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

{"title":"An interactive web application for exploring systemic lupus erythematosus blood transcriptomic diversity.","authors":"Eléonore Bettacchioli, Laurent Chiche, Damien Chaussabel, Divi Cornec, Noémie Jourde-Chiche, Darawan Rinchai","doi":"10.1093/database/baae045","DOIUrl":"10.1093/database/baae045","url":null,"abstract":"<p><p>In the field of complex autoimmune diseases such as systemic lupus erythematosus (SLE), systems immunology approaches have proven invaluable in translational research settings. Large-scale datasets of transcriptome profiling have been collected and made available to the research community in public repositories, but remain poorly accessible and usable by mainstream researchers. Enabling tools and technologies facilitating investigators' interaction with large-scale datasets such as user-friendly web applications could promote data reuse and foster knowledge discovery. Microarray blood transcriptomic data from the LUPUCE cohort (publicly available on Gene Expression Omnibus, GSE49454), which comprised 157 samples from 62 adult SLE patients, were analyzed with the third-generation (BloodGen3) module repertoire framework, which comprises modules and module aggregates. These well-characterized samples corresponded to different levels of disease activity, different types of flares (including biopsy-proven lupus nephritis), different auto-antibody profiles and different levels of interferon signatures. A web application was deployed to present the aggregate-level, module-level and gene-level analysis results from LUPUCE dataset. Users can explore the similarities and heterogeneity of SLE samples, navigate through different levels of analysis, test hypotheses and generate custom fingerprint grids and heatmaps, which may be used in reports or manuscripts. This resource is available via this link: https://immunology-research.shinyapps.io/LUPUCE/. This web application can be employed as a stand-alone resource to explore changes in blood transcript profiles in SLE, and their relation to clinical and immunological parameters, to generate new research hypotheses.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131423/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141160773","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":"MechanoBase: a comprehensive database for the mechanics of tissues and cells.","authors":"Yanhong Xiong, Shiyu Li, Yuxuan Zhang, Qianqian Chen, Mengtan Xing, Yong Zhang, Qi Wang","doi":"10.1093/database/baae040","DOIUrl":"10.1093/database/baae040","url":null,"abstract":"<p><p>Mechanical aspects of tissues and cells critically influence a myriad of biological processes and can substantially alter the course of diverse diseases. The emergence of diverse methodologies adapted from physical science now permits the precise quantification of the cellular forces and the mechanical properties of tissues and cells. Despite the rising interest in tissue and cellular mechanics across fields like biology, bioengineering and medicine, there remains a noticeable absence of a comprehensive and readily accessible repository of this pertinent information. To fill this gap, we present MechanoBase, a comprehensive tissue and cellular mechanics database, curating 57 480 records from 5634 PubMed articles. The records archived in MechanoBase encompass a range of mechanical properties and forces, such as modulus and tractions, which have been measured utilizing various technical approaches. These measurements span hundreds of biosamples across more than 400 species studied under diverse conditions. Aiming for broad applicability, we design MechanoBase with user-friendly search, browsing and data download features, making it a versatile tool for exploring biomechanical attributes in various biological contexts. Moreover, we add complementary resources, including the principles of popular techniques, the concepts of mechanobiology terms and the cellular and tissue-level expression of related genes, offering scientists unprecedented access to a wealth of knowledge in this field of research. Database URL: https://zhanglab-web.tongji.edu.cn/mechanobase/ and https://compbio-zhanglab.org/mechanobase/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131424/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141160712","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":"DUVEL: an active-learning annotated biomedical corpus for the recognition of oligogenic combinations.","authors":"Charlotte Nachtegael, Jacopo De Stefani, Anthony Cnudde, Tom Lenaerts","doi":"10.1093/database/baae039","DOIUrl":"10.1093/database/baae039","url":null,"abstract":"<p><p>While biomedical relation extraction (bioRE) datasets have been instrumental in the development of methods to support biocuration of single variants from texts, no datasets are currently available for the extraction of digenic or even oligogenic variant relations, despite the reports in literature that epistatic effects between combinations of variants in different loci (or genes) are important to understand