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.
The combinatorial effect of histone modifications in regulating gene expression has been studied throughout literature ( @cite_2 ). To better understand this relationship, scientists have generated experimental datasets quantifying gene expression and histone modification signals across different cell-types. These datasets have been made available through large-scale repositories, the latest being the Roadmap Epigenome Project (REMC) ( @cite_27 ). * -12pt
@cite_27 @cite_2
2076154138 2099035207
The reference human genome sequence set the stage for studies of genetic variation and its association with human disease, but epigenomic studies lack a similar reference. To address this need, the NIH Roadmap Epigenomics Consortium generated the largest collection so far of human epigenomes for primary cells and tissues. Here we describe the integrative analysis of 111 reference human epigenomes generated as part of the programme, profiled for histone modification patterns, DNA accessibility, DNA methylation and RNA expression. We establish global maps of regulatory elements, define regulatory modules of coordinated activity, and their likely activators and repressors. We show that disease- and trait-associated genetic variants are enriched in tissue-specific epigenomic marks, revealing biologically relevant cell types for diverse human traits, and providing a resource for interpreting the molecular basis of human disease. Our results demonstrate the central role of epigenomic information for understanding gene regulation, cellular differentiation and human disease. In the nuclei of eukaryotic cells, DNA wraps around the octamer of histone proteins to form the nucleosome, in a structure like ‘beads on a string’, which makes up the basic unit of chromatin. Chromatin further folds into higherlevel structures, loosely or tightly, which helps to determine the accessibility of the DNA. For instance, actively transcribed regions tend to be in looser chromatin structures so that transcription factors and RNA polymerases can access the genes. Chromatin structure can be altered by various post-translational modifications of the N-terminal tail residues of histone proteins. For example, acetylation of a lysine residue can neutralize its positive charge and weaken the binding between the histone and the negatively charged DNA, which exposes the DNA to regulatory proteins. Methylation is another common type of histone modification; for example, the lysine at the fourth position of the H3 histone can be mono-, dior tri-methylated (denoted as H3K4me1, H3K4me2 and H3K4me3, respectively). By examining histone modification patterns at highly conserved noncoding regions in mouse embryonic stem cells, found ‘bivalent domains’ of histone modifications (i.e., harboring both the repressive mark H3K27me3 and the active mark H3K4me3) near genes with poised transcription [1]. When embryonic stem cells differentiate into more specialized cells (e.g., neural precursor cells), a subset of the bivalent domains are resolved (i.e., H3K27me3 becomes weaker, while H3K4me3 becomes stronger, and these loci coincide with genes that are actively transcribed in neural precursor cells). Thus, combinations of histone marks are indicative of transcriptional states. mapped 20 histone methylations of lysine and arginine residues in human CD4 T cells using chromatin immunoprecipitation followed by sequencing (ChIP-seq) [2]. They found that monomethylated H3K27, H3K9, H4K20, H3K79 and H2BK5 were linked to gene activation, while trimethylated H3K27, H3K9 and H3K79 were linked to gene repression. In a later study, the group profiled 39 additional histone modifications in human CD4 T cells [3]. They identified more than 3000 genes that were highly expressed in these cells and the promoters of these genes showed high levels of 17 histone modifications (called a histone modification module). Other studies also investigated the correlation between individual histone marks and gene expression, although not in a quantitative way [4,5].