iRNA-2OM: A Sequence-Based Predictor for Identifying 2'-O-Methylation Sites in Homo sapiens.

Journal: Journal of computational biology : a journal of computational molecular cell biology
PMID: 30113871

Abstract

2'-O-methylation plays an important biological role in gene expression. Owing to the explosive increase in genomic sequencing data, it is necessary to develop a method for quickly and efficiently identifying whether a sequence contains the 2'-O-methylation site. As an additional method to the experimental technique, a computational method may help to identify 2'-O-methylation sites. In this study, based on the experimental 2'-O-methylation data of Homo sapiens, we proposed a support vector machine-based model to predict 2'-O-methylation sites in H. sapiens. In this model, the RNA sequences were encoded with the optimal features obtained from feature selection. In the fivefold cross-validation test, the accuracy reached 97.95%.

Authors

  • Hui Yang
    Department of Neurology, The Second Affiliated Hospital of Guizhou University of Chinese Medicine, Guiyang, China.
  • Hao Lv
    1 Key Laboratory for Neuro-Information of Ministry of Education, School of Life Science and Technology, Center for Informational Biology, University of Electronic Science and Technology of China , Chengdu, China .
  • Hui Ding
    Medical School, Huanghe Science & Technology University, Zhengzhou 450063, PR China.
  • Wei Chen
    Department of Urology, Zigong Fourth People's Hospital, Sichuan, China.
  • Hao Lin
    Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou, Zhejiang, China.

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