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Ag Data Commons migration begins October 18, 2023

The Ag Data Commons is migrating to a new platform – an institutional portal on Figshare. Starting October 18 the current system will be available for search and download only. Submissions will resume after the launch of our portal on Figshare in November. Stay tuned for details!

Maize Genetics Cooperation Stock Center Catalog of Stocks

    The Maize Genetics Cooperation Stock Center is operated by USDA/ARS, located at the University of Illinois, Urbana/Champaign, and integrated with the National Plant Germplasm System (NPGS). The center serves the maize research community by collecting, maintaining and distributing seeds of maize genetic stocks, and providing information about maize stocks and the mutations they carry through the Maize Genetics and Genomics Database (MaizeGDB).

    BrAPI

      The Breeding API (BrAPI) Project is an effort to create a RESTful specification to enable interoperability among plant breeding databases. The Breeding API specifies a standard interface for plant phenotype/genotype databases to serve their data to crop breeding applications. It is a shared, open API, to be used by all data providers and data consumers who wish to participate.

      Plant Expression Database

        [NOTE: PLEXdb is no longer available online. Oct 2019.] PLEXdb (Plant Expression Database) is a unified gene expression resource for plants and plant pathogens. PLEXdb is a genotype to phenotype, hypothesis building information warehouse, leveraging highly parallel expression data with seamless portals to related genetic, physical, and pathway data.

        Ricebase

          Ricebase ([https://ricebase.org](https://ricebase.org)) is an integrative genomic database for rice (Oryza sativa) with an emphasis on combining datasets in a way that maintains the key links between past and current genetic studies. Ricebase includes DNA sequence data, gene annotations, nucleotide variation data and molecular marker fragment size data.

          Musa Germplasm Information System

            The Musa Germplasm Information System (MGIS) contains key information on Musa germplasm diversity, including passport data, botanical classification, morpho-taxonomic descriptors, molecular studies, plant photographs and GIS information on 6829 accessions managed in 30 collections around the world, making it the most extensive source of information on banana genetic resources.

            Animal Trait Correlation Database (CorrDB)

              A genetic correlation is the proportion of shared variance between two traits that is due to genetic causes; a phenotypic correlation is the degree to which two traits co-vary among individuals in a population. In the genomics era, while gene expression, genetic association, and network analysis provide unprecedented means to decode the genetic basis of complex phenotypes, it is important to recognize the possible effects genetic progress in one trait can have on other traits. This database is designed to collect all published livestock genetic/phenotypic trait correlation data, aimed at facilitating genetic network analysis or systems biology studies.

              The Animal Quantitative Trait Loci Database (Animal QTLdb)

                The Animal Quantitative Trait Loci (QTL) Database (Animal QTLdb) strives to collect all publicly available trait mapping data, i.e. QTL (phenotype/expression, eQTL), candidate gene and association data (GWAS), and copy number variations (CNV) mapped to livestock animal genomes, in order to facilitate locating and comparing discoveries within and between species. New data and database tools are continually developed to align various trait mapping data to map-based genome features such as annotated genes.

                Cassavabase

                  The Next Generation Cassava Breeding (NEXTGEN Cassava) project aims to significantly increase the rate of genetic improvement in cassava breeding and unlock the full potential of cassava, a staple crop central to food security and livelihoods across Africa. The project will implement and empirically test a new breeding method known as Genomic Selection that relies on statistical modeling to predict cassava performance before field-testing, and dramatically accelerates the breeding cycle.