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Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules
The use of biological networks such as protein–protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly in...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841433/ https://www.ncbi.nlm.nih.gov/pubmed/33519907 http://dx.doi.org/10.3389/fgene.2020.603264 |
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author | Lim, James T. Chen, Chen Grant, Adam D. Padi, Megha |
author_facet | Lim, James T. Chen, Chen Grant, Adam D. Padi, Megha |
author_sort | Lim, James T. |
collection | PubMed |
description | The use of biological networks such as protein–protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a method for randomizing networks with fixed node strengths. CRANE can be used to generate a null distribution of gene regulatory networks that can in turn be used to rank the most significant changes in candidate disease communities. Compared to other approaches, such as consensus clustering or commonly used generative models, CRANE emulates biologically realistic networks and recovers simulated disease modules with higher accuracy. When applied to breast and ovarian cancer networks, CRANE improves the identification of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes. |
format | Online Article Text |
id | pubmed-7841433 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-78414332021-01-29 Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules Lim, James T. Chen, Chen Grant, Adam D. Padi, Megha Front Genet Genetics The use of biological networks such as protein–protein interaction and transcriptional regulatory networks is becoming an integral part of genomics research. However, these networks are not static, and during phenotypic transitions like disease onset, they can acquire new “communities” (or highly interacting groups) of genes that carry out cellular processes. Disease communities can be detected by maximizing a modularity-based score, but since biological systems and network inference algorithms are inherently noisy, it remains a challenge to determine whether these changes represent real cellular responses or whether they appeared by random chance. Here, we introduce Constrained Random Alteration of Network Edges (CRANE), a method for randomizing networks with fixed node strengths. CRANE can be used to generate a null distribution of gene regulatory networks that can in turn be used to rank the most significant changes in candidate disease communities. Compared to other approaches, such as consensus clustering or commonly used generative models, CRANE emulates biologically realistic networks and recovers simulated disease modules with higher accuracy. When applied to breast and ovarian cancer networks, CRANE improves the identification of cancer-relevant GO terms while reducing the signal from non-specific housekeeping processes. Frontiers Media S.A. 2021-01-14 /pmc/articles/PMC7841433/ /pubmed/33519907 http://dx.doi.org/10.3389/fgene.2020.603264 Text en Copyright © 2021 Lim, Chen, Grant and Padi. 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) and the copyright owner(s) 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 | Genetics Lim, James T. Chen, Chen Grant, Adam D. Padi, Megha Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules |
title | Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules |
title_full | Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules |
title_fullStr | Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules |
title_full_unstemmed | Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules |
title_short | Generating Ensembles of Gene Regulatory Networks to Assess Robustness of Disease Modules |
title_sort | generating ensembles of gene regulatory networks to assess robustness of disease modules |
topic | Genetics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7841433/ https://www.ncbi.nlm.nih.gov/pubmed/33519907 http://dx.doi.org/10.3389/fgene.2020.603264 |
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