Division of Systems Biology, Graduate School of Medicine, Nagoya University, Aichi, Japan.
Nagoya University Hospital, Aichi, Japan.
Genome Biol. 2024 Sep 6;25(1):229. doi: 10.1186/s13059-024-03367-8.
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.
信使 RNA 剪接和降解对于基因表达调控至关重要，其异常会导致疾病。之前的估计动力学速率的方法存在局限性，假设细胞之间的速率是均匀的。DeepKINET 是一种深度生成模型，可以从 scRNA-seq 数据中以单细胞分辨率估计剪接和降解速率。DeepKINET 在模拟和代谢标记数据集上优于现有方法。应用于大脑前脑和乳腺癌数据，它鉴定了负责动力学速率多样性的 RNA 结合蛋白。DeepKINET 还分析了剪接因子突变对红系细胞中靶基因的影响。DeepKINET 有效地揭示了转录后调控中的细胞异质性。
