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

{"title":"BrAPI v2: real-world applications for data integration and collaboration in the breeding and genetics community.","authors":"Peter Selby, Rafael Abbeloos, Anne-Francoise Adam-Blondon, Francisco J Agosto-Pérez, Michael Alaux, Isabelle Alic, Khaled Al-Shamaa, Johan Steven Aparicio, Jan Erik Backlund, Aldrin Batac, Sebastian Beier, Gabriel Besombes, Alice Boizet, Matthijs Brouwer, Terry Casstevens, Arnaud Charleroy, Keo Corak, Chaney Courtney, Mariano Crimi, Gouripriya Davuluri, Kauê de Sousa, Jeremy Destin, Stijn Dhondt, Ajay Dhungana, Bert Droesbeke, Manuel Feser, Mirella Flores-Gonzalez, Valentin Guignon, Corina Habito, Asis Hallab, Jenna Hershberger, Puthick Hok, Amanda M Hulse-Kemp, Lynn Carol Johnson, Sook Jung, Paul Kersey, Andrzej Kilian, Patrick König, Suman Kumar, Josh Lamos-Sweeney, Laszlo Lang, Matthias Lange, Marie-Angélique Laporte, Taein Lee, Erwan Le Floch, Francisco López, Brandon Madriz, Dorrie Main, Marco Marsella, Maud Marty, Célia Michotey, Zachary Miller, Iain Milne, Lukas A Mueller, Moses Nderitu, Pascal Neveu, Nick Palladino, Tim Parsons, Cyril Pommier, Jean-François Rami, Sebastian Raubach, Trevor Rife, Kelly Robbins, Mathieu Rouard, Joseph Ruff, Guilhem Sempéré, Romil Mayank Shah, Paul Shaw, Becky Smith, Nahuel Soldevilla, Anne Tireau, Clarysabel Tovar, Grzegorz Uszynski, Vivian Bass Vega, Stephan Weise, Shawn C Yarnes, The BrAPI Consortium","doi":"10.1093/database/baaf048","DOIUrl":"10.1093/database/baaf048","url":null,"abstract":"<p><p>Population growth and the impacts of climate change are placing increasing pressure on global agriculture and breeding programmes. Recent advancements in phenotyping techniques, genotyping technologies, and predictive modelling are accelerating genetic gains in breeding programmes, helping researchers and breeders develop improved crops more efficiently. However, these advancements have also led to an overwhelming torrent of fragmented data, creating significant challenges in data integration and management. To address this issue, the Breeding Application Programming Interface (BrAPI) project was established as a standardized data model for breeding data. BrAPI is an international, community-driven effort that facilitates interoperability among databases and tools, improving the sharing and interpretation of breeding-related data. This open-source standard is software-agnostic and can be used by anyone interested in breeding, phenotyping, germplasm, genotyping, and agronomy data management. This manuscript provides an overview of the BrAPI project, highlighting the significant progress made in the development of the data standard and the expansion of its community. It also presents a showcase of the wide variety of BrAPI-compatible tools that have been built to enhance breeding and research activities, demonstrating how the project is advancing agricultural innovation and data management practices.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462623/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136701","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":"GExplore 1.5: a comprehensive Caenorhabditis elegans database for the analysis of gene function with a new user-friendly web interface.","authors":"Harald Hutter, Mehrdad Moosavi, Nelly Mafi","doi":"10.1093/database/baaf044","DOIUrl":"10.1093/database/baaf044","url":null,"abstract":"<p><p>GExplore is an online tool to assist with large-scale data mining of selected datasets related to gene and protein function in Caenorhabditis elegans. Here, we describe the current version GExplore 1.5, which contains new datasets and display options as well as a completely redesigned web interface. GExplore now consists of six databases. The gene database contains protein domain information, general expression, and phenotype data as well as interacting genes, gene ontology annotations, and disease associations. The mutation database contains a curated list of more than 200 000 mutations affecting the protein sequences of all protein-coding genes. The protein database contains proteome data from 19 different nematode species, four genetic model organisms and the human proteome for comparison. Three genome-scale RNAseq expression databases contain expression profiles of different developmental stages from embryo to adult, tissues-specific expression profiles at the L2 stage, and expression profiles of the major tissues in the developing embryo at five different time points from gastrulation to the beginning of terminal differentiation. The web-based user interface has been completely redeveloped for the current version. The search interfaces allow users to explore content of the individual databases in detail. The interactive display pages enable the user to fine-tune the results, display additional data, and download the results. GExplore is a tool to quickly obtain an overview of biological and biochemical functions of large groups of genes or identify genes with a certain combination of features for further experimental analysis. Database URL: https://genome.science.sfu.ca/gexplore.