Sook Jung

The Genome Database for Vaccinium (GDV, https://www.vaccinium.org/) is a knowledgebase serving the global Vaccinium research community. GDV centralizes genetics, genomics, and breeding data for blueberry, cranberry, bilberry, and lingonberry. Launched in 2011, GDV transforms research data into an integrated resource through data curation, standardization, and value-added analyses that enable cross-study comparisons and knowledge synthesis. GDV houses diverse data types including germplasm records, genetic markers, linkage maps, QTL/GWAS results, traits, genome assemblies, gene annotations, and expression profiles. The database team perform comprehensive analyses including homology searches against protein databases, InterPro domain identification, and synteny analyses between Vaccinium genomes to identify orthologous relationships. These analyses facilitate knowledge transfer within and between species, enabling researchers to leverage findings across the Vaccinium genus. Users access data through intuitive search interfaces and specialized tools for genetic map visualization, genome browsing, metabolic pathway exploration, and sequence similarity searches. The integrated Breeding Information Management System (BIMS) enables breeders to manage private breeding programs as well as accessing public breeding data. Currently containing 46 genome assemblies, over 287,000 genetic markers, 4,328 QTL, and 3.3 million gene models, GDV continues expanding through collaborations with the VacCAP project and international research groups. The database evolves based on community needs and emerging data types, ensuring its continued relevance for advancing Vaccinium research and crop improvement efforts worldwide.

CottonGen (https://www.cottongen.org) serves as an integrated genomics platform for the cotton research community, combining comprehensive data storage with sophisticated analysis tools built on the Tripal framework. Since its establishment in 2012, CottonGen has consolidated and expanded resources previously scattered across CottonDB and the Cotton Marker Database while developing advanced analytical capabilities. The platform has expanded substantially between 2021 and 2025, with tetraploid genome assemblies and gene annotations increasing 3-fold, genotype datasets doubling, and phenotype records growing 1.8-fold. Recent developments include enhanced search and visualization capabilities through updated Map Viewer and Breeding Information Management System tools, integration of genome-wide association studies and gene expression analysis via new Tripal modules, and implementation of Genotype Investigator for Genome-Wide Analyses for interactive large-scale genotyping data exploration. Beyond data storage, CottonGen provides integrated analysis workflows spanning sequence similarity searches, synteny analysis, expression profiling, marker-trait association studies, and breeding data management. These capabilities support diverse research applications from comparative genomics and gene discovery to marker-assisted selection and cultivar development. As the official platform for the International Cotton Genome Initiative, CottonGen helps coordinate global cotton research efforts and maintains a comprehensive, actively curated resource that evolves with community research priorities.

The AgBioData consortium is a community-driven initiative supporting >40 genomic, genetic, and breeding (GGB) databases for agricultural research. Informed by the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles, AgBioData aims to improve data management, sharing, and reuse across diverse biological data types. Through a National Science Foundation Research Coordination Network grant (award #2126334), the consortium engaged stakeholders via working groups, webinars, and community workshops to identify key data challenges and develop recommendations, tools, and training materials. Major outcomes include guidelines for standardizing genetic and genomic data, SNP harmonization protocols, recommendations for ontology usage, and an open-access curriculum for FAIR data literacy. Other key topics addressed in the first 3 years include data federation, improved single-cell data annotation, and advanced database interoperability. Surveys indicate an increase in community awareness and adoption of FAIR practices. AgBioData represents a scalable model for enhancing agricultural data infrastructure and sustainability, with ongoing efforts focused on AI readiness and the long-term support of curated data resources.

Oxford Nanopore Technologies (ONT) sequencing platforms have enabled scientists to generate sequence reads of 20 kilobase or more, which has facilitated the assembly of complete genomes. Nanopore sequencing involves recording the change in electrical signals as the DNA molecule transverses the pore. Converting this signal information into bases – a process called basecalling – is challenging and require the implementation of machine learning. The models developed for ONT basecallers have continually improved. However, the mean quality of the reads generated fall below a Phred score of 20 (99% accuracy), the target quality for high-quality genome assembly, when used on challenging plant samples. These low-quality base calls limit the ability to conduct de novo genome assembly, accurate phasing of haplotypes, and long-read genotyping. To overcome these shortcomings, we fine-tuned ONT’s high accuracy basecalling model using Bonito, an AI-based deep learning basecaller, to develop crop-specific models for five important species in the Rosaceae family. These species include highly valuable tree crops such as apple, peach, pear, plum, and sweet cherry. Through the development of an automated training pipeline, we were able to achieve ∼14% higher mean and median basecall quality, and a ∼20% increase in total reads with Phred scores >15. These results were achieved without significantly affecting the read length N50s and total reads called. As a result, these new crop-specific models will enable members of the Rosaceae genomics community to produce higher quality ONT sequencing data. Furthermore, we are releasing our automated pipeline to facilitate others to train their own organism or crop-specific models.

