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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, Dufour D. Connecting data for consumer preferences, food quality, and breeding in support of market-oriented breeding of root, tuber, and banana crops. J Sci Food Agric 2024; 104:4514-4526. [PMID: 37226655 DOI: 10.1002/jsfa.12710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 03/25/2023] [Accepted: 05/24/2023] [Indexed: 05/26/2023]
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.
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Affiliation(s)
- Elizabeth Arnaud
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Naama Menda
- Boyce Thompson Institute (BTI), Ithaca, NY, USA
| | - Thierry Tran
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
- International Centre for Tropical Agriculture (CIAT), Cali, Colombia
| | - Amos Asiimwe
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
| | - Michael Kanaabi
- National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | - Karima Meghar
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
| | - Lora Forsythe
- Natural Resources Institute (NRI), Faculty of Engineering & Science, Livelihoods and Institutions Department, University of Greenwich, London, UK
| | - Robert Kawuki
- National Crops Resources Research Institute (NaCRRI), Kampala, Uganda
| | | | | | - Afolabi Agbona
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Xiaofei Zhang
- International Centre for Tropical Agriculture (CIAT), Cali, Colombia
| | | | - Marie-Angélique Laporte
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | | | | | - Asrat Asfaw
- International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | - Brigitte Uwimana
- International Institute of Tropical Agriculture (IITA), Kampala, Uganda
| | | | | | - Isabelle Maraval
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
| | | | - Alexandre Bouniol
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
- CIRAD, UMR QualiSud, Cotonou, Bénin
| | - Eglantine Fauvelle
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
| | - Dominique Dufour
- CIRAD, UMR QualiSud, Montpellier, France
- University of Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
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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. Breedbase: a digital ecosystem for modern plant breeding. G3 Genes|Genomes|Genetics 2022; 12:6564228. [PMID: 35385099 PMCID: PMC9258556 DOI: 10.1093/g3journal/jkac078] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Accepted: 02/14/2022] [Indexed: 01/17/2023]
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.
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Affiliation(s)
- Nicolas Morales
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | - Alex C Ogbonna
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- Cornell University , Ithaca, NY 14853, USA
| | | | | | | | | | | | - David Lyon
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | - Naama Menda
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | - Surya Saha
- Boyce Thompson Institute , Ithaca, NY 14853, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | - Ezenwanyi Uba
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Adeyemi Olojede
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Joseph Onyeka
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | | | | | - Chiedozie Egesi
- Boyce Thompson Institute , Ithaca, NY 14853, USA
- IITA Ibadan , 200001 Ibadan, Nigeria
- National Root Crops Research Institute (NRCRI) , 463109 Umudike, Nigeria
| | - Hale Tufan
- Cornell University , Ithaca, NY 14853, USA
| | | | | | - Jean-Luc Jannink
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | - Clay L Birkett
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | - David J Waring
- Cornell University , Ithaca, NY 14853, USA
- USDA-ARS , Ithaca, NY 14853, USA
| | | | | | | | - Trevor Rife
- Kansas State University , Manhattan, KS 66506, USA
| | | | - Jesse Poland
- Kansas State University , Manhattan, KS 66506, USA
| | | | | | | | | | | | | | | | | | | | - Craig Yencho
- North Carolina State University (NCSU) , Raleigh, NC 27695, USA
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Arnaud E, Laporte MA, Kim S, Aubert C, Leonelli S, Miro B, Cooper L, Jaiswal P, Kruseman G, Shrestha R, Buttigieg PL, Mungall CJ, Pietragalla J, Agbona A, Muliro J, Detras J, Hualla V, Rathore A, Das RR, Dieng I, Bauchet G, Menda N, Pommier C, Shaw F, Lyon D, Mwanzia L, Juarez H, Bonaiuti E, Chiputwa B, Obileye O, Auzoux S, Yeumo ED, Mueller LA, Silverstein K, Lafargue A, Antezana E, Devare M, King B. The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems. Patterns (N Y) 2020; 1:100105. [PMID: 33205138 PMCID: PMC7660444 DOI: 10.1016/j.patter.2020.100105] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/06/2020] [Revised: 05/28/2020] [Accepted: 08/24/2020] [Indexed: 12/15/2022]
Abstract
Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.
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Affiliation(s)
- Elizabeth Arnaud
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Marie-Angélique Laporte
- Digital Solutions Team, Digital Inclusion Lever, Bioversity International, Montpellier Office, Montpellier, France
| | - Soonho Kim
- Markets, Trade and Institutions Division (MTID), International Food Policy Research Institute (IFPRI), Washington, DC, USA
| | - Céline Aubert
- Environment and Production Technology Division (EPTD), International Food Policy Research Institute (IFPRI), Washington, DC, USA
| | - Sabina Leonelli
- Department of Sociology, Philosophy and Anthropology & Exeter Centre for the Study of the Life Sciences (Egenis), University of Exeter, Exeter, UK
| | - Berta Miro
- Agrifood Policy Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Gideon Kruseman
- Socio-Economics Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, State of México, Mexico
| | - Rosemary Shrestha
- Genetic Resources Program, International Maize and Wheat Improvement Center (CIMMYT), Texcoco, State of México, México
| | - Pier Luigi Buttigieg
- Helmholtz Metadata Collaboration, GEOMAR Helmholtz Centre for Ocean Research, Kiel, Germany
| | - Christopher J. Mungall
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | | | - Afolabi Agbona
- Cassava Breeding Program, International Institute of Tropical Agriculture (IITA), Ibadan, Nigeria
| | | | - Jeffrey Detras
- Bioinformatics Cluster, Strategic Innovation Platform, International Rice Research Institute (IRRI), Los Baños, Laguna, Philippines
| | - Vilma Hualla
- Research Informatics Unit (RIU), International Potato Center (CIP), Lima, Peru
| | - Abhishek Rathore
- Statistics, Bioinformatics & Data Management (SBDM) Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Roma Rani Das
- Statistics, Bioinformatics & Data Management (SBDM) Theme, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Hyderabad, Telangana, India
| | - Ibnou Dieng
- Biometrics Unit, International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Guillaume Bauchet
- Mueller Bioinformatics Laboratory, Boyce Thompson Institute for Plant Research, Ithaca, NY, USA
| | - Naama Menda
- Mueller Bioinformatics Laboratory, Boyce Thompson Institute for Plant Research, Ithaca, NY, USA
| | - Cyril Pommier
- BioinfOmics, Plant Bioinformatics Facility, Université Paris-Saclay, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Versailles, France
| | - Felix Shaw
- Digital Biology, Earlham Institute, Norwich, Norfolk, UK
| | - David Lyon
- Mueller Bioinformatics Laboratory, Boyce Thompson Institute for Plant Research, Ithaca, NY, USA
| | - Leroy Mwanzia
- Performance, Innovation and Strategic Analysis, International Center for Tropical Agriculture (CIAT), Regional Office for Africa, Nairobi, Kenya
| | - Henry Juarez
- Research Informatics Unit (RIU), International Potato Center (CIP), Lima, Peru
| | - Enrico Bonaiuti
- Monitoring, Evaluation and Learning Team, International Center for Agricultural Research in the Dry Areas (ICARDA), Beirut, Lebanon
| | - Brian Chiputwa
- Research Methods Group (RMG), World Agroforestry (ICRAF), Nairobi, Kenya
| | - Olatunbosun Obileye
- Data Management Section, International Institute of Tropical Agriculture (IITA), Ibadan, Oyo State, Nigeria
| | - Sandrine Auzoux
- UPR AIDA, The French Agricultural Research Centre for International Development (CIRAD), Sainte-Clotilde, Réunion, France
- Université de Montpellier, Montpellier, France
| | - Esther Dzalé Yeumo
- Unité Délégation à l’Information Scientifique et Technique - DIST, Institut National de la Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Versailles, France
| | - Lukas A. Mueller
- Mueller Bioinformatics Laboratory, Boyce Thompson Institute for Plant Research, Ithaca, NY, USA
| | | | | | - Erick Antezana
- Bayer Crop Science SA-NV, Diegem, Belgium
- Department of Biology, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Medha Devare
- Environment and Production Technology Division (EPTD), International Food Policy Research Institute (IFPRI), Washington, DC, USA
| | - Brian King
- CGIAR Platform for Big Data in Agriculture, International Center for Tropical Agriculture (CIAT), Cali, Colombia
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4
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Mata-Nicolás E, Montero-Pau J, Gimeno-Paez E, Garcia-Carpintero V, Ziarsolo P, Menda N, Mueller LA, Blanca J, Cañizares J, van der Knaap E, Díez MJ. Exploiting the diversity of tomato: the development of a phenotypically and genetically detailed germplasm collection. Hortic Res 2020; 7:66. [PMID: 32377357 PMCID: PMC7192925 DOI: 10.1038/s41438-020-0291-7] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2019] [Revised: 03/06/2020] [Accepted: 03/16/2020] [Indexed: 05/19/2023]
Abstract
A collection of 163 accessions, including Solanum pimpinellifolium, Solanum lycopersicum var. cerasiforme and Solanum lycopersicum var. lycopersicum, was selected to represent the genetic and morphological variability of tomato at its centers of origin and domestication: Andean regions of Peru and Ecuador and Mesoamerica. The collection is enriched with S. lycopersicum var. cerasiforme from the Amazonian region that has not been analyzed previously nor used extensively. The collection has been morphologically characterized showing diversity for fruit, flower and vegetative traits. Their genomes were sequenced in the Varitome project and are publicly available (solgenomics.net/projects/varitome). The identified SNPs have been annotated with respect to their impact and a total number of 37,974 out of 19,364,146 SNPs have been described as high impact by the SnpEeff analysis. GWAS has shown associations for different traits, demonstrating the potential of this collection for this kind of analysis. We have not only identified known QTLs and genes, but also new regions associated with traits such as fruit color, number of flowers per inflorescence or inflorescence architecture. To speed up and facilitate the use of this information, F2 populations were constructed by crossing the whole collection with three different parents. This F2 collection is useful for testing SNPs identified by GWAs, selection sweeps or any other candidate gene. All data is available on Solanaceae Genomics Network and the accession and F2 seeds are freely available at COMAV and at TGRC genebanks. All these resources together make this collection a good candidate for genetic studies.
