DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates.
Genome Biol
; 25(1): 229, 2024 Sep 06.
Article
in En
| MEDLINE
| ID: mdl-39237934
ABSTRACT
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.
Key words
Full text:
1
Collection:
01-internacional
Database:
MEDLINE
Main subject:
RNA Splicing
/
Single-Cell Analysis
Limits:
Animals
/
Female
/
Humans
Language:
En
Journal:
Genome Biol
Journal subject:
BIOLOGIA MOLECULAR
/
GENETICA
Year:
2024
Document type:
Article
Affiliation country:
Japan
Country of publication:
United kingdom