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

Jie Jiang, Bowen Song*, Kunqi Chen, Zhiliang Lu, Rong Rong, Yu Zhong, Jia Meng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 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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