Breedbase: A digital ecosystem for modern plant breeding

Nicolas Morales; Alex C. Ogbonna; Bryan J. Ellerbrock; Guillaume J. Bauchet; Titima Tantikanjana; Isaak Y. Tecle; Adrian F. Powell; David Lyon; Naama Menda; Christiano C. Simoes; Surya Saha; Prashant Hosmani; Mirella Flores; Naftali Panitz; Ryan S. Preble; Afolabi Agbona; Ismail Rabbi; Peter Kulakow; Prasad Peteti; Robert Kawuki; Williams Esuma; Micheal Kanaabi; Doreen M. Chelangat; Ezenwanyi Uba; Adeyemi Olojede; Joseph Onyeka; Trushar Shah; Margaret Karanja; Chiedozie Egesi; Hale Tufan; Agre Paterne; Asrat Asfaw; Jean Luc Jannink; Marnin Wolfe; Clay L. Birkett; David J. Waring; Jenna M. Hershberger; Michael A. Gore; Kelly R. Robbins; Trevor Rife; Chaney Courtney; Jesse Poland; Elizabeth Arnaud; Marie Angelique Laporte; Heneriko Kulembeka; Kasele Salum; Emmanuel Mrema; Allan Brown; Stanley Bayo; Brigitte Uwimana; Violet Akech; Craig Yencho; Bert De Boeck; Hugo Campos; Rony Swennen; Jeremy D. Edwards; Lukas A. Mueller

doi:10.1093/g3journal/jkac078

Breedbase: A digital ecosystem for modern plant breeding

Nicolas Morales, Alex C. Ogbonna, Bryan J. Ellerbrock, Guillaume J. Bauchet, Titima Tantikanjana, Isaak Y. Tecle, Adrian F. Powell, David Lyon, Naama Menda, Christiano C. Simoes, Surya Saha, Prashant Hosmani, Mirella Flores, Naftali Panitz, Ryan S. Preble, Afolabi Agbona, Ismail Rabbi, Peter Kulakow, Prasad Peteti, Robert KawukiWilliams Esuma, Micheal Kanaabi, Doreen M. Chelangat, Ezenwanyi Uba, Adeyemi Olojede, Joseph Onyeka, Trushar Shah, Margaret Karanja, Chiedozie Egesi, Hale Tufan, Agre Paterne, Asrat Asfaw, Jean Luc Jannink, Marnin Wolfe, Clay L. Birkett, David J. Waring, Jenna M. Hershberger, Michael A. Gore, Kelly R. Robbins, Trevor Rife, Chaney Courtney, Jesse Poland, Elizabeth Arnaud, Marie Angelique Laporte, Heneriko Kulembeka, Kasele Salum, Emmanuel Mrema, Allan Brown, Stanley Bayo, Brigitte Uwimana, Violet Akech, Craig Yencho, Bert De Boeck, Hugo Campos, Rony Swennen, Jeremy D. Edwards, Lukas A. Mueller

Research output: Contribution to journal › Article › peer-review

24 Scopus citations

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.

Original language	English (US)
Journal	G3: Genes, Genomes, Genetics
Volume	12
Issue number	7
DOIs	https://doi.org/10.1093/g3journal/jkac078
State	Published - Jul 1 2022
Externally published	Yes

ASJC Scopus subject areas

General Medicine

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10.1093/g3journal/jkac078

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Morales, N., Ogbonna, A. C., Ellerbrock, B. J., Bauchet, G. J., Tantikanjana, T., Tecle, I. Y., Powell, A. F., Lyon, D., Menda, N., Simoes, C. C., Saha, S., Hosmani, P., Flores, M., Panitz, N., Preble, R. S., Agbona, A., Rabbi, I., Kulakow, P., Peteti, P., ... Mueller, L. A. (2022). Breedbase: A digital ecosystem for modern plant breeding. G3: Genes, Genomes, Genetics, 12(7). https://doi.org/10.1093/g3journal/jkac078

