Make No Mistake! Why Do Tools Make Incorrect Long Non-coding RNA Classification?

Abstract

Long non-coding RNAs (lncRNAs) play important roles in various biological processes, and their accurate identification is essential for understanding their functions and potential therapeutic applications. In a previous study, we assessed the impact of short and long reads sequencing technologies on long non-coding RNA computational identification in human and plant data. We provided evidence of where and how to make potential better approaches for the lncRNA classification. In this follow-up study, we investigate the misclassified sequences by five machine learning tools for lncRNA classification in humans to understand the reasons behind the failures of the tools. Our analysis suggests that the primary cause for the failures of these tools is the overlap of two coding regions by lncRNAs, similar to a chimeric sequence. Furthermore, we emphasize the need to view genes as transcriptional units, as the transcript will define the gene function. These insights underscore the need for further refinement and improvement of these tools to enhance their accuracy and reliability in lncRNA prediction and classification, ultimately contributing to a better understanding of the role of lncRNAs in various biological processes and potential therapeutic applications.