DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.
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DOI：
10.1093/nar/gkw226
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发表时间：
2016-06-20
影响因子：
14.9
通讯作者：
Xie X
中科院分区：
文献类型：
--
作者：
Quang D;Xie X
DNA序列的性质和功能的建模是基因组学领域中一项重要但具有挑战性的任务。这项任务对于非编码DNA来说尤其困难，其中绝大多数在功能方面仍然知之甚少。一个强大的非编码DNA功能预测模型可以为基础科学和转化研究带来巨大的好处，因为超过98%的人类基因组是非编码的，93%的疾病相关变异位于这些区域。为了满足这一需求，我们提出了DanQ，一种新的混合卷积和双向长短期记忆递归神经网络框架，用于从序列中从头预测非编码函数。在DanQ模型中，卷积层捕获调控基序，而递归层捕获基序之间的长期依赖关系，以学习调控“语法”来改善预测。DanQ在几个指标上大大优于其他模型。对于一些监管标记物，与相关模型相比，DanQ可以在精确-召回曲线下的面积指标上实现超过50%的相对改善。我们已经在github资源库http://github.com/uci-cbcl/DanQ上提供了源代码。
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.