Engineering Research Center of Intelligent Control for Underground Space, Ministry of Education, China University of Mining and Technology, Xuzhou, 221116, China.
School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, 221116, China.
BMC Bioinformatics. 2020 Oct 9;21(1):447. doi: 10.1186/s12859-020-03787-w.
Recent studies have shown that N-methyladenosine (mA) plays a critical role in numbers of biological processes and complex human diseases. However, the regulatory mechanisms of most methylation sites remain uncharted. Thus, in-depth study of the epi-transcriptomic patterns of mA may provide insights into its complex functional and regulatory mechanisms.
Due to the high economic and time cost of wet experimental methods, revealing methylation patterns through computational models has become a more preferable way, and drawn more and more attention. Considering the theoretical basics and applications of conventional clustering methods, an RNA Expression Weighted Iterative Signature Algorithm (REW-ISA) is proposed to find potential local functional blocks (LFBs) based on MeRIP-Seq data, where sites are hyper-methylated or hypo-methylated simultaneously across the specific conditions. REW-ISA adopts RNA expression levels of each site as weights to make sites of lower expression level less significant. It starts from random sets of sites, then follows iterative search strategies by thresholds of rows and columns to find the LFBs in mA methylation profile. Its application on MeRIP-Seq data of 69,446 methylation sites under 32 experimental conditions unveiled 6 LFBs, which achieve higher enrichment scores than ISA. Pathway analysis and enzyme specificity test showed that sites remained in LFBs are highly relevant to the mA methyltransferase, such as METTL3, METTL14, WTAP and KIAA1429. Further detailed analyses for each LFB even showed that some LFBs are condition-specific, indicating that methylation profiles of some specific sites may be condition relevant.
REW-ISA finds potential local functional patterns presented in mA profiles, where sites are co-methylated under specific conditions.
最近的研究表明，N6-甲基腺苷（m6A）在许多生物过程和复杂人类疾病中发挥着关键作用。然而，大多数甲基化位点的调控机制仍未被发现。因此，深入研究 m6A 的 epi 转录组模式可能有助于深入了解其复杂的功能和调控机制。
由于湿实验方法的经济和时间成本较高，通过计算模型揭示甲基化模式已成为一种更可取的方法，并且越来越受到关注。考虑到传统聚类方法的理论基础和应用，提出了一种基于 MeRIP-Seq 数据的 RNA 表达加权迭代特征算法（REW-ISA），用于寻找潜在的局部功能块（LFBs），这些 LFBs在特定条件下同时发生超甲基化或低甲基化。REW-ISA 采用每个位点的 RNA 表达水平作为权重，使表达水平较低的位点不太重要。它从随机位点集开始，然后通过行和列的阈值迭代搜索策略找到 m6A 甲基化谱中的 LFBs。它在 32 个实验条件下的 69446 个甲基化位点的 MeRIP-Seq 数据上的应用揭示了 6 个 LFBs，其富集得分高于 ISA。通路分析和酶特异性测试表明，留在 LFBs 中的位点与 m6A 甲基转移酶高度相关，如 METTL3、METTL14、WTAP 和 KIAA1429。对每个 LFB 进行更详细的分析甚至表明，一些 LFBs 是条件特异性的，这表明一些特定位点的甲基化谱可能与条件有关。
REW-ISA 发现了 m6A 谱中存在的潜在局部功能模式，其中在特定条件下，位点发生共甲基化。
