Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China.
Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University, Beijing 100191, China; MOE Key Lab of Cardiovascular Sciences, Peking University, Beijing 100191, China.
Methods. 2022 Jul;203:322-327. doi: 10.1016/j.ymeth.2022.01.007. Epub 2022 Jan 25.
Epitranscriptomic mA methylation is shown to mediate extensive regulations under the context of various RNA binding protein (RBP) readers. With mA methylation data has reached a sizable scale, the functional context-aware analysis of mA profiles is becoming more feasible and demanded. In this study, we employed graph regularized non-negative matrix factorization (GNMF) for mA profile analysis and comparison, where the RBP binding preference of mA sites were incorporated as the functional context-based graph constraint term. Compared to the baseline non-negative matrix factorization (NMF) method, this GNMF-based method could better capture the distinctions in multiple functional characteristics between different group of mA sites, including but not limited to the associated biological pathways and disease genes. We further established m6Adecom, an online tool that can be used for correlation and enrichment analysis of mA profiles using the matrix decomposition result from GNMF, and gene set enrichment analysis based on the high-score mA sites. m6Adecom is freely accessible at http://www.rnanut.net/m6adecom.
m6A 修饰的转录后 RNA 甲基化被证明在各种 RNA 结合蛋白 (RBP) 阅读器的背景下介导广泛的调控。随着 mA 甲基化数据已经达到相当大的规模，mA 谱的功能上下文感知分析变得更加可行和迫切。在这项研究中，我们采用了基于图正则化非负矩阵分解 (GNMF) 进行 mA 谱分析和比较，其中将 mA 位点的 RBP 结合偏好纳入基于功能上下文的图约束项。与基线非负矩阵分解 (NMF) 方法相比，这种基于 GNMF 的方法可以更好地捕捉不同 mA 位点组之间在多种功能特征上的区别，包括但不限于相关的生物途径和疾病基因。我们进一步建立了 m6Adecom，这是一个在线工具，可以使用 GNMF 的矩阵分解结果进行 mA 谱的相关性和富集分析，以及基于高分 mA 位点的基因集富集分析。m6Adecom 可在 http://www.rnanut.net/m6adecom 免费访问。
