Identifying 2'-O-methylationation sites by integrating nucleotide chemical properties and nucleotide compositions

2'-O-methylationation is an important post-transcriptional modification and plays important roles in many biological processes. Although experimental technologies have been proposed to detect 2'-O-methylationation sites, they are cost-ineffective. As complements to experimental techniques, computational methods will facilitate the identification of 2'-O-methylationation sites. In the present study, we proposed a support vector machine-based method to identify 2'-O-methylationation sites. In this method, RNA sequences were formulated by nucleotide chemical properties and nucleotide compositions. In the jackknife cross-validation test, the proposed method obtained an accuracy of 95.58% for identifying 2'-O-methylationation sites in the human genome. Moreover, the model was also validated by identifying 2'-O-methylation sites in the Mus musculus and Saccharomyces cerevisiae genomes, and the obtained accuracies are also satisfactory. These results indicate that the proposed method will become a useful tool for the research on 2'-O-methylation.