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

{"title":"A comprehensive morphological database of hognose Porthidium pitvipers (Viperidae: Crotalinae).","authors":"Carlos Patron-Rivero, Carlos Yañez-Arenas, Sara Ruane, Xavier Chiappa-Carrara, Octavio R Rojas-Soto","doi":"10.1093/database/baaf085","DOIUrl":"10.1093/database/baaf085","url":null,"abstract":"<p><p>Generating and sharing primary biological data is essential to support reproducible research, stimulate new hypotheses, and advance our understanding of biodiversity. Here, we present a comprehensive database of morphological traits for snakes of the genus Porthidium (Viperidae: Crotalinae). This database includes linear measurements, pholidosis (scale counts), and head shape data from preserved specimens across five different herpetological collections. These data comprise 13 morphological traits, 8 scale counts, and 55 landmarks collected from 484 individuals across 9 species. The specimens represent both juvenile and adult stages. All data were collected using standardized protocols to ensure comparability across individuals and species. The dataset is a valuable resource for studies in systematics, morphological evolution, ecological adaptation, and ontogeny, as well as facilitating reproducibility and reuse in the fields of evolutionary biology, herpetology, and comparative morphology.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12813580/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145997451","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":"Dataset of xenobiotics human renal clearance values.","authors":"Natalia Łapińska, Sebastian Polak","doi":"10.1093/database/baag018","DOIUrl":"10.1093/database/baag018","url":null,"abstract":"<p><p>Scientific articles have been searched for experimentally established critical pharmacokinetic parameter-renal clearance. The main source of the documents was PubMed database, considered one of the most important and comprehensive repositories of biomedical literature. After manual data collection and thorough quality check database presenting human renal clearance values of exogenous substances was developed. After collecting the data, preliminary processing and simple analysis were carried out. The final database contains over 1700 experimental observations from 761 scientific articles and covers over 500 unique substances studied. Database URL: doi: https://10.17632/3427x3wzzc.2.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13070669/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671198","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":"MOKCa-3D database: functional and structural analysis of missense mutations in cancer.","authors":"Biniam Haile, Adnan Cinar, Sayeda F Banini, Katrin Zhivkova, Finn J Gallagher, Christopher J Richardson, Frances M G Pearl","doi":"10.1093/database/baag001","DOIUrl":"10.1093/database/baag001","url":null,"abstract":"<p><p>Determining the functional consequence of missense mutations acquired in the development of cancer is critical to the understanding of the evolution and the therapeutic vulnerabilities of an individual tumour. Several million missense mutations associated with cancer have been reported across different databases with little functional annotation accompanying each mutation. We have designed the MOKCa-3D database, (https://bioinformaticslab.sussex.ac.uk/MOKCa-3D/) to enable the contextualization and interpretation of cancer somatic missense mutations, including the structural impact of the mutation on the 3D structure, and whether the mutation results in a gain or loss of the protein's function. For each protein, a sequence feature viewer enables interactive visualization of the amino acid sequence, missense mutations, post-translational modification sites, protein domains, active sites, binding sites, protein-protein interaction sites, and mutational frequency. The mutation-level page concisely presents functional insights for each individual mutation, and an interactive MOL* viewer highlights mutated residue on an AlphaFold protein structural model. The SAAP structural impact analysis pipeline was used to identify the structural impact of the mutation. MOKCa-3D concisely presents functional insights and structural impacts of cancer somatic missense mutations enabling users to interpret their functional consequences. It is freely accessible and easy to navigate, making it usable by the widest range of researchers.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13112024/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147765158","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":"huSA: a comprehensive database for multi-dimensional resolution of bulk, single cell and spatial transcription profiles in skin diseases.","authors":"Meiling Zheng, Bao Qian, Zhi Hu, Xingyu Wei, Ke Sun, Wenjuan Jiang, Changxing Gao, Ming Zhao","doi":"10.1093/database/baag009","DOIUrl":"10.1093/database/baag009","url":null,"abstract":"<p><strong>Background: </strong>Skin diseases are among the most prevalent conditions worldwide, posing significant threats to human health by causing physical discomfort, psychological distress, and reduced quality of life. With the rapid advancement of high-throughput technologies, a substantial number of transcriptomic datasets, including single-cell RNA sequencing (scRNA-seq), spatial transcriptomics, and bulk RNA-seq, have been generated in the field of dermatology over the past decade. However, the lack of effective integration and standardized analysis pipelines limits the full utilization of these valuable resources in skin disease research.</p><p><strong>Objectives: </strong>To address this gap, we aimed to construct a comprehensive, integrative, and user-friendly atlas that enables systematic exploration of skin transcriptomic data across multiple diseases and modalities.</p><p><strong>Methods: </strong>We developed the Human Skin Atlas (huSA) ('https://humanskinatlas.com/index.html'), a publicly accessible database that incorporates data from 17 skin diseases and 63 independent datasets, including 1 434 scRNA-seq, 63 spatial transcriptomics, and 1 502 bulk RNA-seq samples. The database provides standardized cell-type annotations, differential gene expression analysis, cell-cell interaction mapping, pathway and metabolic module enrichment, transcription factor regulatory inference, and differentiation state assessment for scRNA-seq data. Data from identical skin diseases were further integrated to enhance biological signal detection. For visualization, we embedded the 'cell × gene' and 'Cirrocumulus' platforms, offering interactive and customizable gene expression visualizations at both single-cell and spatial levels with user-defined parameters.