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1. Connecting data for consumer preferences, food quality, and breeding in support of market-oriented breeding of root, tuber, and banana crops.

Author

Arnaud E, Menda N, Tran T, Asiimwe A, Kanaabi M, Meghar K, Forsythe L, Kawuki R, Ellebrock B, Kayondo IS, Agbona A, Zhang X, Mendes T, Laporte MA, Nakitto M, Ssali RT, Asfaw A, Uwimana B, Ogbete CE, Makunde G, Maraval I, Mueller LA, Bouniol A, Fauvelle E, and Dufour D

Subjects
Humans, Food Quality, Crops, Agricultural growth & development, Food Preferences, Musa, Consumer Behavior, Plant Tubers chemistry, Plant Tubers growth & development, Plant Breeding, Plant Roots chemistry, Plant Roots growth & development
Abstract

The 5-year project 'Breeding roots, tubers and banana products for end user preferences' (RTBfoods) focused on collecting consumers' preferences on 12 food products to guide breeding programmes. It involved multidisciplinary teams from Africa, Latin America, and Europe. Diverse data types were generated on preferred qualities of users (farmers, family and entrepreneurial processors, traders or retailers, and consumers). Country-based target product profiles were produced with a comprehensive market analysis, disaggregating gender's role and preferences, providing prioritised lists of traits for the development of new plant varieties. We describe the approach taken to create, in the roots, tubers, and banana breeding databases, a centralised and meaningful open access to sensory information on food products and genotypes. Biochemical, instrumental textural, and sensory analysis data are then directly connected to the specific plant record while user survey data, bearing personal information, were analysed, anonymised, and uploaded in a repository. Names and descriptions of food quality traits were added into the Crop Ontology for labelling data in the databases, along with the various methods of measurement used by the project. The development and application of standard operating procedures, data templates, and adapted trait ontologies improved the data quality and its format, enabling the linking of these to the plant material studied when uploaded in the breeding databases or in repositories. Some modifications to the database model were necessary to accommodate the food sensory traits and sensory panel trials. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry., (© 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.)

Published
2024
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2. Breedbase: a digital ecosystem for modern plant breeding.

Author

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, and Mueller LA

Subjects
Algorithms, Crops, Agricultural genetics, Software, Ecosystem, 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., (© The Author(s) 2022. Published by Oxford University Press on behalf of Genetics Society of America.)

Published
2022
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3. A snapshot of the emerging tomato genome sequence