Prunus persica (peach) has long served as a model fruit tree for studying phenological events. It has a relatively small genome and exhibits tremendous plasticity in climate tolerances due to the high variation of chill requirements, bloom times, and fruit ripening times. The peach variety ‘Lovell 2D’ was used to generate one of the first high-quality genome assemblies for a tree species, using Sanger sequencing of genetic-map ordered BAC clones. A key to the high quality of this early assembly was the use of a doubled haploid variety, which eliminates the challenges posed by mixed haplotypes. Here, we re-sequenced and assembled the ‘Lovell 2D’ genome along with a doubled haploid sibling ‘Lovell 5D’ using 3rd generation technologies. The resulting genomes were significantly more contiguous than the current ‘Lovell 2D’ reference genome (ver2.0 updated in 2017) and are closer to the estimated total genome size for peach (265Mb). In addition, new gene, transposable element (TE), and Nucleotide-binding domain and Leucine-rich repeat receptor (NLR) annotations were performed to enhance the integrity and utility of the genome. These updated peach doubled-haploid reference assemblies will provide the research community with an improved reference genome for genomics-guided studies and breeding efforts.

The unprecedented volume of big data being routinely generated for nonmodel crop species, coupled with advanced technology enabling the use of big data in breeding, gives further impetus for the need to have access to crop community databases, where all relevant data are curated and integrated. Funding for such databases is, however, insufficient and intermittent, resulting in the data being underutilized. While increased awareness of the importance of funding databases is important, it is practically necessary to find a more efficient way to build a community database. To meet the need for integrated database resources for various crop genomics, genetics, and breeding research communities, we have built five crop databases over the last decade using an open-source database platform and software. We describe the system and methods used for database construction, curation, and analysis protocols, and the data and tools that are available in these five crop databases. Database URL: The Genome Database for Rosaceae (GDR, www.rosaceae.org), the Genome Database for Vaccinium (GDV, www.vaccinium.org), the Citrus Genome Database (CGD, www.citrusgenomedb.org), the Pulse Crop Database (PCD, www.pulsedb.org), and CottonGen (www.cottongen.org)

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.

The Genotype-Phenotype Working Group was established in November 2021 as part of the AgBioData Consortium (https://www.agbiodata.org) with the goal of identifying current challenges in annotating and integrating large-scale genotype and phenotype data. Over the course of the year, the members of this working group identified different types of data sets, explored experimental platforms and methods for data generation, and examined how these data are annotated including the metadata requirements. We conducted a thorough review of publicly funded repositories for raw and processed data for each data type. We also examined several secondary databases and knowledgebases that enable the integration of heterogeneous data types in the context of the Genome Browser, Pathway Networks and tissue-specific gene expression. The review revealed a need for additional infrastructural support, standards, and tools to connect Genotype to Phenotype data and enhance data interoperability for knowledge synthesis and to foster translational research.

ISHS International Symposium on Breeding and Effective Use of Biotechnology and Molecular Tools in Horticultural Crops A strategy for aggregating multi-source historical phenotypic and genotypic data sets containing homonyms for global genomic prediction in apple (Malus domestica)

Large-scale genotype and phenotype data have been increasingly generated to identify genetic markers, understand gene function and evolution and facilitate genomic selection. These datasets hold immense value for both current and future studies, as they are vital for crop breeding, yield improvement and overall agricultural sustainability. However, integrating these datasets from heterogeneous sources presents significant challenges and hinders their effective utilization. We established the Genotype-Phenotype Working Group in November 2021 as a part of the AgBioData Consortium (https://www.agbiodata.org) to review current data types and resources that support archiving, analysis and visualization of genotype and phenotype data to understand the needs and challenges of the plant genomic research community. For 2021–22, we identified different types of datasets and examined metadata annotations related to experimental design/methods/sample collection, etc. Furthermore, we thoroughly reviewed publicly funded repositories for raw and processed data as well as secondary databases and knowledgebases that enable the integration of heterogeneous data in the context of the genome browser, pathway networks and tissue-specific gene expression. Based on our survey, we recommend a need for (i) additional infrastructural support for archiving many new data types, (ii) development of community standards for data annotation and formatting, (iii) resources for biocuration and (iv) analysis and visualization tools to connect genotype data with phenotype data to enhance knowledge synthesis and to foster translational research. Although this paper only covers the data and resources relevant to the plant research community, we expect that similar issues and needs are shared by researchers working on animals. Database URL: https://www.agbiodata.org.