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Integrative Deep Models for Alternative Splicing

Anupama Jha, Matthew R. Gazzara, Yoseph Barash
doi: https://doi.org/10.1101/104869
Anupama Jha
Department of Computer and Information Science, School of Engineering
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Matthew R. Gazzara
Department of Computer and Information Science, School of EngineeringDepartment of GeneticsDepartment of Biochemistry and Biophysics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
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Yoseph Barash
Department of Computer and Information Science, School of EngineeringDepartment of Genetics
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  • For correspondence: yosephb@upenn.edu
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Abstract

Advancements in sequencing technologies have highlighted the role of alternative splicing (AS) in increasing transcriptome complexity. This role of AS, combined with the relation of aberrant splicing to malignant states, motivated two streams of research, experimental and computational. The First involves a myriad of techniques such as RNA-Seq and CLIP-Seq to identify splicing regulators and their putative targets. The second involves probabilistic models, also known as splicing codes, which infer regulatory mechanisms and predict splicing outcome directly from genomic sequence. To date, these models have utilized only expression data. In this work we address two related challenges: Can we improve on previous models for AS outcome prediction and can we integrate additional sources of data to improve predictions for AS regulatory factors. We perform a detailed comparison of two previous modeling approaches, Bayesian and Deep Neural networks, dissecting the confounding effects of datasets and target functions. We then develop a new target function for AS prediction and show that it significantly improves model accuracy. Next, we develop a modeling framework to incorporate CLIP-Seq, knockdown and over-expression experiments, which are inherently noisy and suffer from missing values. Using several datasets involving key splice factors in mouse brain, muscle and heart we demonstrate both the prediction improvements and biological insights offered by our new models. Overall, the framework we propose offers a scalable integrative solution to improve splicing code modeling as vast amounts of relevant genomic data become available.

Availability: code and data will be available on Github following publication.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted January 31, 2017.
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Integrative Deep Models for Alternative Splicing
Anupama Jha, Matthew R. Gazzara, Yoseph Barash
bioRxiv 104869; doi: https://doi.org/10.1101/104869
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Integrative Deep Models for Alternative Splicing
Anupama Jha, Matthew R. Gazzara, Yoseph Barash
bioRxiv 104869; doi: https://doi.org/10.1101/104869

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