Database: The Journal of Biological Databases and Curation最新文献

{"title":"BEDB: a comprehensive binding energy database for molecular docking and dynamics: insights into Human Metapneumovirus (HMPV) Inhibitors.","authors":"Farhan Ullah, Wajeeha Rahman, Anees Ullah, Riffat Jehan, Ali Raza, Shamsher Khan, Zarmeena, Shahid Ullah, Tianshun Gao","doi":"10.1093/database/baag011","DOIUrl":"10.1093/database/baag011","url":null,"abstract":"<p><p>Biological databases play a crucial role in life sciences research by organizing vast amounts of data, enabling efficient access and analysis. Numerous databases have been published across various research areas, yet there remains a need for updated platforms in the field of molecular docking and molecular dynamics simulation research. To address this gap, we have developed an extensive and user-friendly platform focused on compiling the binding energies of compounds associated with a wide range of biological activities. The database offers free access to data on 1321 compounds, including abstracts, references, isomeric SMILES, and 22 molecular properties. Researchers can also securely store their docking and screening data. To demonstrate its capabilities, molecular docking was performed on the top 10 compounds with the best binding energies against human metapneumovirus (HMPV) using AutoDock Vina and the crystal structure (PDB ID: 8FPJ). MK-3207 and Etoposide exhibited docking scores of -10.3 and -9.6, respectively. The top two compounds were further selected for MD simulations, confirming stable binding interactions with the viral protein. Additional compounds, including Teniposide, UK432097, 85019940, Setileuton, Orvepitan, Cep-11981, Tadalafil, and VS-5584, were also analyzed, providing further insights into their binding mechanisms and potential therapeutic relevance. The database is developed using PHP, HTML, CSS, JavaScript, and Python and is freely accessible at https://www.pbed.habdsk.org/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12946676/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147302921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Panorama: a database for the oncogenic evaluation of somatic mutations in pan-cancer.","authors":"Seung-Jin Park, Seon-Young Kim","doi":"10.1093/database/baaf086","DOIUrl":"10.1093/database/baaf086","url":null,"abstract":"<p><p>Somatic mutations, key alterations in cancer development, exert differential effects across tissues and biological layers, such as transcriptomes, proteomes, and post-translational modifications (PTMs). Although previous pan-cancer studies have characterized the molecular landscape of cancer, the effects of individual somatic mutations across different tissues remain insufficiently explored. Here, we developed Panorama to evaluate the oncogenic potential of single somatic mutations across all cancer types. We collected cancer proteogenomics or multiomics data from over 10 000 individuals across 19 cancer types. Based on five evaluation criteria, we assessed whether a specific mutation affects the abundance of a particular gene's transcriptome, proteome, or phosphoproteome; the tumor microenvironment; specific RNA- or protein-based signaling pathways; and outlier-level overexpression of PTMs, aiding in potential drug target identification. By leveraging five oncogenic metrics, Panorama quantifies the oncogenic potential of individual somatic mutations and provides a framework for identifying driver mutations by incorporating their downstream effects. With Panorama, researchers can integrate cancer proteogenomics data, providing a comprehensive approach that enhances our understanding of single somatic mutations in specific tissues. Finally, Panorama was developed as a web-based database to ensure easy access for researchers and is freely available at http://139.150.65.64:8080/or https://github.com/prosium/panorama.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12808847/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SynVectorDB: embedding-based retrieval system for synthetic biology parts.","authors":"Hao Li, Jiani Hu, Jie Song, Wei Zhou","doi":"10.1093/database/baaf088","DOIUrl":"10.1093/database/baaf088","url":null,"abstract":"<p><p>Synthetic biology part discovery faces significant challenges due to inconsistent data organization and limited semantic search capabilities across existing repositories. We developed SynVectorDB, an embedding-based retrieval system that addresses these limitations through methodological innovations in data integration and AI-driven semantic search. Our approach integrates 19 850 biological parts from multiple sources (Addgene, iGEM Registry, laboratory collections), implementing systematic curation protocols that resulted in 7656 parts achieving verified status through literature-based validation and reliability assessment. We introduce a novel three-level hierarchical classification system organizing parts into functionally coherent categories (DNA Elements, RNA Elements, Coding Sequences, and Application Constructs) with detailed subcategorization. The core technical contribution employs BGE-M3 multilingual embeddings within a scalable vector database architecture to enable semantic similarity matching that significantly outperforms keyword-based retrieval methods. Standardized curation workflows enhance data comparability and search accuracy across heterogeneous sources. The dual deployment architecture ensures high performance through cloud services while maintaining open-source accessibility and deployment flexibility. The system maintains SBOL3 compatibility while providing innovative solutions for biological part organization and retrieval. Database URL: SynVectorDB is available in multiple deployment modes: web interface (https://svdb.sjtu.bio), local installation and source code (https://github.com/AilurusBio/synbio-parts-db), and MCP server integration for AI assistants (https://www.npmjs.com/package/synvectordb).</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12805114/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145970546","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"scDrugAtlas: an integrative single-cell drug response database for dissecting tumour heterogeneity in therapeutic efficacy.","authors":"Yanfei Wu, Wei Huang, Xinda Ren, Hui Liu, Ling Xu","doi":"10.1093/database/baag010","DOIUrl":"10.1093/database/baag010","url":null,"abstract":"<p><p>Tumour heterogeneity often leads to substantial differences in responses to same drug treatment. The presence of pre-existing or acquired drug-resistant cell subpopulations within a tumour survive and proliferate, ultimately resulting in tumour relapse and metastasis. The drug resistance is the leading cause of failure in clinical tumour therapy. Therefore, accurate identification of drug-resistant tumour cell subpopulations could greatly facilitate the precision medicine and novel drug development. However, the scarcity of single-cell drug response data significantly hinders the exploration of tumour cell resistance mechanisms and the development of computational predictive methods. In this paper, we propose scDrugAtlas, a comprehensive database devoted to integrating the drug response data at single-cell level. We manually compiled more than 100 datasets containing single-cell drug responses from various public resources. The current version comprises large-scale single-cell transcriptional profiles and drug response labels from 1023 samples, across 77 unique drugs and 31 major cancer types. Particularly, we assigned a confidence level to each response label based on the tissue source (primary or relapse/metastasis), drug exposure time, and drug-induced cell phenotype. We believe scDrugAtlas could greatly facilitate the Bioinformatics community for developing computational models and biologists for identifying drug-resistant tumour cells and underlying molecular mechanism. Database URL: http://drug.hliulab.tech/scDrugAtlas/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923164/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of large language models to the annotation of cell lines and mouse strains in genomics data.","authors":"Sanja Rogic, B Ogan Mancarci, Brianna Xu, Anna Xiao, Carlton Yan, Paul Pavlidis","doi":"10.1093/database/baag041","DOIUrl":"10.1093/database/baag041","url":null,"abstract":"<p><p>Accurate, consistent and comprehensive metadata are essential for the reuse of functional genomics data deposited in repositories such as the Gene Expression Omnibus (GEO), however, achieving this often requires careful manual curation, which is time-consuming, costly and prone to errors. In this paper, we evaluate the performance of Large Language Models (LLMs), focusing on OpenAI's GPT-4o, as an assistive tool for entity-to-ontology annotation of two commonly encountered descriptors in transcriptomic experiments, mouse strains and cell lines. Using over 9 000 manually curated experiments from the Gemma database and over 5 000 associated journal articles, we assess the model's ability to identify relevant free-text entries and map them to appropriate ontology terms. Using zero-shot prompting and retrieval-augmented generation (RAG) to incorporate domain-specific ontology knowledge, GPT-4o correctly annotated 77% of mouse strain and 59% of cell line experiments, and uncovered manual curation errors in Gemma for over 200 experiments (2% of total). GPT-4o substantially outperformed non-LLM alternatives, and was statistically indistinguishable from the highest-performing 2026 frontier models. Model errors often arose from typographical mistakes or inconsistent naming in the GEO record or publication, and resembled those made by human curators. Along with annotations, our approach requests that the model output supporting context and verbatim quotes from the sources. These were typically accurate and enabled rapid curator verification. We further found that for the difficult cell line task, an ensemble of LLMs can boost precision at the cost of recall. These findings suggest that while LLMs are not ready to fully replace manual curators, they can effectively support them. A human-in-the-loop workflow, in which LLM's annotations are provided to human curators for validation, should improve the efficiency and quality of large-scale biomedical metadata curation.