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

{"title":"The Sickle Africa Data Coordinating Centre (SADaCC): a data science hub for interdisciplinary sickle cell disease research and training.","authors":"Ambroise Wonkam, Nchangwi Syntia Munung, Mario Jonas, Wilson Mupfurirwa, Arthemon Nguweneza, Kevin Esoh, Chandre Oosterwyk-Liu, Zimkita Magangana, Khuthala Mnika, Valentina Ngo Bitoungui, Martha Kamkuemah, Kambe Banda, Nabeelah Samie, Jade Hotchkis, Victoria Nembaware, Andre-Pascal Kengne, Nicola Mulder","doi":"10.1093/database/baag007","DOIUrl":"10.1093/database/baag007","url":null,"abstract":"<p><p>Sickle cell disease (SCD) is one of the most prevalent monogenic disorders worldwide, with the highest burden in Africa, where ~75% of the 7.74 million global cases occur. Scientific progress in understanding its epidemiology, clinical heterogeneity, and treatment outcomes has been constrained by heterogeneous, non-standardized, and non-interoperable datasets that limit data integration and cross-country analyses. To address this, the Sickle Africa Data Coordinating Centre (SADaCC) was established as the data science hub of the SickleInAfrica consortium to support the development and expansion of Pan-African SCD registry. SADaCC now coordinates one of the largest patient-consented SCD datasets globally, with data from over 40 000 persons living with SCD in seven countries (Ghana, Mali, Nigeria, Tanzania, Uganda, Zambia, and Zimbabwe) within the Sickle Pan-African Research Consortium (SPARCo), as well as genomic data from SADaCC satellite sites in Cameroon, South Africa, and Malawi. The registry is built on FAIR-compliant architecture, the Sickle Cell Disease Ontology, and powered by a suite of digital platforms such as REDCap, NextCloud, RStudio, GitHub, Docker, and Jupyter. In partnership with SPARCo, SADaCC is also piloting a biobank that will link biospecimens with data in the registry to advance multi-omics research. Beyond infrastructure, SADaCC leads training and/or research in big data analytics, genomics, bioethics, implementation science, qualitative research, and psychosocial studies. Ethical, legal, and social considerations are embedded across all operations with emphasis on equitable intra-African collaboration and patient involvement in research. Looking ahead, SADaCC will integrate real-time data streams, AI-driven analytics, and multi-omics data to drive big data and genetic medicine research for SCD in Africa.</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/PMC12923167/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146257531","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":"TCMToxDB: a comprehensive database for the toxicological analysis of traditional Chinese medicines.","authors":"Yongzheng Zhu, Laihao Fang, Yunbo Miao, Longfei Ma, Rong Sun, Yimin Mao, Wei Guo, Jun Wang, Guoxian Yu","doi":"10.1093/database/baag019","DOIUrl":"10.1093/database/baag019","url":null,"abstract":"<p><p>The safety and modernization of traditional Chinese medicine (TCM) are significant concerns for human beings. Recently, the adverse effects caused by the use of certain TCMs have been frequently reported. Although TCMs may cause toxic reactions, they also play critical roles in treating multiple complex diseases. Therefore, toxicity research is urgently needed for the safe usage of TCMs. However, existing databases for TCMs primarily focus on the pharmacological effects of TCMs, with limited attention to the toxicity. They neither distinguish the toxic effects of formulas, herbs, and ingredients, nor classify and summarize targets for specific toxic manifestations, or assemble evidence from previous studies. We developed TCMToxDB, a comprehensive database that focuses on the toxicity and safe usage of TCMs. TCMToxDB systematically integrates and analyses the research results of toxic TCMs, offering users diverse information acquisition and analysis services. In addition, it assembles five canonical herb-target and ingredient-target interaction prediction algorithms with different advantages, which support the prediction of toxic targets of herbs and ingredients to empower the toxicity research of TCMs and to meet users' personal needs. TCMToxDB is accessible at https://www.sdu-idea.cn/TCMToxDB.</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/PMC13083690/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147688796","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":"VarStack2-an interactive web tool for somatic variant interpretation in cancer.","authors":"Nitin Sreekumar, Benjamin J Ahn, Shulan Tian, Alper Uzun, Ece D Gamsiz Uzun","doi":"10.1093/database/baag016","DOIUrl":"10.1093/database/baag016","url":null,"abstract":"<p><p>The rapid progress in tumour genome sequencing has created a need for bioinformatics tools to interpret the clinical significance of detected variants. VarStack² integrates information from several publicly available resources, including the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, cBioPortal, UCSC Genome Browser, and ClinicalTrials.gov, CIViC and