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Reverse engineering and analysis of large genome-scale gene networks

Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast...

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Detalles Bibliográficos
Autores principales: Aluru, Maneesha, Zola, Jaroslaw, Nettleton, Dan, Aluru, Srinivas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592423/
https://www.ncbi.nlm.nih.gov/pubmed/23042249
http://dx.doi.org/10.1093/nar/gks904
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author Aluru, Maneesha
Zola, Jaroslaw
Nettleton, Dan
Aluru, Srinivas
author_facet Aluru, Maneesha
Zola, Jaroslaw
Nettleton, Dan
Aluru, Srinivas
author_sort Aluru, Maneesha
collection PubMed
description Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web.
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spelling pubmed-35924232013-03-08 Reverse engineering and analysis of large genome-scale gene networks Aluru, Maneesha Zola, Jaroslaw Nettleton, Dan Aluru, Srinivas Nucleic Acids Res Methods Online Reverse engineering the whole-genome networks of complex multicellular organisms continues to remain a challenge. While simpler models easily scale to large number of genes and gene expression datasets, more accurate models are compute intensive limiting their scale of applicability. To enable fast and accurate reconstruction of large networks, we developed Tool for Inferring Network of Genes (TINGe), a parallel mutual information (MI)-based program. The novel features of our approach include: (i) B-spline-based formulation for linear-time computation of MI, (ii) a novel algorithm for direct permutation testing and (iii) development of parallel algorithms to reduce run-time and facilitate construction of large networks. We assess the quality of our method by comparison with ARACNe (Algorithm for the Reconstruction of Accurate Cellular Networks) and GeneNet and demonstrate its unique capability by reverse engineering the whole-genome network of Arabidopsis thaliana from 3137 Affymetrix ATH1 GeneChips in just 9 min on a 1024-core cluster. We further report on the development of a new software Gene Network Analyzer (GeNA) for extracting context-specific subnetworks from a given set of seed genes. Using TINGe and GeNA, we performed analysis of 241 Arabidopsis AraCyc 8.0 pathways, and the results are made available through the web. Oxford University Press 2013-01 2012-10-05 /pmc/articles/PMC3592423/ /pubmed/23042249 http://dx.doi.org/10.1093/nar/gks904 Text en © The Author(s) 2012. Published by Oxford University Press. http://creativecommons.org/licenses/by/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0/), which permits unrestricted, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Methods Online
Aluru, Maneesha
Zola, Jaroslaw
Nettleton, Dan
Aluru, Srinivas
Reverse engineering and analysis of large genome-scale gene networks
title Reverse engineering and analysis of large genome-scale gene networks
title_full Reverse engineering and analysis of large genome-scale gene networks
title_fullStr Reverse engineering and analysis of large genome-scale gene networks
title_full_unstemmed Reverse engineering and analysis of large genome-scale gene networks
title_short Reverse engineering and analysis of large genome-scale gene networks
title_sort reverse engineering and analysis of large genome-scale gene networks
topic Methods Online
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3592423/
https://www.ncbi.nlm.nih.gov/pubmed/23042249
http://dx.doi.org/10.1093/nar/gks904
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