Breedbase: a digital ecosystem for modern plant breeding

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.

Bibliographic Details

Main 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, Esuma, Williams, Kanaabi, Micheal, Chelangat, Doreen M., Uba, Ezenwanyi, Olojede, Adeyemi, Onyeka, Joseph, Shah, Trushar, Karanja, Margaret, Egesi, Chiedozie N., Tufan, Hale, Paterne, Agre, Asfaw, Asrat, Jannink, Jean-Luc, Wolfe, Marnin, Birkett, Clay L., Waring, David J., Hershberger, Jenna M., Gore, Michael A., Robbins, Kelly R., Rife, Trevor, Courtney, Chaney, Poland, Jesse A., Arnaud, Elizabeth, Laporte, Marie-Angélique, Kulembeka, Heneriko, Salum, Kasele, Mrema, Emmanuel, Brown, Allan, Bayo, Stanley, Uwimana, Brigitte, Akech, Violet, Yencho, Craig, De Boeck, Bert, Campos, Hugo, Swennen, Rony L., Edwards, Jeremy D., Mueller, Lukas A.
Format:	Journal Article biblioteca
Language:	English
Published:	Oxford University Press 2022-07-06
Subjects:	data, databases, breeding, phenotypes, genotyping, marker-assisted selection, digital technology, mejora, fenotipos,
Online Access:	https://hdl.handle.net/10568/119327 https://doi.org/10.1093/g3journal/jkac078
Tags:	Add Tag No Tags, Be the first to tag this record!