Variant effect predictions capture some aspects of deep mutational scanning experiments.

ABSTRACT: BACKGROUND:Deep mutational scanning (DMS) studies exploit the mutational landscape of sequence variation by systematically and comprehensively assaying the effect of single amino acid variants (SAVs; also referred to as missense mutations, or non-synonymous Single Nucleotide Variants - missense SNVs or nsSNVs) for particular proteins. We assembled SAV annotations from 22 different DMS experiments and normalized the effect scores to evaluate variant effect prediction methods. Three trained on traditional variant effect data (PolyPhen-2, SIFT, SNAP2), a regression method optimized on DMS data (Envision), and a naïve prediction using conservation information from homologs. RESULTS:On a set of 32,981 SAVs, all methods captured some aspects of the experimental effect scores, albeit not the same

SUBMITTER: Reeb J 

PROVIDER: S-EPMC7077003 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature