StackRAM: a cross-species method for identifying RNA N6-methyladenosine sites based on stacked ensemble

N-6-methyladenosine (m(6)A) is a prevalent RNA methylation modification, which plays an important role in various biological processes. Accurate identification of the m6A sites is fundamental to understand the biological functions and mechanisms of the modification deeply. However, the experimental methods for detecting m(6)A sites are usually time-consuming and expensive, and various computational methods have been developed to identify m6A sites in RNA. This paper proposes a novel cross-species computational method StackRAM using machine learning algorithms to identify the m(6)A sites in Saccharomyces cerevisiae (S. cerevisiae), Homo sapiens (H. sapiens), Arabidopsis thaliana (A. thaliana) and Mus musculus (M. musculus). First, the RNA sequence features are extracted through binary encoding, chemical property, nucleotide frequency, k-mer nucleotide frequency, pseudo dinucleotide composition, and position-specific trinucleotide propensity, and the initial feature dataset is obtained by feature fusion. Second, the Elastic Net is used for the first time to filter redundant and noisy information and retain important features for m(6)A sites classification. Finally, the base-classifiers output probabilities and the optimal feature subset corresponding to the Elastic Net are combined, and the combination feature is put into the second-stage meta-classifier SVM. The result of jackknife test on training dataset S. cerevisiae indicates that the prediction performance of StackRAM is superior to the current state-of-the-art methods. Prediction accuracy of StackRAM for independent test datasets H. sapiens, A. thaliana and M. musculus reach 92.30%, 87.06% and 91.86%, respectively. Therefore, StackRAM has developing potential in cross-species prediction and can be a useful method for identifying m(6)A sites.

Automated summary

This paper proposes a novel cross-species computational method StackRAM for identifying m(6)A sites in RNA. The method utilizes machine learning algorithms and features fusion and selection techniques to improve prediction accuracy. Experimental results demonstrate that StackRAM has superior prediction performance in multiple species and is of great significance for studying the biological functions and mechanisms of m(6)A modification.