ArticlePloS one2013
A biophysical model for identifying splicing regulatory elements and their interactions.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Computational Identification of Tissue-Specific Splicing Regulatory Elements in Human Genes from RNA-Seq Data.PloS one · 2016Article
- CoSREM: a graph mining algorithm for the discovery of combinatorial splicing regulatory elements.BMC bioinformatics · 2015Article
- Genome-wide analysis of alternative transcripts in human breast cancer.Breast cancer research and treatment · 2015Article
- Identifying splicing regulatory elements with de Bruijn graphs.Journal of computational biology : a journal of computational molecular cell biology · 2014Article
- New insights into functional roles of the polypyrimidine tract-binding protein.International journal of molecular sciences · 2013Review
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3 authors.
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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.
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