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The control of RNA alternative splicing is critical for generating biological diversity. Despite emerging genome-wide technologies to study RNA complexity, reliable and comprehensive RNA-regulatory networks have not been defined. Here, we used Bayesian networks to probabilistically model diverse data sets and predict the target networks of specific regulators. We applied this strategy to identify approximately 700 alternative splicing events directly regulated by the neuron-specific factor Nova in the mouse brain, integrating RNA-binding data, splicing microarray data, Nova-binding motifs, and evolutionary signatures. The resulting integrative network revealed combinatorial regulation by Nova and the neuronal splicing factor Fox, interplay between phosphorylation and splicing, and potential links to neurologic disease. Thus, we have developed a general approach to understanding mammalian RNA regulation at the systems level.
Zhang et al. (Fri,) studied this question.