M6A-GSMS: Computational identification of N6-methyladenosine sites with GBDT and stacking learning in multiple species

• Published in: Journal of biomolecular structure & dynamics 2022-01, Vol.40 (22), p.12380-12391
• Main Authors: Zhang, Shengli, Wang, Jinyue, Li, Xinjie, Liang, Yunyun
• Format: Article
• Language: English
• Subjects: GBDT; methyladenosine site; RNA methylation; Stacking learning
• ISSN: 0739-1102; 1538-0254
• DOI: 10.1080/07391102.2021.1970628

N 6 -methyladenosine (m 6 A) is one of the most abundant forms of RNA methylation modifications currently known. It involves a wide range of biological processes, including degradation, stability, alternative splicing, etc. Therefore, the development of convenient and efficient m 6 A prediction technologies are urgent. In this work, a novel predictor based on GBDT and stacking learning is developed to identify m 6 A sites, which is called M6A-GSMS. To achieve accurate prediction, we explore RNA sequence information from four aspects: correlation, structure, physicochemical properties and pseudo ribonucleic acid composition. After using the GBDT algorithm for feature selection, a stacking model is constructed by combining seven basic classifiers. Compared with other state-of-the-art methods, the results show that M6A-GSMS can obtain excellent performance for identifying the m 6 A sites. The prediction accuracy of A.thaliana, D.melanogaster, M.musculus, S.cerevisiae and Human reaches 88.4%, 60.8%, 80.5%, 92.4% and 61.8%, respectively. This method provides an effective prediction for the investigation of m 6 A sites. In addition, all the datasets and codes are currently available at https://github.com/Wang-Jinyue/M6A-GSMS . Communicated by Ramaswamy H. Sarma