We developed PEA-m5C, an accurate transcriptome-wide m5C modification predictor under machine learning framework with random forest algorithm. PEA-m5C was trained with features from the flanking sequences of m5C modifications. In addition, we also deposited all the candidate m5C modification sites in the Ara-m5C database for follow-up functional mechanism researches. Finally, in order to maximize the usage of PEA-m5C, we implement it into a cross-platform, user-friendly and interactive interface and an R package named PEA-m5C based R statistical language and JAVA programming language, which may advance functional researches of m5C.
Figure. illustrates the workflow of PEA-m5C, which consists of three phases, namely, (A) model construction, (B) model optimization, and © model prediction. Model construction and optimization were performed on the DatasetCV.
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- Please cite the following article:
Song J, Zhai J, Bian E, et al. Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning.[J]. Frontiers in Plant Science, 2018, 9:519.