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This article is part of the supplement: Selected articles from The 5th IEEE International Conference on Systems Biology (ISB 2011)

Open Access Research

Integrating many co-splicing networks to reconstruct splicing regulatory modules

Chao Dai12, Wenyuan Li2, Juan Liu1 and Xianghong Jasmine Zhou2*

  • * Corresponding author: Xianghong J Zhou xjzhou@usc.edu

  • † Equal contributors

Author Affiliations

1 School of Computer, Wuhan University, Wuhan 430072, PR China

2 Molecular and Computational Biology, Department of Biological Sciences, University of Southern California, Los Angeles, CA 90089, USA

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BMC Systems Biology 2012, 6(Suppl 1):S17  doi:10.1186/1752-0509-6-S1-S17

Published: 16 July 2012

Abstract

Background

Alternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. However, the mechanism for regulating alternative splicing is poorly understood, and study of coordinated splicing regulation has been limited to individual cases. To study genome-wide splicing regulation, we integrate many human RNA-seq datasets to identify splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors.

Results

We have designed a tensor-based approach to identify co-splicing clusters that appear frequently across multiple conditions, thus very likely to represent splicing modules - a unit in the splicing regulatory network. In particular, we model each RNA-seq dataset as a co-splicing network, where the nodes represent exons and the edges are weighted by the correlations between exon inclusion rate profiles. We apply our tensor-based method to the 38 co-splicing networks derived from human RNA-seq datasets and indentify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent potential splicing modules by validating against four biological knowledge databases. The likelihood that a frequent co-splicing cluster is biologically meaningful increases with its recurrence across multiple datasets, highlighting the importance of the integrative approach.

Conclusions

Co-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, particularly they can grant new insights into functions associated with post-transcriptional regulation, and the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced. We propose that by identifying splicing module, a unit in the splicing regulatory network can serve as an important step to decipher the splicing code.