Interpretation of RNA Universe and Coding Potential Using IntRNA

This study constructs a multi‐channel deep learning framework IntRNA to interpret RNA universe and coding potential. IntRNA consistently performed the best among existing methods in various benchmarks. Moreover, IntRNA’s interpretability is also validated by real‐world analysis, which found some features indicating long‐distance contact between nucleobase pair to be critical in determining coding potential. Abstract The interpretation of RNA universe and coding potential are long‐standing issues in modern RNA studies, and three crucial questions remain unanswered: a) how to detect and interpret the coding potential of RNA, b) how to annotate the sophisticated taxonomy of the sncRNAs, and c) how to successfully distinguish between circular and linear lncRNAs. In this study, a multi‐channel deep learning framework, IntRNA, is thus constructed to interpret RNA universe and coding potential. First, a large number of RNA encoding features are proposed, which dramatically enlarged the available feature space. Second, a method realizing image‐like representation of RNA sequences is developed to describe the intrinsic correlation among the encoding features generated above. Third, a dual‐path model is constructed, which consistently performed the best among existing methods in various benchmarks. IntRNA’s interpretability is also validated by analysis, and all source codes are accessible at: https://idrblab.org/intrna/ and https://github.com/idrblab/intrna. This study constructs a multi-channel deep learning framework IntRNA to interpret RNA universe and coding potential. IntRNA consistently performed the best among existing methods in various benchmarks. Moreover, IntRNA ’s interpretability is also validated by real-world analysis, which found some features indicating long-distance contact between nucleobase pair to be critical in determining coding potential. Abstract The interpretation of RNA universe and coding potential are long-standing issues in modern RNA studies, and three crucial questions remain unanswered: a) how to detect and interpret the coding potential of RNA, b) how to annotate the sophisticated taxonomy of the sncRNAs, and c) how to successfully distinguish between circular and linear lncRNAs. In this study, a multi-channel deep learning framework, IntRNA, is thus constructed to interpret RNA universe and coding potential. First, a large number of RNA encoding features are proposed, which dramatically enlarged the available feature space. Second, a method realizing image-like representation of RNA sequences is developed to describe the intrinsic correlation among the encoding features generated above. Third, a dual-path model is constructed, which consistently performed the best among existing methods in various benchmarks. IntRNA ’s interpretability is also validated by analysis, and all source codes are accessible at: https://idrblab.org/intrna/ and https://github.com/idrblab/intrna. Advanced Science, Volume 12, Issue 43, November 20, 2025.