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Graphlet Based Metrics for the Comparison of Gene Regulatory Networks
Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047442/ https://www.ncbi.nlm.nih.gov/pubmed/27695050 http://dx.doi.org/10.1371/journal.pone.0163497 |
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author | Martin, Alberto J. M. Dominguez, Calixto Contreras-Riquelme, Sebastián Holmes, David S. Perez-Acle, Tomas |
author_facet | Martin, Alberto J. M. Dominguez, Calixto Contreras-Riquelme, Sebastián Holmes, David S. Perez-Acle, Tomas |
author_sort | Martin, Alberto J. M. |
collection | PubMed |
description | Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). |
format | Online Article Text |
id | pubmed-5047442 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-50474422016-10-27 Graphlet Based Metrics for the Comparison of Gene Regulatory Networks Martin, Alberto J. M. Dominguez, Calixto Contreras-Riquelme, Sebastián Holmes, David S. Perez-Acle, Tomas PLoS One Research Article Understanding the control of gene expression remains one of the main challenges in the post-genomic era. Accordingly, a plethora of methods exists to identify variations in gene expression levels. These variations underlay almost all relevant biological phenomena, including disease and adaptation to environmental conditions. However, computational tools to identify how regulation changes are scarce. Regulation of gene expression is usually depicted in the form of a gene regulatory network (GRN). Structural changes in a GRN over time and conditions represent variations in the regulation of gene expression. Like other biological networks, GRNs are composed of basic building blocks called graphlets. As a consequence, two new metrics based on graphlets are proposed in this work: REConstruction Rate (REC) and REC Graphlet Degree (RGD). REC determines the rate of graphlet similarity between different states of a network and RGD identifies the subset of nodes with the highest topological variation. In other words, RGD discerns how th GRN was rewired. REC and RGD were used to compare the local structure of nodes in condition-specific GRNs obtained from gene expression data of Escherichia coli, forming biofilms and cultured in suspension. According to our results, most of the network local structure remains unaltered in the two compared conditions. Nevertheless, changes reported by RGD necessarily imply that a different cohort of regulators (i.e. transcription factors (TFs)) appear on the scene, shedding light on how the regulation of gene expression occurs when E. coli transits from suspension to biofilm. Consequently, we propose that both metrics REC and RGD should be adopted as a quantitative approach to conduct differential analyses of GRNs. A tool that implements both metrics is available as an on-line web server (http://dlab.cl/loto). Public Library of Science 2016-10-03 /pmc/articles/PMC5047442/ /pubmed/27695050 http://dx.doi.org/10.1371/journal.pone.0163497 Text en © 2016 Martin et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Martin, Alberto J. M. Dominguez, Calixto Contreras-Riquelme, Sebastián Holmes, David S. Perez-Acle, Tomas Graphlet Based Metrics for the Comparison of Gene Regulatory Networks |
title | Graphlet Based Metrics for the Comparison of Gene Regulatory Networks |
title_full | Graphlet Based Metrics for the Comparison of Gene Regulatory Networks |
title_fullStr | Graphlet Based Metrics for the Comparison of Gene Regulatory Networks |
title_full_unstemmed | Graphlet Based Metrics for the Comparison of Gene Regulatory Networks |
title_short | Graphlet Based Metrics for the Comparison of Gene Regulatory Networks |
title_sort | graphlet based metrics for the comparison of gene regulatory networks |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5047442/ https://www.ncbi.nlm.nih.gov/pubmed/27695050 http://dx.doi.org/10.1371/journal.pone.0163497 |
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