Wang Qi, Wu Yonghe, Vorberg Tim, Eils Roland, Herrmann Carl
Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg, Germany.
Faculty of Biosciences, Heidelberg University, Heidelberg, Germany.
Front Genet. 2021 May 31;12:664654. doi: 10.3389/fgene.2021.664654. eCollection 2021.
Regulation of gene expression through multiple epigenetic components is a highly combinatorial process. Alterations in any of these layers, as is commonly found in cancer diseases, can lead to a cascade of downstream effects on tumor suppressor or oncogenes. Hence, deciphering the effects of epigenetic alterations on regulatory elements requires innovative computational approaches that can benefit from the huge amounts of epigenomic datasets that are available from multiple consortia, such as Roadmap or BluePrint. We developed a software tool named IRENE (Integrative Ranking of Epigenetic Network of Enhancers), which performs quantitative analyses on differential epigenetic modifications through an integrated, network-based approach. The method takes into account the additive effect of alterations on multiple regulatory elements of a gene. Applying this tool to well-characterized test cases, it successfully found many known cancer genes from publicly available cancer epigenome datasets.
通过多种表观遗传成分对基因表达进行调控是一个高度组合的过程。正如在癌症疾病中常见的那样,这些层面中的任何一个发生改变,都可能导致对肿瘤抑制基因或癌基因产生一系列下游效应。因此,解读表观遗传改变对调控元件的影响需要创新的计算方法,这些方法能够从多个联盟(如路线图或蓝图)提供的大量表观基因组数据集中受益。我们开发了一种名为IRENE(增强子表观遗传网络综合排名)的软件工具,它通过基于网络的综合方法对差异表观遗传修饰进行定量分析。该方法考虑了改变对基因多个调控元件的累加效应。将此工具应用于特征明确的测试案例,它成功地从公开可用的癌症表观基因组数据集中发现了许多已知的癌症基因。