Detection of alternative splicing: deep sequencing or deep learning?

ABSTRACT: Alternative splicing is a crucial mechanism of gene regulation that enables condition- and tissue-specific expression of gene isoforms. Its dysregulation plays a role in various diseases such as cancer, neurological disorders, and metabolic conditions. Despite its importance, accurate detection of alternative splicing events remains challenging. Comprehensive alternative splicing event detection typically requires deep sequencing with over 100 million reads; however, much of the publicly accessible RNA sequencing data is of lower sequencing depth. Recent advances, particularly deep learning models working with genomic sequences, offer new avenues for predicting alternative splicing without reliance on high sequencing depth data. Our study addresses the question: Can we utilize the vast rep

SUBMITTER: Hackl LM 

PROVIDER: S-EPMC12790623 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature