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m6AmPred: Identifying RNA N6, 2′-O-dimethyladenosine (m6Am) sites based on sequence-derived information

    • University of Liverpool
    • Xi'an Jiaotong-Liverpool University

    Research output: Contribution to journal › Article › peer-review

    21 Citations (Scopus)

    Abstract

    N6,2′-O-dimethyladenosine (m6Am) is a reversible modification widely occurred on varied RNA molecules. The biological function of m6Am is yet to be known though recent studies have revealed its influences in cellular mRNA fate. Precise identification of m6Am sites on RNA is vital for the understanding of its biological functions. We present here m6AmPred, the first web server for in silico identification of m6Am sites from the primary sequences of RNA. Built upon the eXtreme Gradient Boosting with Dart algorithm (XgbDart) and EIIP-PseEIIP encoding scheme, m6AmPred achieved promising prediction performance with the AUCs greater than 0.954 when tested by 10-fold cross-validation and independent testing datasets. To critically test and validate the performance of m6AmPred, the experimentally verified m6Am sites from two data sources were cross-validated. The m6AmPred web server is freely accessible at: https://www.xjtlu.edu.cn/biologicalsciences/m6am, and it should make a useful tool for the researchers who are interested in N6,2′-O-dimethyladenosine RNA modification.

    Original languageEnglish
    Pages (from-to)328-334
    Number of pages7
    JournalMethods
    Volume203
    DOIs
    Publication statusPublished - Jul 2022

    Keywords

    • EIIP-PseEIIP
    • Feature analysis
    • N6,2′-O-dimethyladenosine (mA)
    • Sequence-derived features
    • eXtreme Gradient Boosting (XgbDart)

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