Singh Ritambhara, Lanchantin Jack, Robins Gabriel, Qi Yanjun
Department of Computer Science, University of Virginia, Charlottesville, VA 22904, USA.
Bioinformatics. 2016 Sep 1;32(17):i639-i648. doi: 10.1093/bioinformatics/btw427.
Histone modifications are among the most important factors that control gene regulation. Computational methods that predict gene expression from histone modification signals are highly desirable for understanding their combinatorial effects in gene regulation. This knowledge can help in developing 'epigenetic drugs' for diseases like cancer. Previous studies for quantifying the relationship between histone modifications and gene expression levels either failed to capture combinatorial effects or relied on multiple methods that separate predictions and combinatorial analysis. This paper develops a unified discriminative framework using a deep convolutional neural network to classify gene expression using histone modification data as input. Our system, called DeepChrome, allows automatic extraction of complex interactions among important features. To simultaneously visualize the combinatorial interactions among histone modifications, we propose a novel optimization-based technique that generates feature pattern maps from the learnt deep model. This provides an intuitive description of underlying epigenetic mechanisms that regulate genes.
We show that DeepChrome outperforms state-of-the-art models like Support Vector Machines and Random Forests for gene expression classification task on 56 different cell-types from REMC database. The output of our visualization technique not only validates the previous observations but also allows novel insights about combinatorial interactions among histone modification marks, some of which have recently been observed by experimental studies.
Codes and results are available at www.deepchrome.org
Supplementary data are available at Bioinformatics online.
组蛋白修饰是控制基因调控的最重要因素之一。从组蛋白修饰信号预测基因表达的计算方法对于理解它们在基因调控中的组合效应非常有必要。这些知识有助于开发针对癌症等疾病的“表观遗传药物”。先前用于量化组蛋白修饰与基因表达水平之间关系的研究要么未能捕捉到组合效应，要么依赖于多种将预测和组合分析分开的方法。本文开发了一个统一的判别框架，使用深度卷积神经网络以组蛋白修饰数据作为输入来对基因表达进行分类。我们的系统名为DeepChrome，能够自动提取重要特征之间的复杂相互作用。为了同时可视化组蛋白修饰之间的组合相互作用，我们提出了一种基于优化的新技术，该技术从学习到的深度模型生成特征模式图。这提供了对调控基因的潜在表观遗传机制的直观描述。
我们表明，在来自REMC数据库的56种不同细胞类型上，对于基因表达分类任务，DeepChrome优于支持向量机和随机森林等现有模型。我们可视化技术的输出不仅验证了先前的观察结果，还提供了关于组蛋白修饰标记之间组合相互作用的新见解，其中一些最近已通过实验研究观察到。
代码和结果可在www.deepchrome.org获取。
补充数据可在《生物信息学》在线获取。