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462625/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136731","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":"Empirical substitution models of protein evolution: database, relationships, and modeling considerations.","authors":"Paula Iglesias-Rivas, Roberto Del Amparo, Javier A Cabaleiro, Miguel Arenas","doi":"10.1093/database/baaf052","DOIUrl":"10.1093/database/baaf052","url":null,"abstract":"<p><p>Substitution models of protein evolution describe the patterns of amino acid substitutions over evolutionary time and are fundamental for probabilistic methods of phylogenetic inference. At the protein level, a variety of substitution models are available, but only empirical substitution models are well established in phylogenetics due to their mathematical simplicity. Despite their importance, a database compiling the large number of currently available empirical substitution models of protein evolution is lacking, although such a resource could facilitate access, assessment, and subsequent implementation of these models into phylogenetic frameworks. Besides, little is known about formal comparisons between the current set of empirical substitution models. We present EModelDB, a database of empirical substitution models of protein evolution required for probabilistic protein phylogenetics that includes the corresponding exchangeability matrices, model classification, and model-specific biological information. The database is integrated into a graphical user interface, written in Python and SQL, that facilitates its usability. We also compared common empirical substitution models in terms of the distance between their relative rates of amino acid substitution and amino frequencies at equilibrium. We found that substitution models derived from proteins related in nature tend to cluster together, reflecting similar evolutionary patterns. Indeed, we evaluated the empirical substitution models in terms of the folding stability of the derived modeled proteins and found that they generally produce less stable proteins compared to real proteins, suggesting that substitution models with additional evolutionary constraints can be preferred for studying protein evolution accounting for folding stability. Database URL: https://github.com/Paula-Iglesias-Rivas/EModelDB.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462380/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136768","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":"MRdb: a comprehensive database of univariate and multivariate Mendelian randomization with large-scale GWAS summary data.","authors":"Qian Liu, Yujie Zhang, Houxing Li, Jiatong Li, Mengyu Xin, Rui Sun, Yifan Dai, Xinxin Shan, Yuting He, Borui Xu, Shangwei Ning, Peng Wang, Qiuyan Guo","doi":"10.1093/database/baaf054","DOIUrl":"10.1093/database/baaf054","url":null,"abstract":"<p><p>Recent advancements highlight the importance of large-scale causal inference in elucidating disease mechanisms and guiding public health strategies. Mendelian randomization (MR) has become a cornerstone method for identifying causal relationships by leveraging genetic variants as instrumental variables. However, existing tools lack flexibility for multivariable analyses and fail to integrate diverse datasets effectively. To address these challenges, we introduce MRdb, a comprehensive database designed for conducting both univariable and multivariable MR analyses. MRdb encompasses 12 distinct categories of exposure data, including but not limited to 19 126 expression quantitative trait loci genes, 4907 plasma proteins, and 1400 plasma metabolites. Additionally, it integrates 48 507 disease outcomes sourced from FinnGen R10 and the IEU Open GWAS Project. MRdb offers robust data preprocessing features, including handling missing statistics, harmonizing datasets, and selecting instrumental variables to ensure high-quality analyses. Collectively, MRdb bridges the gaps in existing tools by integrating diverse datasets with user-friendly functionalities, empowering researchers to explore complex causal mechanisms.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462376/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136794","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":"ScLineageAtlas: a comprehensive single-cell genomics database for characterizing cellular clones in cancer.","authors":"Jinyang Liu, Rui Hou, Junlin Xu, Tingting Hui, Haotian Tian, Yankun Liu, Meijun Zhang, Geng Tian, Jialiang Yang","doi":"10.1093/database/baaf046","DOIUrl":"10.1093/database/baaf046","url":null,"abstract":"<p><p>Accurate identification of clonal relationships between cell populations is crucial for investigating cellular differentiation trajectories and gaining insights into the underlying mechanisms of cancer initiation and development. The Single Cell Lineage Atlas (ScLineageAtlas; https://www.scladb.geneis.org.cn) is a comprehensive single-cell genomics database that characterizes cellular clones across various cancer types. The database currently includes 24 processed single-cell RNA sequencing datasets spanning 13 different cancer types. ScLineageAtlas leverages advanced computational methods to identify cellular clones, providing researchers with a detailed understanding of clone relationships and evolutionary dynamics. Additionally, the database offers comprehensive metadata for each sample, enabling researchers to explore contextual information and sample characteristics. The spatial visualization of cell clones presented in the ScLineageAtlas provides a valuable tool for enhancing our understanding of the genetic heterogeneity within the tumour microenvironment. Through the analysis of biological differences between these diverse cell populations, researchers can explore key genes and signalling pathways associated with cancer initiation, development, and therapeutic efficacy. In summary, the ScLineageAtlas serves as a user-friendly platform for data operations on cellular clones, facilitating the understanding of tumour heterogeneity, differentiation trajectories, and evolution. It thus contributes significantly to cancer research and clinical practice.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462628/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136872","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":"Review of autism spectrum disorder databases for the identification of candidate genes.","authors":"Diana Martínez-Minguet, René Noel, Alberto García S, Mireia Costa, Oscar Pastor","doi":"10.1093/database/baaf067","DOIUrl":"https://doi.org/10.1093/database/baaf067","url":null,"abstract":"<p><p>Research into the genetics of autism spectrum disorder (ASD) seeks to unravel its complex genetic background by identifying genes associated with the condition at varying levels of confidence. While these findings hold significant potential for clinical applications, the dispersed nature of scientific evidence presents a challenge for the reliable identification of ASD candidate genes. Although ASD candidate genes are gathered in genetic databases, these vary widely in the gene sets, biological information, and confidence level classification methods, leading to inconsistencies and complicating research efforts. This study aims to identify and assess the quality and reliability of ASD genetic databases to support more robust identification of ASD candidate genes. Using a Systematic Mapping Study, we identified 13 specialized databases. We then followed a Data Quality Approach in two stages, first assessing Accessibility, Currency, and Relevance dimensions to select the potentially relevant databases to be used as ASD candidate gene sources. The selected databases were analysed, assessing Completeness-at schema and data level-, and Consistency between high-confidence ASD genes. The four selected databases are: AutDB, SFARI Gene, GeisingerDBD, and SysNDD. SFARI Gene demonstrated the highest completeness at schema level (89%), while AutDB showed the highest completeness at data level (90%). However, only 1.5% consistency was observed across the four databases in their classification of high-confidence ASD candidate genes. Our findings highlight the unique contributions of each database and reveal substantial inconsistencies in gene classification, driven by differences in scoring criteria and the scientific evidence considered. These inconsistencies have important implications for both clinical users and researchers, as conclusions may vary depending on the database used. This study supports researchers when using ASD genetic databases, promoting consistent interpretation and improved clinical decisions.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145298968","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Zoonosis: a comprehensive database of zoonotic pathogens.","authors":"Boyuan Zhang, Dan Liu, Weiwen Wang, Xiaoqiang Li, Yifei Xie, Huitong Li, Hongmei Zhou, Jianshuai Gao, Hui Jiang, Xuezheng Fan, Jiabo Ding, Qingchun Shen, Xizheng Ma","doi":"10.1093/database/baaf062","DOIUrl":"https://doi.org/10.1093/database/baaf062","url":null,"abstract":"<p><p>Emerging infectious diseases pose a significant threat to global public health and economic security, with zoonoses accounting for a substantial proportion. Livestock such as cattle and sheep are critical reservoirs for zoonotic pathogens and play a key role in transmitting these pathogens to humans and other animals, including dogs and wildlife, due to their close interaction with diverse populations. In this study, we introduce Zoonosis (http://zoonosis.cn/zoonosis/), a comprehensive database that integrates data on pathogens, including Brucella, Mycobacterium tuberculosis, and Bacillus anthracis. Currently, Zoonosis integrates over 4500 samples from more than 60 countries, with a total data volume of 1.8TB, providing a global perspective on zoonotic disease distribution. Equipped with user-friendly visualization and analysis tools, Zoonosis enables rapid biological data interpretation, aiding disease diagnosis and prevention. This resource supports virology, zoology, and epidemiology experts in monitoring cross-species transmissions and mitigating future zoonotic outbreaks. Database URL: http://zoonosis.cn/zoonosis/.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145299034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The state of the human coding gene catalogues.","authors":"Miguel Maquedano, Daniel Cerdán-Vélez, Michael L Tress","doi":"10.1093/database/baaf045","DOIUrl":"10.1093/database/baaf045","url":null,"abstract":"<p><p>In 2018, we analysed the three main repositories for the human proteome: Ensembl/GENCODE, RefSeq, and UniProtKB. At that time the three gene sets disagreed on the coding status of one of every eight annotated coding genes, and our results suggested that as many as 4234 of these genes might not be correctly classified. Here, we have repeated the analysis with updated versions of the three reference gene sets. Superficially, little appears to have changed. The three sets annotate 21 871 coding genes, slightly fewer than previously, and still disagree on the status of 2603 annotated genes, almost one in eight. However, we show that collaborations between the three reference gene sets have led to greater consensus. Reference catalogues have agreed on the coding status of another 249 genes since the last analysis while at least 700 genes have been reclassified. We still find that there are >2000 coding genes with at least one potential non-coding feature to indicate that they may not be coding genes. This includes a large majority of the 2603 genes for which annotators do not agree on coding status. In total, we believe that as many as 3000 genes may be misclassified as coding and could be annotated as non-coding genes, pseudogenes, or cancer antigens.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462614/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145136841","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":"VineColD: an integrative database for global historical tracing and real-time monitoring of grapevine cold hardiness.","authors":"Hongrui Wang, Jason P Londo","doi":"10.1093/database/baaf055","DOIUrl":"10.1093/database/baaf055","url":null,"abstract":"<p><p>Cold hardiness is a crucial physiological parameter that determines the survival of grapevines during the dormant season. Accurate modelling and large-scale prediction of grapevine cold hardiness are essential for assessing the potential geographic distribution of grapevine cultivation, quantifying the impact of climate change on grapevine habitats, and ensuring the sustainability of the grape and wine industries worldwide in the regions characterized by cool or cold dormant seasons. However, until now, no comprehensive database has been available. In this research, we combined advanced automated machine learning techniques with extensive historical and current weather data to create an integrative database for grapevine cold hardiness: VineColD (https://cornell-tree-fruit-physiology.shinyapps.io/VineColD/). We developed the NYUS.2.1 model, an automated machine learning-based system for predicting grapevine cold hardiness and applied it to global historical weather data from 17,985 curated weather stations between latitudes 30° and 55° in both hemispheres from 1960 to 2024, resulting in the development of an integrative grapevine cold hardiness database and monitoring system, VineColD. VineColD integrates both a global historical dataset and a daily updated regional cold hardiness system, offering a comprehensive resource to study grape cold hardiness for 54 grapevine cultivars. The platform provides multiple download options, from single-station data to complete datasets, and the interactive multifunctional R Shiny application facilitates data analysis and visualization. VineColD delivers critical insights into the impact of climate change on grapevine cultivation and supports a range of analytical functions, making it a valuable tool for grape growers and researchers.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":"2025 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12475903/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145174122","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":"BgDB: a comprehensive genomic resource information system of bitter gourd for accelerated breeding programme.","authors":"Princy Saini, Ankita Singh, Tilak Chandra, Dheeraj Kumar Chaurasia, Kunal Chaudhary, Priyanka Jain, G Boopalakrishnan, Sarika Jaiswal, Shyam Sunder Dey, Tusar Kanti Behera, Ulavappa Basavanneppa Angadi, Mir Asif Iquebal, Dinesh Kumar","doi":"10.1093/database/baaf039","DOIUrl":"10.1093/database/baaf039","url":null,"abstract":"<p><p>Bitter gourd, scientifically known as Momordica charantia L. with 2n = 22, is a widely recognized medicinal vegetable, renowned for its multifaceted health benefits, primarily acclaimed for its lipid- and glucose-lowering effects. Its growing demands as a food source and for industrial applications necessitate value addition in ongoing breeding initiatives to enhance genotypic traits in multifarious ways. A thorough understanding of the underlying molecular footprint is warranted for characterization, which still remains underexplored relative to other cash crops. Though a chromosome-level genome assembly of bitter gourd is available, scattered and fragmented information becomes an obstacle for assisted breeding and gene editing. Therefore, it is crucial to further dissect structural and molecular variants, noncoding RNAs (ncRNAs), transcription factors, and transcripts from whole-genome and resequencing projects. The present study leads to the development of a comprehensive genomic resource, BgDB (Bitter Gourd Resource Database) at a single platform, vital for advanced bitter gourd breeding programmes for raising bitter gourd varieties with traits of significant social and economic value. BgDB, available at https://bgdb.daasbioinfromaticsteam.in/index.php, is a user-friendly, three-tier database that offers a comprehensive interface with detailed analysed information, including 114 598 transcripts, 4914 differentially expressed genes, 32 570 predicted simple sequence repeat markers, and 162 850 primers for downstream applications. It also catalogues extensive annotations of bitter gourd-specific single nucleotide polymorphisms/insertions and deletions, long noncoding RNAs, circular RNAs, microRNAs, 1220 transcription factors, 295 transcription regulators, and 146 quantitative trait loci (QTL) distributed throughout the chromosomes. This genomic resource is poised to significantly advance genetic diversity analyses, population and varietal differentiation, and trait optimization. It further facilitates the exploration of regulatory ncRNA elements, key transcripts, and essential transcription factors and regulators. The discovery of QTL will aid in the development of improved bitter gourd varieties in the endeavour of enhanced productivity. Beyond comprehensive datasets, the future integration of multi-omics resources could profoundly advance and fully unlock the potential of databases. Database URL: https://bgdb.daasbioinfromaticsteam.in/index.php.</p>","PeriodicalId":10923,"journal":{"name":"Database: The Journal of Biological Databases and Curation","volume":" ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12462627/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144583338","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}