disease etiologies. This work presents the creation of a unique dataset of oligogenic variant combinations, geared to train tools to help in the curation of scientific literature. To overcome the hurdles associated with the number of unlabelled instances and the cost of expertise, active learning (AL) was used to optimize the annotation, thus getting assistance in finding the most informative subset of samples to label. By pre-annotating 85 full-text articles containing the relevant relations from the Oligogenic Diseases Database (OLIDA) with PubTator, text fragments featuring potential digenic variant combinations, i.e. gene-variant-gene-variant, were extracted. The resulting fragments of texts were annotated with ALAMBIC, an AL-based annotation platform. The resulting dataset, called DUVEL, is used to fine-tune four state-of-the-art biomedical language models: BiomedBERT, BiomedBERT-large, BioLinkBERT and BioM-BERT. More than 500 000 text fragments were considered for annotation, finally resulting in a dataset with 8442 fragments, 794 of them being positive instances, covering 95% of the original annotated articles. When applied to gene-variant pair detection, BiomedBERT-large achieves the highest F1 score (0.84) after fine-tuning, demonstrating significant improvement compared to the non-fine-tuned model, underlining the relevance of the DUVEL dataset. This study shows how AL may play an important role in the creation of bioRE dataset relevant for biomedical curation applications. DUVEL provides a unique biomedical corpus focusing on 4-ary relations between two genes and two variants. It is made freely available for research on GitHub and Hugging Face. Database URL: https://huggingface.co/datasets/cnachteg/duvel or https://doi.org/10.57967/hf/1571.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11131422/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141160776","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":"PDB NextGen Archive: centralizing access to integrated annotations and enriched structural information by the Worldwide Protein Data Bank.","authors":"Preeti Choudhary, Zukang Feng, John Berrisford, Henry Chao, Yasuyo Ikegawa, Ezra Peisach, Dennis W Piehl, James Smith, Ahsan Tanweer, Mihaly Varadi, John D Westbrook, Jasmine Y Young, Ardan Patwardhan, Kyle L Morris, Jeffrey C Hoch, Genji Kurisu, Sameer Velankar, Stephen K Burley","doi":"10.1093/database/baae041","DOIUrl":"10.1093/database/baae041","url":null,"abstract":"<p><p>The Protein Data Bank (PDB) is the global repository for public-domain experimentally determined 3D biomolecular structural information. The archival nature of the PDB presents certain challenges pertaining to updating or adding associated annotations from trusted external biodata resources. While each Worldwide PDB (wwPDB) partner has made best efforts to provide up-to-date external annotations, accessing and integrating information from disparate wwPDB data centers can be an involved process. To address this issue, the wwPDB has established the PDB Next Generation (or NextGen) Archive, developed to centralize and streamline access to enriched structural annotations from wwPDB partners and trusted external sources. At present, the NextGen Archive provides mappings between experimentally determined 3D structures of proteins and UniProt amino acid sequences, domain annotations from Pfam, SCOP2 and CATH databases and intra-molecular connectivity information. Since launch, the PDB NextGen Archive has seen substantial user engagement with over 3.5 million data file downloads, ensuring researchers have access to accurate, up-to-date and easily accessible structural annotations. Database URL: http://www.wwpdb.org/ftp/pdb-nextgen-archive-site.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11130521/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158130","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":"CoSFISH: a comprehensive reference database of COI and 18S rRNA barcodes for fish.","authors":"Yuanyuan Wang, Yexin Yang, Yi Liu, Chao Liu, Meng Xu, Miao Fang, Xidong Mu","doi":"10.1093/database/baae038","DOIUrl":"10.1093/database/baae038","url":null,"abstract":"<p><p>Fish, being a crucial component of aquatic ecosystems, holds significant importance from both economic and ecological perspectives. However, the identification of fish at the species level remains challenging, and there is a lack of a taxonomically complete and comprehensive reference sequence database for fish. Therefore, we developed CoSFISH, an online fish database. Currently, the database contains 21 535 cytochrome oxidase I sequences and 1074 18S rRNA sequences of 21 589 species, belonging to 8 classes and 90 orders. We additionally incorporate online analysis tools to aid users in comparing, aligning and analyzing sequences, as well as designing primers. Users can upload their own data for analysis, in addition to using the data stored in the database directly. CoSFISH offers an extensive fish database and incorporates online analysis tools, making it a valuable resource for the study of fish diversity, phylogenetics and biological evolution. Database URL:  http://210.22.121.250:8888/CoSFISH/home/indexPage.