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Nucleic Acids Res. 2016 Jun 20;44(11):e107. doi: 10.1093/nar/gkw226. Epub 2016 Apr 15.

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.

Author information

1
Department of Computer Science University of California, Irvine, CA 92697, USA Center for Complex Biological Systems University of California, Irvine, CA 92697, USA.
2
Department of Computer Science University of California, Irvine, CA 92697, USA Center for Complex Biological Systems University of California, Irvine, CA 92697, USA xhx@uci.edu.

Abstract

Modeling the properties and functions of DNA sequences is an important, but challenging task in the broad field of genomics. This task is particularly difficult for non-coding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of non-coding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is non-coding and 93% of disease-associated variants lie in these regions. To address this need, we propose DanQ, a novel hybrid convolutional and bi-directional long short-term memory recurrent neural network framework for predicting non-coding function de novo from sequence. In the DanQ model, the convolution layer captures regulatory motifs, while the recurrent layer captures long-term dependencies between the motifs in order to learn a regulatory 'grammar' to improve predictions. DanQ improves considerably upon other models across several metrics. For some regulatory markers, DanQ can achieve over a 50% relative improvement in the area under the precision-recall curve metric compared to related models. We have made the source code available at the github repository http://github.com/uci-cbcl/DanQ.

PMID:
27084946
PMCID:
PMC4914104
DOI:
10.1093/nar/gkw226
[Indexed for MEDLINE]
Free PMC Article

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