Developing integrated crop knowledge networks to advance candidate gene discovery
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...
| Autores principales: | , , , , , , |
|---|---|
| Formato: | info:ar-repo/semantics/artículo |
| Lenguaje: | Inglés |
| Publicado: |
Elsevier
2019
|
| Materias: | |
| Acceso en línea: | 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 |
| _version_ | 1855035402581180416 |
|---|---|
| author | Hassani-Pak, Keywan Castellote, Martín Alfredo Esch, Maria Hindle, Matthew Lysenko, Artem Taubert, Jan Rawlings, Christopher John |
| author_browse | Castellote, Martín Alfredo Esch, Maria Hassani-Pak, Keywan Hindle, Matthew Lysenko, Artem Rawlings, Christopher John Taubert, Jan |
| author_facet | Hassani-Pak, Keywan Castellote, Martín Alfredo Esch, Maria Hindle, Matthew Lysenko, Artem Taubert, Jan Rawlings, Christopher John |
| author_sort | Hassani-Pak, Keywan |
| collection | INTA Digital |
| 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. |
| format | info:ar-repo/semantics/artículo |
| id | INTA4894 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2019 |
| publishDateRange | 2019 |
| publishDateSort | 2019 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | INTA48942019-04-12T13:59:54Z Developing integrated crop knowledge networks to advance candidate gene discovery Hassani-Pak, Keywan Castellote, Martín Alfredo Esch, Maria Hindle, Matthew Lysenko, Artem Taubert, Jan Rawlings, Christopher John Bioinformática Cultivos Gestión del Conocimiento Genómica Bioinformatics Crops Knowledge Management Genomics 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 2019-04-12T13:58:39Z 2019-04-12T13:58:39Z 2016-12 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf Elsevier Applied & Translational Genomics 11 : 18-26 (December 2016) |
| spellingShingle | Bioinformática Cultivos Gestión del Conocimiento Genómica Bioinformatics Crops Knowledge Management Genomics Hassani-Pak, Keywan Castellote, Martín Alfredo Esch, Maria Hindle, Matthew Lysenko, Artem Taubert, Jan Rawlings, Christopher John Developing integrated crop knowledge networks to advance candidate gene discovery |
| title | 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_short | Developing integrated crop knowledge networks to advance candidate gene discovery |
| title_sort | developing integrated crop knowledge networks to advance candidate gene discovery |
| topic | Bioinformática Cultivos Gestión del Conocimiento Genómica Bioinformatics Crops Knowledge Management Genomics |
| 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 |
| work_keys_str_mv | AT hassanipakkeywan developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery AT castellotemartinalfredo developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery AT eschmaria developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery AT hindlematthew developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery AT lysenkoartem developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery AT taubertjan developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery AT rawlingschristopherjohn developingintegratedcropknowledgenetworkstoadvancecandidategenediscovery |