DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates

• DOI: 10.1186/s13059-024-03367-8
• Publication Date: 2024-09-05T23:02:49Z
• Authors: Chikara Mizukoshi; Yasuhiro Kojima; Satoshi Nomura; Shuto Hayashi; Ko Abe; Teppei Shimamura
• Keywords: Single-cell RNA sequencing (scRNA-seq); RNA splicing; QH301-705.5; RNA Splicing; RNA Stability; Method; RNA-Binding Proteins; Transcriptome dynamics; RNA-binding proteins; Breast Neoplasms; QH426-470; Splicing kinetics; Prosencephalon; RNA degradation; Genetics; Humans; Animals; Female; Biology (General); Single-Cell Analysis

Messenger RNA splicing and degradation are critical for gene expression regulation, the abnormality of which leads to diseases. Previous methods for estimating kinetic rates have limitations, assuming uniform rates across cells. DeepKINET is a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperforms existing methods on simulated and metabolic labeling datasets. Applied to forebrain and breast cancer data, it identifies RNA-binding proteins responsible for kinetic rate diversity. DeepKINET also analyzes the effects of splicing factor mutations on target genes in erythroid lineage cells. DeepKINET effectively reveals cellular heterogeneity in post-transcriptional regulation.