Interpretable prioritization of splice variants in diagnostic next-generation sequencing.

ABSTRACT: A critical challenge in genetic diagnostics is the computational assessment of candidate splice variants, specifically the interpretation of nucleotide changes located outside of the highly conserved dinucleotide sequences at the 5' and 3' ends of introns. To address this gap, we developed the Super Quick Information-content Random-forest Learning of Splice variants (SQUIRLS) algorithm. SQUIRLS generates a small set of interpretable features for machine learning by calculating the information-content of wild-type and variant sequences of canonical and cryptic splice sites, assessing changes in candidate splicing regulatory sequences, and incorporating characteristics of the sequence such as exon length, disruptions of the AG exclusion zone, and conservation. We curated a comprehensive coll

SUBMITTER: Danis D 

PROVIDER: S-EPMC8456162 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature