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Learning from Co-expression Networks: Possibilities and Challenges
Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biologica...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Frontiers Media S.A.
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4825623/ https://www.ncbi.nlm.nih.gov/pubmed/27092161 http://dx.doi.org/10.3389/fpls.2016.00444 |
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author | Serin, Elise A. R. Nijveen, Harm Hilhorst, Henk W. M. Ligterink, Wilco |
author_facet | Serin, Elise A. R. Nijveen, Harm Hilhorst, Henk W. M. Ligterink, Wilco |
author_sort | Serin, Elise A. R. |
collection | PubMed |
description | Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes. |
format | Online Article Text |
id | pubmed-4825623 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-48256232016-04-18 Learning from Co-expression Networks: Possibilities and Challenges Serin, Elise A. R. Nijveen, Harm Hilhorst, Henk W. M. Ligterink, Wilco Front Plant Sci Plant Science Plants are fascinating and complex organisms. A comprehensive understanding of the organization, function and evolution of plant genes is essential to disentangle important biological processes and to advance crop engineering and breeding strategies. The ultimate aim in deciphering complex biological processes is the discovery of causal genes and regulatory mechanisms controlling these processes. The recent surge of omics data has opened the door to a system-wide understanding of the flow of biological information underlying complex traits. However, dealing with the corresponding large data sets represents a challenging endeavor that calls for the development of powerful bioinformatics methods. A popular approach is the construction and analysis of gene networks. Such networks are often used for genome-wide representation of the complex functional organization of biological systems. Network based on similarity in gene expression are called (gene) co-expression networks. One of the major application of gene co-expression networks is the functional annotation of unknown genes. Constructing co-expression networks is generally straightforward. In contrast, the resulting network of connected genes can become very complex, which limits its biological interpretation. Several strategies can be employed to enhance the interpretation of the networks. A strategy in coherence with the biological question addressed needs to be established to infer reliable networks. Additional benefits can be gained from network-based strategies using prior knowledge and data integration to further enhance the elucidation of gene regulatory relationships. As a result, biological networks provide many more applications beyond the simple visualization of co-expressed genes. In this study we review the different approaches for co-expression network inference in plants. We analyse integrative genomics strategies used in recent studies that successfully identified candidate genes taking advantage of gene co-expression networks. Additionally, we discuss promising bioinformatics approaches that predict networks for specific purposes. Frontiers Media S.A. 2016-04-08 /pmc/articles/PMC4825623/ /pubmed/27092161 http://dx.doi.org/10.3389/fpls.2016.00444 Text en Copyright © 2016 Serin, Nijveen, Hilhorst and Ligterink. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Plant Science Serin, Elise A. R. Nijveen, Harm Hilhorst, Henk W. M. Ligterink, Wilco Learning from Co-expression Networks: Possibilities and Challenges |
title | Learning from Co-expression Networks: Possibilities and Challenges |
title_full | Learning from Co-expression Networks: Possibilities and Challenges |
title_fullStr | Learning from Co-expression Networks: Possibilities and Challenges |
title_full_unstemmed | Learning from Co-expression Networks: Possibilities and Challenges |
title_short | Learning from Co-expression Networks: Possibilities and Challenges |
title_sort | learning from co-expression networks: possibilities and challenges |
topic | Plant Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4825623/ https://www.ncbi.nlm.nih.gov/pubmed/27092161 http://dx.doi.org/10.3389/fpls.2016.00444 |
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