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An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs

Richard Brown, View ORCID ProfileGerton Lunter
doi: https://doi.org/10.1101/351254
Richard Brown
1Wellcome Trust Centre of Human Genetics, University of Oxford, Oxford, OX3 7BN, U.K.
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Gerton Lunter
1Wellcome Trust Centre of Human Genetics, University of Oxford, Oxford, OX3 7BN, U.K.
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Abstract

Motivation Convolutional neural networks (CNNs) have been trememdously successful in many contexts, particularly where training data is abundant and signal-to-noise ratios are large. However, when predicting noisily observed biological phenotypes from DNA sequence, each training instance is only weakly informative, and the amount of training data is often fundamentally limited, emphasizing the need for methods that make optimal use of training data and any structure inherent in the model.

Results Here we show how to combine equivariant networks, a general mathematical framework for handling exact symmetries in CNNs, with Bayesian dropout, a version of MC dropout suggested by a reinterpretation of dropout as a variational Bayesian approximation, to develop a model that exhibits exact reverse-complement symmetry and is more resistant to overtraining. We find that this model has increased power and generalizability, resulting in significantly better predictive accuracy compared to standard CNN implementations and state-of-art deep-learning-based motif finders. We use our network to predict recombination hotspots from sequence, and identify high-resolution binding motifs for the recombination-initiation protein PRDM9, which were recently validated by high-resolution assays. The network achieves a predictive accuracy comparable to that attainable by a direct assay of the H3K4me3 histone mark, a proxy for PRDM9 binding.

Availability https://github.com/luntergroup/EquivariantNetworks

Contact richard.brown{at}well.ox.ac.uk, gerton.lunter{at}well.ox.ac.uk

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-ND 4.0 International license.
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Posted June 20, 2018.
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An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs
Richard Brown, Gerton Lunter
bioRxiv 351254; doi: https://doi.org/10.1101/351254
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An Equivariant Bayesian Convolutional Network predicts recombination hotspots and accurately resolves binding motifs
Richard Brown, Gerton Lunter
bioRxiv 351254; doi: https://doi.org/10.1101/351254

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