MOTIVATION: MirtronDB provides a comprehensive and up-to-date resource for advancing mirtron research within RNA biology. Therefore, maintaining a specialized and continuously updated resource for mirtrons is essential to support ongoing discoveries and to serve as a key reference for researchers investigating the roles of mirtrons. RESULTS: Here, we present mirtronDB 2.0, an enhanced version that expands both content and functionality. This version integrates mirtron data published between 2017 and 2025, increasing the number of documented mirtrons across various species. In addition, it incorporates newly predicted mirtrons identified through a robust pipeline that combines advanced bioinformatics and machine learning approaches, with specific coverage of six mammalian species. We have introduced new website features, including an interactive dashboard to enhance usability and facilitate intuitive data exploration. These rigorous updates consolidate mirtronDB as a key resource for mirtron to the RNA biology community. AVAILABILITY AND IMPLEMENTATION: mirtronDB can be found under http://mirtrondb.cp.utfpr.edu.br/. The complete content of Database 2.0 and the source code for the analyses are also freely available in the FigShare repository: https://figshare.com/articles/dataset/MirtronDB_version2/29344775.

The correct annotation of non-coding RNAs, especially long non-coding RNAs (lncRNAs), is still a critial challenge in genome analyses due to their highly heterogeneous characteristics. Due to this heterogeneity, transcriptome data sources can be an important factor that might affect lncRNA annotation quality. Long-read technologies now bring the potential to improve the quality of transcriptome annotation, specially in genome entities that are not “classic” coding genes. However, there is a gap regarding benchmarking studies that test if the direct use of lncRNA predictors in long-reads makes more precise identification of these transcripts. Considering that lncRNA identification tools were not trained with these reads, our study want to address: how is the performance of these tools? Are they also able to efficiently identify lncRNAs? For this, we used short and long-read data from human and selected plants transcriptomes to test our questions. We can provide evidence of where and how to make potential better approaches for the lncRNA annotation by understanding these issues.