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COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning

Hannes Bretschneider, Shreshth Gandhi, Amit G Deshwar, Khalid Zuberi, Brendan J Frey
doi: https://doi.org/10.1101/255257
Hannes Bretschneider
1Deep Genomics Inc., Toronto ON, M5G 1L7, Canada
2Department of Computer Science, University of Toronto,Toronto ON, M5S 2E4, Canada
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Shreshth Gandhi
1Deep Genomics Inc., Toronto ON, M5G 1L7, Canada
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Amit G Deshwar
1Deep Genomics Inc., Toronto ON, M5G 1L7, Canada
3Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto ON, M5S 2E3, Canada
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Khalid Zuberi
1Deep Genomics Inc., Toronto ON, M5G 1L7, Canada
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Brendan J Frey
1Deep Genomics Inc., Toronto ON, M5G 1L7, Canada
2Department of Computer Science, University of Toronto,Toronto ON, M5S 2E4, Canada
3Edward S. Rogers Sr. Department of Electrical & Computer Engineering, University of Toronto, Toronto ON, M5S 2E3, Canada
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  • For correspondence: hannes@deepgenomics.com
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Abstract

Motivation Alternative splice site selection is inherently competitive and the probability of a given splice site to be used also depends strongly on the strength of neighboring sites. Here we present a new model named Competitive Splice Site Model (COSSMO), which explicitly models these competitive effects and predict the PSI distribution over any number of putative splice sites. We model an alternative splicing event as the choice of a 3’ acceptor site conditional on a fixed upstream 5’ donor site, or the choice of a 5’ donor site conditional on a fixed 3’ acceptor site. We build four different architectures that use convolutional layers, communication layers, LSTMS, and residual networks, respectively, to learn relevant motifs from sequence alone. We also construct a new dataset from genome annotations and RNA-Seq read data that we use to train our model.

Results COSSMO is able to predict the most frequently used splice site with an accuracy of 70% on unseen test data, and achieve an R2 of 60% in modeling the PSI distribution. We visualize the motifs that COSSMO learns from sequence and show that COSSMO recognizes the consensus splice site sequences as well as many known splicing factors with high specificity.

Availability Our dataset is available from http://cossmo.deepgenomics.com.

Contact frey@deepgenomics.com

Supplementary information Supplementary data are available at Bioinformatics online.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted March 25, 2018.
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COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning
Hannes Bretschneider, Shreshth Gandhi, Amit G Deshwar, Khalid Zuberi, Brendan J Frey
bioRxiv 255257; doi: https://doi.org/10.1101/255257
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COSSMO: Predicting Competitive Alternative Splice Site Selection using Deep Learning
Hannes Bretschneider, Shreshth Gandhi, Amit G Deshwar, Khalid Zuberi, Brendan J Frey
bioRxiv 255257; doi: https://doi.org/10.1101/255257

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