Song, Jie Zhai, Jingjing Bian, Enze Song, Yujia Yu, Jiantao Ma, Chuang Table2_Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning.XLSX <p>The emergence of epitranscriptome opened a new chapter in gene regulation. 5-methylcytosine (m<sup>5</sup>C), 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 m<sup>5</sup>C modifications under certain conditions, transcriptome-wide studies of m<sup>5</sup>C modifications are still hindered by the dynamic nature of m<sup>5</sup>C and the lack of computational prediction methods. In this study, we introduced PEA-m5C, a machine learning-based m<sup>5</sup>C predictor trained with features extracted from the flanking sequence of m<sup>5</sup>C 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 m<sup>5</sup>C 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 m<sup>5</sup>C predictor iRNAm5C-PseDNC (Acc = 0.665, MCC = 0.332). PEA-m5C has been applied to predict candidate m<sup>5</sup>C modifications in annotated Arabidopsis transcripts. Further analysis of these m<sup>5</sup>C 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.</p> AUC;Epitranscriptome;machine learning;RNA modification;RNA 5-methylcytosine 2018-12-03
    https://frontiersin.figshare.com/articles/dataset/Table2_Transcriptome-Wide_Annotation_of_m5C_RNA_Modifications_Using_Machine_Learning_XLSX/7411481
10.3389/fpls.2018.00519.s007