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

{"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":2.7,"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}

{"title":"Mapping of EFO terms from the GWAS catalog data to multiple ontologies at the Rat Genome Database.","authors":"Stanley J F Laulederkind, G T Hayman, Shur-Jen Wang, Carissa A Park, Mary L Kaldunski, Mahima Vedi, Marek A Tutaj, Logan Lamers, Jennifer R Smith, Jeffrey L de Pons, Melinda R Dwinell, Anne E Kwitek","doi":"10.1093/database/baag008","DOIUrl":"10.1093/database/baag008","url":null,"abstract":"<p><p>The laboratory rat, Rattus norvegicus, is an important model of many human diseases, and experimental findings in the rat have relevance to both human physiology and disease. The Rat Genome Database (RGD, https://rgd.mcw.edu/) is a model organism database that provides access to a wide variety of curated rat data including disease associations, phenotypes, pathways, molecular functions, biological processes, cellular components, and chemical interactions for genes, quantitative trait loci (QTL), and strains. Because the laboratory rat is used often as a model of human physiology and disease, RGD has incorporated data from the NHGRI-EBI (National Human Genome Research Institute-European Bioinformatics Institute) catalog of human genome-wide association studies (GWAS) (https://www.ebi.ac.uk/gwas/). This provides the basis for easy integration of RGD data for rat and other species with human SNP (single nucleotide polymorphism)-phenotype associations. When data from different sources use different vocabularies, there must be some standard way to compare the data. Since the human GWAS data are annotated with Experimental Factor Ontology (EFO) terms, RGD needed to map those EFO terms to various ontologies used at RGD for annotating rat genomic and strain data, so the human data can be translationally associated with the wealth of preclinical data including rat genes, rat QTL, and rat strains. To bridge the ontology/vocabulary gap, the curators at RGD have mapped all the EFO terms (http://www.ebi.ac.uk/efo) used to annotate the human GWAS SNPs in the NHGRI-EBI Catalog of human GWAS Catalog Data (https://www.ebi.ac.uk/gwas/) to multiple ontologies used at RGD. RGD has used the mappings to translate human GWAS disease/phenotype EFO annotations into RGD Disease Ontology annotations, human phenotype ontology annotations, clinical measurement ontology annotations, and vertebrate trait ontology annotations. Also, RGD has made the ontological mappings available via Simple Standard for Sharing Ontological Mappings (SSSOM) files on the RGD download site (https://download.rgd.mcw.edu/ontology/mappings/).</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/PMC12946677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147302963","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}

{"title":"Design and implementation of a web-based platform for biodiversity valorization data management: from process modeling to user-centred evaluation.","authors":"Beatrice Amico, Carlo Combi, Mauro Commisso, Stefano Negri, Flavia Guzzo","doi":"10.1093/database/baag052","DOIUrl":"10.1093/database/baag052","url":null,"abstract":"<p><p>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.</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/PMC13563465/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148944430","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":"KitBase Expanded: An Integrated Genomic and Phenotypic Resource for 3,268 Fast-Neutron-Irradiated Rice Mutants.","authors":"Artur Teixeira de Araujo, Rashmi Jain, Deling Ruan, Mawsheng Chern, Nguyen Ho, Rohan Bhushan Jhingan, Guotian Li, Phat Q Duong, Maria Florencia Ercoli, Pamela C Ronald","doi":"10.1093/database/baag024","DOIUrl":"10.1093/database/baag024","url":null,"abstract":"<p><p>Fast-neutron mutagenesis creates diverse genome-wide mutations, providing a powerful tool for crop functional genomics. Here, we present an expanded genomic and phenotypic analysis of 3268 fast-neutron (FN)-induced mutant rice lines (Oryza sativa L. cv. Kitaake). All FN lines were whole-genome sequenced, and mutations were identified by alignment in the Nipponbare and KitaakeX reference genomes. We cataloged over 428,000 mutations affecting 78.49% of Nipponbare genes and 70.38% of KitaakeX genes. In silico expression analysis indicates that 575 non-mutated Nipponbare genes are highly expressed and likely essential for viability. Each mutant carries, on average, 68.5 mutations in the Nipponbare alignments or 63.2 mutations for KitaakeX alignments, distributed randomly across all 12 chromosomes with no evident hotspots. FN lines have approximately 8.5% fewer mutations when using the KitaakeX alignment, underscoring the unique contributions of each reference genome and the importance of utilizing both for comprehensive mutation discovery. The majority of mutations are small deletions and single-base substitutions, with deletions predominating in their effect on genes. We found that 74.4% of all transcription factor Nipponbare genes were mutated at least once. Phenotypic characterization of over 2700 lines revealed a broad spectrum of variation in core agronomic traits (heading date, tiller number, plant height, panicle weight, seed yield components) and other morphological variants of interest. The integration of genomic and phenotypic data through the KitBase platform enabled the identification of candidate genes for several traits of interest. The KitBase website (https://kitbase.ucdavis.edu) has been updated to provide open access to all mutation data and seed stocks, as well as an intuitive query interface, facilitating forward and reverse genetic analyses in rice. This expanded resource enriches the rice functional genomics toolkit and highlights the value of coupling high-density mutation mapping with phenotypic data for rapid gene discovery and crop improvement.