Genomic sequences and RNA-binding proteins predict RNA splicing efficiency in various single-cell contexts

Area of Science:

• Molecular Biology
• Genomics
• Bioinformatics

Background:

• RNA splicing efficiency is crucial for gene expression regulation and RNA velocity estimation in single-cell studies.
• Genomic regulation and context-dependent stochasticity of RNA splicing remain poorly understood.

Purpose of the Study:

• To estimate relative RNA splicing efficiency across diverse single-cell RNA sequencing datasets.
• To identify genomic and RNA-binding protein features predictive of RNA splicing efficiency.

Main Methods:

• Utilized a recent RNA velocity tool to estimate splicing efficiency.
• Extracted extensive genomic and RNA-binding protein features.
• Applied predictive modeling across human and mouse tissues.

Main Results:

• Identified numerous genomic and RNA-binding protein features highly predictive of relative RNA splicing efficiency.
• Demonstrated predictive power across multiple tissues and organs in human and mouse.
• Showcased the potential to elucidate RNA processing complexity.

Conclusions:

• Genomic and RNA-binding protein features are strong predictors of RNA splicing efficiency.
• This predictive framework can enhance the analysis of single-cell transcription activities.
• Findings contribute to a deeper understanding of gene expression regulation.