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

Abstract

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 noncoding DNA, the vast majority of which is still poorly understood in terms of function. A powerful predictive model for the function of noncoding DNA can have enormous benefit for both basic science and translational research because over 98% of the human genome is noncoding 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 noncoding 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. Availability and implementation All source code is available at the github repository http://github.com/uci-cbcl/DanQ .