Xiao Ming, Xiao Yi, Yu Jun, Zhang Le
College of Computer Science, Sichuan University, Chengdu, China.
Tianfu Engineering-oriented Numerical Simulation and Software Innovation Center, Chengdu, China.
Front Genet. 2024 Mar 13;15:1367731. doi: 10.3389/fgene.2024.1367731. eCollection 2024.
CpG island (CGI) methylation is one of the key epigenomic mechanisms for gene expression regulation and chromosomal integrity. However, classical CGI prediction methods are neither easy to locate those short and position-sensitive CGIs (CpG islets), nor investigate genetic and expression pattern for CGIs under different CpG position- and interval- sensitive parameters in a genome-wide perspective. Therefore, it is urgent for us to develop such a bioinformatic algorithm that not only can locate CpG islets, but also provide CGI methylation site annotation and functional analysis to investigate the regulatory mechanisms for CGI methylation. This study develops Human position-defined CGI prediction method to locate CpG islets using high performance computing, and then builds up a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate these functions into PCGIMA to provide relevant online computing and visualization service. The main results include: (1) Human position-defined CGI prediction method is more efficient to predict position-defined CGIs with multiple consecutive (d) values and locate more potential short CGIs than previous CGI prediction methods. (2) Our annotation and analysis method not only can investigate the connections between position-defined CGI methylation and gene expression specificity from a genome-wide perspective, but also can analysis the potential association of position-defined CGIs with gene functions. (3) PCGIMA (http://www.combio-lezhang.online/pcgima/home.html) provides an easy-to-use analysis and visualization platform for human CGI prediction and methylation. This study not only develops Human position-defined CGI prediction method to locate short and position-sensitive CGIs (CpG islets) using high performance computing to construct MR-CpGCluster algorithm, but also a novel human genome annotation and analysis method to investigate the connections among CGI, gene expression and methylation. Finally, we integrate them into PCGIMA for online computing and visualization.
CpG岛（CGI）甲基化是基因表达调控和染色体完整性的关键表观基因组机制之一。然而，传统的CGI预测方法既不易定位那些短的且对位置敏感的CGI（CpG小胰岛），也难以在全基因组范围内研究不同CpG位置和间隔敏感参数下CGI的遗传和表达模式。因此，我们迫切需要开发一种生物信息学算法，它不仅能够定位CpG小胰岛，还能提供CGI甲基化位点注释和功能分析，以研究CGI甲基化的调控机制。本研究开发了人类位置定义的CGI预测方法，利用高性能计算定位CpG小胰岛，然后建立了一种新的人类基因组注释和分析方法，以研究CGI、基因表达和甲基化之间的联系。最后，我们将这些功能整合到PCGIMA中，提供相关的在线计算和可视化服务。主要结果包括：（1）人类位置定义的CGI预测方法在预测具有多个连续（d）值的位置定义的CGI方面更有效，并且比以前的CGI预测方法能定位更多潜在的短CGI。（2）我们的注释和分析方法不仅能够从全基因组角度研究位置定义的CGI甲基化与基因表达特异性之间的联系，还能分析位置定义的CGI与基因功能的潜在关联。（3）PCGIMA（http://www.combio-lezhang.online/pcgima/home.html）为人类CGI预测和甲基化提供了一个易于使用的分析和可视化平台。本研究不仅开发了人类位置定义的CGI预测方法，利用高性能计算定位短的且对位置敏感的CGI（CpG小胰岛）以构建MR-CpGCluster算法，还开发了一种新的人类基因组注释和分析方法，以研究CGI、基因表达和甲基化之间的联系。最后，我们将它们整合到PCGIMA中进行在线计算和可视化。