</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/PMC13262976/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148223791","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":"H-SPAR DB: human spaceflight platform for analysis and research-an integrative omics database for space health.","authors":"Marios Tomazou, Marilena M Bourdakou, Eleni Nicolaidou, Grigoris Georgiou, Kyriaki Savva, Efi Athieniti, Styliana Menelaou, Sotiroula Afxenti, George M Spyrou","doi":"10.1093/database/baaf083","DOIUrl":"10.1093/database/baaf083","url":null,"abstract":"<p><p>H-SPAR DB is a comprehensive database designed to support space health research by providing a unified platform for data integration, analysis, and interpretation. The database simplifies the complex workflows associated with spaceflight-related biology studies by combining curated molecular lists, transcriptomic datasets from NASA's GeneLab, and user-uploaded data into a streamlined, user-friendly interface. H-SPAR DB enables researchers to perform differential expression analysis, set operations, and association analyses while also generating integrative knowledge graphs around a space-related biological theme. The platform reduces the time required for data gathering and processing by offering a single platform for data exploration, analysis, and visualization. By integrating interactive visualizations and data tables, H-SPAR DB facilitates the interpretation of results, ultimately enhancing the efficiency of space biology research and fostering discoveries that address human health challenges in space. Researchers can access H-SPAR DB freely at https://bioinformatics.cing.ac.cy/H-SPARDB/ without login or other requirements.</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/PMC12805116/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145970438","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":"predicTox: an integrated database of clinical risk frequencies and human gene expression signatures for cardiotoxic drugs.","authors":"Jens Hansen, Pedro Martinez, Arjun S Yadaw, Yuguang Xiong, Rebecca Racz, Michael R Pacanowski, Laura L Hopkins, Nicholas M P King, Darrell Abernethy, Eric Sobie, Ravi Iyengar","doi":"10.1093/database/baag003","DOIUrl":"10.1093/database/baag003","url":null,"abstract":"<p><p>We recently used drug-induced transcriptomic responses and whole-genome sequences in healthy human induced pluripotent stem cell (iPSC)-derived cardiomyocytes to identify cellular pathways and genomic variants potentially associated with the cardiotoxic effects of tyrosine kinase inhibitors (TKIs) and anthracyclines. Here, we describe predicTox (www.predictox.org), an interactive website that organizes our data and its integration with knowledge from cell pathways and genomic databases. DrugTox summary cards give results of these analyses and metadata for each drug. Fields include cardiotoxicity risk scores curated from the FDA Adverse Event Reporting System, cell pathways, and genomic variants potentially associated with drug-induced cardiotoxicity. At a detailed level, predicTox provides a ranked list of up- and downregulated pathways that are predominantly induced by cardiotoxic TKIs as well as lists of their pathway genes and the specific cardiotoxic TKIs inducing those pathways. predicTox provides downloadable lists of drug-induced differentially expressed genes and pathways as well as drug-related genomic variants associated with cardiotoxicity. Statistical metrics are given. Mathematical models allow simulation of drug effects on heart physiology. Building on the results of our algorithm for independent reidentification of the well-known rs2229774 variant for anthracycline-induced cardiotoxicity, we describe how our data can be queried to identify potential variants associated with drug-induced cardiotoxicity by affecting a drug's pharmacodynamics and pharmacokinetics.</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/PMC12863071/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146104166","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":"neomerDB: a comprehensive database of neomer biomarkers in cancer.","authors":"Kimonas Provatas, Candace S Y Chan, Ioannis Kerasiotis, Eleftherios Bochalis, Akshatha Nayak, Brad E Zacharia, Georgios A Pavlopoulos, Wei Li, Ilias Georgakopoulos-Soares","doi":"10.1093/database/baag006","DOIUrl":"10.1093/database/baag006","url":null,"abstract":"<p><p>The development of biomarkers for population screening, early cancer detection, monitoring, and recurrence surveillance offers substantial potential to improve patient outcomes and save lives. Nullomers are short k-mers that are absent from a human genome, and neomers are the subset of nullomers that emerge recurrently due to somatic mutations during cancer development. Here, we have developed neomerDB, a database that encompasses a catalogue of neomers across cancer types and organs. We examined 10 000 whole exome sequencing and 2658 whole genome sequencing tumour-matched samples and identified the set of neomers associated with each cancer type and organ. We also analysed 76 215 whole genomes and 730 947 whole exomes of individuals from diverse ancestries, from which we removed nullomers and neomers that can arise due to germline variants in the population. Finally, we conducted a case study demonstrating that neomers can be utilized to detect glioblastoma from liquid biopsy samples (n = 38), utilizing cell-free DNA and cell-free RNA, achieving a Receiver Operating Characteristic - Area Under the Curve score of 0.98 and a precision-recall score of 0.99. neomerDB is a user-friendly database that enables advanced searches, provides interactive visualizations, and download options for neomer biomarkers. neomerDB is publicly available at https://neomerDB.com/.