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Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks

Peter K. Koo, Praveen Anand, Steffan B. Paul, Sean R. Eddy
doi: https://doi.org/10.1101/418459
Peter K. Koo
1Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
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  • For correspondence: peter_koo@harvard.edu seaneddy@fas.harvard.edu
Praveen Anand
1Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
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Steffan B. Paul
1Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
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Sean R. Eddy
1Howard Hughes Medical Institute, Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA
2John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
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  • For correspondence: peter_koo@harvard.edu seaneddy@fas.harvard.edu
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Abstract

To infer the sequence and RNA structure specificities of RNA-binding proteins (RBPs) from experiments that enrich for bound sequences, we introduce a convolutional residual network which we call ResidualBind. ResidualBind significantly outperforms previous methods on experimental data from many RBP families. We interrogate ResidualBind to identify what features it has learned from high-affinity sequences with saliency analysis along with 1st-order and 2nd-order in silico mutagenesis. We show that in addition to sequence motifs, ResidualBind learns a model that includes the number of motifs, their spacing, and both positive and negative effects of RNA structure context. Strikingly, ResidualBind learns RNA structure context, including detailed base-pairing relationships, directly from sequence data, which we confirm on synthetic data. ResidualBind is a powerful, flexible, and interpretable model that can uncover cis-recognition preferences across a broad spectrum of RBPs.

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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-ND 4.0 International license.
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Posted September 15, 2018.
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Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks
Peter K. Koo, Praveen Anand, Steffan B. Paul, Sean R. Eddy
bioRxiv 418459; doi: https://doi.org/10.1101/418459
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Inferring Sequence-Structure Preferences of RNA-Binding Proteins with Convolutional Residual Networks
Peter K. Koo, Praveen Anand, Steffan B. Paul, Sean R. Eddy
bioRxiv 418459; doi: https://doi.org/10.1101/418459

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