iRNA(m6A)-PseDNC: Identifying N-methyladenosine sites using pseudo dinucleotide composition.

As a prevalent post-transcriptional modification, N-methyladenosine (mA) plays key roles in a series of biological processes. Although experimental technologies have been developed and applied to identify mA sites, they are still cost-ineffective for transcriptome-wide detections of mA. As good complements to the experimental techniques, some computational methods have been proposed to identify mA sites. However, their performance remains unsatisfactory. In this study, we firstly proposed an Euclidean distance based method to construct a high quality benchmark dataset. By encoding the RNA sequences using pseudo nucleotide composition, a new predictor called iRNA(m6A)-PseDNC was developed to identify mA sites in the Saccharomyces cerevisiae genome. It has been demonstrated by the 10-fold cross validation test that the performance of iRNA(m6A)-PseDNC is superior to the existing methods. Meanwhile, for the convenience of most experimental scientists, established at the site http://lin-group.cn/server/iRNA(m6A)-PseDNC.php is its web-server, by which users can easily get their desired results without need to go through the detailed mathematics. It is anticipated that iRNA(m6A)-PseDNC will become a useful high throughput tool for identifying mA sites in the S. cerevisiae genome.