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.

MOTIVATION: Nanobodies are single domain antibodies derived from the unique heavy chain-only immunoglobulins of the camelid family, and have broad applications in biosciences, including as imaging and diagnostic agents. The generation of nanobodies is a multi-stage experimental process for which we have developed AntigenApp a laboratory data management system for nanobody generation and sequence analysis. RESULTS: AntigenApp is a web application and database built to capture data generated during all steps of the nanobody discovery pipeline and to provide built-in analysis capabilities of the experimental results. This includes enzyme-linked immunosorbent assay optical density measurements, sequencing results, nanobody domain labelling, and search/clustering capabilities using protein BLAST. The tool incorporates a standardized naming convention that is automatically applied to nanobodies stored in the database and we envisage the tool being of interest to the wider nanobody community for in-house use. AVAILABILITY AND IMPLEMENTATION: AntigenApp is available from https://github.com/rosalindfranklininstitute/antigen-app under the Apache 2.0 licence and published https://doi.org/10.5281/zenodo.17397055.

Abstract Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available.