably distinguish truly deleterious mutations from neutral variation. We developed MutAnt, a mutation meta‑annotator based on machine‑learning. It is trained on a large, clinically relevant dataset of variants using multiple variant properties, including s

SUBMITTER: Sarachakov A 

PROVIDER: S-EPMC12689701 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature