Class prediction of closely related plant varieties using gene expression profiling
In recent years, class prediction experiments have been largely developed in cancer research with the aim of classifying unknown samples by examining their expression signature. In natural populations, a significant component of gene expression variability is also heritable. Citrus species are an id...
| Autores principales: | , , , , |
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| Formato: | Artículo |
| Lenguaje: | Inglés |
| Publicado: |
2017
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| Acceso en línea: | http://hdl.handle.net/20.500.11939/5676 |
| _version_ | 1855491964375400448 |
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| author | Ancillo, Gema Gadea, Jose Forment, Javier Guerri, José Navarro, Luis |
| author_browse | Ancillo, Gema Forment, Javier Gadea, Jose Guerri, José Navarro, Luis |
| author_facet | Ancillo, Gema Gadea, Jose Forment, Javier Guerri, José Navarro, Luis |
| author_sort | Ancillo, Gema |
| collection | ReDivia |
| description | In recent years, class prediction experiments have been largely developed in cancer research with the aim of classifying unknown samples by examining their expression signature. In natural populations, a significant component of gene expression variability is also heritable. Citrus species are an ideal model to accomplish the study of these questions in plants, due to the existence of varieties derived from somatic mutations that are likely to differ from each other by one or a few point mutations but are phenotypically indistinguishable at early vegetative stages. The small genetic variability existing among these varieties makes molecular markers ineffective in distinguishing genotypes within a particular species. Gene expression profiles have been used to predict mandarin clementine varieties (Citrus clementina, Hort. ex Tan.) by means of two independent supervised learning algorithms: Support Vector Machines and Prediction Analysis of Microarrays. The results show that transcriptional variation is variety-dependent in citrus, and supervised clustering methods may correctly assign blind samples to varieties when both training and test samples are under the same experimental conditions. |
| format | Artículo |
| id | ReDivia5676 |
| institution | Instituto Valenciano de Investigaciones Agrarias (IVIA) |
| language | Inglés |
| publishDate | 2017 |
| publishDateRange | 2017 |
| publishDateSort | 2017 |
| record_format | dspace |
| spelling | ReDivia56762025-04-25T14:44:34Z Class prediction of closely related plant varieties using gene expression profiling Ancillo, Gema Gadea, Jose Forment, Javier Guerri, José Navarro, Luis In recent years, class prediction experiments have been largely developed in cancer research with the aim of classifying unknown samples by examining their expression signature. In natural populations, a significant component of gene expression variability is also heritable. Citrus species are an ideal model to accomplish the study of these questions in plants, due to the existence of varieties derived from somatic mutations that are likely to differ from each other by one or a few point mutations but are phenotypically indistinguishable at early vegetative stages. The small genetic variability existing among these varieties makes molecular markers ineffective in distinguishing genotypes within a particular species. Gene expression profiles have been used to predict mandarin clementine varieties (Citrus clementina, Hort. ex Tan.) by means of two independent supervised learning algorithms: Support Vector Machines and Prediction Analysis of Microarrays. The results show that transcriptional variation is variety-dependent in citrus, and supervised clustering methods may correctly assign blind samples to varieties when both training and test samples are under the same experimental conditions. 2017-06-01T10:12:48Z 2017-06-01T10:12:48Z 2007 article Ancillo, G., Gadea, J., Forment, J., Guerri, J., Navarro, L. (2007). Class prediction of closely related plant varieties using gene expression profiling. Journal of experimental botany, 58(8), 1927-1933. 0022-0957 http://hdl.handle.net/20.500.11939/5676 10.1093/jxb/erm054 en openAccess Impreso |
| spellingShingle | Ancillo, Gema Gadea, Jose Forment, Javier Guerri, José Navarro, Luis Class prediction of closely related plant varieties using gene expression profiling |
| title | Class prediction of closely related plant varieties using gene expression profiling |
| title_full | Class prediction of closely related plant varieties using gene expression profiling |
| title_fullStr | Class prediction of closely related plant varieties using gene expression profiling |
| title_full_unstemmed | Class prediction of closely related plant varieties using gene expression profiling |
| title_short | Class prediction of closely related plant varieties using gene expression profiling |
| title_sort | class prediction of closely related plant varieties using gene expression profiling |
| url | http://hdl.handle.net/20.500.11939/5676 |
| work_keys_str_mv | AT ancillogema classpredictionofcloselyrelatedplantvarietiesusinggeneexpressionprofiling AT gadeajose classpredictionofcloselyrelatedplantvarietiesusinggeneexpressionprofiling AT formentjavier classpredictionofcloselyrelatedplantvarietiesusinggeneexpressionprofiling AT guerrijose classpredictionofcloselyrelatedplantvarietiesusinggeneexpressionprofiling AT navarroluis classpredictionofcloselyrelatedplantvarietiesusinggeneexpressionprofiling |