HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures
Abstract Background Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. The histone code is complex and consists of more than 100 marks. Therefore, biologists need computational tools to characterize general signatures representing the distri...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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BMC
2018-09-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12859-018-2312-1 |
| _version_ | 1818126596318429184 |
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| author | Hani Z. Girgis Alfredo Velasco Zachary E. Reyes |
| author_facet | Hani Z. Girgis Alfredo Velasco Zachary E. Reyes |
| author_sort | Hani Z. Girgis |
| collection | DOAJ |
| description | Abstract Background Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. 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. Results To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications — H3K36me3, H3K79me1, H3K79me2, and H4K8ac — that are associated with coding regions of active genes. Other marks — H4K12ac, H3K14ac, H3K27me3, and H2AK5ac — were found to be weakly associated with coding regions of inactive genes. Conclusions This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code. |
| first_indexed | 2024-12-11T07:03:58Z |
| format | Article |
| id | doaj.art-fa7c0b1053d543d6817e3a98502a9e3a |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-11T07:03:58Z |
| publishDate | 2018-09-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-fa7c0b1053d543d6817e3a98502a9e3a2022-12-22T01:16:33ZengBMCBMC Bioinformatics1471-21052018-09-0119111810.1186/s12859-018-2312-1HebbPlot: an intelligent tool for learning and visualizing chromatin mark signaturesHani Z. Girgis0Alfredo Velasco1Zachary E. Reyes2Tandy School of Computer Science, University of TulsaTandy School of Computer Science, University of TulsaTandy School of Computer Science, University of TulsaAbstract Background Histone modifications play important roles in gene regulation, heredity, imprinting, and many human diseases. 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. Results To this end, we developed a software tool, HebbPlot, which utilizes a Hebbian neural network in learning a general chromatin signature from regions with a common function. Hebbian networks can learn the associations between tens of marks and thousands of regions. HebbPlot presents a signature as a digital image, which can be easily interpreted. Moreover, signatures produced by HebbPlot can be compared quantitatively. We validated HebbPlot in six case studies. The results of these case studies are novel or validating results already reported in the literature, indicating the accuracy of HebbPlot. Our results indicate that promoters have a directional chromatin signature; several marks tend to stretch downstream or upstream. H3K4me3 and H3K79me2 have clear directional distributions around active promoters. In addition, the signatures of high- and low-CpG promoters are different; H3K4me3, H3K9ac, and H3K27ac are the most different marks. When we studied the signatures of enhancers active in eight tissues, we observed that these signatures are similar, but not identical. Further, we identified some histone modifications — H3K36me3, H3K79me1, H3K79me2, and H4K8ac — that are associated with coding regions of active genes. Other marks — H4K12ac, H3K14ac, H3K27me3, and H2AK5ac — were found to be weakly associated with coding regions of inactive genes. Conclusions This study resulted in a novel software tool, HebbPlot, for learning and visualizing the chromatin signature of a genetic element. Using HebbPlot, we produced a visual catalog of the signatures of multiple genetic elements in 57 cell types available through the Roadmap Epigenomics Project. Furthermore, we made a progress toward a functional catalog consisting of 22 histone marks. In sum, HebbPlot is applicable to a wide array of studies, facilitating the deciphering of the histone code.http://link.springer.com/article/10.1186/s12859-018-2312-1Histone marksChromatin modificationsEpigenetic signaturesVisualizationArtificial neural networksHebbian learning |
| spellingShingle | Hani Z. Girgis Alfredo Velasco Zachary E. Reyes HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures BMC Bioinformatics Histone marks Chromatin modifications Epigenetic signatures Visualization Artificial neural networks Hebbian learning |
| title | HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures |
| title_full | HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures |
| title_fullStr | HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures |
| title_full_unstemmed | HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures |
| title_short | HebbPlot: an intelligent tool for learning and visualizing chromatin mark signatures |
| title_sort | hebbplot an intelligent tool for learning and visualizing chromatin mark signatures |
| topic | Histone marks Chromatin modifications Epigenetic signatures Visualization Artificial neural networks Hebbian learning |
| url | http://link.springer.com/article/10.1186/s12859-018-2312-1 |
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