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.

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.

Chuang Ma
Professor, Doctoral Supervisor

My research interests include artificial intelligence, abiotic stress and plant breeding.