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13389303/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148548127","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GeneReL: a large language model-powered platform for gene regulatory relationship extraction with community curation.","authors":"Joo-Seok Park, Suyeon Ha, Yejin Lee, Yang Jae Kang","doi":"10.1093/database/baag062","DOIUrl":"10.1093/database/baag062","url":null,"abstract":"<p><p>Gene regulatory networks provide fundamental insights into plant biology, yet extracting structured interaction data from scientific literature remains a significant bottleneck. Traditional manual curation cannot scale to meet the demands of modern research, while automated text mining approaches struggle with the complexity of gene nomenclature and relationship classification. Large language models (LLMs) offer promising capabilities for information extraction, but integrated platforms combining LLM extraction with community validation for plant regulatory databases remain scarce. We developed GeneReL, an integrated platform combining LLM-based extraction with community-driven curation for gene regulatory networks in Arabidopsis thaliana. The system employs a tiered pipeline using Claude Haiku 4.5 for screening, Claude Sonnet 4 for extraction, and Claude Opus 4 for verification, along with a novel five-step gene normalization pipeline incorporating paper-text search and LLM-based disambiguation with UniProt annotations. The database contains 13,710 curated interactions across 51 relationship types, with 90.2% classified as high confidence based on linguistic certainty markers in source text. Comparison with IntAct reveals 86.0% of interactions are unique to our literature-derived database, demonstrating complementary coverage to existing resources. The web platform provides card-based browsing with voting capabilities, interactive network visualization using Cytoscape.js with locus-ID-based node consolidation, and administrative interfaces for curator review of ambiguous gene mappings. GeneReL is freely accessible at https://generel.newgenes.me.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13643336/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149126845","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"FAD: a comprehensive database merging information about food allergens.","authors":"Wafa Mokhtari, Souad Khemili-Talbi, Jean Kwasigroch, Dimitri Gilis","doi":"10.1093/database/baag028","DOIUrl":"10.1093/database/baag028","url":null,"abstract":"<p><p>Food allergies represent a significant and growing global challenge. However, researchers and risk assessors still lack a dedicated and comprehensive resource that centralizes food allergen information. As a result, obtaining the necessary structural, biochemical, and immunological data often requires time-consuming searches across multiple heterogeneous databases, hindering efficient analysis and slowing scientific progress. The Food Allergen Database (FAD) merges biochemical, structural, immunological, and functional properties of all known food allergens and isoallergens (1168 entries). It is an essential resource that facilitates the extraction of all information about food allergens.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13213374/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148027925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rosetta Statements: simplifying FAIR knowledge graph construction with a user-centred approach.","authors":"Lars Vogt, Kheir Eddine Farfar, Pallavi Karanth, Marcel Konrad, Allard Oelen, Manuel Prinz, Philip Strömert","doi":"10.1093/database/baag030","DOIUrl":"10.1093/database/baag030","url":null,"abstract":"<p><p>Knowledge graphs and ontologies are promising technologies for achieving FAIR (findable, accessible, interoperable, and reusable) data. We identify four challenges as high barriers for the effective use of knowledge graphs. Since the construction of knowledge graphs is a modelling task and every model serves a purpose against which it is optimized, we question the central paradigm of modelling a mind-independent reality. Instead, we propose the Rosetta Statement approach, which models English natural language statements and displays them as natural language sentences in its user interface. We suggest a Resource Description Framework (RDF)-native metamodel, from which semantic data schemata can be derived for any type of simple English statement. We provide a light and a full version, with the latter supporting versioning and a change-track. We implemented the full version in the Open Research Knowledge Graph (ORKG), an open, domain-agnostic, community-driven knowledge graph for documenting research findings from scholarly publications. The ORKG allows domain experts, with short training but without formal expertise in semantics, to define RDF schemata for new types of Rosetta Statements. We discuss how the Rosetta Statement