MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning

Authors

Language

English

Publication Date

10-8-2022

Journal

Genome Medicine

DOI

10.1186/s13073-022-01120-z

PMID

36209109

PMCID

PMC9548151

PubMedCentral® Posted Date

10-8-2022

PubMedCentral® Full Text Version

Post-print

Abstract

Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning methods, we present pathogenicity prediction models, MetaRNN and MetaRNN-indel, to help identify and prioritize rare nonsynonymous single nucleotide variants (nsSNVs) and non-frameshift insertion/deletions (nfINDELs). We use independent test sets to demonstrate that these new models outperform state-of-the-art competitors and achieve a more interpretable score distribution. Importantly, prediction scores from both models are comparable, enabling easy adoption of integrated genotype-phenotype association analysis methods. All pre-computed nsSNV scores are available at http://www.liulab.science/MetaRNN. The stand-alone program is also available at https://github.com/Chang-Li2019/MetaRNN.

Keywords

Computational Biology, Deep Learning, INDEL Mutation, Nucleotides

Published Open-Access

yes

Recommended Citation

, "MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning" (2022). Faculty, Staff and Student Publications. 650.
https://digitalcommons.library.tmc.edu/uthshis_docs/650