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Computational aspects underlying genome to phenome analysis in plants
Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high‐throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While t...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849790/ https://www.ncbi.nlm.nih.gov/pubmed/30500991 http://dx.doi.org/10.1111/tpj.14179 |
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author | Bolger, Anthony M. Poorter, Hendrik Dumschott, Kathryn Bolger, Marie E. Arend, Daniel Osorio, Sonia Gundlach, Heidrun Mayer, Klaus F. X. Lange, Matthias Scholz, Uwe Usadel, Björn |
author_facet | Bolger, Anthony M. Poorter, Hendrik Dumschott, Kathryn Bolger, Marie E. Arend, Daniel Osorio, Sonia Gundlach, Heidrun Mayer, Klaus F. X. Lange, Matthias Scholz, Uwe Usadel, Björn |
author_sort | Bolger, Anthony M. |
collection | PubMed |
description | Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high‐throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait−trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features. |
format | Online Article Text |
id | pubmed-6849790 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68497902019-11-15 Computational aspects underlying genome to phenome analysis in plants Bolger, Anthony M. Poorter, Hendrik Dumschott, Kathryn Bolger, Marie E. Arend, Daniel Osorio, Sonia Gundlach, Heidrun Mayer, Klaus F. X. Lange, Matthias Scholz, Uwe Usadel, Björn Plant J Si Genome to Phenome Recent advances in genomics technologies have greatly accelerated the progress in both fundamental plant science and applied breeding research. Concurrently, high‐throughput plant phenotyping is becoming widely adopted in the plant community, promising to alleviate the phenotypic bottleneck. While these technological breakthroughs are significantly accelerating quantitative trait locus (QTL) and causal gene identification, challenges to enable even more sophisticated analyses remain. In particular, care needs to be taken to standardize, describe and conduct experiments robustly while relying on plant physiology expertise. In this article, we review the state of the art regarding genome assembly and the future potential of pangenomics in plant research. We also describe the necessity of standardizing and describing phenotypic studies using the Minimum Information About a Plant Phenotyping Experiment (MIAPPE) standard to enable the reuse and integration of phenotypic data. In addition, we show how deep phenotypic data might yield novel trait−trait correlations and review how to link phenotypic data to genomic data. Finally, we provide perspectives on the golden future of machine learning and their potential in linking phenotypes to genomic features. John Wiley and Sons Inc. 2019-01-12 2019-01 /pmc/articles/PMC6849790/ /pubmed/30500991 http://dx.doi.org/10.1111/tpj.14179 Text en © 2018 The Authors The Plant Journal published by John Wiley & Sons Ltd and Society for Experimental Biology. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Si Genome to Phenome Bolger, Anthony M. Poorter, Hendrik Dumschott, Kathryn Bolger, Marie E. Arend, Daniel Osorio, Sonia Gundlach, Heidrun Mayer, Klaus F. X. Lange, Matthias Scholz, Uwe Usadel, Björn Computational aspects underlying genome to phenome analysis in plants |
title | Computational aspects underlying genome to phenome analysis in plants |
title_full | Computational aspects underlying genome to phenome analysis in plants |
title_fullStr | Computational aspects underlying genome to phenome analysis in plants |
title_full_unstemmed | Computational aspects underlying genome to phenome analysis in plants |
title_short | Computational aspects underlying genome to phenome analysis in plants |
title_sort | computational aspects underlying genome to phenome analysis in plants |
topic | Si Genome to Phenome |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6849790/ https://www.ncbi.nlm.nih.gov/pubmed/30500991 http://dx.doi.org/10.1111/tpj.14179 |
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