Table4_Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning.XLSX

The emergence of epitranscriptome opened a new chapter in gene regulation. 5-methylcytosine (m5C), as an important post-transcriptional modification, has been identified to be involved in a variety of biological processes such as subcellular localization and translational fidelity. Though high-throughput experimental technologies have been developed and applied to profile m5C modifications under certain conditions, transcriptome-wide studies of m5C modifications are still hindered by the dynamic and reversible nature of m5C and the lack of computational prediction methods. In this study, we introduced PEA-m5C, a machine learning-based m5C predictor trained with features extracted from the flanking sequence of m5C modifications. PEA-m5C yielded an average AUC (area under the receiver operating characteristic) of 0.939 in 10-fold cross-validation experiments based on known Arabidopsis m5C modifications. A rigorous independent testing showed that PEA-m5C (Accuracy [Acc] = 0.835, Matthews correlation coefficient [MCC] = 0.688) is remarkably superior to the recently developed m5C predictor iRNAm5C-PseDNC (Acc = 0.665, MCC = 0.332). PEA-m5C has been applied to predict candidate m5C modifications in annotated Arabidopsis transcripts. Further analysis of these m5C candidates showed that 4nt downstream of the translational start site is the most frequently methylated position. PEA-m5C is freely available to academic users at: https://github.com/cma2015/PEA-m5C.

表观转录组（epitranscriptome）的出现为基因调控研究翻开了全新篇章。5-甲基胞嘧啶（5-methylcytosine，m5C）作为一类关键的转录后修饰，现已被证实参与亚细胞定位、翻译保真度等多种生物学过程。尽管现已开发出高通量实验技术，可在特定条件下对m5C修饰进行表征分析，但受限于m5C的动态可逆特性与计算预测方法的缺失，全转录组层面的m5C修饰研究仍面临诸多阻碍。本研究中，我们提出了一款基于机器学习的m5C修饰预测工具PEA-m5C，其训练所用特征提取自m5C修饰位点的侧翼序列。在基于已知拟南芥（Arabidopsis）m5C修饰位点的10折交叉验证实验中，PEA-m5C取得了0.939的平均受试者工作特征曲线下面积（area under the receiver operating characteristic，AUC）。严格的独立测试结果显示，PEA-m5C（准确率[Acc]=0.835，马修斯相关系数[MCC]=0.688）的性能显著优于近年开发的m5C预测工具iRNAm5C-PseDNC（Acc=0.665，MCC=0.332）。研究人员已利用PEA-m5C对已注释的拟南芥转录本中的候选m5C修饰位点进行预测。对这些候选m5C修饰位点的进一步分析显示，翻译起始位点下游4个核苷酸（4nt）的位置是最常见的甲基化位点。学术用户可通过以下网址免费获取PEA-m5C工具：https://github.com/cma2015/PEA-m5C。