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Network and gene expression analyses for understanding protein function
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics.
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 [en]
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: urn:nbn:se:su:diva-89055ISBN: 978-91-7447-674-3 (print)OAI: oai:DiVA.org:su-89055DiVA: diva2:615438
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
List of papers
1. Comparative interactomics with Funcoup 2.0
Open this publication in new window or tab >>Comparative interactomics with Funcoup 2.0
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2012 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 40, no D1, D821-D828 p.Article in journal (Refereed) Published
Abstract [en]

FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-76759 (URN)10.1093/nar/gkr1062 (DOI)000298601300123 ()
Note

AuthorCount; 6

Available from: 2013-04-11 Created: 2012-05-16 Last updated: 2017-09-29Bibliographically approved
2. MGclus: network clustering employing shared neighbors
Open this publication in new window or tab >>MGclus: network clustering employing shared neighbors
2013 (English)In: Molecular BioSystems, ISSN 1742-206X, Vol. 9, no 7, 1670-1675 p.Article in journal (Refereed) Published
Abstract [en]

Network analysis is an important tool for functional annotation of genes and proteins. A common approach to discern structure in a global network is to infer network clusters, or modules, and assume a functional coherence within each module, which may represent a complex or a pathway. It is however not trivial to define optimal modules. Although many methods have been proposed, it is unclear which methods perform best in general. It seems that most methods produce far from optimal results but in different ways. MGclus is a new algorithm designed to detect modules with a strongly interconnected neighborhood in large scale biological interaction networks. In our benchmarks we found MGclus to outperform other methods when applied to random graphs with varying degree of noise, and to perform equally or better when applied to biological protein interaction networks. MGclus is implemented in Java and utilizes the JGraphT graph library. It has an easy to use command-line interface and is available for download from http://sonnhammer.sbc.su.se/download/software/MGclus/.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-89051 (URN)10.1039/c3mb25473a (DOI)000319882200014 ()
Available from: 2013-04-10 Created: 2013-04-10 Last updated: 2013-07-12Bibliographically approved
3. Statistical Assessment of Crosstalk Enrichment between Gene Groups in Biological Networks
Open this publication in new window or tab >>Statistical Assessment of Crosstalk Enrichment between Gene Groups in Biological Networks
2013 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 8, no 1, e54945- p.Article in journal (Refereed) Published
Abstract [en]

Motivation: Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Results: Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). Availability and Implementation: CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-88245 (URN)10.1371/journal.pone.0054945 (DOI)000314021500148 ()
Funder
Swedish Research Council
Note

AuthorCount:4;

Available from: 2013-03-15 Created: 2013-03-12 Last updated: 2017-12-06Bibliographically approved
4. Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
Open this publication in new window or tab >>Network Analysis of Functional Genomics Data: Application to Avian Sex-Biased Gene Expression
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.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-87667 (URN)10.1100/2012/130491 (DOI)000313179900001 ()
Funder
Swedish Research Council
Note

AuthorCount:4;

Available from: 2013-02-18 Created: 2013-02-14 Last updated: 2017-12-06Bibliographically approved
5. Prognostic significance in breast cancer of a gene signature capturing stromal PDGF signaling
Open this publication in new window or tab >>Prognostic significance in breast cancer of a gene signature capturing stromal PDGF signaling
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(English)In: American Journal of Pathology, ISSN 0002-9440, E-ISSN 1525-2191Article in journal (Refereed) Accepted
National Category
Bioinformatics (Computational Biology)
Research subject
Biochemistry with Emphasis on Theoretical Chemistry
Identifiers
urn:nbn:se:su:diva-89054 (URN)
Available from: 2013-04-10 Created: 2013-04-10 Last updated: 2017-12-06Bibliographically approved

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