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MaxLink: network-based prioritization of genes tightly linked to a disease seed set
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm Bioinformatics Centre, Sweden.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm Bioinformatics Centre, Sweden.
Stockholm University, Faculty of Science, Department of Biochemistry and Biophysics. Stockholm Bioinformatics Centre, Sweden; Swedish eScience Research Center, Sweden.
2014 (English)In: Bioinformatics, ISSN 1367-4803, E-ISSN 1367-4811, Vol. 30, no 18, 2689-2690 p.Article 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.

Place, publisher, year, edition, pages
2014. Vol. 30, no 18, 2689-2690 p.
National Category
Bioinformatics and Systems Biology
Research subject
Biochemistry towards Bioinformatics
Identifiers
URN: urn:nbn:se:su:diva-109021DOI: 10.1093/bioinformatics/btu344ISI: 000342913000029OAI: oai:DiVA.org:su-109021DiVA: diva2:778178
Note

AuthorCount:3;

Available from: 2015-01-09 Created: 2014-11-10 Last updated: 2017-09-29Bibliographically approved
In thesis
1. Functional association networks for disease gene prediction
Open this publication in new window or tab >>Functional association networks for disease gene prediction
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. 64 p.
Keyword
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:nbn:se:su:diva-147217 (URN)978-91-7649-976-4 (ISBN)978-91-7649-977-1 (ISBN)
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: 2017-10-16Bibliographically approved

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