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Breedbase: a digital ecosystem for modern plant breeding.

Authors :
Morales, Nicolas
Ogbonna, Alex C.
Ellerbrock, Bryan J.
Bauchet, Guillaume J.
Tantikanjana, Titima
Tecle, Isaak Y.
Powell, Adrian F.
Lyon, David
Menda, Naama
Simoes, Christiano C.
Saha, Surya
Hosmani, Prashant
Flores, Mirella
Panitz, Naftali
Preble, Ryan S.
Agbona, Afolabi
Rabbi, Ismail
Kulakow, Peter
Peteti, Prasad
Kawuki, Robert
Source :
G3: Genes | Genomes | Genetics. Jul2022, Vol. 12 Issue 7, p1-12. 12p.
Publication Year :
2022

Abstract

Modern breeding methods integrate next-generation sequencing 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/, last accessed 4/18/2022). Originally initiated as Cassavabase (https://cassavabase.org/, last accessed 4/18/2022) with the NextGen Cassava project (https://www.nextgencassava. org/, last accessed 4/18/2022), 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/, last accessed 4/18/2022) and packaged in a Docker image for deployment (https://hub.docker.com/u/breedbase, last accessed 4/18/2022). The Breedbase system enables breeding programs to better manage and leverage their data for decision making within a fully integrated digital ecosystem. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21601836
Volume :
12
Issue :
7
Database :
Academic Search Index
Journal :
G3: Genes | Genomes | Genetics
Publication Type :
Academic Journal
Accession number :
157862528
Full Text :
https://doi.org/10.1093/g3journal/jkac078