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

Abstract

N⁶-methyladenosine (m⁶A) 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 m⁶A prediction technologies are urgent. In this work, a novel predictor based on GBDT and stacking learning is developed to identify m⁶A 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 m⁶A 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 m⁶A sites. In addition, all the datasets and codes are currently available at https://github.com/Wang-Jinyue/M6A-GSMS.

Communicated by Ramaswamy H. Sarma

Keywords:

• N⁶-methyladenosine site
• RNA methylation
• GBDT
• Stacking learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Research involving human participants and/or animals

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

There was no human participant and consent was not required.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (No.12101480), the Natural Science Basic Research Program of Shaanxi (No. 2021JM-115), and the Fundamental Research Funds for the Central Universities (No. JB210715).