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Updated: Jun 30, 2026

Using the E1A Minigene Tool to Study mRNA Splicing Changes
10:25

Using the E1A Minigene Tool to Study mRNA Splicing Changes

Published on: April 22, 2021

Predicting human mRNA isoform levels from site-specific splicing kinetics in silico

Zane R Thornburg1,2, You Jin Song2,3, Jiaxi Yan3

  • 1Beckman Institute for Advanced Science and Technology, University of Illinois Urbana-Champaign, Urbana, IL, 61801.

Biorxiv : the Preprint Server for Biology
|June 29, 2026
PubMed

Insights

We developed a new computational model for pre-messenger RNA (mRNA) splicing kinetics. This model can predict how changes in cellular conditions affect mRNA isoforms and is applicable to most human genes.

Area of Science:

  • Molecular Biology
  • Computational Biology
  • Genetics

Background:

  • Messenger RNA (mRNA) splicing is crucial for gene expression, influencing cell physiology, development, and disease.
  • Existing computational models for splicing are limited in their ability to account for splicing chemistry and genome-wide applicability.
  • Alternative splicing generates diverse mRNA isoforms from a single gene, with frequencies dependent on splicing kinetics and intracellular conditions.

Purpose of the Study:

  • To develop a stochastic kinetic model of pre-mRNA splicing that is extensible to most human protein-coding genes.
  • To create a computational platform for rapidly assessing how variations in intracellular and environmental conditions affect splicing kinetics and mRNA isoforms.
  • To provide a resource for quantitative, computational analysis of pre-mRNA splicing, enabling rapid computational-experimental hypothesis testing.

Main Methods:

  • Developed a stochastic kinetic model of splicing incorporating site-specific reaction rates and the ability to include additional splicing factors.
  • The model is designed for extensibility to most protein-coding genes in the human genome.
  • Experimentally validated the model's predictive capability by analyzing the spliced isoform ratio of the SRSF6 gene under normoxia and hypoxia.

Main Results:

  • The developed stochastic kinetic model successfully simulates pre-mRNA splicing kinetics.
  • The model demonstrates extensibility to a large portion of the human genome.
  • Experimental validation confirmed the model's predictive accuracy in assessing the impact of hypoxia on SRSF6 pre-mRNA splicing.

Conclusions:

  • The new computational model overcomes limitations of previous approaches, offering a versatile tool for studying splicing.
  • This model provides a computational benchtop for probing splicing kinetics across diverse genes and conditions.
  • The developed framework facilitates a rapid computational-experimental approach to investigate biological hypotheses related to splicing.

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