Genomic sequences and RNA binding proteins predict RNA splicing kinetics in various single-cell contexts
Hou, R.; Huang, Y.
Show abstract
RNA splicing is a key step of gene expression in higher organisms. Accurate quantification of the two-step splicing kinetics is of high interests not only for understanding the regulatory machinery, but also for estimating the RNA velocity in single cells. However, the kinetic rates remain poorly understood due to the intrinsic low content of unspliced RNAs and its stochasticity across contexts. Here, we estimated the relative splicing efficiency across a variety of single-cell RNA-Seq data with scVelo. We further extracted three large feature sets including 92 basic genomic sequence features, 65,536 octamers and 120 RNA binding proteins features and found they are highly predictive to RNA splicing efficiency across multiple tissues on human and mouse. A set of important features have been identified with strong regulatory potentials on splicing efficiency. This predictive power brings promise to reveal the complexity of RNA processing and to enhance the estimation of single-cell RNA velocity.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Tissue-specific regulation of gene expression via unproductive splicing 95%
- Shiba: A versatile computational method for systematic identification of differential RNA splicing across platforms 95%
- Insplico: Effective computational tool for studying intron splicing order genome-wide with short and long RNA-seq reads 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.