Abstract

Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases including diabetes, obesity, and cancer. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distributions of tens of chromatin marks around thousands of regions. To this end, we developed a software tool called HebbPlot, which utilizes a Hebb neural network in learning a general chromatin signature from regions with a common function. Hebb networks can learn the associations between tens of marks and thousands of regions. This is the first application of Hebb networks in the epigenetics field. HebbPlot presents a signature as a digitized image, in which a bright pixel indicates the presence of a mark around a part of the genetic element, and a black pixel indicates the absence of the mark. A row of pixels represents one mark. Similar rows are clustered in the image. We validated HebbPlot on synthetic data and on 111 epigenomes provided by the Roadmap Epigenomics Project. HebbPlot was able to retrieve distinct chromatin signatures for promoters, enhancers, and genes active in each of the 111 cell types. Our analysis reveals that active promoters have a directional signature; marks such as H3K79(me1/me2), H3K4(me1,me2,me3), and H3K9ac stretch toward coding regions. The plots of inactive promoters show that H3K27me3 is consistently present around them. Further, the signatures of enhancers that are fully included in repetitive regions are almost identical to those located outside repeats, indicating that transposons have an enhancer-like function in the human genome. Furthermore, the chromatin signature of active elements consists of the presence of H3K79me1 and the absence of H3K9me3 and H3K27me3. In sum, HebbPlot is a general tool that can be applied to wide array of studies, facilitating the deciphering of the histone code.\n\nAuthor summaryChromatin marks have gained much attention because of their important roles in gene regulation, cell differentiation, Lamarckian inheritance, and imprinting. A chromatin signature of a genetic element, such as genes or enhancers, consists of multiple marks and may differ from a tissue to a tissue. Currently, tens of histone modifications are known. Several marks of more than 100 human cell types have been determined. Many epigenomes of other normal and pathological cell types will be available soon.\n\nExtracting a chromatin signature representing the distributions of tens of marks around thousands of regions is a challenging task. Hebb networks are a special type of artificial neural networks known for their ability to learn associations. We developed a software tool called HebbPlot. The tool uses a Hebb network to learn how a mark is distributed around a set of regions that have the same function, e.g. promoters active in the same tissue. HebbPlot produces a pattern representing mark distributions around all of the regions. Mark patterns are clustered based on their similarity to one another. Then a digitized image representing the learned pattern is generated. HebbPlot will help biologist with characterizing and visualizing chromatin signatures in numerous studies.