DeepKINET: a Deep Generative Model for Estimating Single-cell RNA Splicing and Degradation Rates

Overview

Journal Genome Biol

Specialties Biology
Genetics

Date 2024 Sep 5

PMID 39237934

Authors

Chikara Mizukoshi

Yasuhiro Kojima

Satoshi Nomura

Shuto Hayashi

Ko Abe

Teppei Shimamura

Affiliations

Soon will be listed here.

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.

Citing Articles

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

Mizukoshi C, Kojima Y, Nomura S, Hayashi S, Abe K, Shimamura T Genome Biol. 2024; 25(1):229.

PMID: 39237934 PMC: 11378460. DOI: 10.1186/s13059-024-03367-8.

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