</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/PMC12895195/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146164635","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":"Desert Plant Endophyte Genome Database: a curated repository of endophytic bacterial genomes across arid ecosystems.","authors":"Sabiha Parween, Arun Prasanna Nagarajan, Amal K Alghamdi, Abdul Aziz Eida, Feras F Lafi, Luma Albanna, Nida Salem, Barakat Abu-Irmaileh, Zaid A Pirzada, Shahid Siddique, Ruben Garrido-Oter, Paul Schulze-Lefert, Maged M Saad, Heribert Hirt","doi":"10.1093/database/baag020","DOIUrl":"10.1093/database/baag020","url":null,"abstract":"<p><p>Microbial communities associated with desert plants play a pivotal role in enhancing host survival under extreme environmental stressors, including drought, salinity, and nutrient limitation. The Desert Plant Endophyte Microbial Collection is one of the largest curated repositories of 2500 cultivable endophytic bacteria isolated from 23 native desert plant species across Saudi Arabia, Jordan, and Pakistan. Representing a broad spectrum of arid microhabitats from inland deserts and mountain wadis to coastal mangroves and date palm oases, the collection supports integrative studies on microbial ecology and plant-microbe interactions in water-limited ecosystems. A central component of this initiative is the Desert Plant Endophyte Genome Database, which currently hosts whole-genome sequences of 534 endophytic bacterial isolates annotated with extensive ecological metadata, assembly statistics, functional traits, and host associations. The database interface provides tools for genome exploration, metadata filtering, and functional gene mining, enabling users to identify taxa and traits of agronomic interest, particularly for applications in sustainable agriculture and sustainable desert revegetation. By combining genomic, ecological, and functional data, the Desert Plant Endophyte Genome Database serves as a foundational platform for the development of targeted microbial inoculants and fosters data-driven research into desert microbiomes and plant resilience mechanisms.</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/PMC13092982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147697668","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":"CysDBase: a comprehensive database of cysteine post-translational modifications across protein sequence, structure, microenvironment, class, cellular localization, biological pathway, and taxonomy.","authors":"Devarakonda Himaja, Debashree Bandyopadhyay","doi":"10.1093/database/baag021","DOIUrl":"10.1093/database/baag021","url":null,"abstract":"<p><p>The reactive thiol group of cysteine (Cys) acts as a nucleophile and undergoes many cysteine post-translational modifications (Cys-PTMs). Cys-PTMs, called protein redox switch, contribute to various cellular and physiological processes, including reactive oxygen species (ROS)-induced signalling, ROS mitigation, and scavenging. Consolidation of Cys-PTMs into a database would facilitate the mechanistic elucidation of biological processes and therapeutic applications. The existing databases store information on cysteine motifs, oxidation states, a few of the Cys-PTMs, etc., specific to species or kingdoms, and lack general applicability. There was no mention of the impact of the protein microenvironments and cellular localizations on the Cys-PTMs. The current study reports a database containing 7 Cys-PTMs (disulphide, S-nitrosylation, S-palmitoylation, S-glutathionylation, S-sulphenylation, metal-binding, and thioether), 11 features, 33 06 395 UniProt IDs, and 1 14 56 639 cysteine residues, across the taxonomy, encompassing cellular organelles, enzyme classes, sequence motifs, protein structures, and microenvironments. The maximum number of cysteine residues is reported here compared to 16 contemporary cysteine databases. Twenty-one types of metal-binding cysteines and thioether modifications are reported for the first time. Enzyme classes, cellular localization, taxonomic preferences, and microenvironment around Cys-PTMs were systematically analysed and curated, indicating the pathogenic involvement of those Cys-PTMs. The database has a web access (https://cysdbase.bits-hyderabad.ac.in/) and a programmatic access via GitHub link (https://github.com/devhimd19/CysDBase). The query inputs to the repositories are UniProt ID, biological pathway, location, or genus name. Query outputs are 11 biological features, namely, protein name, Cys-PTMs, cysteine residue number, cysteine sequence motif, cell organelle, biological pathway, protein microenvironment (buried fraction and relative hydrophobicity [rHpy]), EC number and enzyme class, secondary structure, organism, and PubMed ID.</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/PMC13161762/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147873684","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}