Tripal, a community update after 10 years of supporting open source, standards-based genetic, genomic and breeding databases

Building resource-efficient community databases using open-source software

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).

Building a community-driven bioinformatics platform to facilitate Cannabis sativa multi-omics research

Global changes in cannabis legislation after decades of stringent regulation and heightened demand for its industrial and medicinal applications have spurred recent genetic and genomics research. An international research community emerged and identified the need for a web portal to host cannabis-specific datasets that seamlessly integrates multiple data sources and serves omics-type analyses, fostering information sharing. The Tripal platform was used to host public genome assemblies, gene annotations, quantitative trait loci and genetic maps, gene and protein expression data, metabolic profiles and their sample attributes. Single nucleotide polymorphisms were called using public resequencing datasets on three genomes. Additional applications, such as SNP-Seek and MapManJS, were embedded into Tripal. A multi-omics data integration web-service Application Programming Interface (API), developed on top of existing Tripal modules, returns generic tables of samples, properties and values. Use cases demonstrate the API's utility for various omics analyses, enabling researchers to perform multi-omics analyses efficiently.

Availability and implementation

The web portal can be accessed at www.icgrc.info.

OysterDB: A Genome Database for Ostreidae

The molluscan family Ostreidae, commonly known as oysters, is an important molluscan group due to its economic and ecological importance. In recent years, an abundance of genomic data of Ostreidae species has been generated and available in public domain. However, there is still a lack of a high-efficiency database platform to store and distribute these data with comprehensive tools. In this study, we developed an oyster genome database (OysterDB) to consolidate oyster genomic data. This database includes eight oyster genomes and 208,923 protein-coding gene annotations. Bioinformatic tools, such as BLAST and JBrowse, are integrated into the database to provide a user-friendly platform for homologous sequence searching, visualization of genomes, and screen for candidate gene information. Moreover, OysterDB will be continuously updated with ever-growing oyster genomic resources and facilitate future studies for comparative and functional genomic analysis of oysters ( http://oysterdb.com.cn/ ).

FoPGDB: a pangenome database of Fusarium oxysporum, a cross-kingdom fungal pathogen

Pangenomes, capturing the genetic diversity of a species or genus, are essential to understanding the ecology, pathobiology and evolutionary mechanisms of fungi that cause infection in crops and humans. However, fungal pangenome databases remain unavailable. Here, we report the first fungal pangenome database, specifically for Fusarium oxysporum species complex (FOSC), a group of cross-kingdom pathogens causing devastating vascular wilt to over 100 plant species and life-threatening fusariosis to immunocompromised humans. The F. oxysporum Pangenome Database (FoPGDB) is a comprehensive resource integrating 35 high-quality FOSC genomes, coupled with robust analytical tools. FoPGDB allows for both gene-based and graph-based exploration of the F. oxysporum pangenome. It also curates a large repository of putative effector sequences, crucial for understanding the mechanisms of FOSC pathogenicity. With an assortment of functionalities including gene search, genomic variant exploration and tools for functional enrichment, FoPGDB provides a platform for in-depth investigations of the genetic diversity and adaptability of F. oxysporum. The modular and user-friendly interface ensures efficient data access and interpretation. FoPGDB promises to be a valuable resource for F. oxysporum research, contributing to our understanding of this pathogen's pangenomic landscape and aiding in the development of novel disease management strategies. Database URL: http://www.fopgdb.site.

PearMODB: a multiomics database for pear (Pyrus) genomics, genetics and breeding study

Pear (Pyrus ssp.) belongs to Rosaceae and is an important fruit tree widely cultivated around the world. Currently, challenges to cope with the burgeoning sets of multiomics data are rapidly increasing. Here, we constructed the Pear Multiomics Database (PearMODB) by integrating genome, transcriptome, epigenome and population variation data, and aimed to provide a portal for accessing and analyzing pear multiomics data. A variety of online tools were built including gene search, BLAST, JBrowse, expression heatmap, synteny analysis and primer design. The information of DNA methylation sites and single-nucleotide polymorphisms can be retrieved through the custom JBrowse, providing an opportunity to explore the genetic polymorphisms linked to phenotype variation. Moreover, different gene families involving transcription factors, transcription regulators and disease resistance (nucleotide-binding site leucine-rich repeat) were identified and compiled for quick search. In particular, biosynthetic gene clusters (BGCs) were identified in pear genomes, and specialized webpages were set up to show detailed information of BGCs, laying a foundation for studying metabolic diversity among different pear varieties. Overall, PearMODB provides an important platform for pear genomics, genetics and breeding studies. Database URL http://pearomics.njau.edu.cn.