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11130519/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141158172","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":"MSGD: a manually curated database of genomic, transcriptomic, proteomic and drug information for multiple sclerosis.","authors":"Tao Wu, Yaopan Hou, Guanghao Xin, Jingyan Niu, Shanshan Peng, Fanfan Xu, Ying Li, Yuling Chen, Yifangfei Yu, Huixue Zhang, Xiaotong Kong, Yuze Cao, Shangwei Ning, Lihua Wang, Junwei Hao","doi":"10.1093/database/baae037","DOIUrl":"10.1093/database/baae037","url":null,"abstract":"<p><p>Multiple sclerosis (MS) is the most common inflammatory demyelinating disease of the central nervous system. 'Omics' technologies (genomics, transcriptomics, proteomics) and associated drug information have begun reshaping our understanding of multiple sclerosis. However, these data are scattered across numerous references, making them challenging to fully utilize. We manually mined and compiled these data within the Multiple Sclerosis Gene Database (MSGD) database, intending to continue updating it in the future. We screened 5485 publications and constructed the current version of MSGD. MSGD comprises 6255 entries, including 3274 variant entries, 1175 RNA entries, 418 protein entries, 313 knockout entries, 612 drug entries and 463 high-throughput entries. Each entry contains detailed information, such as species, disease type, detailed gene descriptions (such as official gene symbols), and original references. MSGD is freely accessible and provides a user-friendly web interface. Users can easily search for genes of interest, view their expression patterns and detailed information, manage gene sets and submit new MS-gene associations through the platform. The primary principle behind MSGD's design is to provide an exploratory platform, aiming to minimize filtration and interpretation barriers while ensuring highly accessible presentation of data. This initiative is expected to significantly assist researchers in deciphering gene mechanisms and improving the prevention, diagnosis and treatment of MS. Database URL: http://bio-bigdata.hrbmu.edu.cn/MSGD.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11126313/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141093077","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":"Collecting and managing in situ banana genetic resources information (Musa spp.) using online resources and citizen science.","authors":"Christophe Jenny, Valentin Guignon, Felip Manyer I Ballester, Max Ruas, Mathieu Rouard","doi":"10.1093/database/baae036","DOIUrl":"10.1093/database/baae036","url":null,"abstract":"<p><p>The Musa Germplasm Information System (MGIS) stands as a pivotal database for managing global banana genetic resources information. In our latest effort, we have expanded MGIS to incorporate in situ observations. We thus incorporated more than 3000 in situ observations from 133 countries primarily sourced from iNaturalist, GBIF, Flickr, Pl@ntNet, Google Street view and expert curation of the literature. This addition provides a more comprehensive and detailed view of banana diversity and its distribution. Additional graphical interfaces, supported by new Drupal modules, were developed, allowing users to compare banana accessions and explore them based on various filters including taxonomy and geographic location. The integrated maps present a unified view, showcasing both in situ observations and the collecting locations of accessions held in germplasm collections. This enhancement not only broadens the scope of MGIS but also promotes a collaborative and open approach in documenting banana diversity, to allow more effective conservation and use of banana germplasm. Furthermore, this work documents a citizen-science approach that could be relevant for other communities. Database URL: https://www.crop-diversity.org/mgis/musa-in-situ.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110932/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141080783","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 terpenoids database with the chemical content as a novel agronomic trait.","authors":"Wenqian Li, Yinliang Chen, Ruofei Yang, Zilong Hu, Shaozhong Wei, Sheng Hu, Xinjun Xiong, Meijuan Wang, Ammar Lubeiny, Xiaohua Li, Minglei Feng, Shuang Dong, Xinlu Xie, Chao Nie, Jingyi Zhang, Yunhao Luo, Yichen Zhou, Ruodi Liu, Jinhai Pan, De-Xin Kong, Xuebo Hu","doi":"10.1093/database/baae027","DOIUrl":"10.1093/database/baae027","url":null,"abstract":"<p><p>Natural products play a pivotal role in drug discovery, and the richness of natural products, albeit significantly influenced by various environmental factors, is predominantly determined by intrinsic genetics of a series of enzymatic reactions and produced as secondary metabolites of organisms. Heretofore, few natural product-related databases take the chemical content into consideration as a prominent property. To gain unique insights into the quantitative diversity