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Affiliation(s)
- Estefanía Mata-Nicolás
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
| | - Javier Montero-Pau
- Department of Biochemistry and Molecular Biology, Universitat de València, Valencia, Spain
| | - Esther Gimeno-Paez
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
| | - Víctor Garcia-Carpintero
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
| | - Peio Ziarsolo
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
| | | | | | - José Blanca
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
| | - Joaquín Cañizares
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
| | - Esther van der Knaap
- Institute of Plant Breeding, Genetics and Genomics, University of Georgia, Georgia, GA USA
- Department of Horticulture, University of Georgia, Georgia, GA USA
| | - María José Díez
- Instituto Universitario de Conservación y Mejora de la Agrodiversidad Valenciana. COMAV. Universitat Politècnica de València, Valencia, Spain
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Harper L, Campbell J, Cannon EKS, Jung S, Poelchau M, Walls R, Andorf C, Arnaud E, Berardini TZ, Birkett C, Cannon S, Carson J, Condon B, Cooper L, Dunn N, Elsik CG, Farmer A, Ficklin SP, Grant D, Grau E, Herndon N, Hu ZL, Humann J, Jaiswal P, Jonquet C, Laporte MA, Larmande P, Lazo G, McCarthy F, Menda N, Mungall CJ, Munoz-Torres MC, Naithani S, Nelson R, Nesdill D, Park C, Reecy J, Reiser L, Sanderson LA, Sen TZ, Staton M, Subramaniam S, Tello-Ruiz MK, Unda V, Unni D, Wang L, Ware D, Wegrzyn J, Williams J, Woodhouse M, Yu J, Main D. AgBioData consortium recommendations for sustainable genomics and genetics databases for agriculture. Database (Oxford) 2018; 2018:5096675. [PMID: 30239679 PMCID: PMC6146126 DOI: 10.1093/database/bay088] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2018] [Revised: 07/19/2018] [Accepted: 07/30/2018] [Indexed: 01/07/2023]
Abstract
The future of agricultural research depends on data. The sheer volume of agricultural biological data being produced today makes excellent data management essential. Governmental agencies, publishers and science funders require data management plans for publicly funded research. Furthermore, the value of data increases exponentially when they are properly stored, described, integrated and shared, so that they can be easily utilized in future analyses. AgBioData (https://www.agbiodata.org) is a consortium of people working at agricultural biological databases, data archives and knowledgbases who strive to identify common issues in database development, curation and management, with the goal of creating database products that are more Findable, Accessible, Interoperable and Reusable. We strive to promote authentic, detailed, accurate and explicit communication between all parties involved in scientific data. As a step toward this goal, we present the current state of biocuration, ontologies, metadata and persistence, database platforms, programmatic (machine) access to data, communication and sustainability with regard to data curation. Each section describes challenges and opportunities for these topics, along with recommendations and best practices.
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Affiliation(s)
- Lisa Harper
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | | | - Ethalinda K S Cannon
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
- Computer Science, Iowa State University, Ames, IA, USA
| | - Sook Jung
- Horticulture, Washington State University, Pullman, WA, USA
| | - Monica Poelchau
- National Agricultural Library, USDA Agricultural Research Service, Beltsville, MD, USA
| | | | - Carson Andorf
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
- Computer Science, Iowa State University, Ames, IA, USA
| | - Elizabeth Arnaud
- Bioversity International, Informatics Unit, Conservation and Availability Programme, Parc Scientifique Agropolis II, Montpellier, France
| | - Tanya Z Berardini
- The Arabidopsis Information Resource, Phoenix Bioinformatics, Fremont, CA, USA
| | | | - Steve Cannon
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | - James Carson
- Texas Advanced Computing Center, The University of Texas at Austin, Austin, TX, USA
| | - Bradford Condon
- Entomology and Plant Pathology, University of Tennessee Knoxville, Knoxville, TN, USA
| | - Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Nathan Dunn
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Christine G Elsik
- Division of Animal Sciences and Division of Plant Sciences, University of Missouri, Columbia, MO, USA
| | - Andrew Farmer
- National Center for Genome Resources, Santa Fe, NM, USA
| | | | - David Grant
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | - Emily Grau
- National Center for Genome Resources, Santa Fe, NM, USA
| | - Nic Herndon
- Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
| | - Zhi-Liang Hu
- Animal Science, Iowa State University, Ames, USA
| | - Jodi Humann
- Horticulture, Washington State University, Pullman, WA, USA
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Clement Jonquet
- Laboratory of Informatics, Robotics, Microelectronics of Montpellier, University of Montpellier & CNRS, Montpellier, France
| | - Marie-Angélique Laporte
- Bioversity International, Informatics Unit, Conservation and Availability Programme, Parc Scientifique Agropolis II, Montpellier, France
| | | | - Gerard Lazo
- Crop Improvement and Genetics Research Unit, USDA-ARS, Albany, CA, USA
| | - Fiona McCarthy
- School of Animal and Comparative Biomedical Sciences, University of Arizona, Tucson, AZ, USA
| | | | | | | | - Sushma Naithani
- Department of Botany and Plant Pathology, Oregon State University, Corvallis, OR, USA
| | - Rex Nelson
- Corn Insects and Crop Genetics Research Unit, USDA-ARS, Ames, IA, USA
| | - Daureen Nesdill
- Marriott Library, University of Utah, Salt Lake City, UT, USA
| | - Carissa Park
- Animal Science, Iowa State University, Ames, USA
| | - James Reecy
- Animal Science, Iowa State University, Ames, USA
| | - Leonore Reiser
- The Arabidopsis Information Resource, Phoenix Bioinformatics, Fremont, CA, USA
| | | | - Taner Z Sen
- Crop Improvement and Genetics Research Unit, USDA-ARS, Albany, CA, USA
| | - Margaret Staton
- Entomology and Plant Pathology, University of Tennessee Knoxville, Knoxville, TN, USA
| | | | | | - Victor Unda
- Horticulture, Washington State University, Pullman, WA, USA
| | - Deepak Unni
- Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
| | - Liya Wang
- Plant Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Doreen Ware
- USDA, Plant, Soil and Nutrition Research, Ithaca, NY, USA
- Plant Genomics, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, USA
| | - Jill Wegrzyn
- Ecology and Evolutionary Biology, University of Connecticut, Storrs, CT, USA
| | - Jason Williams
- Cold Spring Harbor Laboratory, DNA Learning Center, Cold Spring Harbor, NY, USA
| | - Margaret Woodhouse
- Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA, USA
| | - Jing Yu
- Horticulture, Washington State University, Pullman, WA, USA
| | - Doreen Main
- Horticulture, Washington State University, Pullman, WA, USA
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6
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Li J, Chitwood J, Menda N, Mueller L, Hutton SF. Linkage between the I-3 gene for resistance to Fusarium wilt race 3 and increased sensitivity to bacterial spot in tomato. Theor Appl Genet 2018; 131:145-155. [PMID: 28986627 DOI: 10.1007/s00122-017-2991-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/03/2017] [Accepted: 09/19/2017] [Indexed: 06/07/2023]
Abstract
