Abstract

Alternative splicing (AS) of precursor mRNA (pre-mRNA) is a crucial step in the expression of most eukaryotic genes. Splicing factors (SFs) play an important role in AS regulation by binding to the cis-regulatory elements on the pre-mRNA. Although many splicing factors (SFs) and their binding sites have been identified, their combinatorial regulatory effects remain to be elucidated. In this paper, we derive a biophysical model for AS regulation that integrates combinatorial signals of cis-acting splicing regulatory elements (SREs) and their interactions. We also develop a systematic framework for model inference. Applying the biophysical model to a human RNA-Seq data set, we demonstrate that our model can explain 49.1%–66.5% variance of the data, which is comparable to the best result achieved by biophysical models for transcription. In total, we identified 119 SRE pairs between different regions of cassette exons that may regulate exon or intron definition in splicing, and 77 SRE pairs from the same region that may arise from a long motif or two different SREs bound by different SFs. Particularly, putative binding sites of polypyrimidine tract-binding protein (PTB), heterogeneous nuclear ribonucleoprotein (hnRNP) F/H and E/K are identified as interacting SRE pairs, and have been shown to be consistent with the interaction models proposed in previous experimental results. These results show that our biophysical model and inference method provide a means of quantitative modeling of splicing regulation and is a useful tool for identifying SREs and their interactions. The software package for model inference is available under an open source license.

Highlights

  • A key step in eukaryotic gene expression is to remove introns from precursor messenger RNA so that exons can be joined together to form the mature mRNA

  • This simplification is similar to the one used in the derivation of a biophysical model [26,31,32] for transcription where assembly of the RNA polymerase (RNAP) complex is simplified to one reaction

  • The first probability can be expressed as EI1 =(EI1 zEI2 ), while the second probability can be derived from the biophysical chemical reactions modeling spliceosome assembly and binding of splicing regulatory elements (SREs) to the pre-mRNA as detailed in Materials and Methods

Read more

Summary

Introduction

A key step in eukaryotic gene expression is to remove introns from precursor messenger RNA (pre-mRNA) so that exons can be joined together to form the mature mRNA. One mechanism is that recognition of splice sites by the spliceosome is influenced by a class of RNA binding proteins named splicing factors (SFs) that can bind to the cis-acting splicing regulatory elements (SREs) on the pre-mRNA. These SREs are categorized as exonic splicing enhancers (ESEs) and silencers (ESSs), and intronic splicing enhancers (ISEs) and silencers (ISSs) based on their locations and effects on splicing [7]. Comparing with the other two approaches, the regression-based approach offers flexibility of identifying combinatorial regulatory effects of multiple SREs. the current regression methods for AS were not developed systematically from a theoretical base, which may limit their performance

Objectives
Methods
Results
Conclusion
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call