approach contributes to addressing the four challenges and how its structural proximity to natural language supports the development of tools for data entry and summarization using Large Language Models. We further discuss how the Rosetta approach supports a three-step procedure for FAIR knowledge graph construction: (1) domain experts using Rosetta Statements and Wikidata terms to create a FAIR knowledge graph with basic search functionality; (2) the addition of semantic search capability by replacing Wikidata terms with ontology terms; and (3) the transformation of selected Rosetta Statement types into reasoning-capable graphs with support from ontology engineers. We argue that this three-step procedure is designed to substantially lower the entry barrier for knowledge graph construction while increasing their cognitive interoperability, consistent with the CLEAR Principle.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13227103/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148142971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Tear fluid database: a reference website for tear fluid proteomics.","authors":"Drew Mayernik, Saleh Ahmed, Eliza Williams, Tae Jin Lee, Amy Estes, Pamela Martin, Wenbo Zhi, Vishal Jhanji, Shruti Sharma, Ashok Sharma","doi":"10.1093/database/baaf091","DOIUrl":"10.1093/database/baaf091","url":null,"abstract":"<p><p>Tear fluid is a clinically accessible, minimally invasive biofluid with a complex and dynamic proteome. Molecular alterations in tear composition have been linked to a broad spectrum of ocular and systemic diseases; however, the small volume of tear samples presents substantial challenges for obtaining high-quality proteomic data. To overcome this limitation, we developed a highly sensitive mass spectrometry workflow capable of identifying more than 1,000 proteins from individual tear samples. Applying this workflow to a large and diverse cohort, we generated a representative and comprehensive profile of the human tear fluid proteome and established reference abundance ranges for proteins commonly detected in tear fluid. In parallel with protein quantification, we collected detailed clinical annotations for each participant. As the database continues to grow, these analyses will increasingly support the identification of disease-associated proteomic signatures, deepen our understanding of underlying biological mechanisms, and accelerate the discovery of clinically relevant biomarkers. To make these data broadly accessible, we created a user-friendly website for exploring protein measurements alongside associated clinical metadata. The current release includes proteomic profiles from 74 human tear samples, encompassing 2,134 unique proteins. The TearFluid Database serves as a foundational resource for biomarker discovery, comparative proteomics, and systems-level investigations in tear biology, offering the scientific community a robust and expandable platform for advancing tear fluid proteomics research. Database URL: https://tearfluid.org/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12808846/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145988626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"BDCD: a comprehensive Brain Disease Cell-cell communication Database.","authors":"Xinyi Liu, Citu Citu, Gang Qu, Wendao Liu, Nitesh Enduru, Andi Liu, Chia-Hao Tung, Zhongming Zhao","doi":"10.1093/database/baag017","DOIUrl":"10.1093/database/baag017","url":null,"abstract":"<p><p>Dysregulated cell-cell communication (CCC) is increasingly recognized as a driver of brain disease pathology, contributing to neuroinflammation, synaptic dysfunction, and neurodegeneration. Nevertheless, existing resources remain limited in brain specificity, regional coverage, and functional annotation. To address this gap, we develop the Brain Disease Cell-cell communication Database (BDCD), the first comprehensive resource focused on CCC networks across major brain diseases. BDCD integrates 38 manually curated datasets, comprising 8 519 425 single cells from single-cell RNA-seq studies and 140 744 spots from spatial transcriptomic maps, spanning 14 brain regions and 13 canonical cell types covering Alzheimer's disease, Parkinson's disease, schizophrenia, bipolar disorder, and multiple sclerosis. BDCD reconstructs more than 495 000 ligand-receptor interaction events and links them to structural features, genetic associations, pathways, and therapeutic modulators, including 6100 single nucleotide polymorphisms from genome-wide association studies, 3350 drugs, and 72 477 allosteric modulators. This comprehensive atlas enables cross-disease comparison and supports dynamic hypothesis generation, providing a foundation for mechanistic insights and therapeutic discovery. BDCD is publicly available at https://bioinfo.uth.edu/bdcd/. Database URL: https://bioinfo.uth.edu/bdcd/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":2.7,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13044719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147590552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}