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cDeepbind: A context sensitive deep learning model of RNA-protein binding

Shreshth Gandhi, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey
doi: https://doi.org/10.1101/345140
Shreshth Gandhi
Department of Electrical and Computer Engineering
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Leo J. Lee
Department of Electrical and Computer Engineering
Donnely Centre for Cellular and Biomolecular Research and
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Andrew Delong
Department of Electrical and Computer Engineering
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David Duvenaud
Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Brendan J. Frey
Department of Electrical and Computer Engineering
Donnely Centre for Cellular and Biomolecular Research and
Department of Computer Science, University of Toronto, Toronto, ON, Canada
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Abstract

Motivation Determining RNA binding protein(RBP) binding specificity is crucial for understanding many cellular processes and genetic disorders. RBP binding is known to be affected by both the sequence and structure of RNAs. Deep learning can be used to learn generalizable representations of raw data and has improved state of the art in several fields such as image classification, speech recognition and even genomics. Previous work on RBP binding has either used shallow models that combine sequence and structure or deep models that use only the sequence. Here we combine both abilities by augmenting and refining the original Deepbind architecture to capture structural information and obtain significantly better performance.

Results We propose two deep architectures, one a lightweight convolutional network for transcriptome wide inference and another a Long Short-Term Memory(LSTM) network that is suitable for small batches of data. We incorporate computationally predicted secondary structure features as input to our models and show its effectiveness in boosting prediction performance. Our models achieved significantly higher correlations on held out in-vitro test data compared to previous approaches, and generalise well to in-vivo CLIP-SEQ data achieving higher median AUCs than other approaches. We analysed the output from our model for VTS1 and CPO and provided intuition into its working. Our models confirmed known secondary structure preferences for some proteins as well as found new ones where secondary structure might play a role. We also demonstrated the strengths of our model compared to other approaches such as the ability to combine information from long distances along the input.

Availability Software and models are available at https://github.com/shreshthgandhi/cDeepbind

Contact ljlee{at}psi.toronto.edu, frey{at}psi.toronto.edu

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 June 12, 2018.
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cDeepbind: A context sensitive deep learning model of RNA-protein binding
Shreshth Gandhi, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey
bioRxiv 345140; doi: https://doi.org/10.1101/345140
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cDeepbind: A context sensitive deep learning model of RNA-protein binding
Shreshth Gandhi, Leo J. Lee, Andrew Delong, David Duvenaud, Brendan J. Frey
bioRxiv 345140; doi: https://doi.org/10.1101/345140

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