TetraRNA, a tetra-class machine learning model for deciphering the coding potential derivation of RNA world - Hanrui Bai; Jie Wang; Xiaoke Jiang; Zhen Guo; Wenjing Yang; Zitian Yang; Jing Li; Changning Liu (Computational and Structural Biotechnology Journal)
CncRNAs (coding and noncoding RNAs) are a class of bifunctional RNAs that that has both coding and noncoding biological activity. An increasing number of cncRNAs are being identified, prompting reassessment of our knowledge of RNA. However, most existing RNA classification tools are based on binary classification models which are not effective in distinguishing cncRNAs from mRNAs or long noncoding RNAs (lncRNAs). Our statistical analysis demonstrated that mRNA-derived cncRNAs (untranslated mRNAs, untr-mRNAs) and lncRNA-derived cncRNAs (translated ncRNAs, tr-ncRNAs) do not fall in the same cluster. Therefore, in this study, we devised a novel tetra-class RNA classification model that is systematically optimized for RNA feature extraction. According to our model, all human RNAs can be reclassified into one of four categories — mRNA, untr-mRNA, lncRNA, and tr-ncRNA — representing a novel RNA classification system and allowing the discovery of more potential cncRNAs. Further analysis revealed significant differences among the four types of RNAs in tissue-specific expression, functional annotation, sequence composition, and other factors, providing insights into their divergent evolution trajectories. Moreover, investigation of the small tr-ncRNA peptides demonstrated that their evolution is coordinated with that of the the conserved functional small RNAs associated with them. All analysis results have been integrated into a database — TetraRNADB accessible online (http://tetrarnadb.liu-lab.com/).