The negative association between the I - 3 gene and increased sensitivity to bacterial spot is due to linkage drag (not pleiotropy) and may be remedied by reducing the introgression size. Fusarium wilt is one of the most serious diseases of tomato (Solanum lycopersicum L.) throughout the world. There are three races of the pathogen (races 1, 2 and 3), and the deployment of three single, dominant resistance genes corresponding to each of these has been the primary means of controlling the disease. The I-3 gene was introgressed from S. pennellii and confers resistance to race 3. Although I-3 provides effective control, it is negatively associated with several horticultural traits, including increased sensitivity to bacterial spot disease (Xanthomonas spp.). To test the hypothesis that this association is due to linkage with unfavorable alleles rather than to pleiotropy, we used a map-based approach to develop a collection of recombinant inbred lines varying for portions of I-3 introgression. Progeny of recombinants were evaluated for bacterial spot severity in the field for three seasons, and disease severities were compared between I-3 introgression haplotypes for each recombinant. Results indicated that increased sensitivity to bacterial spot is not associated with the I-3 gene, but rather with an upstream region of the introgression. A survey of public and private inbred lines and hybrids indicates that the majority of modern I-3 germplasm contains a similarly sized introgression for which the negative association with bacterial spot likely persists. In light of this, it is expected that the development and utilization of a reduced I-3 introgression will significantly improve breeding efforts for resistance to Fusarium wilt race 3.
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Affiliation(s)
- Jian Li
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, 14625 CR 672, Wimauma, FL, 33598-6101, USA
| | - Jessica Chitwood
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, 14625 CR 672, Wimauma, FL, 33598-6101, USA
| | - Naama Menda
- Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY, 14853, USA
| | - Lukas Mueller
- Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY, 14853, USA
| | - Samuel F Hutton
- Gulf Coast Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, 14625 CR 672, Wimauma, FL, 33598-6101, USA.
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7
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Strickler SR, Bombarely A, Munkvold JD, York T, Menda N, Martin GB, Mueller LA. Comparative genomics and phylogenetic discordance of cultivated tomato and close wild relatives. PeerJ 2015; 3:e793. [PMID: 25780758 PMCID: PMC4358695 DOI: 10.7717/peerj.793] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2014] [Accepted: 02/04/2015] [Indexed: 01/27/2023] Open
Abstract
Background. Studies of ancestry are difficult in the tomato because it crosses with many wild relatives and species in the tomato clade that have diverged very recently. As a result, the phylogeny in relation to its closest relatives remains uncertain. By using the coding sequence from Solanum lycopersicum, S. galapagense, S. pimpinellifolium, S. corneliomuelleri, and S. tuberosum and the genomic sequence from S. lycopersicum ‘Heinz’, an heirloom line, S. lycopersicum ‘Yellow Pear’, and two of cultivated tomato’s closest relatives, S. galapagense and S. pimpinellifolium, we have aimed to resolve the phylogenies of these closely related species as well as identify phylogenetic discordance in the reference cultivated tomato. Results. Divergence date estimates suggest that the divergence of S. lycopersicum, S. galapagense, and S. pimpinellifolium happened less than 0.5 MYA. Phylogenies based on 8,857 coding sequences support grouping of S. lycopersicum and S. galapagense, although two secondary trees are also highly represented. A total of 25 genes in our analysis had sites with evidence of positive selection along the S. lycopersicum lineage. Whole genome phylogenies showed that while incongruence is prevalent in genomic comparisons between these genotypes, likely as a result of introgression and incomplete lineage sorting, a primary phylogenetic history was strongly supported. Conclusions. Based on analysis of these genotypes, S. galapagense appears to be closely related to S. lycopersicum, suggesting they had a common ancestor prior to the arrival of an S. galapagense ancestor to the Galápagos Islands, but after divergence of the sequenced S. pimpinellifolium. Genes showing selection along the S. lycopersicum lineage may be important in domestication or selection occurring post-domestication. Further analysis of intraspecific data in these species will help to establish the evolutionary history of cultivated tomato. The use of an heirloom line is helpful in deducing true phylogenetic information of S. lycopersicum and identifying regions of introgression from wild species.
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Affiliation(s)
| | - Aureliano Bombarely
- Department of Horticulture, Virginia Polytechnic Institute and State University , Blacksburg, VA , USA
| | | | - Thomas York
- Boyce Thompson Institute for Plant Research , Ithaca, NY , USA
| | - Naama Menda
- Boyce Thompson Institute for Plant Research , Ithaca, NY , USA
| | - Gregory B Martin
- Boyce Thompson Institute for Plant Research , Ithaca, NY , USA ; Department of Plant Pathology and Plant-Microbe Biology, Cornell University , Ithaca, NY , USA
| | - Lukas A Mueller
- Boyce Thompson Institute for Plant Research , Ithaca, NY , USA
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8
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Oellrich A, Walls RL, Cannon EKS, Cannon SB, Cooper L, Gardiner J, Gkoutos GV, Harper L, He M, Hoehndorf R, Jaiswal P, Kalberer SR, Lloyd JP, Meinke D, Menda N, Moore L, Nelson RT, Pujar A, Lawrence CJ, Huala E. An ontology approach to comparative phenomics in plants. Plant Methods 2015; 11:10. [PMID: 25774204 PMCID: PMC4359497 DOI: 10.1186/s13007-015-0053-y] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2014] [Accepted: 02/05/2015] [Indexed: 05/29/2023]
Abstract
BACKGROUND Plant phenotype datasets include many different types of data, formats, and terms from specialized vocabularies. Because these datasets were designed for different audiences, they frequently contain language and details tailored to investigators with different research objectives and backgrounds. Although phenotype comparisons across datasets have long been possible on a small scale, comprehensive queries and analyses that span a broad set of reference species, research disciplines, and knowledge domains continue to be severely limited by the absence of a common semantic framework. RESULTS We developed a workflow to curate and standardize existing phenotype datasets for six plant species, encompassing both model species and crop plants with established genetic resources. Our effort focused on mutant phenotypes associated with genes of known sequence in Arabidopsis thaliana (L.) Heynh. (Arabidopsis), Zea mays L. subsp. mays (maize), Medicago truncatula Gaertn. (barrel medic or Medicago), Oryza sativa L. (rice), Glycine max (L.) Merr. (soybean), and Solanum lycopersicum L. (tomato). We applied the same ontologies, annotation standards, formats, and best practices across all six species, thereby ensuring that the shared dataset could be used for cross-species querying and semantic similarity analyses. Curated phenotypes were first converted into a common format using taxonomically broad ontologies such as the Plant Ontology, Gene Ontology, and Phenotype and Trait Ontology. We then compared ontology-based phenotypic descriptions with an existing classification system for plant phenotypes and evaluated our semantic similarity dataset for its ability to enhance predictions of gene families, protein functions, and shared metabolic pathways that underlie informative plant phenotypes. CONCLUSIONS The use of ontologies, annotation standards, shared formats, and best practices for cross-taxon phenotype data analyses represents a novel approach to plant phenomics that enhances the utility of model genetic organisms and can be readily applied to species with fewer genetic resources and less well-characterized genomes. In addition, these tools should enhance future efforts to explore the relationships among phenotypic similarity, gene function, and sequence similarity in plants, and to make genotype-to-phenotype predictions relevant to plant biology, crop improvement, and potentially even human health.
