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A deep learning approach to pattern recognition for short DNA sequences

Akosua Busia, George E. Dahl, Clara Fannjiang, David H. Alexander, Elizabeth Dorfman, Ryan Poplin, Cory Y. McLean, Pi-Chuan Chang, Mark DePristo
doi: https://doi.org/10.1101/353474
Akosua Busia
Google Brain, Mountain View, California, USA.
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George E. Dahl
Google Brain, Mountain View, California, USA.
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Clara Fannjiang
Google Brain, Mountain View, California, USA.
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David H. Alexander
Google Brain, Mountain View, California, USA.
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Elizabeth Dorfman
Google Brain, Mountain View, California, USA.
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Ryan Poplin
Google Brain, Mountain View, California, USA.
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Cory Y. McLean
Google Brain, Mountain View, California, USA.
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Pi-Chuan Chang
Google Brain, Mountain View, California, USA.
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Mark DePristo
Google Brain, Mountain View, California, USA.
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  • For correspondence: mdepristo@google.com
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Abstract

Motivation Inferring properties of biological sequences--such as determining the species-of-origin of a DNA sequence or the function of an amino-acid sequence--is a core task in many bioinformatics applications. These tasks are often solved using string-matching to map query sequences to labeled database sequences or via Hidden Markov Model-like pattern matching. In the current work we describe and assess an deep learning approach which trains a deep neural network (DNN) to predict database-derived labels directly from query sequences.

Results We demonstrate this DNN performs at state-of-the-art or above levels on a difficult, practically important problem: predicting species-of-origin from short reads of 16S ribosomal DNA. When trained on 16S sequences of over 13,000 distinct species, our DNN achieves read-level species classification accuracy within 2.0% of perfect memorization of training data, and produces more accurate genus-level assignments for reads from held-out species than k-mer, alignment, and taxonomic binning baselines. Moreover, our models exhibit greater robustness than these existing approaches to increasing noise in the query sequences. Finally, we show that these DNNs perform well on experimental 16S mock community dataset. Overall, our results constitute a first step towards our long-term goal of developing a general-purpose deep learning approach to predicting meaningful labels from short biological sequences.

Availability TensorFlow training code is available through GitHub (https://github.com/tensorflow/models/tree/master/research). Data in TensorFlow TFRecord format is available on Google Cloud Storage (gs://brain-genomics-public/research/seq2species/).

Contact seq2species-interest{at}google.com

Footnotes

  • ↵† Work completed as part of the Google AI Residency Program. Present address: University of California, Berkeley, California, USA.

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 January 28, 2019.
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A deep learning approach to pattern recognition for short DNA sequences
Akosua Busia, George E. Dahl, Clara Fannjiang, David H. Alexander, Elizabeth Dorfman, Ryan Poplin, Cory Y. McLean, Pi-Chuan Chang, Mark DePristo
bioRxiv 353474; doi: https://doi.org/10.1101/353474
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A deep learning approach to pattern recognition for short DNA sequences
Akosua Busia, George E. Dahl, Clara Fannjiang, David H. Alexander, Elizabeth Dorfman, Ryan Poplin, Cory Y. McLean, Pi-Chuan Chang, Mark DePristo
bioRxiv 353474; doi: https://doi.org/10.1101/353474

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