presents it through a user-friendly interface. VarStack2 simplifies the process of retrieving data, saving users significant time compared to manually navigating each database individually. Users can input a variant by specifying a gene symbol, amino acid change, and coding sequence change, with the option to search tumour-specific studies in cBioPortal alongside their primary query. Results are organized into separate sections and can be exported in CSV format for further analysis. Additionally, VarStack2 offers a smart search feature that suggests variants for the gene of interest based on its database search results. These features make VarStack2 a useful tool for scientists and clinicians by enhancing the variant interpretation process and integrating somatic variant information into workflows. VarStack² is freely available at http://varstack.brown.edu/.</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/PMC13070670/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147671206","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":"IFNIKB: a type I interferon database for antitumuor immunity studies.","authors":"Fubo Ma, Kang Li, Yangchao Yu, Lei Zhang, Liguo Zhang, Bing Li, Le Zhang","doi":"10.1093/database/baag053","DOIUrl":"10.1093/database/baag053","url":null,"abstract":"<p><p>Type I interferon (IFN-I) is an important class of cytokines that can inhibit tumuor progression through mechanisms such as immunomodulation of the tumuor microenvironment or targeting cellular components. Although various endogenous and exogenous IFN-I therapeutic strategies have been developed, reports of immune cell dysfunction resulting from IFN-I signal enhancement indicate that optimal strategies have yet to be established. However, heterogeneous data of IFN-I are currently spread across multiple public databases and lack systematic integration, which poses challenges for knowledge acquisition and clinical research promotion. Herein, we develop the IFN-I Knowledge Base (IFNIKB), the first specific database for IFN-I. It integrates 26 273 literature articles on IFN-I antitumuor immunity, 2202 clinical trial records, and data on 8372 genes and 7164 proteins across 654 species. Furthermore, we design an automated workflow for knowledge discovery from literature. Users can construct on-demand knowledge graphs to explore entities and relationships through interactive visualizations, temporal trends, and network topology analyses. Additionally, we provide tools for multiple sequence alignment, sequence identity computation, and phylogenetic analysis to interpret IFN-I from molecular perspectives. Therefore, researchers can employ IFNIKB to conveniently acquire knowledge, propose hypothesis, optimize experimental design, and identify potential clinical therapeutic targets. Database URL:  http://www.combio-lezhang.online/IFNIKB/home.</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/PMC13548876/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148905243","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":"scRiskDB: a single-cell epigenomic resource linking complex traits to regulatory mechanisms across human tissues.","authors":"Gefei Zhao, Xianfeng Ping, Binbin Lai","doi":"10.1093/database/baag048","DOIUrl":"10.1093/database/baag048","url":null,"abstract":"<p><p>Understanding the regulatory impact of non-coding genetic variants remains a major challenge in human genetics. Here, we present scRiskDB, a comprehensive and user-friendly database that maps genetic risk variants to their downstream regulatory elements, target genes, and relevant cell types at single-cell resolution. By integrating genome-wide association studies (GWAS) with single-cell datasets across 45 tissues and developmental stages, scRiskDB implements a variant-to-function framework that systematically outlines potential regulatory cascades from single nucleotide variants to cell-specific risk mechanisms. This multi-layered design allows users to explore trait-associated regulatory architectures across cell types and developmental stages. The platform provides interactive, multi-level visualizations and curated results, facilitating hypothesis generation and mechanistic insights into disease aetiology.</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/PMC13439126/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148673223","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":"U-Plant and U-Plant DISCOVER: an open-source database for botanic garden collections management and public engagement.","authors":"Marco D'Antraccoli, Pietro Picconi, Giuseppe Maccioni, Davide Borgioli, Alessio Mo, Lorenzo Peruzzi","doi":"10.1093/database/baag059","DOIUrl":"10.1093/database/baag059","url":null,"abstract":"<p><p>Botanic gardens manage diverse plant collections and associated data that support research, conservation, education, and public engagement. Effective management of these collections requires robust, sustainable, and adaptable databases. Although several documentation solutions are available, they present different challenges: proprietary systems may