Biology is generating more data than ever. As a result, there is an ever increasing number of publicly available databases that analyse, integrate and summarize the available data, providing an invaluable resource for the biological community. As this trend continues, there is a pressing need to organize, catalogue and rate these resources, so that the information they contain can be most effectively exploited. MetaBase (MB) (http://MetaDatabase.Org) is a community-curated database containing more than 2000 commonly used biological databases. Each entry is structured using templates and can carry various user comments and annotations. Entries can be searched, listed, browsed or queried. The database was created using the same MediaWiki technology that powers Wikipedia, allowing users to contribute on many different levels. The initial release of MB was derived from the content of the 2007 Nucleic Acids Research (NAR) Database Issue. Since then, approximately 100 databases have been manually collected from the literature, and users have added information for over 240 databases. MB is synchronized annually with the static Molecular Biology Database Collection provided by NAR. To date, there have been 19 significant contributors to the project; each one is listed as an author here to highlight the community aspect of the project.

Cross-reactivity between soybean allergens and bovine caseins has been previously reported. In this study we aimed to map epitopes of the major soybean allergen Gly m 5 that are co-recognized by casein specific antibodies, and to identify a peptide responsible for the cross-reactivity. Cow's milk protein (CMP)-specific antibodies were used in different immunoassays (immunoblotting, ELISA, ELISA inhibition test) to evaluate the in vitro recognition of soybean proteins (SP). Recombinant Gly m 5 (α), a truncated fragment containing the C-terminal domain (α-T) and peptides of α-T were obtained and epitope mapping was performed with an overlapping peptide assay. Bioinformatics tools were used for epitope prediction by sequence alignment, and for modelling the cross-recognized soy proteins and peptides. The binding of SP to a monoclonal antibody was studied by surface Plasmon resonance (SPR). Finally, the in vivo cross-recognition of SP was assessed in a mouse model of milk allergy. Both α and α-T reacted with the different CMP-specific antibodies. α-T contains IgG and IgE epitopes in several peptides, particularly in the peptide named PA. Besides, we found similar values of association and dissociation constants between the α-casein specific mAb and the different milk and soy components. The food allergy mouse model showed that SP and PA contain the cross-reactive B and T epitopes, which triggered hypersensitivity reactions and a Th2-mediated response on CMP-sensitized mice. Gly m 5 is a cross-reactive soy allergen and the α-T portion of the molecule contains IgG and IgE immunodominant epitopes, confined to PA, a region with enough conformation to be bound by antibodies. These findings contribute to explain the intolerance to SP observed in IgE-mediated CMA patients, primarily not sensitised to SP, as well as it sets the basis to propose a mucosal immunotherapy for milk allergy using this soy peptide.

Background: The economic importance of Solanaceae plant species is well documented and tomato has become a model for functional genomics studies. In plants, important processes are regulated by microRNAs (miRNA). Description: We describe here a data base integrating genetic map positions of miRNA-targeted genes, their expression profiles and their relations with quantitative fruit metabolic loci and yield associated traits. miSolRNA provides a metadata source to facilitate the construction of hypothesis aimed at defining physiological modes of action of regulatory process underlying the metabolism of the tomato fruit. Conclusions: The MiSolRNA database allows the simple extraction of metadata for the proposal of new hypothesis concerning possible roles of miRNAs in the regulation of tomato fruit metabolism. It permits i) to map miRNAs and their predicted target sites both on expressed (SGN-UNIGENES) and newly annotated sequences (BAC sequences released), ii) to co-locate any predicted miRNA-target interaction with metabolic QTL found in tomato fruits, iii) to retrieve expression data of target genes in tomato fruit along their developmental period and iv) to design further experiments for unresolved questions in complex trait biology based on the use of genetic materials that have been proven to be a useful tools for map-based cloning experiments in Solanaceae plant species.

Omputational biology is an interdisciplinary science bred from fields as disparate as mathematics, chemistry, statistics, physics, biology, and computer science. Although the exact definition of computational biology is far from being precise and unambiguous, it is a fact that hundreds of scientists around the world have been increasingly using skills from the above-mentioned fields to approach different biological questions. It is not our aim to elucidate here the definition of computational biology and its differences from related fields such as bioinformatics. However, to review the state of this discipline in Argentina, we need at least a working definition. In this sense, any interdisciplinary research in which the main interest is in studying biological problems and where the working hypothesis can be tested by means of simulation and computational modeling will be considered as belonging to the computational biology field or at least as employing a computational biology approach. It is interesting to note that computational biology research can be implemented in two different ways depending on how the ''interdisciplinary'' nature of this field is assembled. On one hand, a scientist with formal training in a given field (for example, a molecular biologist) could acquire other skills (such as programming or mathematical training) in his attempt to answer a given biological question. Alternatively, working teams made up of members specializing in different fields may work together to reach a scientific explanation of a problem. We think that the first approach is more common among the scientific community because it depends entirely on the scientist's desire to discover an explanation for their problems and on their capacity to explore different areas of science. The second approach is generally dependent on the availability of research and development programs, funded by public or private resources, that favor collaborations between different research institutions to form multidisciplinary teams. We will see that the first approach is more common in Argentina. This paper summarizes the state of the art of computational biology in Argentina. Our aim is to offer as broad a view as possible of the different groups of scientists and their main research interests. Also, we present a brief review of educational, research, and development policies related to the field. We hope that this review may encourage overseas researchers to contact and collaborate with Argentinean teams, as well as organizing and facilitating the exchange of information between researchers in our country. However, this review will probably be far from complete, due to the lack of centralized information on computational biology, and for this reason we apologize for any potential omissions. (For author information, see Box 1.)

A bstract: The General Feature Format (GFF) is widely used to represent genomic annotations, but its hierarchical, multi-attribute structure makes manual querying and analysis challenging. Existing libraries such as gffutils provide programmatic interfaces, yet they require coding proficiency. gffutilsAI is a novel AI-powered command-line agent that enables researchers to perform interactive, natural-language-driven exploration of GFF files. Built on top of the gffutils library and the Strands AI agent framework, gffutilsAI integrates local and cloud-based large language models (LLMs) such as Llama 3.1, GPT-5, and Claude 3.5 to translate human queries into executable actions. The tool supports coordinate-based queries, attribute and GO searches, hierarchical traversal, statistical summaries, and CSV export, offering a new paradigm for accessible conversational genomics.