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Affiliation(s)
- Anika Oellrich
- />Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton, CB10 1SA UK
| | - Ramona L Walls
- />iPlant Collaborative, University of Arizona, 1657 E. Helen St., Tucson, Arizona 85721 USA
| | - Ethalinda KS Cannon
- />Department of Electrical and Computer Engineering Iowa State University, 1018 Crop Informatics Lab, Ames, Iowa 50011 USA
| | - Steven B Cannon
- />USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Crop Genome Informatics Lab, Iowa State University, Ames, IA 50011 USA
- />Department of Agronomy, Agronomy Hall, Iowa State University, Ames, IA 50010 USA
| | - Laurel Cooper
- />Department of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University, Corvallis, OR 97331 USA
| | - Jack Gardiner
- />Department of Genetics, Development and Cell Biology, Roy J Carver Co-Laboratory, Iowa State University, Ames, IA 50010 USA
| | - Georgios V Gkoutos
- />Department of Computer Science, Aberystwyth University, Llandinam Building, Aberystwyth, SY23 3DB UK
| | - Lisa Harper
- />USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Crop Genome Informatics Lab, Iowa State University, Ames, IA 50011 USA
| | - Mingze He
- />Department of Genetics, Development and Cell Biology, Roy J Carver Co-Laboratory, Iowa State University, Ames, IA 50010 USA
| | - Robert Hoehndorf
- />Computer, Electrical and Mathematical Sciences & Engineering Division and Computational Bioscience Research Center, King Abdullah University of Science and Technology, 4700 King Abdullah University of Science and Technology, P.O. Box 2882, Thuwal, 23955-6900 Kingdom of Saudi Arabia
| | - Pankaj Jaiswal
- />Department of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University, Corvallis, OR 97331 USA
| | - Scott R Kalberer
- />USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Crop Genome Informatics Lab, Iowa State University, Ames, IA 50011 USA
| | - John P Lloyd
- />Department of Plant Biology, Michigan State University, 220 Trowbridge Rd, East Lansing, MI 48824 USA
| | - David Meinke
- />Department of Botany, Oklahoma State University, 301 Physical Sciences, Stillwater, OK 74078 USA
| | - Naama Menda
- />Boyce Thompson Institute for Plant Research, 533 Tower Road, Ithaca, NY 14853 USA
| | - Laura Moore
- />Department of Botany and Plant Pathology, 2082 Cordley Hall, Oregon State University, Corvallis, OR 97331 USA
| | - Rex T Nelson
- />USDA-ARS Corn Insects and Crop Genetics Research Unit, Iowa State University, Crop Genome Informatics Lab, Iowa State University, Ames, IA 50011 USA
| | - Anuradha Pujar
- />Boyce Thompson Institute for Plant Research, 533 Tower Road, Ithaca, NY 14853 USA
| | - Carolyn J Lawrence
- />Department of Agronomy, Agronomy Hall, Iowa State University, Ames, IA 50010 USA
- />Department of Genetics, Development and Cell Biology, Roy J Carver Co-Laboratory, Iowa State University, Ames, IA 50010 USA
| | - Eva Huala
- />Phoenix Bioinformatics, 643 Bair Island Rd Suite 403, Redwood City, CA 94063 USA
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9
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Tecle IY, Edwards JD, Menda N, Egesi C, Rabbi IY, Kulakow P, Kawuki R, Jannink JL, Mueller LA. solGS: a web-based tool for genomic selection. BMC Bioinformatics 2014; 15:398. [PMID: 25495537 PMCID: PMC4269960 DOI: 10.1186/s12859-014-0398-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2014] [Accepted: 11/26/2014] [Indexed: 11/18/2022] Open
Abstract
Background Genomic selection (GS) promises to improve accuracy in estimating breeding values and genetic gain for quantitative traits compared to traditional breeding methods. Its reliance on high-throughput genome-wide markers and statistical complexity, however, is a serious challenge in data management, analysis, and sharing. A bioinformatics infrastructure for data storage and access, and user-friendly web-based tool for analysis and sharing output is needed to make GS more practical for breeders. Results We have developed a web-based tool, called solGS, for predicting genomic estimated breeding values (GEBVs) of individuals, using a Ridge-Regression Best Linear Unbiased Predictor (RR-BLUP) model. It has an intuitive web-interface for selecting a training population for modeling and estimating genomic estimated breeding values of selection candidates. It estimates phenotypic correlation and heritability of traits and selection indices of individuals. Raw data is stored in a generic database schema, Chado Natural Diversity, co-developed by multiple database groups. Analysis output is graphically visualized and can be interactively explored online or downloaded in text format. An instance of its implementation can be accessed at the NEXTGEN Cassava breeding database, http://cassavabase.org/solgs. Conclusions solGS enables breeders to store raw data and estimate GEBVs of individuals online, in an intuitive and interactive workflow. It can be adapted to any breeding program. Electronic supplementary material The online version of this article (doi:10.1186/s12859-014-0398-7) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Isaak Y Tecle
- Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, NY, USA.
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10
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Fernandez-Pozo N, Menda N, Edwards JD, Saha S, Tecle IY, Strickler SR, Bombarely A, Fisher-York T, Pujar A, Foerster H, Yan A, Mueller LA. The Sol Genomics Network (SGN)--from genotype to phenotype to breeding. Nucleic Acids Res 2014; 43:D1036-41. [PMID: 25428362 PMCID: PMC4383978 DOI: 10.1093/nar/gku1195] [Citation(s) in RCA: 363] [Impact Index Per Article: 36.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Sol Genomics Network (SGN, http://solgenomics.net) is a web portal with genomic and phenotypic data, and analysis tools for the Solanaceae family and close relatives. SGN hosts whole genome data for an increasing number of Solanaceae family members including tomato, potato, pepper, eggplant, tobacco and Nicotiana benthamiana. The database also stores loci and phenotype data, which researchers can upload and edit with user-friendly web interfaces. Tools such as BLAST, GBrowse and JBrowse for browsing genomes, expression and map data viewers, a locus community annotation system and a QTL analysis tools are available. A new tool was recently implemented to improve Virus-Induced Gene Silencing (VIGS) constructs called the SGN VIGS tool. With the growing genomic and phenotypic data in the database, SGN is now advancing to develop new web-based breeding tools and implement the code and database structure for other species or clade-specific databases.
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Affiliation(s)
| | - Naama Menda
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | - Jeremy D Edwards
- Dale Bumpers National Rice Research Center, Stuttgart, AR 72160, USA
| | - Surya Saha
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Ithaca, NY 14853, USA
| | - Isaak Y Tecle
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | | | - Aureliano Bombarely
- Department of Horticulture, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0002, USA
| | | | - Anuradha Pujar
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | - Hartmut Foerster
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | - Aimin Yan
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA
| | - Lukas A Mueller
- Boyce Thompson Institute for Plant Research, Ithaca, NY 14853, USA Department of Plant Breeding, Cornell University, Ithaca, NY 14853, USA
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11
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Menda N, Strickler SR, Edwards JD, Bombarely A, Dunham DM, Martin GB, Mejia L, Hutton SF, Havey MJ, Maxwell DP, Mueller LA. Analysis of wild-species introgressions in tomato inbreds uncovers ancestral origins. BMC Plant Biol 2014; 14:287. [PMID: 25348801 PMCID: PMC4219026 DOI: 10.1186/s12870-014-0287-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/29/2014] [Accepted: 10/15/2014] [Indexed: 05/03/2023]
Abstract
BACKGROUND Decades of intensive tomato breeding using wild-species germplasm have resulted in the genomes of domesticated germplasm (Solanum lycopersicum) being intertwined with introgressions from their wild relatives. Comparative analysis of genomes among cultivated tomatoes and wild species that have contributed genetic variation can help identify desirable genes, such as those conferring disease resistance. The ability to identify introgression position, borders, and contents can reveal ancestral origins and facilitate harnessing of wild variation in crop breeding. RESULTS Here we present the whole-genome sequences of two tomato inbreds, Gh13 and BTI-87, both carrying the begomovirus resistance locus Ty-3 introgressed from wild tomato species. Introgressions of different sizes on chromosome 6 of Gh13 and BTI-87, both corresponding to the Ty-3 region, were identified as from a source close to the wild species S. chilense. Other introgressions were identified throughout the genomes of the inbreds and showed major differences in the breeding pedigrees of the two lines. Interestingly, additional large introgressions from the close tomato relative S. pimpinellifolium were identified in both lines. Some of the polymorphic regions were attributed to introgressions in the reference Heinz 1706 genome, indicating wild genome sequences in the reference tomato genome. CONCLUSIONS The methods developed in this work can be used to delineate genome introgressions, and subsequently contribute to development of molecular markers to aid phenotypic selection, fine mapping and discovery of candidate genes for important phenotypes, and for identification of novel variation for tomato improvement. These universal methods can easily be applied to other crop plants.
