Identification of alternative splicing regulatory patterns and characteristic splicing factors in heart failure using RNA-seq data and machine learning.

ABSTRACT: Heart failure (HF) represents the advanced stage of several cardiovascular disorders. This study aimed to build an alternative splicing regulatory network and identify potential splicing factors involved in HF utilizing RNA-seq data and machine learning algorithms. We performed bioinformatics analysis on RNA-seq datasets containing samples from HF patients and normal individuals to obtain gene expression matrices and identify differently regulated alternative splicing events in HF. By calculating percent spliced-in (PSI) value, we identified 4055 abnormal alternative splicing events of 3142 genes in HF. These genes were significantly enriched in PPAR signaling, regulation of actin cytoskeleton, and muscle contraction. Interestingly, based on abnormal alternative splicing events, two distin

SUBMITTER: Li J 

PROVIDER: S-EPMC11336631 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature