ArticleNucleic acids research2022
A large-scale benchmark study of tools for the classification of protein-coding and non-coding RNAs.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- HyLnc: a hybrid deep learning and feature-based approach for long non-coding RNA prediction.RNA biology · 2026Article
- Uncertainty-aware benchmarking reveals ambiguous transcripts in mRNA-lncRNA classification.bioRxiv : the preprint server for biology · 2026Article
- Metamorphosis and lncRNAs: A Close Relationship.Genesis (New York, N.Y. : 2000) · 2026Review
- Targeting the nuclear factor kappa B pathway for the medical management of intervertebral disc degeneration.Frontiers in neuroscience · 2026Review
- LncPlankton: a comprehensive database of candidate lncRNAs from marine microbial eukaryotes.NAR genomics and bioinformatics · 2025Article
- Interpretation of RNA Universe and Coding Potential Using IntRNA.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Evaluating computational tools for protein-coding sequence detection: Are they up to the task?RNA (New York, N.Y.) · 2025Article
- Comparison and benchmark of deep learning methods for non-coding RNA classification.PLoS computational biology · 2024Article
- Optimizing protein sequence classification: integrating deep learning models with Bayesian optimization for enhanced biological analysis.BMC medical informatics and decision making · 2024Article
- Role of noncoding RNAs in orthodontic tooth movement: new insights into periodontium remodeling.Journal of translational medicine · 2023Review
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Authors and funding
2 authors.
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Abstract
Identification of protein-coding and non-coding transcripts is paramount for understanding their biological roles. Computational approaches have been addressing this task for over a decade; however, generalized and high-performance models are still unreliable. This benchmark study assessed the performance of 24 tools producing >55 models on the datasets covering a wide range of species. We have collected 135 small and large transcriptomic datasets from existing studies for comparison and identified the potential bottlenecks hampering the performance of current tools. The key insights of this study include lack of standardized training sets, reliance on homogeneous training data, gradual changes in annotated data, lack of augmentation with homology searches, the presence of false positives and negatives in datasets and the lower performance of end-to-end deep learning models. We also derived a new dataset, RNAChallenge, from the benchmark considering hard instances that may include potential false alarms. The best and least well performing models under- and overfit the dataset, respectively, thereby serving a dual purpose. For computational approaches, it will be valuable to develop accurate and unbiased models. The identification of false alarms will be of interest for genome annotators, and experimental study of hard RNAs will help to untangle the complexity of the RNA world.
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