PhenoSV: interpretable phenotype-aware model for the prioritization of genes affected by structural variants.

ABSTRACT: Structural variants (SVs) represent a major source of genetic variation associated with phenotypic diversity and disease susceptibility. While long-read sequencing can discover over 20,000 SVs per human genome, interpreting their functional consequences remains challenging. Existing methods for identifying disease-related SVs focus on deletion/duplication only and cannot prioritize individual genes affected by SVs, especially for noncoding SVs. Here, we introduce PhenoSV, a phenotype-aware machine-learning model that interprets all major types of SVs and genes affected. PhenoSV segments and annotates SVs with diverse genomic features and employs a transformer-based architecture to predict their impacts under a multiple-instance learning framework. With phenotype information, PhenoSV furthe

SUBMITTER: Xu Z 

PROVIDER: S-EPMC10684511 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature