Motivation: 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. Results: 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.
Keji Zhao and colleagues report genome-wide maps of 18 histone lysine acetylations in human CD4+ T cells as detected by ChIP-sequencing. Analysis of the data along with genome-wide maps of histone lysine methylations revealed a common module of 17 modifications associated with 25 of genes. Gene regulation, despite being investigated in a large number of works, is still yet to be well understood. The mechanisms that control gene expression is one of the open problems. Epigenetic factors, among others, are assumed to have a role to play. In this work, we focus on DNA methylation and post-translational histone modifications (PTMs). These individually have been shown to contribute to controlling of gene expression. However, neither can totally account for the expression levels, i.e. low or high. Therefore, the hypothesis of their combinatorial role, as two of the most influencing factors, has been established and discussed in literature. Taking a computational approach based on rule induction, we derived (83 ) rules and identified some key PTMs that have considerable effects such as H2BK5ac, H3K79me123, H4K91ac, and H3K4me3. Also, some interesting patterns of DNA methylation and PTMs that can explain the low expression of genes in CD4 (+ ) T cell. The results include previously reported patterns as well as some new valid ones which could give some new insights to the process in question.