Identification of 2'-O-methylation Site by Investigating Multi-feature Extracting Techniques

Abstract

Background: RNA methylation is a reversible post-transcriptional modification involving numerous biological processes. Ribose 2'-O-methylation is part of RNA methylation. It has shown that ribose 2'-O-methylation plays an important role in immune recognition and other pathogenesis.

Objective: We aim to design a computational method to identify 2'-O-methylation.

Methods: Different from the experimental method, we propose a computational workflow to identify the methylation site based on the multi-feature extracting algorithm.

Results: With a voting procedure based on 7 best feature-classifier combinations, we achieved Accuracy of 76.5% in 10-fold cross-validation. Furthermore, we optimized features and input the optimized features into SVM. As a result, the AUC reached to 0.813.

Conclusion: The RNA sample, especially the negative samples, used in this study are more objective and strict, so we obtained more representative results than state-of-arts studies.

Keywords: Urinary tract infection; antibiotic resistance; beta-lactam antibiotics; iucC gene; uropathogenic E. coli; virulence.