Abstract Middle East respiratory syndrome coronavirus (MERS-CoV) remains a high-consequence zoonotic pathogen with recurrent outbreak potential and substantially higher reported case fatality than many other human coronaviruses. Although increasing numbers of MERS-CoV genomes and protein structures are available, these data remain distributed across independent sequence, proteome, and structural repositories, limiting systematic interpretation of viral mutations in their functional and three-dimensional contexts. Here, we present an integrated and automated MERS-CoV structural mutation database that connects whole-genome variation, mature viral protein coordinates, experimentally available structures, and residue-level structural annotations. The genome-wide pipeline curates complete or near-complete good-quality MERS-CoV genomes, performs whole-genome alignment, phylogenetic clade and lineage assignment, and identifies amino-acid mutations relative to the reference genome. Replicase mutations are further converted from ORF1a/ORF1ab polyprotein coordinates into mature non-structural protein positions. In parallel, the proteome-wide pipeline automatically retrieves MERS-CoV-related structural records, extracts coordinate-derived chain sequences, performs strict chain-level mapping to the reference proteome, and generates structure-specific mutation mappings. The resulting database contains 1715 unique protein-level mutations, 35 995 cumulative genome-level mutation observations, 456 mapped structure-chain records, 213 unique structures, and 13 542 residue-level structural annotations. The web interface (https://merscov3d.org/version 1.0) lets users browse mutation frequencies, clade distributions, mapped structures, and structural annotations, and inspect mutation positions interactively in a three-dimensional molecular context. Therefore, integrating automated genome surveillance with proteome-scale structural mapping, this resource provides a platform for prioritizing biologically relevant mutations, supporting public health and national surveillance interpretation of emerging MERS-CoV variants, identifying structurally important viral regions, and assisting antiviral-development organizations in drug-target prioritization and resistance-monitoring studies.
Abstract Robust and trustworthy biomedical question answering (QA) remains a critical challenge for large language models (LLMs), especially in complex domains such as rare diseases, where information is fragmented across multiple sources. MedHopQA 2025 benchmark introduces 10 000 multi-step reasoning questions curated from Wikipedia, requiring systems to extract, connect, and synthesize biomedical knowledge across interlinked documents. In this work, we present a retrieval-augmented generation (RAG) and decision-making framework that integrates diverse retrieval strategies, including Query2Doc-based, Rationale-based, and Web-augmented retrieval, and employs a dedicated Decision Maker model to select or directly generate the most accurate and well-reasoned answers. Our system not only leverages evidence from both Wikipedia and the web but also explicitly evaluates and compares candidate answers to ensure answer reliability and reasoning transparency. Experiments on the test set demonstrate that our ensemble approach achieves state-of-the-art performance, yielding an Exact Match score of 0.84, and highlighting the importance of hybrid retrieval and robust decision-making in advancing biomedical multi-step QA. Furthermore, comprehensive analyses across retrieval stages reveal that mitigating model self-bias and ensuring a balanced integration of diverse sources are crucial for maximizing overall system performance, which ultimately reaches a score of 0.87.
Abstract Research aimed to valorize biodiversity, such as studies based on phytochemical characterization and bioactivity assessment, produces wide and heterogeneous datasets in which data collection is often non-standardized. This paper presents the design and implementation of a web-based plant metabolite database platform specifically developed to support biodiversity valorization research within the activities of the National Biodiversity Future Center, with a focus on the analysis of the Italian flora. The platform was designed to manage plant species, biological samples, experimental metabolites, molecular information, bioactivity data, and spectrometry results within an integrated and structured data environment. The system development followed an advanced, agile, and process-oriented methodology with strong and continuous involvement of biologists (i.e., the final users), from the collection and analysis of process- and data-related requirements to database modeling, considering the integration of existing external public databases. Following this initial phase, the development of the user interface proceeded, enabling researchers to insert data, perform advanced queries, and analyze and visualize spectral data through a dedicated tool. The web application was evaluated using the Questionnaire for User Interaction Satisfaction, which received high ratings from domain experts. This work demonstrates how the integration of process modeling and database design can enhance the reproducibility and standardization of research aimed at the valorization of biodiversity.
Butterflies have been a model system for studying the evolution of colour. This is partly due to their complex patterns that reflect human-visible (VIS) and ultraviolet (UV) light, which are perceived by conspecifics and predators. Many studies have sourced data from publicly available images, but most of these images only consider the visible spectrum of light. Including the UV spectrum is crucial for fully understanding the evolution of butterfly morphology and behavioural ecology. Here we provide standardized images (VIS and UV) of over 4 000 individuals from 16 communities of Australian butterflies. These communities represent different climates and urbanization levels spanning over 2 500 km. The dataset contains at least one individual of 125 different species from five families, constituting over one quarter of Australian butterfly diversity. In addition to photographs, we provide spectral measurements of butterfly wings for at least one individual of each species and sex, and Cytochrome Oxidase subunit 1 (CO1) sequences of 1 635 individuals. All these data are accessible in Zenodo and an associated R package simplifies the download of subsets of the database. This database will be of use to evolutionary biologists and ecologists interested in a broad range of topics related to phenotypic variation.
Querying the RDF Portal knowledge graph maintained by DBCLS-which aggregates ~60 life-science databases-requires proficiency in both SPARQL and database-specific RDF schemas, placing this resource beyond the reach of most researchers. Large Language Models (LLMs) can, in principle, translate natural-language questions into executable SPARQL, but without schema-level context, they frequently fabricate non-existent predicates or fail to resolve entity names to database-specific identifiers. We present TogoMCP, a system that recasts the LLM as a protocol-driven inference engine orchestrating specialized tools via the Model Context Protocol (MCP). Two mechanisms are essential to its design: (i) the MIE (Metadata-Interoperability-Exchange) file, a concise YAML document that dynamically supplies the LLM with each target database's structural and semantic context at query time; and (ii) a two-stage workflow separating entity resolution via external REST APIs from schema-guided SPARQL generation. On a benchmark of 50 biologically grounded questions spanning five types and 23 databases, TogoMCP achieved a large improvement over an unaided baseline (Cohen's $d = 1.82$, Wilcoxon $P \lt .001$), with win rates exceeding 80% for question types with precise, verifiable answers. An ablation study shows that all component configurations deliver significant improvements, with MIE schema files providing the largest marginal contribution on mean per-question score ($\Delta = +0.50$ relative to a no-MIE condition, two-sided Wilcoxon $P = .067$; 90% bootstrap CI $[+0.04,\,\,+0.94]$ excludes zero); a one-line instruction to load the relevant MIE file recovers the same mean improvement as a full procedural protocol, while the protocol additionally reduces downside risk (loss rate 1.6% vs. 4.8%, Fisher $P = .036$). These results suggest a general design principle: concise, dynamically delivered schema context is more valuable than complex orchestration logic for mean score performance, while procedural guidance plays a complementary role in narrowing variance. Database URL: https://togomcp.rdfportal.org/.