involve costs, licensing constraints or limited institutional customization; locally developed databases may be difficult to maintain and update over time; spreadsheets may compromise data consistency and reliability. Here, we present U-Plant, a fully open-source database developed to support botanic garden collection management through a web-based interface. U-Plant records accession-level and individual-level data, including taxonomic identity, acquisition and source-locality information, garden location, condition history, and associated documentation. Its companion portal, U-Plant DISCOVER, provides public access to a curated subset of collection data through search tools, maps, and thematic visits, supporting both visitor engagement and external discovery of the collections. We describe the database structure, core functionalities, current implementation, and limitations, and outline priorities for future development, highlighting its potential as an open-source, community-supported database for botanic garden collections. GitHub repository link (U-Plant and U-Plant DISCOVER source code):  https://github.com/Unipisa/U-Plant Database URL (U-Plant DISCOVER):  https://uplantdiscover.sma.unipi.it/.</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/PMC13591270/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149012080","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":"NORG - Nitrogen ORGanizer: a novel web-based system for cryopreservation and freezer management.","authors":"Marvin Keller, Maximilian Przybilla, Jannik Sehring, Kai Schmid, Thomas Kauer, Felix Dörmer, Nadja Ritschel, Annika May, Hannah Schlierbach, Gudrun Schmidt, Kerstin Leib, Chiana Stephan, Elena Jenike, Carmen Selignow, Angelina Frank, Vincent Umathum, Anne Schänzer, Attila Németh, Keywan Sohrabi, Till Acker, Nick Heller, Hildegard Dohmen, Daniel Amsel","doi":"10.1093/database/baag037","DOIUrl":"10.1093/database/baag037","url":null,"abstract":"<p><p>The preservation of samples, whether they are from humans, cell lines, animals, plants, or other resources, usually follows a similar process. They are packaged in appropriate containers, whether tubes for cryogenic containers or boxes in freezers or other systems. For this biological sample database to be and remain useful, the samples must be appropriately cataloged and consecutively maintained in a document. After all, only when it is clear where which sample is located, the biological database has a value. In many cases this documentation is done in local spreadsheets and saved in e.g. CSV or XLSX format. In some cases, there are even handwritten records to comprehend where which sample is located. The intention behind a biological database is mainly the collection of rare material, but also the withdrawal of this material from the storage for further analysis, partly in-house but presumably also with external cooperation partners. If a sample is now taken from a container that has already been completely filled and stored, this empty space can only be re-allocated with a sample with great administrative effort, since new samples would otherwise be stored in the container that is currently active. With NORG we address this problem. NORG offers a stand-alone software solution via webservices to catalog e.g. nitrogen tanks and store the contents digitally with a color-coding for the filling level of the storages. In addition, NORG can trace who sent which sample to whom and when, for example to have it analyzed by a cooperation partner.</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/PMC13355526/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148419531","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":"PhaLP 2.0: extending the community-oriented phage lysin database with a SUBLYME pipeline for metagenomic discovery.","authors":"Alexandre Boulay, Victor Németh, Bjorn Criel, Michiel Stock, Bernard De Baets, Clovis Galiez, Elsa Rousseau, Yves Briers, Roberto Vázquez","doi":"10.1093/database/baag033","DOIUrl":"10.1093/database/baag033","url":null,"abstract":"<p><p>As biology becomes increasingly data-driven, so does the field of phage lysins, enzymes that degrade bacterial cell walls and offer promising alternatives to traditional antibiotics. Five years ago, we introduced PhaLP, a centralized resource for Phage Lytic Protein sequences and associated metadata to support global research efforts. Here, we present PhaLP 2.0, an enhanced database designed to address key challenges in computational lysin research by integrating newly identified lysins from thousands of metagenomes. To expand the known diversity of lysins beyond that of cultured phages, we developed SUBLYME, a protein-embedding-based machine-learning Software designed to Uncover and classify Bacteriophage Lysins from Metagenomic datasets. Using embeddings derived from the well-curated sequences of the original PhaLP database, we trained support vector machines to distinguish lysins from non-lysins in viromes and classify them as endolysins or virion-associated lysins. The models achieved an average F1 score of 98% on held-out clusters. SUBLYME enabled the discovery of 743 000 new lysin sequences from EnVhogDB, a virome-derived protein database, increasing the number of known lysin clusters 40-fold, from 1000 to 40 000. SUBLYME and PhaLP 2.0 are accessible online at https://github.com/Rousseau-Team/sublyme and https://phalp.ugent.be, respectively. Together, these advances establish PhaLP 2.0 as a comprehensive and scalable portal for lysin discovery, classification, and sequence analysis, paving the way for future antibacterial applications and evolutionary insights.