Staem5: A novel computational approachfor accurate prediction of m5C site

Di Chai; Cangzhi Jia; Jia Zheng; Quan Zou; Fuyi Li

doi:10.1016/j.omtn.2021.10.012

Staem5: A novel computational approachfor accurate prediction of m5C site

Mol Ther Nucleic Acids. 2021 Oct 20:26:1027-1034. doi: 10.1016/j.omtn.2021.10.012. eCollection 2021 Dec 3.

Authors

Di Chai¹, Cangzhi Jia¹, Jia Zheng¹, Quan Zou², Fuyi Li³

Affiliations

¹ School of Science, Dalian Maritime University, Dalian 116026, China.
² Yangtze Delta Region Institute (Quzhou), Quzhou, China.
³ Department of Microbiology and Immunology, The Peter Doherty Institute for Infection and Immunity, The University of Melbourne, 792 Elizabeth Street, Melbourne, VIC 3000, Australia.

Abstract

5-Methylcytosine (m5C) is an important post-transcriptional modification that has been extensively found in multiple types of RNAs. Many studies have shown that m5C plays vital roles in many biological functions, such as RNA structure stability and metabolism. Computational approaches act as an efficient way to identify m5C sites from high-throughput RNA sequence data and help interpret the functional mechanism of this important modification. This study proposed a novel species-specific computational approach, Staem5, to accurately predict RNA m5C sites in Mus musculus and Arabidopsis thaliana. Staem5 was developed by employing feature fusion tactics to leverage informatic sequence profiles, and a stacking ensemble learning framework combined five popular machine learning algorithms. Extensive benchmarking tests demonstrated that Staem5 outperformed state-of-the-art approaches in both cross-validation and independent tests. We provide the source code of Staem5, which is publicly available at https://github.com/Cxd-626/Staem5.git.

Keywords: RNA modification; ensemble learning; m5C sites; machine learning; sequence analysis; stacking.