DeepKINET: a deep generative model for estimating single-cell RNA splicing and degradation rates - Mizukoshi, Chikara; Kojima, Yasuhiro; Nomura, Satoshi; Hayashi, Shuto; Abe, Ko; Shimamura, Teppei (Genome Biology, 2024)
Keywords: Accuracy; Animals; brain; Breast cancer; breast neoplasms; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Cancer; Cell cycle; data collection; Datasets; Degradation; Female; Forebrain; Gene expression; gene expression regulation; Gene regulation; Genes; genome; Humans; Kinetics; messenger RNA; Metabolism; Methods; Neural networks; Parameter estimation; Post-transcription; Prosencephalon - metabolism; Proteins; RNA degradation; RNA Splicing; RNA Stability; RNA-binding protein; RNA-binding proteins; RNA-Binding Proteins - genetics; RNA-Binding Proteins - metabolism; Single-Cell Analysis; Single-cell RNA sequencing (scRNA-seq); Splicing factors; Splicing kinetics; Transcriptome dynamics; Velocity; Dimensionality reduction; Variational autoencoder (VAE); RNA velocity; Deep generative model; RNA degradation; RNA-binding proteins; Neural network; Cell differentiation; Transcriptome dynamics; Single-cell RNA sequencing (scRNA-seq); Splicing kinetics; Metabolic labeling; RNA splicing