Article

A deep generative model for estimating single-cell RNA splicing and degradation rates

2023-11-26

Abstract excerpt

<h4> A bstract </h4> 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. We introduce DeepKINET, a deep generative model that estimates splicing and degradation rates at single-cell resolution from scRNA-seq data. DeepKINET outperformed...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: f340444a-a979-50e3-a609-f740c643e901
DOI: 10.1101/2023.11.25.568659

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

A deep generative model for estimating single-cell RNA splicing and degradation ratesDOI 10.1101/2023.11.25.568659Read

scRegulate: Single-Cell Regulatory-Embedded Variational Inference of Transcription Factor Activity from Gene ExpressionSemantic proximity Read

A Cross-Species Generative Cell Atlas Across 1.5 Billion Years of Evolution: The TranscriptFormer Single-cell ModelSemantic proximity Read

mmVelo: A deep generative model for estimating cell state-dependent dynamics across multiple modalitiesSemantic proximity Read

Generative modeling for RNA splicing prediction and designSemantic proximity Read

Generating Synthetic Single Cell Data from Bulk RNA-seq Using a Pretrained Variational AutoencoderSemantic proximity Read

Squidiff: Predicting cellular development and responses to perturbations using a diffusion modelSemantic proximity Read

Genomic sequences and RNA binding proteins predict RNA splicing kinetics in various single-cell contextsSemantic proximity Read

ARTEMIS integrates autoencoders and Schrödinger Bridges to predict continuous dynamics of gene expression, cell population and perturbation from time-series single-cell dataSemantic proximity Read

Computational identification of splicing phenotypes from single cell transcriptomic experimentsSemantic proximity Read

Squidiff: Predicting cellular development and responses to perturbations using a diffusion modelSemantic proximity Read

BRIE2: computational identification of splicing phenotypes from single-cell transcriptomic experiments.Semantic proximity Read

Learning interpretable cellular and gene signature embeddings from single-cell transcriptomic dataSemantic proximity Read