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    Breedbase: a digital ecosystem for modern plant breeding

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    Journal Article (2.864Mb)
    Date
    2022
    Author
    Morales, N.
    Ogbonna, A.C.
    Ellerbrock, B.J.
    Bauchet, G.J.
    Tantikanjana, T.
    Tecle, I.Y.
    Powell, A.
    Lyon, D.
    Menda, N.
    Simoes, C.C.
    Saha, S.
    Hosmani, P.
    Flores, M.
    Panitz, N.
    Preble, R.S.
    Agbona, A.
    Rabbi, I.Y.
    Kulakow, P.
    Peteti, P.
    Kawuki, R.
    Esuma, W.
    Kanaabi, M.
    Chelangat, D.M.
    Uba, E.
    Olojede, A.O.
    Onyeka, J.
    Shah, T.
    Karanja, M.
    Egesi, C.
    Tufan, H.A.
    Agre, A.P.
    Asfaw, A.
    Jannink, J.
    Wolfe, M.
    Birkett, C.L.
    Waring, D.J.
    Hershberger, J.M.
    Gore, M.A.
    Robbins, K.
    Rife, T.
    Courtney, C.
    Poland, J.
    Arnaud, E.
    Laporte, M.A.
    Kulembeka, H.
    Salum, K.
    Mrema, E.
    Brown, A.
    Bayo, S.
    Uwimana, B.
    Akech, V.
    Yencho, C.
    de Boeck, B.
    Campos, H.
    Swennen, R.
    Edwards, J.D.
    Mueller, L.
    Type
    Journal Article
    Review Status
    Peer Review
    Target Audience
    Scientists
    Metadata
    Show full item record
    Abstract/Description
    Modern breeding methods integrate next-generation sequencing (NGS) and phenomics to identify plants with the best characteristics and greatest genetic merit for use as parents in subsequent breeding cycles to ultimately create improved cultivars able to sustain high adoption rates by farmers. This data-driven approach hinges on strong foundations in data management, quality control, and analytics. Of crucial importance is a central database able to 1) track breeding materials, 2) store experimental evaluations, 3) record phenotypic measurements using consistent ontologies, 4) store genotypic information, and 5) implement algorithms for analysis, prediction and selection decisions. Because of the complexity of the breeding process, breeding databases also tend to be complex, difficult, and expensive to implement and maintain. Here, we present a breeding database system, Breedbase (https://breedbase.org/). Originally initiated as Cassavabase (https://cassavabase.org/) with the NextGen Cassava project (https://www.nextgencassava.org/), and later developed into a crop-agnostic system, it is presently used by dozens of different crops and projects. The system is web-based and is available as open source software. It is available on GitHub (https://github.com/solgenomics/) and packaged in a Docker image for deployment (https://dockerhub.com/breedbase/). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem.
    https://doi.org/10.1093/g3journal/jkac078
    Multi standard citation
    Permanent link to this item
    https://hdl.handle.net/20.500.12478/7499
    IITA Authors ORCID
    Ismail Rabbihttps://orcid.org/0000-0001-9966-2941
    Peter Kulakowhttps://orcid.org/0000-0002-7574-2645
    Trushar Shahhttps://orcid.org/0000-0002-0091-7981
    Chiedozie Egesihttps://orcid.org/0000-0002-9063-2727
    Paterne AGREhttps://orcid.org/0000-0003-1231-2530
    Asrat Asfawhttps://orcid.org/0000-0002-4859-0631
    Allen Brownhttps://orcid.org/0000-0002-4468-5932
    Brigitte Uwimanahttps://orcid.org/0000-0001-7460-9001
    Rony Swennenhttps://orcid.org/0000-0002-5258-9043
    Digital Object Identifier (DOI)
    https://doi.org/10.1093/g3journal/jkac078
    Research Themes
    Biotech and Plant Breeding
    IITA Subjects
    Agronomy; Food Security; Genetic Improvement; Plant Breeding; Plant Genetic Resources; Plant Production
    Agrovoc Terms
    Databases; Breeding; Phenotypes; Genotyping; Marker-Assisted Selection; Digital Technology
    Regions
    ACP; North America
    Countries
    United States of America
    Hubs
    Headquarters and Western Africa Hub
    Journals
    G3-Genes Genomes Genetics
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    • Annual Reports28
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