of natural products, we have developed the first TerPenoids database embedded with Content information (TPCN) with features such as compound browsing, structural search, scaffold analysis, similarity analysis and data download. This database can be accessed through a web-based computational toolkit available at http://www.tpcn.pro/. By conducting meticulous manual searches and analyzing over 10 000 reference papers, the TPCN database has successfully integrated 6383 terpenoids obtained from 1254 distinct plant species. The database encompasses exhaustive details including isolation parts, comprehensive molecule structures, chemical abstracts service registry number (CAS number) and 7508 content descriptions. The TPCN database accentuates both the qualitative and quantitative dimensions as invaluable phenotypic characteristics of natural products that have undergone genetic evolution. By acting as an indispensable criterion, the TPCN database facilitates the discovery of drug alternatives with high content and the selection of high-yield medicinal plant species or phylogenetic alternatives, thereby fostering sustainable, cost-effective and environmentally friendly drug discovery in pharmaceutical farming. Database URL: http://www.tpcn.pro/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11110934/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141080781","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":"CardioHotspots: a database of mutational hotspots for cardiac disorders.","authors":"Alberto García S, Mireia Costa, Alba García-Zarzoso, Oscar Pastor","doi":"10.1093/database/baae034","DOIUrl":"10.1093/database/baae034","url":null,"abstract":"<p><p>Mutational hotspots are DNA regions with an abnormally high frequency of genetic variants. Identifying whether a variant is located in a mutational hotspot is critical for determining the variant's role in disorder predisposition, development, and treatment response. Despite their significance, current databases on mutational hotspots are limited to the oncology domain. However, identifying mutational hotspots is critical for any disorder in which genetics plays a role. This is true for the world's leading cause of death: cardiac disorders. In this work, we present CardioHotspots, a literature-based database of manually curated hotspots for cardiac diseases. This is the only database we know of that provides high-quality and easily accessible information about hotspots associated with cardiac disorders. CardioHotspots is publicly accessible via a web-based platform (https://genomics-hub.pros.dsic.upv.es:3099/). Database URL: https://genomics-hub.pros.dsic.upv.es:3099/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11096770/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140944457","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":"PMBC: a manually curated database for prognostic markers of breast cancer.","authors":"Jiabei Liu, Yiyi Yu, Mingyue Li, Yixuan Wu, Weijun Chen, Guanru Liu, Lingxian Liu, Jiechun Lin, Chujun Peng, Weijun Sun, Xiaoli Wu, Xin Chen","doi":"10.1093/database/baae033","DOIUrl":"10.1093/database/baae033","url":null,"abstract":"<p><p>Breast cancer is notorious for its high mortality and heterogeneity, resulting in different therapeutic responses. Classical biomarkers have been identified and successfully commercially applied to predict the outcome of breast cancer patients. Accumulating biomarkers, including non-coding RNAs, have been reported as prognostic markers for breast cancer with the development of sequencing techniques. However, there are currently no databases dedicated to the curation and characterization of prognostic markers for breast cancer. Therefore, we constructed a curated database for prognostic markers of breast cancer (PMBC). PMBC consists of 1070 markers covering mRNAs, lncRNAs, miRNAs and circRNAs. These markers are enriched in various cancer- and epithelial-related functions including mitogen-activated protein kinases signaling. We mapped the prognostic markers into the ceRNA network from starBase. The lncRNA NEAT1 competes with 11 RNAs, including lncRNAs and mRNAs. The majority of the ceRNAs in ABAT belong to pseudogenes. The topology analysis of the ceRNA network reveals that known prognostic RNAs have higher closeness than random. Among all the biomarkers, prognostic lncRNAs have a higher degree, while prognostic mRNAs have significantly higher closeness than random RNAs. These results indicate that the lncRNAs play important roles in maintaining the interactions between lncRNAs and their ceRNAs, which might be used as a characteristic to prioritize prognostic lncRNAs based on the ceRNA network. PMBC renders a user-friendly interface and provides detailed information about individual prognostic markers, which will facilitate the precision treatment of breast cancer. PMBC is available at the following URL: http://www.pmbreastcancer.com/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11095525/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140944463","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}