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Affiliation(s)
- Naama Menda
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
| | - Susan R Strickler
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
| | - Jeremy D Edwards
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
| | - Aureliano Bombarely
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
| | - Diane M Dunham
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
| | - Gregory B Martin
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
- />Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Ithaca, NY 14853 USA
| | - Luis Mejia
- />Facultad de Agronomía, Universidad de San Carlos de Guatemala, Guatemala City, 01012 Guatemala
| | - Samuel F Hutton
- />University of Florida, Gulf Coast Research and Education Center, 14625 CR 672, Wimauma, FL 33598 USA
| | - Michael J Havey
- />USDA-ARS Department of Horticulture, University of Wisconsin, 1575 Linden Drive, Madison, WI 53706 USA
| | - Douglas P Maxwell
- />Department of Plant Pathology, University of Wisconsin-Madison, Madison, WI 53706 USA
| | - Lukas A Mueller
- />Boyce Thompson Institute for Plant Research, 533 Tower Rd, Ithaca, NY 14853 USA
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12
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Pujar A, Menda N, Bombarely A, Edwards JD, Strickler SR, Mueller LA. From manual curation to visualization of gene families and networks across Solanaceae plant species. Database (Oxford) 2013; 2013:bat028. [PMID: 23681907 PMCID: PMC3655285 DOI: 10.1093/database/bat028] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
High-quality manual annotation methods and practices need to be scaled to the increased rate of genomic data production. Curation based on gene families and gene networks is one approach that can significantly increase both curation efficiency and quality. The Sol Genomics Network (SGN; http://solgenomics.net) is a comparative genomics platform, with genetic, genomic and phenotypic information of the Solanaceae family and its closely related species that incorporates a community-based gene and phenotype curation system. In this article, we describe a manual curation system for gene families aimed at facilitating curation, querying and visualization of gene interaction patterns underlying complex biological processes, including an interface for efficiently capturing information from experiments with large data sets reported in the literature. Well-annotated multigene families are useful for further exploration of genome organization and gene evolution across species. As an example, we illustrate the system with the multigene transcription factor families, WRKY and Small Auxin Up-regulated RNA (SAUR), which both play important roles in responding to abiotic stresses in plants. Database URL:http://solgenomics.net/
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Affiliation(s)
- Anuradha Pujar
- Boyce Thompson Institute for Plant Research, 533, Tower Road, Ithaca, NY 14853, USA
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13
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Cooper L, Walls RL, Elser J, Gandolfo MA, Stevenson DW, Smith B, Preece J, Athreya B, Mungall CJ, Rensing S, Hiss M, Lang D, Reski R, Berardini TZ, Li D, Huala E, Schaeffer M, Menda N, Arnaud E, Shrestha R, Yamazaki Y, Jaiswal P. The plant ontology as a tool for comparative plant anatomy and genomic analyses. Plant Cell Physiol 2013; 54:e1. [PMID: 23220694 PMCID: PMC3583023 DOI: 10.1093/pcp/pcs163] [Citation(s) in RCA: 87] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
The Plant Ontology (PO; http://www.plantontology.org/) is a publicly available, collaborative effort to develop and maintain a controlled, structured vocabulary ('ontology') of terms to describe plant anatomy, morphology and the stages of plant development. The goals of the PO are to link (annotate) gene expression and phenotype data to plant structures and stages of plant development, using the data model adopted by the Gene Ontology. From its original design covering only rice, maize and Arabidopsis, the scope of the PO has been expanded to include all green plants. The PO was the first multispecies anatomy ontology developed for the annotation of genes and phenotypes. Also, to our knowledge, it was one of the first biological ontologies that provides translations (via synonyms) in non-English languages such as Japanese and Spanish. As of Release #18 (July 2012), there are about 2.2 million annotations linking PO terms to >110,000 unique data objects representing genes or gene models, proteins, RNAs, germplasm and quantitative trait loci (QTLs) from 22 plant species. In this paper, we focus on the plant anatomical entity branch of the PO, describing the organizing principles, resources available to users and examples of how the PO is integrated into other plant genomics databases and web portals. We also provide two examples of comparative analyses, demonstrating how the ontology structure and PO-annotated data can be used to discover the patterns of expression of the LEAFY (LFY) and terpene synthase (TPS) gene homologs.
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Affiliation(s)
- Laurel Cooper
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902, USA
- These authors contributed equally to this work
- These authors contributed equally to the development of the Plant Ontology
| | - Ramona L. Walls
- New York Botanical Garden, 2900 Southern Blvd., Bronx, NY 10458-5126, USA
- These authors contributed equally to this work
- These authors contributed equally to the development of the Plant Ontology
| | - Justin Elser
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902, USA
- These authors contributed equally to the development of the Plant Ontology
| | - Maria A. Gandolfo
- L.H. Bailey Hortorium, Department of Plant Biology, Cornell University, 412 Mann Library Building, Ithaca, NY 14853, USA
- These authors contributed equally to the development of the Plant Ontology
| | - Dennis W. Stevenson
- New York Botanical Garden, 2900 Southern Blvd., Bronx, NY 10458-5126, USA
- These authors contributed equally to the development of the Plant Ontology
| | - Barry Smith
- Department of Philosophy, University at Buffalo, 126 Park Hall, Buffalo, NY 14260, USA
- These authors contributed equally to the development of the Plant Ontology
| | - Justin Preece
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902, USA
| | - Balaji Athreya
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902, USA
| | - Christopher J. Mungall
- Berkeley Bioinformatics Open-Source Projects, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Mailstop 64-121, Berkeley, CA 94720, USA
| | - Stefan Rensing
- Faculty of Biology and BIOSS Centre for Biological Signalling Studies, University of Freiburg, Schänzlestr. 1, D-79104 Freiburg, Germany
| | - Manuel Hiss
- Faculty of Biology and BIOSS Centre for Biological Signalling Studies, University of Freiburg, Schänzlestr. 1, D-79104 Freiburg, Germany
| | - Daniel Lang
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Germany
| | - Ralf Reski
- Plant Biotechnology, Faculty of Biology, University of Freiburg, Germany
- FRIAS - Freiburg Institute for Advanced Studies, University of Freiburg, Freiburg, Germany
| | - Tanya Z. Berardini
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
| | - Donghui Li
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
| | - Eva Huala
- Department of Plant Biology, Carnegie Institution for Science, Stanford, CA 94305, USA
| | - Mary Schaeffer
- Agriculture Research Services, United States Department of Agriculture, Columbia, MO 65211, USA
- Division of Plant Sciences, Department of Agronomy, University of Missouri, Columbia, MO 65211, USA
| | - Naama Menda
- Boyce Thompson Institute for Plant Research, 533 Tower Road, Ithaca, NY 148533, USA
| | - Elizabeth Arnaud
- Bioversity International, via dei Tre Denari, 174/a, Maccarese, Rome, Italy
| | - Rosemary Shrestha
- Genetic Resources Program, Centro Internacional de Mejoramiento de Maiz y Trigo (CIMMYT), Apdo. Postal 6-641, 06600 Mexico, D.F., Mexico
| | - Yukiko Yamazaki
- Center for Genetic Resource Information, National Institute of Genetics, Mishima, Shizuoka, 411-8540 Japan
| | - Pankaj Jaiswal
- Department of Botany and Plant Pathology, Oregon State University, 2082 Cordley Hall, Corvallis, OR 97331-2902, USA
- These authors contributed equally to the development of the Plant Ontology
- *Corresponding author: E-mail,: ; Fax, +1-541-737-3573
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14
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Jung S, Menda N, Redmond S, Buels RM, Friesen M, Bendana Y, Sanderson LA, Lapp H, Lee T, MacCallum B, Bett KE, Cain S, Clements D, Mueller LA, Main D. The Chado Natural Diversity module: a new generic database schema for large-scale phenotyping and genotyping data. Database (Oxford) 2011; 2011:bar051. [PMID: 22120662 PMCID: PMC3225077 DOI: 10.1093/database/bar051] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Linking phenotypic with genotypic diversity has become a major requirement for basic and applied genome-centric biological research. To meet this need, a comprehensive database backend for efficiently storing, querying and analyzing large experimental data sets is necessary. Chado, a generic, modular, community-based database schema is widely used in the biological community to store information associated with genome sequence data. To meet the need to also accommodate large-scale phenotyping and genotyping projects, a new Chado module called Natural Diversity has been developed. The module strictly adheres to the Chado remit of being generic and ontology driven. The flexibility of the new module is demonstrated in its capacity to store any type of experiment that either uses or generates specimens or stock organisms. Experiments may be grouped or structured hierarchically, whereas any kind of biological entity can be stored as the observed unit, from a specimen to be used in genotyping or phenotyping experiments, to a group of species collected in the field that will undergo further lab analysis. We describe details of the Natural Diversity module, including the design approach, the relational schema and use cases implemented in several databases.
