Systematic reconstruction of splicing regulatory modules by integrating many RNA-seq datasets

Alternative splicing is a ubiquitous gene regulatory mechanism that dramatically increases the complexity of the proteome. In this paper we study splicing module, which we define as a set of cassette exons co-regulated by the same splicing factors. 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 19 co-splicing networks derived from RNA-seq datasets and identify an atlas of frequent co-splicing clusters. We demonstrate that these identified clusters represent 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. We also demonstrate that the co-splicing clusters reveal novel functional groups which cannot be identified by co-expression clusters, and that the same exons can dynamically participate in different pathways depending on different conditions and different other exons that are co-spliced.