Kim Anastasiia, Lubbers Nicholas, Steadman Christina R, Sanbonmatsu Karissa Y
Computing and AI division at Los Alamos National Laboratory, Los Alamos, NM 87544, United States.
Bioscience division at Los Alamos National Laboratory, Los Alamos, NM 87544, United States.
Bioinform Adv. 2025 Jul 23;5(1):vbaf175. doi: 10.1093/bioadv/vbaf175. eCollection 2025.
Recent advances in genomics and sequencing platforms have revolutionized our ability to create immense data sets, particularly for studying epigenetic regulation of gene expression. However, the avalanche of epigenomic data is difficult to parse for biological interpretation given nonlinear complex patterns and relationships. This attractive challenge in epigenomic data lends itself to machine learning for discerning infectivity and susceptibility. In this study, we explore over 3000 epigenomes of uninfected individuals and provide a framework to characterize the relationships among epigenetic modifiers, their modifiers, genetic loci, and specific immune cell types across all chromosomes using hierarchical clustering.
Hierarchical clustering of epigenomic data revealed consistent epigenetic patterns across chromosomes, demonstrating that variation due to epigenetic modifiers is greater than variation between cell types. Gene Ontology and KEGG pathway analyses indicated significant enrichment of genes involved in chromatin remodeling, mRNA splicing, immune responses, and the regulation of microRNAs and snoRNAs. Epigenetic modifiers frequently formed biologically relevant clusters, including the cohesin complex, RNA Polymerase II transcription factors, and PRC2 complex members. These clustering behaviors remained consistent across all chromosomes, supported by entropy analysis and high Adjusted Rand Index scores, indicating robust cross-chromosomal similarity. Co-occurrence analysis further revealed specific sets of modifiers that consistently appeared together within clusters, reflecting shared biological functions and interactions. Validation using another dataset confirmed the reproducibility of these clustering patterns and modifier co-occurrence relationships, underscoring the reliability and generalizability of the methodology.
The analysis pipeline for this study is freely available online at the GitHub repository: https://github.com/lanl/epigen.
基因组学和测序平台的最新进展彻底改变了我们创建海量数据集的能力，特别是用于研究基因表达的表观遗传调控。然而，鉴于非线性复杂模式和关系，大量的表观基因组数据难以进行生物学解释。表观基因组数据中的这一引人关注的挑战适合采用机器学习来识别感染性和易感性。在本研究中，我们探索了3000多个未感染个体的表观基因组，并提供了一个框架，使用层次聚类来表征所有染色体上表观遗传修饰因子、它们的修饰、基因座和特定免疫细胞类型之间的关系。
表观基因组数据的层次聚类揭示了跨染色体的一致表观遗传模式，表明表观遗传修饰因子引起的变异大于细胞类型之间的变异。基因本体论和KEGG通路分析表明，参与染色质重塑、mRNA剪接、免疫反应以及微小RNA和核仁小RNA调控的基因显著富集。表观遗传修饰因子经常形成生物学相关的聚类，包括黏连蛋白复合体、RNA聚合酶II转录因子和PRC2复合体成员。这些聚类行为在所有染色体上保持一致，得到熵分析和高调整兰德指数得分的支持，表明存在强大的跨染色体相似性。共现分析进一步揭示了在聚类中始终共同出现的特定修饰因子集，反映了共享的生物学功能和相互作用。使用另一个数据集进行验证证实了这些聚类模式和修饰因子共现关系的可重复性，强调了该方法的可靠性和通用性。
本研究的分析流程可在GitHub仓库上免费在线获取：https://github.com/lanl/epigen 。
