Tandy School of Computer Science, University of Tulsa, 800 South Tucker Drive, Tulsa, 74104-9700, OK, USA.
BMC Bioinformatics. 2018 Sep 3;19(1):310. doi: 10.1186/s12859-018-2312-1.
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.
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.
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.
组蛋白修饰在基因调控、遗传、印迹和许多人类疾病中发挥着重要作用。组蛋白密码非常复杂，包含超过 100 种标记。因此，生物学家需要计算工具来描述代表数千个区域周围数十种染色质标记分布的一般特征。
为此，我们开发了一种软件工具 HebbPlot，它利用赫布神经网络从具有共同功能的区域学习一般的染色质特征。赫布网络可以学习数十种标记与数千个区域之间的关联。HebbPlot 将特征表示为数字图像，易于解释。此外，HebbPlot 生成的特征可以进行定量比较。我们在六个案例研究中验证了 HebbPlot。这些案例研究的结果是新颖的或已经在文献中报道的验证结果，表明了 HebbPlot 的准确性。我们的结果表明，启动子具有定向染色质特征；几种标记倾向于向下游或上游延伸。H3K4me3 和 H3K79me2 在活性启动子周围具有明显的定向分布。此外，高和低 CpG 启动子的特征不同；H3K4me3、H3K9ac 和 H3K27ac 是最不同的标记。当我们研究在八种组织中活跃的增强子的特征时，我们观察到这些特征相似，但不完全相同。此外，我们确定了一些与活性基因的编码区相关的组蛋白修饰 - H3K36me3、H3K79me1、H3K79me2 和 H4K8ac。其他标记 - H4K12ac、H3K14ac、H3K27me3 和 H2AK5ac - 被发现与非活性基因的编码区弱相关。
本研究开发了一种新的软件工具 HebbPlot，用于学习和可视化遗传元件的染色质特征。使用 HebbPlot，我们制作了 Roadmap Epigenomics Project 中 57 种细胞类型的多种遗传元件特征的可视化目录。此外，我们朝着由 22 种组蛋白标记组成的功能目录迈出了一步。总之，HebbPlot 适用于广泛的研究，有助于破译组蛋白密码。
