A novel binary k-mer approach for classification of coding and non-coding RNAs across diverse species
Classification among coding (CDS) and non-coding RNA (ncRNA) sequences is a challenge and several machine learning models have been developed for the same. Since the frequency of curated coding sequences is many-folds as compared to that of the ncRNAs, we devised a novel approach to work with the complete datasets from fifteen diverse species. In our proposed novel binary approach, we replaced all the As and Ts with 0s and Gs and Cs with 1s to obtain a binary form of coding and ncRNAs. The k-mer analysis of these binary sequences revealed that the frequency of binary patterns among the coding and ncRNAs can be used as features to distinguish among them. Using insights from these distinguishing frequencies, we used k-nearest neighbour classifier to classify among them. Our strategy is not only time-efficient but leads to significantly increased performance metrics including Matthews correlation coefficient (MCC) for some species like P. paniscus, M. mulatta, M. lucifugus, G. gallus, C. japonica, C. abingdonii, A. carolinensis, D. melanogaster and C. elegans when compared with the conventional ATGC approach. Additionally, we also show that the values of MCC obtained for diverse species tested on the model based on H. sapiens correlated with the geological evolutionary timeline thereby further strengthening our approach. Therefore, we propose that CDS and ncRNAs can be efficiently classified using "2-character" frequency as compared to "4-character" frequency of ATGC approach. Thus, our highly efficient binary approach can replace the more complex ATGC approach successfully.