</p><p><strong>Results: </strong>The huSA enables both individual dataset analysis and cross-dataset integration, providing robust, consistent, and scalable insights into skin disease biology. Demonstration analyses confirmed that results derived from either single datasets or aggregated multi-dataset integrations exhibited high reliability and biological relevance. The platform successfully supports diverse research needs, including cell-type-specific expression profiling, regulatory network construction, and spatial transcriptomic exploration.</p><p><strong>Conclusions: </strong>The Human Skin Atlas (huSA) represents a state-of-the-art integrative resource for the skin research community. By offering multiscale analyses and interactive visualization tools, the huSA accelerates the discovery of molecular mechanisms underlying skin diseases and facilitates translational research efforts aimed at improving skin health.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12923168/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257469","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":"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":3.6,"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":"CysDuF database: annotation and characterization of cysteine residues in domain of unknown function proteins based on cysteine post-translational modifications, their protein microenvironments, biochemical pathways, taxonomy, and diseases.","authors":"Devarakonda Himaja, Debashree Bandyopadhyay","doi":"10.1093/database/baag002","DOIUrl":"10.1093/database/baag002","url":null,"abstract":"<p><p>Experimental characterization and annotation of amino acids belonging to domains of unknown function (DUF) proteins are expensive and time-consuming, which could be complemented by computational methods. Cysteine, being the second most reactive amino acid at the catalytic sites of enzymes, was selected for functional annotation and characterization on DUF proteins. Earlier, we reported functional annotation of cysteine on DUF proteins belonging to the COX-II family. However, holistic characterization of cysteine functions on DUF proteins was not known, to the best of our knowledge. Here, we annotated and characterized cysteine residues based on post-translational modifications (PTMs), biochemical pathways, diseases, taxonomy, and protein microenvironment. The information on uncharacterized DUF proteins was initially obtained from the literature, and the sequence, structure, pathways, taxonomy, and disease information were retrieved from the SCOPe database using DUF IDs. Protein microenvironments (MENV) around cysteine residues from DUF proteins were computed using protein structures (n = 70 342). The cysteine PTMs were predicted using the in-house cysteine-function prediction server, DeepCys https://deepcys.bits-hyderabad.ac.in). The accuracy of the prediction, validated against known experimental cysteine PTMs (n = 18 626), was 0.79. The information was consolidated in the database (https://cysduf.bits-hyderabad.ac.in/), retrievable in downloadable formats (CSV, JSON, or TXT) using the following inputs, DUF ID, PFAM ID, or PDB ID. For the first time, we annotated cysteine PTMs in DUF proteins belonging to seven different biochemical pathways and various species across the taxonomy, notably for the SARS-CoV-2 virus. The nature of MENV around cysteine from DUF proteins was mainly buried and hydrophobic. However, in the SARS-CoV-2 virus, a significant number of functional cysteine residues were exposed on the surface with hydrophilic microenvironment.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12828279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146028686","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":3.6,"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":3.6,"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":3.6,"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":"A comprehensive database for biological data derived from sewage in five European cities.","authors":"Ágnes Becsei, Patrick Munk, Alessandro Fuschi, Saria Otani, József Stéger, Dávid Visontai, Krisztián Papp, Christian Brinch, Ravi Kant, Ilya Weinstein, Olli Vapalahti, Miranda de Graaf, Claudia M E Schapendonk, Jeroen Roelfsema, Maaike van den Beld, Roan Pijnacker, Eelco Franz, Patricia Alba, Antonio Battisti, Alessandra De Cesare, Valentina Indio, Fulvia Troja, Tarja Sironen, Chiara Oliveri, Frédérique Pasquali, Ivan Liachko, Benjamin Auch, Colman O'Cathail, Krisztián Bányai, Magdolna Makó, Péter Pollner, Marion Koopmans, Istvan Csabai, Daniel Remondini, Frank M Aarestrup","doi":"10.1093/database/baaf089","DOIUrl":"10.1093/database/baaf089","url":null,"abstract":"<p><p>Sewage metagenomics is a powerful tool for proactive pathogen surveillance and understanding microbial community dynamics. To support such efforts, we present a highly curated and accessible longitudinal dataset of 239 sewage samples collected from five European cities. The dataset, processed through metagenomic sequencing, includes rich analytical outputs such as taxonomic profiles, identified antimicrobial resistance genes, assembled contigs with annotated origins, metagenome-assembled genomes with functional gene annotations, and metadata. Given the computational intensity and time required to reproduce such analyses, we share this dataset to promote reuse and advance research. In addition to the metagenomic data, qPCR was used to identify specific pathogens, and Hi-C sequencing was performed on a subset of the samples to strengthen genomic linkage analysis. Central to this resource is a publicly available PostgreSQL database, designed to facilitate efficient exploration and reuse of the data. This comprehensive database allows users to perform targeted queries, subset data, and streamline access to this extensive resource.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2026 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12817144/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146009259","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}