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

Abstract

The RNA splicing efficiency is of high interest for both understanding the regulatory machinery of gene expression and estimating the RNA velocity in single cells. However, its genomic regulation and stochasticity across contexts remain poorly understood. Here, by leveraging the recent RNA velocity tool, we estimated the relative splicing efficiency across a variety of single-cell RNA-Seq data sets. We further extracted large sets of genomic features and 120 RNA-binding protein features and found they are highly predictive to relative RNA splicing efficiency across multiple tissues and organs on human and mouse. This predictive power brings promise to reveal the complexity of RNA processing and to enhance the analysis of single-cell transcription activities. In order to ensure reproducibility, all preprocessed datasets and scripts used for the prediction and figure generation are publicly available at https://doi.org/10.5281/zenodo.6513669. Supplementary data are available at Bioinformatics online.