Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression

Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a n...

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Main Authors: Oliver Frings, Judith E. Mank, Andrey Alexeyenko, Erik L. L. Sonnhammer
Format: Article
Language:English
Published: Hindawi Limited 2012-01-01
Series:The Scientific World Journal
Online Access:http://dx.doi.org/10.1100/2012/130491
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spelling doaj-f7b72005408145e5a0a59faadc0491792020-11-25T01:21:53ZengHindawi LimitedThe Scientific World Journal1537-744X2012-01-01201210.1100/2012/130491130491Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene ExpressionOliver Frings0Judith E. Mank1Andrey Alexeyenko2Erik L. L. Sonnhammer3Stockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, SE-171 21 Solna, SwedenDepartment of Genetics, Evolution and the Environment, University College London, WC1E 6BT, UKStockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, SE-171 21 Solna, SwedenStockholm Bioinformatics Centre, Science for Life Laboratory, Box 1031, SE-171 21 Solna, SwedenGene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at different developmental stages. We first constructed a global chicken interaction network. Combining the network with the expression data showed that most sex-biased genes tend to have lower network connectivity, that is, act within local network environments, although some interesting exceptions were found. Genes of the same sex bias were generally more strongly connected with each other than expected. We further studied the fates of duplicated sex-biased genes and found that there is a significant trend to keep the same pattern of sex bias after duplication. We also identified sex-biased modules in the network, which reveal pathways or complexes involved in sex-specific processes. Altogether, this work integrates evolutionary genomics with systems biology in a novel way, offering new insights into the modular nature of sex-biased genes.http://dx.doi.org/10.1100/2012/130491
collection DOAJ
language English
format Article
sources DOAJ
author Oliver Frings
Judith E. Mank
Andrey Alexeyenko
Erik L. L. Sonnhammer
spellingShingle Oliver Frings
Judith E. Mank
Andrey Alexeyenko
Erik L. L. Sonnhammer
Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
The Scientific World Journal
author_facet Oliver Frings
Judith E. Mank
Andrey Alexeyenko
Erik L. L. Sonnhammer
author_sort Oliver Frings
title Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
title_short Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
title_full Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
title_fullStr Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
title_full_unstemmed Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
title_sort network analysis of functional genomics data: application to avian sex-biased gene expression
publisher Hindawi Limited
series The Scientific World Journal
issn 1537-744X
publishDate 2012-01-01
description Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at different developmental stages. We first constructed a global chicken interaction network. Combining the network with the expression data showed that most sex-biased genes tend to have lower network connectivity, that is, act within local network environments, although some interesting exceptions were found. Genes of the same sex bias were generally more strongly connected with each other than expected. We further studied the fates of duplicated sex-biased genes and found that there is a significant trend to keep the same pattern of sex bias after duplication. We also identified sex-biased modules in the network, which reveal pathways or complexes involved in sex-specific processes. Altogether, this work integrates evolutionary genomics with systems biology in a novel way, offering new insights into the modular nature of sex-biased genes.
url http://dx.doi.org/10.1100/2012/130491
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AT andreyalexeyenko networkanalysisoffunctionalgenomicsdataapplicationtoaviansexbiasedgeneexpression
AT erikllsonnhammer networkanalysisoffunctionalgenomicsdataapplicationtoaviansexbiasedgeneexpression
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