Developing integrated crop knowledge networks to advance candidate gene discovery

Autores
Hassani-Pak, Keywan; Castellote, Martín Alfredo; Esch, Maria; Hindle, Matthew; Lysenko, Artem; Taubert, Jan; Rawlings, Christopher John
Año de publicación
2016
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.
EEA Balcarce
Fil: Hassani-Pak, Keywan. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Castellote, Martín Alfredo. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Laboratorio de Agrobiotecnología; Argentina
Fil: Esch, Maria. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Hindle, Matthew. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Lysenko, Artem. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Taubert, Jan. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Rawlings, Christopher John. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fuente
Applied & Translational Genomics 11 : 18-26 (December 2016)
Materia
Bioinformática
Cultivos
Gestión del Conocimiento
Genómica
Bioinformatics
Crops
Knowledge Management
Genomics
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/4894
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oai_identifier_str oai:localhost:20.500.12123/4894
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spelling Developing integrated crop knowledge networks to advance candidate gene discoveryHassani-Pak, KeywanCastellote, Martín AlfredoEsch, MariaHindle, MatthewLysenko, ArtemTaubert, JanRawlings, Christopher JohnBioinformáticaCultivosGestión del ConocimientoGenómicaBioinformaticsCropsKnowledge ManagementGenomicsThe chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.EEA BalcarceFil: Hassani-Pak, Keywan. Rothamsted Research. Department of Computational and Systems Biology; Gran BretañaFil: Castellote, Martín Alfredo. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Laboratorio de Agrobiotecnología; ArgentinaFil: Esch, Maria. Rothamsted Research. Department of Computational and Systems Biology; Gran BretañaFil: Hindle, Matthew. Rothamsted Research. Department of Computational and Systems Biology; Gran BretañaFil: Lysenko, Artem. Rothamsted Research. Department of Computational and Systems Biology; Gran BretañaFil: Taubert, Jan. Rothamsted Research. Department of Computational and Systems Biology; Gran BretañaFil: Rawlings, Christopher John. Rothamsted Research. Department of Computational and Systems Biology; Gran BretañaElsevier2019-04-12T13:58:39Z2019-04-12T13:58:39Z2016-12info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttps://www.sciencedirect.com/science/article/pii/S2212066116300308http://hdl.handle.net/20.500.12123/48942212-0661https://doi.org/10.1016/j.atg.2016.10.003Applied & Translational Genomics 11 : 18-26 (December 2016)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-03-26T11:22:39Zoai:localhost:20.500.12123/4894instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-03-26 11:22:40.191INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Developing integrated crop knowledge networks to advance candidate gene discovery
title Developing integrated crop knowledge networks to advance candidate gene discovery
spellingShingle Developing integrated crop knowledge networks to advance candidate gene discovery
Hassani-Pak, Keywan
Bioinformática
Cultivos
Gestión del Conocimiento
Genómica
Bioinformatics
Crops
Knowledge Management
Genomics
title_short Developing integrated crop knowledge networks to advance candidate gene discovery
title_full Developing integrated crop knowledge networks to advance candidate gene discovery
title_fullStr Developing integrated crop knowledge networks to advance candidate gene discovery
title_full_unstemmed Developing integrated crop knowledge networks to advance candidate gene discovery
title_sort Developing integrated crop knowledge networks to advance candidate gene discovery
dc.creator.none.fl_str_mv Hassani-Pak, Keywan
Castellote, Martín Alfredo
Esch, Maria
Hindle, Matthew
Lysenko, Artem
Taubert, Jan
Rawlings, Christopher John
author Hassani-Pak, Keywan
author_facet Hassani-Pak, Keywan
Castellote, Martín Alfredo
Esch, Maria
Hindle, Matthew
Lysenko, Artem
Taubert, Jan
Rawlings, Christopher John
author_role author
author2 Castellote, Martín Alfredo
Esch, Maria
Hindle, Matthew
Lysenko, Artem
Taubert, Jan
Rawlings, Christopher John
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv Bioinformática
Cultivos
Gestión del Conocimiento
Genómica
Bioinformatics
Crops
Knowledge Management
Genomics
topic Bioinformática
Cultivos
Gestión del Conocimiento
Genómica
Bioinformatics
Crops
Knowledge Management
Genomics
dc.description.none.fl_txt_mv The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.
EEA Balcarce
Fil: Hassani-Pak, Keywan. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Castellote, Martín Alfredo. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Balcarce. Laboratorio de Agrobiotecnología; Argentina
Fil: Esch, Maria. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Hindle, Matthew. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Lysenko, Artem. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Taubert, Jan. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
Fil: Rawlings, Christopher John. Rothamsted Research. Department of Computational and Systems Biology; Gran Bretaña
description The chances of raising crop productivity to enhance global food security would be greatly improved if we had a complete understanding of all the biological mechanisms that underpinned traits such as crop yield, disease resistance or nutrient and water use efficiency. With more crop genomes emerging all the time, we are nearer having the basic information, at the gene-level, to begin assembling crop gene catalogues and using data from other plant species to understand how the genes function and how their interactions govern crop development and physiology. Unfortunately, the task of creating such a complete knowledge base of gene functions, interaction networks and trait biology is technically challenging because the relevant data are dispersed in myriad databases in a variety of data formats with variable quality and coverage. In this paper we present a general approach for building genome-scale knowledge networks that provide a unified representation of heterogeneous but interconnected datasets to enable effective knowledge mining and gene discovery. We describe the datasets and outline the methods, workflows and tools that we have developed for creating and visualising these networks for the major crop species, wheat and barley. We present the global characteristics of such knowledge networks and with an example linking a seed size phenotype to a barley WRKY transcription factor orthologous to TTG2 from Arabidopsis, we illustrate the value of integrated data in biological knowledge discovery. The software we have developed (www.ondex.org) and the knowledge resources (http://knetminer.rothamsted.ac.uk) we have created are all open-source and provide a first step towards systematic and evidence-based gene discovery in order to facilitate crop improvement.
publishDate 2016
dc.date.none.fl_str_mv 2016-12
2019-04-12T13:58:39Z
2019-04-12T13:58:39Z
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.sciencedirect.com/science/article/pii/S2212066116300308
http://hdl.handle.net/20.500.12123/4894
2212-0661
https://doi.org/10.1016/j.atg.2016.10.003
url https://www.sciencedirect.com/science/article/pii/S2212066116300308
http://hdl.handle.net/20.500.12123/4894
https://doi.org/10.1016/j.atg.2016.10.003
identifier_str_mv 2212-0661
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv Applied & Translational Genomics 11 : 18-26 (December 2016)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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score 13.231807

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