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Affiliation(s)
- Sook Jung
- Department of Horticulture and Landscape, Washington State University, Pullman, WA 99164, USA.
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15
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Tecle IY, Menda N, Buels RM, van der Knaap E, Mueller LA. solQTL: a tool for QTL analysis, visualization and linking to genomes at SGN database. BMC Bioinformatics 2010; 11:525. [PMID: 20964836 PMCID: PMC2984588 DOI: 10.1186/1471-2105-11-525] [Citation(s) in RCA: 19] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2010] [Accepted: 10/21/2010] [Indexed: 11/23/2022] Open
Abstract
Background A common approach to understanding the genetic basis of complex traits is through identification of associated quantitative trait loci (QTL). Fine mapping QTLs requires several generations of backcrosses and analysis of large populations, which is time-consuming and costly effort. Furthermore, as entire genomes are being sequenced and an increasing amount of genetic and expression data are being generated, a challenge remains: linking phenotypic variation to the underlying genomic variation. To identify candidate genes and understand the molecular basis underlying the phenotypic variation of traits, bioinformatic approaches are needed to exploit information such as genetic map, expression and whole genome sequence data of organisms in biological databases. Description The Sol Genomics Network (SGN, http://solgenomics.net) is a primary repository for phenotypic, genetic, genomic, expression and metabolic data for the Solanaceae family and other related Asterids species and houses a variety of bioinformatics tools. SGN has implemented a new approach to QTL data organization, storage, analysis, and cross-links with other relevant data in internal and external databases. The new QTL module, solQTL, http://solgenomics.net/qtl/, employs a user-friendly web interface for uploading raw phenotype and genotype data to the database, R/QTL mapping software for on-the-fly QTL analysis and algorithms for online visualization and cross-referencing of QTLs to relevant datasets and tools such as the SGN Comparative Map Viewer and Genome Browser. Here, we describe the development of the solQTL module and demonstrate its application. Conclusions solQTL allows Solanaceae researchers to upload raw genotype and phenotype data to SGN, perform QTL analysis and dynamically cross-link to relevant genetic, expression and genome annotations. Exploration and synthesis of the relevant data is expected to help facilitate identification of candidate genes underlying phenotypic variation and markers more closely linked to QTLs. solQTL is freely available on SGN and can be used in private or public mode.
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Affiliation(s)
- Isaak Y Tecle
- Boyce Thompson Institute for Plant Research, Tower Rd, Ithaca, NY 14853, USA
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16
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Bombarely A, Menda N, Tecle IY, Buels RM, Strickler S, Fischer-York T, Pujar A, Leto J, Gosselin J, Mueller LA. The Sol Genomics Network (solgenomics.net): growing tomatoes using Perl. Nucleic Acids Res 2010; 39:D1149-55. [PMID: 20935049 PMCID: PMC3013765 DOI: 10.1093/nar/gkq866] [Citation(s) in RCA: 213] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/04/2022] Open
Abstract
The Sol Genomics Network (SGN; http://solgenomics.net/) is a clade-oriented database (COD) containing biological data for species in the Solanaceae and their close relatives, with data types ranging from chromosomes and genes to phenotypes and accessions. SGN hosts several genome maps and sequences, including a pre-release of the tomato (Solanum lycopersicum cv Heinz 1706) reference genome. A new transcriptome component has been added to store RNA-seq and microarray data. SGN is also an open source software project, continuously developing and improving a complex system for storing, integrating and analyzing data. All code and development work is publicly visible on GitHub (http://github.com). The database architecture combines SGN-specific schemas and the community-developed Chado schema (http://gmod.org/wiki/Chado) for compatibility with other genome databases. The SGN curation model is community-driven, allowing researchers to add and edit information using simple web tools. Currently, over a hundred community annotators help curate the database. SGN can be accessed at http://solgenomics.net/.
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Affiliation(s)
- Aureliano Bombarely
- Boyce Thompson Institute for Plant Research, Tower Road, Ithaca, NY 14853, USA
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17
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Menda N, Buels RM, Tecle I, Mueller LA. A community-based annotation framework for linking solanaceae genomes with phenomes. Plant Physiol 2008; 147:1788-1799. [PMID: 18539779 PMCID: PMC2492603 DOI: 10.1104/pp.108.119560] [Citation(s) in RCA: 18] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2008] [Accepted: 05/09/2008] [Indexed: 05/26/2023]
Abstract
The amount of biological data available in the public domain is growing exponentially, and there is an increasing need for infrastructural and human resources to organize, store, and present the data in a proper context. Model organism databases (MODs) invest great efforts to functionally annotate genomes and phenomes by in-house curators. The SOL Genomics Network (SGN; http://www.sgn.cornell.edu) is a clade-oriented database (COD), which provides a more scalable and comparative framework for biological information. SGN has recently spearheaded a new approach by developing community annotation tools to expand its curational capacity. These tools effectively allow some curation to be delegated to qualified researchers, while, at the same time, preserving the in-house curators' full editorial control. Here we describe the background, features, implementation, results, and development road map of SGN's community annotation tools for curating genotypes and phenotypes. Since the inception of this project in late 2006, interest and participation from the Solanaceae research community has been strong and growing continuously to the extent that we plan to expand the framework to accommodate more plant taxa. All data, tools, and code developed at SGN are freely available to download and adapt.
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Affiliation(s)
- Naama Menda
- Department of Plant Breeding and Genetics, and Boyce Thompson Institute for Plant Research, Cornell University, Ithaca, New York 14853, USA
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18
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Galpaz N, Wang Q, Menda N, Zamir D, Hirschberg J. Abscisic acid deficiency in the tomato mutant high-pigment 3 leading to increased plastid number and higher fruit lycopene content. Plant J 2008; 53:717-30. [PMID: 17988221 DOI: 10.1111/j.1365-313x.2007.03362.x] [Citation(s) in RCA: 228] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Carotenoids are present in most tissues of higher plants where they play a variety of essential roles. To study the regulation of carotenoid biosynthesis, we have isolated novel mutations in tomato (Solanum lycopersicum) with altered pigmentation of fruit or flowers. Here we describe the isolation and analysis of a tomato mutant, high-pigment 3 (hp3), that accumulates 30% more carotenoids in the mature fruit. Higher concentrations of carotenoids and chlorophyll were also measured in leaves and the pericarp of green fruit. The mutation in hp3 had occurred in the gene for zeaxanthin epoxidase (Zep), which converts zeaxanthin to violaxanthin. Consequently, leaves of the mutant lack violaxanthin and neoxanthin, and flowers contain only minute quantities of these xanthophylls. The concentration in the hp3 mutant of abscisic acid (ABA), which is derived from xanthophylls, is 75% lower than the normal level, making hp3 an ABA-deficient mutant. The plastid compartment size in fruit cells is at least twofold larger in hp3 plants compared with the wild-type. The transcript level in the green fruit of FtsZ, which encodes a tubulin-like protein involved in plastid division, is 60% higher in hp3 than in the wild-type, suggesting that increased plastid division is responsible for this phenomenon. Elevated fruit pigmentation and plastid compartment size were also observed in the ABA-deficient mutants flacca and sitiens. Taken together, these results suggest that ABA deficiency in the tomato mutant hp3 leads to enlargement of the plastid compartment size, probably by increasing plastid division, thus enabling greater biosynthesis and a higher storage capacity of the pigments.
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Affiliation(s)
- Navot Galpaz
- Department of Genetics, Alexander Silberman Institute of Life Sciences, The Hebrew University of Jerusalem, Jerusalem 91904, Israel
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19
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Abstract
MOTIVATION With the rapid accumulation of genetic data for a multitude of different species, the availability of intuitive comparative genomic tools becomes an important requirement for the research community. Here we describe a web-based comparative viewer for mapping data, including genetic, physical and cytological maps, that is part of the SGN website (http://sgn.cornell.edu/) but that can also be installed and adapted for other websites. In addition to viewing and comparing different maps stored in the SGN database, the viewer allows users to upload their own maps and compare them to other maps in the system. The viewer is implemented in object oriented Perl, with a simple extensible interface to write data adapters for other relational database schemas and flat file formats.
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Affiliation(s)
- Lukas A Mueller
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY 14853, USA.
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20
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Ori N, Cohen AR, Etzioni A, Brand A, Yanai O, Shleizer S, Menda N, Amsellem Z, Efroni I, Pekker I, Alvarez JP, Blum E, Zamir D, Eshed Y. Regulation of LANCEOLATE by miR319 is required for compound-leaf development in tomato. Nat Genet 2007; 39:787-91. [PMID: 17486095 DOI: 10.1038/ng2036] [Citation(s) in RCA: 334] [Impact Index Per Article: 19.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2007] [Accepted: 04/13/2007] [Indexed: 11/09/2022]
Abstract
Plant leaves show pronounced plasticity of size and form. In the classical, partially dominant mutation Lanceolate (La), the large compound leaves of tomato (Solanum lycopersicum) are converted into small simple ones. We show that LA encodes a transcription factor from the TCP family containing an miR319-binding site. Five independent La isolates are gain-of-function alleles that result from point mutations within the miR319-binding site and confer partial resistance of the La transcripts to microRNA (miRNA)-directed inhibition. The reduced sensitivity to miRNA regulation leads to elevated LA expression in very young La leaf primordia and to precocious differentiation of leaf margins. In contrast, downregulation of several LA-like genes using ectopic expression of miR319 resulted in larger leaflets and continuous growth of leaf margins. Our results imply that regulation of LA by miR319 defines a flexible window of morphogenetic competence along the developing leaf margin that is required for leaf elaboration.
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Affiliation(s)
- Naomi Ori
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel.
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21
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Semel Y, Nissenbaum J, Menda N, Zinder M, Krieger U, Issman N, Pleban T, Lippman Z, Gur A, Zamir D. Overdominant quantitative trait loci for yield and fitness in tomato. Proc Natl Acad Sci U S A 2006; 103:12981-6. [PMID: 16938842 PMCID: PMC1552043 DOI: 10.1073/pnas.0604635103] [Citation(s) in RCA: 158] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
Abstract
Heterosis, or hybrid vigor, is a major genetic force that contributes to world food production. The genetic basis of heterosis is not clear, and the importance of loci with overdominant (ODO) effects is debated. One problem has been the use of whole-genome segregating populations, where interactions often mask the effects of individual loci. To assess the contribution of ODO to heterosis in the absence of epistasis, we carried out quantitative genetic and phenotypic analyses on a population of tomato (Solanum lycopersicum) introgression lines (ILs), which carry single marker-defined chromosome segments from the distantly related wild species Solanum pennellii. The ILs revealed 841 quantitative trait loci (QTL) for 35 diverse traits measured in the field on homozygous and heterozygous plants. ILs showing greater reproductive fitness were characterized by the prevalence of ODO QTL, which were virtually absent for the nonreproductive traits. ODO can result from true ODO due to allelic interactions of a single gene or from pseudoODO that involves linked loci with dominant alleles in repulsion. The fact that we detected dominant and recessive QTL for all phenotypic categories but ODO only for the reproductive traits indicates that pseudoODO due to random linkage is unlikely to explain heterosis in the ILs. Thus, we favor the true ODO model involving a single functional Mendelian locus. We propose that the alliance of ODO QTL with higher reproductive fitness was selected for in evolution and was domesticated by man to improve yields of crop plants.
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Affiliation(s)
- Yaniv Semel
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Jonathan Nissenbaum
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Naama Menda
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Michael Zinder
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Uri Krieger
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Noa Issman
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Tzili Pleban
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Zachary Lippman
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Amit Gur
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
| | - Dani Zamir
- Institute of Plant Sciences and Genetics, Faculty of Agriculture, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
- To whom correspondence should be addressed. E-mail:
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22
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Dafny-Yelin M, Guterman I, Menda N, Ovadis M, Shalit M, Pichersky E, Zamir D, Lewinsohn E, Adam Z, Weiss D, Vainstein A. Flower proteome: changes in protein spectrum during the advanced stages of rose petal development. Planta 2005; 222:37-46. [PMID: 15883834 DOI: 10.1007/s00425-005-1512-x] [Citation(s) in RCA: 12] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2004] [Accepted: 02/03/2005] [Indexed: 05/02/2023]
Abstract
Flowering is a unique and highly programmed process, but hardly anything is known about the developmentally regulated proteome changes in petals. Here, we employed proteomic technologies to study petal development in rose (Rosa hybrida). Using two-dimensional polyacrylamide gel electrophoresis, we generated stage-specific (closed bud, mature flower and flower at anthesis) petal protein maps with ca. 1,000 unique protein spots. Expression analyses of all resolved protein spots revealed that almost 30% of them were stage-specific, with ca. 90 protein spots for each stage. Most of the proteins exhibited differential expression during petal development, whereas only ca. 6% were constitutively expressed. Eighty-two of the resolved proteins were identified by mass spectrometry and annotated. Classification of the annotated proteins into functional groups revealed energy, cell rescue, unknown function (including novel sequences) and metabolism to be the largest classes, together comprising ca. 90% of all identified proteins. Interestingly, a large number of stress-related proteins were identified in developing petals. Analyses of the expression patterns of annotated proteins and their corresponding RNAs confirmed the importance of proteome characterization.
