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.

Clustering methods are essential to partitioning biological samples being useful to minimize the information complexity in large datasets. Tools in this context usually generates data with greed algorithms that solves some Data Mining difficulties which can degrade biological relevant information during the clustering process. The lack of standardization of metrics and consistent bases also raises questions about the clustering efficiency of some methods. Benchmarks are needed to explore the full potential of clustering methods - in which alignment-free methods stand out - and the good choice of dataset makes it essentials. Here we present a new approach to Data Mining in large protein sequences datasets, the Rapid Alignment Free Tool for Sequences Similarity Search to Groups (RAFTS 3 G), a method to clustering aiming of losing less biological information in the processes of generation groups. The strategy developed in our algorithm is optimized to be more astringent which reflects increase in accuracy and sensitivity in the generation of clusters in a wide range of similarity. RAFTS 3 G is the better choice compared to three main methods when the user wants more reliable result even ignoring the ideal threshold to clustering. In general, RAFTS 3 G is able to group up to millions of biological sequences into large datasets, which is a remarkable option of efficiency in clustering. RAFTS 3 G compared to other “standard-gold” methods in the clustering of large biological data maintains the balance between the reduction of biological information redundancy and the creation of consistent groups. We bring the binary search concept applied to grouped sequences which shows maintaining sensitivity/accuracy relation and up to minimize the time of data generated with RAFTS 3 G process.