Reference-informed prediction of alternative splicing and splicing-altering mutations from sequences.

ABSTRACT: Alternative splicing plays a crucial role in protein diversity and gene expression regulation in higher eukaryotes and mutations causing dysregulated splicing underlie a range of genetic diseases. Computational prediction of alternative splicing from genomic sequences not only provides insight into gene-regulatory mechanisms but also helps identify disease-causing mutations and drug targets. However, the current methods for the quantitative prediction of splice site usage still have limited accuracy. Here, we present DeltaSplice, a deep neural network model optimized to learn the impact of mutations on quantitative changes in alternative splicing from the comparative analysis of homologous genes. The model architecture enables DeltaSplice to perform "reference-informed prediction" by incor

SUBMITTER: Xu C 

PROVIDER: S-EPMC10996483 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature