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Functional association networks for disease gene prediction
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm, Bioinformatics Center, Science for Life Laboratory.ORCID iD: 0000-0003-2245-7557
2017 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mapping of the human genome has been instrumental in understanding diseasescaused by changes in single genes. However, disease mechanisms involvingmultiple genes have proven to be much more elusive. Their complexityemerges from interactions of intracellular molecules and makes them immuneto the traditional reductionist approach. Only by modelling this complexinteraction pattern using networks is it possible to understand the emergentproperties that give rise to diseases.The overarching term used to describe both physical and indirect interactionsinvolved in the same functions is functional association. FunCoup is oneof the most comprehensive networks of functional association. It uses a naïveBayesian approach to integrate high-throughput experimental evidence of intracellularinteractions in humans and multiple model organisms. In the firstupdate, both the coverage and the quality of the interactions, were increasedand a feature for comparing interactions across species was added. The latestupdate involved a complete overhaul of all data sources, including a refinementof the training data and addition of new class and sources of interactionsas well as six new species.Disease-specific changes in genes can be identified using high-throughputgenome-wide studies of patients and healthy individuals. To understand theunderlying mechanisms that produce these changes, they can be mapped tocollections of genes with known functions, such as pathways. BinoX wasdeveloped to map altered genes to pathways using the topology of FunCoup.This approach combined with a new random model for comparison enables BinoXto outperform traditional gene-overlap-based methods and other networkbasedtechniques.Results from high-throughput experiments are challenged by noise and biases,resulting in many false positives. Statistical attempts to correct for thesechallenges have led to a reduction in coverage. Both limitations can be remediedusing prioritisation tools such as MaxLink, which ranks genes using guiltby association in the context of a functional association network. MaxLink’salgorithm was generalised to work with any disease phenotype and its statisticalfoundation was strengthened. MaxLink’s predictions were validatedexperimentally using FRET.The availability of prioritisation tools without an appropriate way to comparethem makes it difficult to select the correct tool for a problem domain.A benchmark to assess performance of prioritisation tools in terms of theirability to generalise to new data was developed. FunCoup was used for prioritisationwhile testing was done using cross-validation of terms derived fromGene Ontology. This resulted in a robust and unbiased benchmark for evaluationof current and future prioritisation tools. Surprisingly, previously superiortools based on global network structure were shown to be inferior to a localnetwork-based tool when performance was analysed on the most relevant partof the output, i.e. the top ranked genes.This thesis demonstrates how a network that models the intricate biologyof the cell can contribute with valuable insights for researchers that study diseaseswith complex genetic origins. The developed tools will help the researchcommunity to understand the underlying causes of such diseases and discovernew treatment targets. The robust way to benchmark such tools will help researchersto select the proper tool for their problem domain.

Place, publisher, year, edition, pages
Stockholm: Department of Biochemistry and Biophysics, Stockholm University , 2017. , p. 64
Keyword [en]
network biology, biological networks, network prediction, functional association, functional coupling, network integration, functional association networks, genome-wide association networks, gene networks, protein networks, fret, functional enrichment analysis, network cross-talk, pathway annotation, gene prioritisation, network-based gene prioritization, benchmarking
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-147217ISBN: 978-91-7649-976-4 (print)ISBN: 978-91-7649-977-1 (print)OAI: oai:DiVA.org:su-147217DiVA, id: diva2:1145879
Public defence
2017-11-10, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 14:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 5: Manuscript. Paper 6: Manuscript.

Available from: 2017-10-18 Created: 2017-09-29 Last updated: 2018-04-27Bibliographically 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, p. D821-D828Article 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. MaxLink: network-based prioritization of genes tightly linked to a disease seed set
Open this publication in new window or tab >>MaxLink: network-based prioritization of genes tightly linked to a disease seed set
2014 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 18, p. 2689-2690Article in journal (Refereed) Published
Abstract [en]

A Summary: MaxLink, a guilt-by-association network search algorithm, has been made available as a web resource and a stand-alone version. Based on a user-supplied list of query genes, MaxLink identifies and ranks genes that are tightly linked to the query list. This functionality can be used to predict potential disease genes from an initial set of genes with known association to a disease. The original algorithm, used to identify and rank novel genes potentially involved in cancer, has been updated to use a more statistically sound method for selection of candidate genes and made applicable to other areas than cancer. The algorithm has also been made faster by re-implementation in C + +, and the Web site uses FunCoup 3.0 as the underlying network.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-109021 (URN)10.1093/bioinformatics/btu344 (DOI)000342913000029 ()
Note

AuthorCount:3;

Available from: 2015-01-09 Created: 2014-11-10 Last updated: 2017-09-29Bibliographically approved
3. A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation
Open this publication in new window or tab >>A novel method for crosstalk analysis of biological networks: improving accuracy of pathway annotation
2017 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 45, no 2, article id e8Article in journal (Refereed) Published
Abstract [en]

Analyzing gene expression patterns is a mainstay to gain functional insights of biological systems. A plethora of tools exist to identify significant enrichment of pathways for a set of differentially expressed genes. Most tools analyze gene overlap between gene sets and are therefore severely hampered by the current state of pathway annotation, yet at the same time they run a high risk of false assignments. A way to improve both true positive and false positive rates (FPRs) is to use a functional association network and instead look for enrichment of network connections between gene sets. We present a new network crosstalk analysis method BinoX that determines the statistical significance of network link enrichment or depletion between gene sets, using the binomial distribution. This is a much more appropriate statistical model than previous methods have employed, and as a result BinoX yields substantially better true positive and FPRs than was possible before. A number of benchmarks were performed to assess the accuracy of BinoX and competing methods. We demonstrate examples of how BinoX finds many biologically meaningful pathway annotations for gene sets from cancer and other diseases, which are not found by other methods.

National Category
Biological Sciences
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-141250 (URN)10.1093/nar/gkw849 (DOI)000396576300003 ()27664219 (PubMedID)
Available from: 2017-04-12 Created: 2017-04-12 Last updated: 2017-09-29Bibliographically approved
4. A large-scale benchmark of gene prioritization methods
Open this publication in new window or tab >>A large-scale benchmark of gene prioritization methods
2017 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 7, article id 46598Article in journal (Refereed) Published
Abstract [en]

In order to maximize the use of results from high-throughput experimental studies, e.g. GWAS, for identification and diagnostics of new disease-associated genes, it is important to have properly analyzed and benchmarked gene prioritization tools. While prospective benchmarks are underpowered to provide statistically significant results in their attempt to differentiate the performance of gene prioritization tools, a strategy for retrospective benchmarking has been missing, and new tools usually only provide internal validations. The Gene Ontology (GO) contains genes clustered around annotation terms. This intrinsic property of GO can be utilized in construction of robust benchmarks, objective to the problem domain. We demonstrate how this can be achieved for network-based gene prioritization tools, utilizing the FunCoup network. We use cross-validation and a set of appropriate performance measures to compare state-of-the-art gene prioritization algorithms: three based on network diffusion, NetRank and two implementations of Random Walk with Restart, and MaxLink that utilizes network neighborhood. Our benchmark suite provides a systematic and objective way to compare the multitude of available and future gene prioritization tools, enabling researchers to select the best gene prioritization tool for the task at hand, and helping to guide the development of more accurate methods.

National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-143570 (URN)10.1038/srep46598 (DOI)000399996300001 ()28429739 (PubMedID)
Available from: 2017-06-01 Created: 2017-06-01 Last updated: 2017-09-29Bibliographically approved
5. FunCoup 4: new species, data, and visualization
Open this publication in new window or tab >>FunCoup 4: new species, data, and visualization
2018 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 46, no D1, p. D601-D607Article in journal (Refereed) Published
Abstract [en]

This release of the FunCoup database ( http://funcoup.sbc.su.se) is the fourth generation of one of the most comprehensive databases for genome-wide functional association networks. These functional associations are inferred via integrating various data types using a naive Bayesian algorithm and orthology based information transfer across different species. This approach provides high coverage of the included genomes as well as high quality of inferred interactions. In this update of FunCoup we introduce four new eukaryotic species: Schizosaccharomyces pombe, Plasmodium falciparum, Bos taurus, Oryza sativa and open the database to the prokaryotic domain by including networks for Escherichia coli and Bacillus subtilis. The latter allows us to also introduce a new class of functional association between genes - co-occurrence in the same operon. We also supplemented the existing classes of functional association: metabolic, signaling, complex and physical protein interaction with up-to-date information. In this release we switched to InParanoid v8 as the source of orthology and base for calculation of phylogenetic profiles. While populating all other evidence types with new data we introduce a new evidence type based on quantitative mass spectrometry data. Finally, the newJavaScript based network viewer provides the user an intuitive and responsive platform to further evaluate the results.

National Category
Biological Sciences
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-152557 (URN)10.1093/nar/gkx1138 (DOI)000419550700091 ()29165593 (PubMedID)
Available from: 2018-02-19 Created: 2018-02-19 Last updated: 2018-04-27Bibliographically approved
6. Experimental validation of predicted cancer genes using FRET
Open this publication in new window or tab >>Experimental validation of predicted cancer genes using FRET
Show others...
(English)Manuscript (preprint) (Other academic)
Keyword
gene prioritisation, experimental validation, FRET, functional association network, cancer genes
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
urn:nbn:se:su:diva-147216 (URN)
Available from: 2017-09-18 Created: 2017-09-18 Last updated: 2017-09-29Bibliographically approved

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