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Affiliation(s)
- Mery Dafny-Yelin
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, The Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
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23
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Mueller LA, Solow TH, Taylor N, Skwarecki B, Buels R, Binns J, Lin C, Wright MH, Ahrens R, Wang Y, Herbst EV, Keyder ER, Menda N, Zamir D, Tanksley SD. The SOL Genomics Network: a comparative resource for Solanaceae biology and beyond. Plant Physiol 2005; 138:1310-7. [PMID: 16010005 PMCID: PMC1176404 DOI: 10.1104/pp.105.060707] [Citation(s) in RCA: 128] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The SOL Genomics Network (SGN; http://sgn.cornell.edu) is a rapidly evolving comparative resource for the plants of the Solanaceae family, which includes important crop and model plants such as potato (Solanum tuberosum), eggplant (Solanum melongena), pepper (Capsicum annuum), and tomato (Solanum lycopersicum). The aim of SGN is to relate these species to one another using a comparative genomics approach and to tie them to the other dicots through the fully sequenced genome of Arabidopsis (Arabidopsis thaliana). SGN currently houses map and marker data for Solanaceae species, a large expressed sequence tag collection with computationally derived unigene sets, an extensive database of phenotypic information for a mutagenized tomato population, and associated tools such as real-time quantitative trait loci. Recently, the International Solanaceae Project (SOL) was formed as an umbrella organization for Solanaceae research in over 30 countries to address important questions in plant biology. The first cornerstone of the SOL project is the sequencing of the entire euchromatic portion of the tomato genome. SGN is collaborating with other bioinformatics centers in building the bioinformatics infrastructure for the tomato sequencing project and implementing the bioinformatics strategy of the larger SOL project. The overarching goal of SGN is to make information available in an intuitive comparative format, thereby facilitating a systems approach to investigations into the basis of adaptation and phenotypic diversity in the Solanaceae family, other species in the Asterid clade such as coffee (Coffea arabica), Rubiaciae, and beyond.
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Affiliation(s)
- Lukas A Mueller
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, New York 14853, USA.
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24
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Abstract
A comprehensive mutant population is a basic resource for exploring gene function. We developed an isogenic tomato 'mutation library' in the genetic background of the inbred variety M82. A total of 13 000 M(2) families, derived from EMS (ethyl methane sulfonate) and fast-neutron mutagenesis, were visually phenotyped in the field and categorized into a morphological catalog that includes 15 primary and 48 secondary categories. Currently, 3417 mutations have been cataloged; among them are most of the previously described phenotypes from the monogenic mutant collection of The Tomato Genetics Resource Center, and over a thousand new mutants, with multiple alleles per locus. The phenotypic database indicates that most mutations fall into more than a single category (pleiotropic), with some organs such as leaves more prone to alterations than others. All data and images can be searched and accessed in the Solanaceae Genome Network (SGN) on a site called 'The Genes That Make Tomatoes' (http://zamir.sgn.cornell.edu/mutants/).
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Affiliation(s)
- Naama Menda
- The Robert H. Smith Institute of Plant Sciences and Genetics in Agriculture, and the Otto Warburg Center for Agricultural Biotechnology, Faculty of Agriculture, The Hebrew University of Jerusalem, PO Box 12, Rehovot 76100, Israel
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25
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Shalit M, Guterman I, Volpin H, Bar E, Tamari T, Menda N, Adam Z, Zamir D, Vainstein A, Weiss D, Pichersky E, Lewinsohn E. Volatile ester formation in roses. Identification of an acetyl-coenzyme A. Geraniol/Citronellol acetyltransferase in developing rose petals. Plant Physiol 2003; 131:1868-76. [PMID: 12692346 PMCID: PMC166943 DOI: 10.1104/pp.102.018572] [Citation(s) in RCA: 110] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
The aroma of roses (Rosa hybrida) is due to more than 400 volatile compounds including terpenes, esters, and phenolic derivatives. 2-Phenylethyl acetate, cis-3-hexenyl acetate, geranyl acetate, and citronellyl acetate were identified as the main volatile esters emitted by the flowers of the scented rose var. "Fragrant Cloud." Cell-free extracts of petals acetylated several alcohols, utilizing acetyl-coenzyme A, to produce the corresponding acetate esters. Screening for genes similar to known plant alcohol acetyltransferases in a rose expressed sequence tag database yielded a cDNA (RhAAT1) encoding a protein with high similarity to several members of the BAHD family of acyltransferases. This cDNA was functionally expressed in Escherichia coli, and its gene product displayed acetyl-coenzyme A:geraniol acetyltransferase enzymatic activity in vitro. The RhAAT1 protein accepted other alcohols such as citronellol and 1-octanol as substrates, but 2-phenylethyl alcohol and cis-3-hexen-1-ol were poor substrates, suggesting that additional acetyltransferases are present in rose petals. The RhAAT1 protein is a polypeptide of 458 amino acids, with a calculated molecular mass of 51.8 kD, pI of 5.45, and is active as a monomer. The RhAAT1 gene was expressed exclusively in floral tissue with maximum transcript levels occurring at stage 4 of flower development, where scent emission is at its peak.
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Affiliation(s)
- Moshe Shalit
- Department of Vegetable Crops, Newe Ya'ar Research Center, Agricultural Research Organization, P.O. Box 1021, Ramat Yishay 30095, Israel
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26
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Guterman I, Shalit M, Menda N, Piestun D, Dafny-Yelin M, Shalev G, Bar E, Davydov O, Ovadis M, Emanuel M, Wang J, Adam Z, Pichersky E, Lewinsohn E, Zamir D, Vainstein A, Weiss D. Rose scent: genomics approach to discovering novel floral fragrance-related genes. Plant Cell 2002; 14:2325-38. [PMID: 12368489 PMCID: PMC151220 DOI: 10.1105/tpc.005207] [Citation(s) in RCA: 150] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
For centuries, rose has been the most important crop in the floriculture industry; its economic importance also lies in the use of its petals as a source of natural fragrances. Here, we used genomics approaches to identify novel scent-related genes, using rose flowers from tetraploid scented and nonscented cultivars. An annotated petal EST database of approximately 2100 unique genes from both cultivars was created, and DNA chips were prepared and used for expression analyses of selected clones. Detailed chemical analysis of volatile composition in the two cultivars, together with the identification of secondary metabolism-related genes whose expression coincides with scent production, led to the discovery of several novel flower scent-related candidate genes. The function of some of these genes, including a germacrene D synthase, was biochemically determined using an Escherichia coli expression system. This work demonstrates the advantages of using the high-throughput approaches of genomics to detail traits of interest expressed in a cultivar-specific manner in nonmodel plants. EST sequences were submitted to the GenBank database (accession numbers BQ 103855 to BQ 106728).
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Affiliation(s)
- Inna Guterman
- Institute of Plant Sciences and Genetics in Agriculture, Faculty of Agricultural, Food, and Environmental Quality Sciences, Hebrew University of Jerusalem, P.O. Box 12, Rehovot 76100, Israel
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27
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Lavid N, Wang J, Shalit M, Guterman I, Bar E, Beuerle T, Menda N, Shafir S, Zamir D, Adam Z, Vainstein A, Weiss D, Pichersky E, Lewinsohn E. O-methyltransferases involved in the biosynthesis of volatile phenolic derivatives in rose petals. Plant Physiol 2002; 129:1899-907. [PMID: 12177504 PMCID: PMC166779 DOI: 10.1104/pp.005330] [Citation(s) in RCA: 94] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Rose (Rosa hybrida) flowers produce and emit a diverse array of volatiles, characteristic to their unique scent. One of the most prominent compounds in the floral volatiles of many rose varieties is the methoxylated phenolic derivative 3,5-dimethoxytoluene (orcinol dimethyl ether). Cell-free extracts derived from developing rose petals displayed O-methyltransferase (OMT) activities toward several phenolic substrates, including 3,5-dihydroxytoluene (orcinol), 3-methoxy,5-hydroxytoluene (orcinol monomethyl ether), 1-methoxy, 2-hydroxy benezene (guaiacol), and eugenol. The activity was most prominent in rose cv Golden Gate, a variety that produces relatively high levels of orcinol dimethyl ether, as compared with rose cv Fragrant Cloud, an otherwise scented variety but which emits almost no orcinol dimethyl ether. Using a functional genomics approach, we have identified and characterized two closely related cDNAs from a rose petal library that each encode a protein capable of methylating the penultimate and immediate precursors (orcinol and orcinol monomethyl ether, respectively) to give the final orcinol dimethyl ether product. The enzymes, designated orcinol OMTs (OOMT1 and OOMT2), are closely related to other plant methyltransferases whose substrates range from isoflavones to phenylpropenes. The peak in the levels of OOMT1 and OOMT2 transcripts in the flowers coincides with peak OMT activity and with the emission of orcinol dimethyl ether.
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Affiliation(s)
- Noa Lavid
- Vegetable Crops, Newe Ya'ar Research Center, Agricultural Research Organization, P.O. Box 1021, Ramat Yishay, 30095, Israel
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