Conserving Citrus Diversity: From Vavilov's Early Explorations to Genebanks around the World

Citrus is among the most economically important fruit crops. Its vast species diversity and global production was observed by N.I. Vavilov during his international plant explorations from the early to mid-1900s. Currently, ex situ citrus collections located around the world conserve and protect citrus genetic resources, as revealed in a survey conducted in 2021. Responses were received from 43 collections in 27 countries, of which 35 provided data regarding collection composition, management practices, and security, as well as other information. The six largest citrus collections have between 1000 and 1735 accessions. The largest accession holdings are mandarins and sweet oranges, although all citrus fruit types are maintained: mandarin, sweet orange, lemon, pummelo, grapefruit, hybrids, lime, sour orange, citron, kumquat, papeda, finger lime, and crop wild relatives. Diseases pose significant threats to collections, though some collections are maintained in a clean-plant state as a result of intensive sanitation efforts. National and regional quarantine regulations often limit the export and import of citrus plants or propagative materials, thus limiting the availability of materials at an international level. Resources, both financial and human, are necessary to ensure the long-term safety and security of citrus collections on a global scale. Future efforts to develop citrus genebanking communities will provide opportunities for improved conservation, as well as collaborations and training.

Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction

The first paradigm of plant breeding involves direct selection-based phenotypic observation, followed by predictive breeding using statistical models for quantitative traits constructed based on genetic experimental design and, more recently, by incorporation of molecular marker genotypes. However, plant performance or phenotype (P) is determined by the combined effects of genotype (G), envirotype (E), and genotype by environment interaction (GEI). Phenotypes can be predicted more precisely by training a model using data collected from multiple sources, including spatiotemporal omics (genomics, phenomics, and enviromics across time and space). Integration of 3D information profiles (G-P-E), each with multidimensionality, provides predictive breeding with both tremendous opportunities and great challenges. Here, we first review innovative technologies for predictive breeding. We then evaluate multidimensional information profiles that can be integrated with a predictive breeding strategy, particularly envirotypic data, which have largely been neglected in data collection and are nearly untouched in model construction. We propose a smart breeding scheme, integrated genomic-enviromic prediction (iGEP), as an extension of genomic prediction, using integrated multiomics information, big data technology, and artificial intelligence (mainly focused on machine and deep learning). We discuss how to implement iGEP, including spatiotemporal models, environmental indices, factorial and spatiotemporal structure of plant breeding data, and cross-species prediction. A strategy is then proposed for prediction-based crop redesign at both the macro (individual, population, and species) and micro (gene, metabolism, and network) scales. Finally, we provide perspectives on translating smart breeding into genetic gain through integrative breeding platforms and open-source breeding initiatives. We call for coordinated efforts in smart breeding through iGEP, institutional partnerships, and innovative technological support.

The banana genome hub: a community database for genomics in the Musaceae

The Banana Genome Hub provides centralized access for genome assemblies, annotations, and the extensive related omics resources available for bananas and banana relatives. A series of tools and unique interfaces are implemented to harness the potential of genomics in bananas, leveraging the power of comparative analysis, while recognizing the differences between datasets. Besides effective genomic tools like BLAST and the JBrowse genome browser, additional interfaces enable advanced gene search and gene family analyses including multiple alignments and phylogenies. A synteny viewer enables the comparison of genome structures between chromosome-scale assemblies. Interfaces for differential expression analyses, metabolic pathways and GO enrichment were also added. A catalogue of variants spanning the banana diversity is made available for exploration, filtering, and export to a wide variety of software. Furthermore, we implemented new ways to graphically explore gene presence-absence in pangenomes as well as genome ancestry mosaics for cultivated bananas. Besides, to guide the community in future sequencing efforts, we provide recommendations for nomenclature of locus tags and a curated list of public genomic resources (assemblies, resequencing, high density genotyping) and upcoming resources-planned, ongoing or not yet public. The Banana Genome Hub aims at supporting the banana scientific community for basic, translational, and applied research and can be accessed at https://banana-genome-hub.southgreen.fr.

CottonGen: The Community Database for Cotton Genomics, Genetics, and Breeding Research

Over the last eight years, the volume of whole genome, gene expression, SNP genotyping, and phenotype data generated by the cotton research community has exponentially increased. The efficient utilization/re-utilization of these complex and large datasets for knowledge discovery, translation, and application in crop improvement requires them to be curated, integrated with other types of data, and made available for access and analysis through efficient online search tools. Initiated in 2012, CottonGen is an online community database providing access to integrated peer-reviewed cotton genomic, genetic, and breeding data, and analysis tools. Used by cotton researchers worldwide, and managed by experts with crop-specific knowledge, it continuous to be the logical choice to integrate new data and provide necessary interfaces for information retrieval. The repository in CottonGen contains colleague, gene, genome, genotype, germplasm, map, marker, metabolite, phenotype, publication, QTL, species, transcriptome, and trait data curated by the CottonGen team. The number of data entries housed in CottonGen has increased dramatically, for example, since 2014 there has been an 18-fold increase in genes/mRNAs, a 23-fold increase in whole genomes, and a 372-fold increase in genotype data. New tools include a genetic map viewer, a genome browser, a synteny viewer, a metabolite pathways browser, sequence retrieval, BLAST, and a breeding information management system (BIMS), as well as various search pages for new data types. CottonGen serves as the home to the International Cotton Genome Initiative, managing its elections and serving as a communication and coordination hub for the community. With its extensive curation and integration of data and online tools, CottonGen will continue to facilitate utilization of its critical resources to empower research for cotton crop improvement.