title: Deep learning of the tissue-regulated splicing code.
abstract: BACKGROUND
  Alternative splicing (AS) is a regulated process that directs the generation of different transcripts from single genes. A computational model that can accurately predict splicing patterns based on genomic features and cellular context is highly desirable, both in understanding this widespread phenomenon, and in exploring the effects of genetic variations on AS.
  
  
  METHODS
  Using a deep neural network, we developed a model inferred from mouse RNA-Seq data that can predict splicing patterns in individual tissues and differences in splicing patterns across tissues. Our architecture uses hidden variables that jointly represent features in genomic sequences and tissue types when making predictions. A graphics processing unit was used to greatly reduce the training time of our models with millions of parameters.
  
  
  RESULTS
  We show that the deep architecture surpasses the performance of the previous Bayesian method for predicting AS patterns. With the proper optimization procedure and selection of hyperparameters, we demonstrate that deep architectures can be beneficial, even with a moderately sparse dataset. An analysis of what the model has learned in terms of the genomic features is presented.
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.
authors.forename: Michael K K
authors.initials: MK
authors.lastname: Leung
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.
authors.forename: Hui Yuan
authors.initials: HY
authors.lastname: Xiong
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.
authors.forename: Leo J
authors.initials: LJ
authors.lastname: Lee
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, CanadaDepartment of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario M5S 3G4, Banting and Best Department of Medical Research, University of Toronto, Toronto, Ontario M5S 3E1, Canada and Canadian Institute for Advanced Research, Toronto, Ontario M5G 1Z8, Canada.
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Frey
mesh_terms: D000465:Algorithms; D017398:Alternative Splicing; D000818:Animals; D001185:Artificial Intelligence; D001499:Bayes Theorem; D023281:Genomics / Q000379:methods; D006801:Humans; D051379:Mice; D016571:Neural Networks, Computer; D017423:Sequence Analysis, RNA