</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/PMC13237592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175510","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":"Enhancing the SIB Literature Services (SIBiLS) with annotations to support biocuration.","authors":"Déborah Caucheteur, Alexandre Flament, Julien Gobeill, Emilie Pasche, Pierre-André Michel, Luc Mottin, Jeevanthi Liyanapathirana, Anaïs Mottaz, Patrick Ruch","doi":"10.1093/database/baag034","DOIUrl":"10.1093/database/baag034","url":null,"abstract":"<p><p>Text-mining techniques are essential tools to efficiently access information within an increasing number of publications. The SIB Literature Services (SIBiLS) are dedicated to the text-mining and biocuration communities. SIBiLS features an extensive repository comprising ~40 million abstracts from MEDLINE, 8 million full-text articles accessible through the National Library of Medicine (NLM) via PMC, 29 million supplementary data files, and ~1 million taxonomic treatments sourced from Plazi and other contributors. The full-text articles are also containing original Open Access journals, which are not commonly indexed by PMC but relevant for research and scientific communities, the so-called PMC + collection. SIBiLS is thus a superset of the NLM collections. Several text-processing strategies have been implemented (e.g. tokenization, hyphenation, concept normalization), leading to the enrichment of SIBiLS with automatic annotations based on term mapping from ~30 ontologies. The aim is to improve literature triage and curation time by automating the extraction and organization of relevant information from large volumes of scientific texts. The annotation process has generated over 16 billion annotations. One of the goals of this enhancement is to improve the recall of rare contents, including genomic variants and information on rare diseases. It is also used in various contexts, including studying biotic interactions and curating genomic variants through dedicated front-end applications such as the BiotXplorer and Variomes. Annotations are performed using the BioC standard. By combining JATS and BioC, SIBiLS enables curators to perform high-precision evidence tracking at any textual representation levels via an original annotation schema (e.g. IDs, onto-terminological sources, provenance). An assessment of the annotation quality has been carried out for the different annotated entities. The average annotation accuracy is approaching 90% but exhibits high variation depending on both the entity and the source vocabulary.</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/PMC13593564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149015974","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 similarity metric, rubric, and unified hierarchy for biomedical publication types and study designs.","authors":"Neil R Smalheiser, Joe D Menke, Arthur W Holt","doi":"10.1093/database/baag022","DOIUrl":"10.1093/database/baag022","url":null,"abstract":"<p><p>Our goal is to unify the 72 biomedical publication types and study designs (collectively, PTs) into a single rubric and hierarchy. This is carried out in a data-driven manner by computing pairwise similarities of each PT against all others to form a similarity matrix. By performing hierarchical clustering we place each PT in a specific category and collect these into broader categories. Spearman correlations among PT pairs ranged from strongly negative to strongly positive (-0.732 to +0.997), with a mean of 0.176. Overall, we obtained 13 clusters of PTs and 5 more general categories: Observational Clinical Research, Qualitative and Genetic Methods, Clinical Evaluation and Validation, Interventional Trial Research, and Scholarly Synthesis and Discourse. These were then utilized to construct a unified hierarchy of PT terms. The rubric provides a flexible classification scheme for publication types and study designs that can accommodate new PTs as they are added over time. The similarity metric has the potential to improve the modelling, implementation, and evaluation of automated indexing systems. The PT rubric provides an overview that complements the existing NIH MeSH Hierarchy trees, and the unified hierarchy permits proper automated expansion for PT indexing and PubMed user queries involving PT terms.</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/PMC13237591/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148175512","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}