Author

Sandra Knapp, Ying Wang, Antonio Granell, Dongyu Qu, Erika Asamizu, Pierre Frasse, Hongling Jiang, Mohamed Zouine, Pradeep Kumar Singh, Vivek Dalal, Luigi Frusciante, Robert M. Buels, Hans de Jong, Dongyuan Liu, James J. Giovannoni, Sander Peters, Sarita, Satoshi Tabata, Isaak Y. Tecle, Mara Ercolano, Jun Wang, Longfei Liu, Rekha Dixit, Heiko Schoof, Yongbiao Xue, Kishor Gaikwad, Julia Vrebalov, Alessandra Traini, Nunzio D’Agostino, Ruth White, Zhibiao Ye, Amparo Mico, Cheol-Goo Hur, Jitendra P. Khurana, Roderic Guigó, Arun Sharma, Paramjit Khurana, Jiuhai Zhao, Hiroyuki Fukuoka, Byung-Dong Kim, Smriti Shridhar, René Klein Lankhorst, Yuanyuan Dai, Dani Zamir, Sumera Praveen, Helen Beasley, Manuel Spannagl, Erwin Datema, Klaus X.F. Mayer, Yves Van de Peer, Akhilesh K. Tyagi, Aureliano Bombarely, P. Lindhout, Mark Fiers, Silvana Grandillo, Jane Rogers, Zhangjun Fei, Changbao Li, Giorgio Valle, Karen McLaren, Alok Singh, Sung-Hwan Jo, Sarah Butcher, Willem J. Stiekema, Murielle Philippot, Huajie Fan, Glenn J. Bryan, Fei Lu, Doil Choi, Jun He, Daniel W. A. Buchan, Stephane Rombauts, Jinfeng Chen, Yongchen Du, Xiao-Hua Yang, Shailendra Vyas, Daisuke Shibata, Maria Luisa Chiusano, Rajesh Kumar, Song Bin Chang, Marjo J. van Staveren, Gerard J. Bishop, Victoria Fernandez-Pedrosa, Hong-Qing Ling, Graham B. Seymour, Lukas A. Mueller, Mondher Bouzayen, Stephen M. Stack, Rémy Bruggmann, Ajay Kumar, Zhonghua Zhang, Christine Nicholson, Guoping Wang, Saloni Mathur, Sean Humphray, Vikrant Gupta, Jinfeng Shi, Roeland C. H. J. van Ham, Debasis Chattopadhyay, Amolkumar U. Solanke, Mingsheng Chen, Shusei Sato, Sanwen Huang, Sonia Osorio, Chen Lu, Zhukuan Cheng, Tilak Raj Sharma, Dóra Szinay, James Abbott, Awadhesh Pandit, Yu Geng, Mahavir Yadav, Sara Todesco, Manuel Pérez-Alonso, Giovanni Giuliano, Amalia Barone, Trilochan Mohapatra, Irfan Ahmad Ghazi, Wencai Yang, Francisco Camara, Giulia Falcone, Anika Jöcker, Clare Riddle, Alessandro Vezzi, Jianjun Chen, Shouhong Sun, Marco Pietrella, Joyce Van Eck, Lindsay A. Shearer, Adri A. Mills, Steven D. Tanksley, Miguel A. Botella, Chuanyou Li, Sarah Sims, Farid Regad, Jeffrey A. Fawcett, Parul Chowdhury, Naama Menda, Suzanne M. Royer, Nagendra K. Singh, Mueller, L. A., Lankhorst, R. K., Tanksley, S. D., Giovannoni, J. J., White, R., Vrebalov, J., Fei, Z., van Eck, J., Buels, R., Mills, A. A., Menda, N., Tecle, I. Y., Bombarely, A., Stack, S., Royer, S. M., Chang, S. B., Shearer, L. A., Kim, B. D., Jo, S. H., Hur, C. G., Choi, D., Li, C. B., Zhao, J., Jiang, H., Geng, Y., Dai, Y., Fan, H., Chen, J., Lu, F., Shi, J., Sun, S., Yang, X., Lu, C., Chen, M., Cheng, Z., Li, C., Ling, H., Xue, Y., Wang, Y., Seymour, G. B., Bishop, G. J., Bryan, G., Rogers, J., Sims, S., Butcher, S., Buchan, D., Abbott, J., Beasley, H., Nicholson, C., Riddle, C., Humphray, S., Mclaren, K., Mathur, S., Vyas, S., Solanke, A. U., Kumar, R., Gupta, V., Sharma, A. K., Khurana, P., Khurana, J. P., Tyagi, A., Sarita, Chowdhury, P., Shridhar, S., Chattopadhyay, D., Pandit, A., Singh, P., Kumar, A., Dixit, R., Singh, A., Praveen, S., Dalal, V., Yadav, M., Ghazi, I. A., Gaikwad, K., Sharma, T. R., Mohapatra, T., Singh, N. K., Szinay, D., de Jong, H., Peters, S., van Staveren, M., Datema, E., Fiers, M. W. E. J., van Ham, R. C. H. J., Lindhout, P., Philippot, M., Frasse, P., Regad, F., Zouine, M., Bouzayen, M., Asamizu, E., Sato, S., Fukuoka, H., Tabata, S., Shibata, D., Botella, M. A., Perez Alonso, M., Fernandez Pedrosa, V., Osorio, S., Mico, A., Granell, A., Zhang, Z., He, J., Huang, S., Du, Y., Qu, D., Liu, L., Liu, D., Wang, J., Ye, Z., Yang, W., Wang, G., Vezzi, A., Todesco, S., Valle, G., Falcone, G., Pietrella, M., Giuliano, G., Grandillo, S., Traini, A., D'Agostino, Nunzio, Chiusano, MARIA LUISA, Ercolano, MARIA RAFFAELLA, Barone, Amalia, Frusciante, Luigi, Schoof, H., Jöcker, A., Bruggmann, R., Spannagl, M., Mayer, K. X. F., Guigó, R., Camara, F., Rombauts, S., Fawcett, J. A., Van de Peer, Y., Knapp, S., Zamir, D., and Stiekema, W.

Subjects
0106 biological sciences, lcsh:QH426-470, Bioinformatics, Genomics, Plant Science, Computational biology, lcsh:Plant culture, Biology, Laboratorium voor Erfelijkheidsleer, ENCODE, 01 natural sciences, Genome, 03 medical and health sciences, Laboratorium voor Plantenveredeling, Bioinformatica, Genetics, Life Science, lcsh:SB1-1110, 030304 developmental biology, Whole genome sequencing, 0303 health sciences, Bacterial artificial chromosome, EPS-4, fungi, food and beverages, Biology and Life Sciences, Genome project, PRI Bioscience, lcsh:Genetics, Plant Breeding, GenBank, Laboratory of Genetics, Agronomy and Crop Science, 010606 plant biology & botany, Reference genome
Abstract

The genome of tomato (Solanum lycopersicum L.) is being sequenced by an international consortium of 10 countries (Korea, China, the United Kingdom, India, the Netherlands, France, Japan, Spain, Italy, and the United States) as part of the larger “International Solanaceae Genome Project (SOL): Systems Approach to Diversity and Adaptation” initiative. The tomato genome sequencing project uses an ordered bacterial artificial chromosome (BAC) approach to generate a high-quality tomato euchromatic genome sequence for use as a reference genome for the Solanaceae and euasterids. Sequence is deposited at GenBank and at the SOL Genomics Network (SGN). Currently, there are around 1000 BACs finished or in progress, representing more than a third of the projected euchromatic portion of the genome. An annotation effort is also underway by the International Tomato Annotation Group. The expected number of genes in the euchromatin is ∼40,000, based on an estimate from a preliminary annotation of 11% of finished sequence. Here, we present this first snapshot of the emerging tomato genome and its annotation, a short comparison with potato (Solanum tuberosum L.) sequence data, and the tools available for the researchers to exploit this new resource are also presented. In the future, whole-genome shotgun techniques will be combined with the BAC-by-BAC approach to cover the entire tomato genome. The high-quality reference euchromatic tomato sequence is expected to be near completion by 2010.

Published
2009

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