iMethyl-STTNC: Identification of N-methyladenosine sites by extending the idea of SAAC into Chou's PseAAC to formulate RNA sequences.

N- methyladenosine (mA) is a vital post-transcriptional modification, which adds another layer of epigenetic regulation at RNA level. It chemically modifies mRNA that effects protein expression. RNA sequence contains many genetic code motifs (GAC). Among these codes, identification of methylated or not methylated GAC motif is highly indispensable. However, with a large number of RNA sequences generated in post-genomic era, it becomes a challenging task how to accurately and speedily characterize these sequences. In view of this, the concept of an intelligent is incorporated with a computational model that truly and fast reflects the motif of the desired classes. An intelligent computational model "iMethyl-STTNC" model is proposed for identification of methyladenosine sites in RNA. In the proposed study, four feature extraction techniques, such as; Pseudo-dinucleotide-composition, Pseudo-trinucleotide-composition, split-trinucleotide-composition, and split-tetra-nucleotides-composition (STTNC) are utilized for genuine numerical descriptors. Three different classification algorithms including probabilistic neural network, Support vector machine (SVM), and K-nearest neighbor are adopted for prediction. After examining the outcomes of prediction model on each feature spaces, SVM using STTNC feature space reported the highest accuracy of 69.84%, 91.84% on dataset1 and dataset2, respectively. The reported results show that our proposed predictor has achieved encouraging results compared to the present approaches, so far in the research. It is finally reckoned that our developed model might be beneficial for in-depth analysis of genomes and drug development.