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Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm University, Science for Life Laboratory (SciLifeLab).
2012 (English)In: Scientific World Journal, ISSN 1537-744X, E-ISSN 1537-744X, 130491- p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
2012. 130491- p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-87667DOI: 10.1100/2012/130491ISI: 000313179900001OAI: oai:DiVA.org:su-87667DiVA: diva2:606091
Funder
Swedish Research Council
Note

AuthorCount:4;

Available from: 2013-02-18 Created: 2013-02-14 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Network and gene expression analyses for understanding protein function
Open this publication in new window or tab >>Network and gene expression analyses for understanding protein function
2013 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Biological function is the result of a complex network of functional associations between genes or their products. Modeling the dynamics underlying biological networks is one of the big challenges in bioinformatics. A first step towards solving this problem is to predict and study the networks of functional associations underlying various conditions.

An improved version of the FunCoup network inference method that features networks for three new species and updated versions of the existing networks is presented. Network clustering, i.e. partitioning networks into highly connected components is an important tool for network analysis. We developed MGclus, a clustering method for biological networks that scores shared network neighbors. We found MGclus to perform favorably compared to other methods popular in the field. Studying sets of experimentally derived genes in the context of biological networks is a common strategy to shed light on their underlying biology. The CrossTalkZ method presented in this work assesses the statistical significance of crosstalk enrichment, i.e. the extent of connectivity between or within groups of functionally coupled genes or proteins in biological networks. We further demonstrate that CrossTalkZ is a valuable method to functionally annotate experimentally derived gene sets.

Males and females differ in the expression of an extensive number of genes. The methods developed in the first part of this work were applied to study sex-biased genes in chicken and several network properties related to the molecular mechanisms of sex-biased gene regulation in chicken were deduced. Cancer studies have shown that tumor progression is strongly determined by the tumor microenvironment. We derived a gene expression signature of PDGF-activated fibroblasts that shows a strong prognostic significance in breast cancer in univariate and multivariate survival analyses when compared to established markers for prognosis.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University, 2013. 86 p.
Keyword
biological networks, network inference, network analysis, clustering, network module, network crosstalk, expression analysis, gene signature, biomarker
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-89055 (URN)978-91-7447-674-3 (ISBN)
Public defence
2013-05-23, Nordenskiöldsalen, Geovetenskapens hus, Svante Arrhenius väg 12, Stockholm, 09:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 5: Accepted.

 

Available from: 2013-05-01 Created: 2013-04-10 Last updated: 2013-04-22Bibliographically approved

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Frings, OliverSonnhammer, Erik L. L.
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