Abstract：
As the most abundant mRNA modification, m6A controls and influences many aspects of mRNA metabolism including the mRNA stability and degradation. However, the role of specific m6A sites in regulating gene expression still remains unclear. In additional, the multicollinearity problem caused by the correlation of methylation level of multiple m6A sites in each gene could influence the prediction performance. To address the above challenges, we propose an elastic-net regularized negative binomial regression model (called m6Aexpress-enet) to predict which m6A site could potentially regulate its gene expression. Comprehensive evaluations on simulated datasets demonstrate that m6Aexpress-enet could achieve the top prediction performance. Applying m6Aexpress-enet on real MeRIP-seq data from human lymphoblastoid cell lines, we have uncovered the complex regulatory pattern of predicted m6A sites and their unique enrichment pathway of the constructed co-methylation modules. m6Aexpress-enet proves itself as a powerful tool to enable biologists to discover the mechanism of m6A regulatory gene expression. Furthermore, the source code and the step-by-step implementation of m6Aexpress-enet is freely accessed at https://github.com/tengzhangs/m6Aexpress-enet.
摘要：
作为最丰富的mRNA修饰，m6A控制和影响mRNA代谢的许多方面，包括mRNA的稳定性和降解。然而,特定m6A位点在调节基因表达中的作用仍不清楚。Inadditional,每个基因中多个m6A位点甲基化水平的相关性引起的多重共线性问题可能会影响预测性能。为应对上述挑战,我们提出了一个弹性网络正则化负二项回归模型（称为m6Aexpress-enet）来预测哪个m6A位点可能潜在地调节其基因表达。对模拟数据集的综合评估表明，m6Aexpress-enet可以达到最高的预测性能。将m6Aexpress-enet应用于来自人类淋巴母细胞细胞系的真实MeRIP-seq数据，我们已经发现了预测的m6A位点的复杂调控模式及其所构建的共甲基化模块的独特富集途径。m6Aexpress-enet证明自己是使生物学家能够发现m6A调控基因表达机制的强大工具。此外，m6Aexpress-enet的源代码和分步实现可在https://github.com/tengzhangs/m6Aexpress-enet上自由访问。