@article{45aaf478b7314df7afe546a646a04465,

title = "Breedbase: A digital ecosystem for modern plant breeding",

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.",

author = "Nicolas Morales and Ogbonna, {Alex C.} and Ellerbrock, {Bryan J.} and Bauchet, {Guillaume J.} and Titima Tantikanjana and Tecle, {Isaak Y.} and Powell, {Adrian F.} and David Lyon and Naama Menda and Simoes, {Christiano C.} and Surya Saha and Prashant Hosmani and Mirella Flores and Naftali Panitz and Preble, {Ryan S.} and Afolabi Agbona and Ismail Rabbi and Peter Kulakow and Prasad Peteti and Robert Kawuki and Williams Esuma and Micheal Kanaabi and Chelangat, {Doreen M.} and Ezenwanyi Uba and Adeyemi Olojede and Joseph Onyeka and Trushar Shah and Margaret Karanja and Chiedozie Egesi and Hale Tufan and Agre Paterne and Asrat Asfaw and Jannink, {Jean Luc} and Marnin Wolfe and Birkett, {Clay L.} and Waring, {David J.} and Hershberger, {Jenna M.} and Gore, {Michael A.} and Robbins, {Kelly R.} and Trevor Rife and Chaney Courtney and Jesse Poland and Elizabeth Arnaud and Laporte, {Marie Angelique} and Heneriko Kulembeka and Kasele Salum and Emmanuel Mrema and Allan Brown and Stanley Bayo and Brigitte Uwimana and Violet Akech and Craig Yencho and {De Boeck}, Bert and Hugo Campos and Rony Swennen and Edwards, {Jeremy D.} and Mueller, {Lukas A.}",

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doi = "10.1093/g3journal/jkac078",

language = "English (US)",

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Morales, N, Ogbonna, AC, Ellerbrock, BJ, Bauchet, GJ, Tantikanjana, T, Tecle, IY, Powell, AF, Lyon, D, Menda, N, Simoes, CC, Saha, S, Hosmani, P, Flores, M, Panitz, N, Preble, RS, Agbona, A, Rabbi, I, Kulakow, P, Peteti, P, Kawuki, R, Esuma, W, Kanaabi, M, Chelangat, DM, Uba, E, Olojede, A, Onyeka, J, Shah, T, Karanja, M, Egesi, C, Tufan, H, Paterne, A, Asfaw, A, Jannink, JL, Wolfe, M, Birkett, CL, Waring, DJ, Hershberger, JM, Gore, MA, Robbins, KR, Rife, T, Courtney, C, Poland, J, Arnaud, E, Laporte, MA, Kulembeka, H, Salum, K, Mrema, E, Brown, A, Bayo, S, Uwimana, B, Akech, V, Yencho, C, De Boeck, B, Campos, H, Swennen, R, Edwards, JD & Mueller, LA 2022, 'Breedbase: A digital ecosystem for modern plant breeding', G3: Genes, Genomes, Genetics, vol. 12, no. 7. https://doi.org/10.1093/g3journal/jkac078

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T1 - Breedbase: A digital ecosystem for modern plant breeding

AU - Morales, Nicolas

AU - Ogbonna, Alex C.

AU - Ellerbrock, Bryan J.

AU - Bauchet, Guillaume J.

AU - Tantikanjana, Titima

AU - Tecle, Isaak Y.

AU - Powell, Adrian F.

AU - Lyon, David

AU - Menda, Naama

AU - Simoes, Christiano C.

AU - Saha, Surya

AU - Hosmani, Prashant

AU - Flores, Mirella

AU - Panitz, Naftali

AU - Preble, Ryan S.

AU - Agbona, Afolabi

AU - Rabbi, Ismail

AU - Kulakow, Peter

AU - Peteti, Prasad

AU - Kawuki, Robert

AU - Esuma, Williams

AU - Kanaabi, Micheal

AU - Chelangat, Doreen M.

AU - Uba, Ezenwanyi

AU - Olojede, Adeyemi

AU - Onyeka, Joseph

AU - Shah, Trushar

AU - Karanja, Margaret

AU - Egesi, Chiedozie

AU - Tufan, Hale

AU - Paterne, Agre

AU - Asfaw, Asrat

AU - Jannink, Jean Luc

AU - Wolfe, Marnin

AU - Birkett, Clay L.

AU - Waring, David J.

AU - Hershberger, Jenna M.

AU - Gore, Michael A.

AU - Robbins, Kelly R.

AU - Rife, Trevor

AU - Courtney, Chaney

AU - Poland, Jesse

AU - Arnaud, Elizabeth

AU - Laporte, Marie Angelique

AU - Kulembeka, Heneriko

AU - Salum, Kasele

AU - Mrema, Emmanuel

AU - Brown, Allan

AU - Bayo, Stanley

AU - Uwimana, Brigitte

AU - Akech, Violet

AU - Yencho, Craig

AU - De Boeck, Bert

AU - Campos, Hugo

AU - Swennen, Rony

AU - Edwards, Jeremy D.

AU - Mueller, Lukas A.

N1 - Generated from Scopus record by KAUST IRTS on 2022-09-13

PY - 2022/7/1

Y1 - 2022/7/1

N2 - 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.

AB - 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.

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Breedbase: A digital ecosystem for modern plant breeding

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