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.
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Song, J., Zhai, J., Bian, E., Song, Y., Yu, J., Ma, C. (2018). Transcriptome-Wide Annotation of m5C RNA Modifications Using Machine Learning Frontiers in Plant Science 9(), 519. https://dx.doi.org/10.3389/fpls.2018.00519