title: Integrative modeling defines the Nova splicing-regulatory network and its combinatorial controls.
abstract: 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.
authors.affiliation: Laboratory of Molecular Neuro-Oncology, Howard Hughes Medical Institute, The Rockefeller University, 1230 York Avenue, New York, NY 10021, USA. czhang@rockefeller.edu
authors.forename: Chaolin
authors.initials: C
authors.lastname: Zhang
authors.forename: Maria A
authors.initials: MA
authors.lastname: Frias
authors.forename: Aldo
authors.initials: A
authors.lastname: Mele
authors.forename: Matteo
authors.initials: M
authors.lastname: Ruggiu
authors.forename: Taesun
authors.initials: T
authors.lastname: Eom
authors.forename: Christina B
authors.initials: CB
authors.lastname: Marney
authors.forename: Huidong
authors.initials: H
authors.lastname: Wang
authors.forename: Donny D
authors.initials: DD
authors.lastname: Licatalosi
authors.forename: John J
authors.initials: JJ
authors.lastname: Fak
authors.forename: Robert B
authors.initials: RB
authors.lastname: Darnell
mesh_terms: D017398:Alternative Splicing; D000818:Animals; D000951:Antigens, Neoplasm / Q000378:metabolism*; D001185:Artificial Intelligence; D001499:Bayes Theorem; D001665:Binding Sites; D001921:Brain / Q000378:metabolism*; D002460:Cell Line; D019295:Computational Biology; D019143:Evolution, Molecular; D005091:Exons; D053263:Gene Regulatory Networks; D006801:Humans; D007438:Introns; D051379:Mice; D008957:Models, Genetic; D015233:Models, Statistical; D009419:Nerve Tissue Proteins / Q000378:metabolism*; D009422:Nervous System Diseases / Q000235:genetics; D000090682:Neuro-Oncological Ventral Antigen; D020411:Oligonucleotide Array Sequence Analysis; D010766:Phosphorylation; D011485:Protein Binding; D011506:Proteins / Q000235:genetics / Q000378:metabolism; D012313:RNA / Q000378:metabolism; D016601:RNA-Binding Proteins / Q000378:metabolism*
chemical_list: D000951:Antigens, Neoplasm; D009419:Nerve Tissue Proteins; D000090682:Neuro-Oncological Ventral Antigen; C515624:Nova2 protein, mouse; D011506:Proteins; D016601:RNA-Binding Proteins; D012313:RNA