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PheNetic: network-based interpretation of molecular profiling data
Department of Microbial and Molecular Systems, KULeuven, Leuven, Belgium; Department of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, Belgium.
Department of Microbial and Molecular Systems, KULeuven, Leuven, Belgium; Department of Information Technology (INTEC, iMINDS), U.Ghent, Ghent, Belgium.
Department of Computer Science, KULeuven, Leuven, Belgium.
Department of Computer Science, KULeuven, Leuven, Belgium.ORCID iD: 0000-0002-6860-6303
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2015 (English)In: Nucleic Acids Research, ISSN 0305-1048, E-ISSN 1362-4962, Vol. 43, no W1, p. 244-250Article in journal (Refereed) Published
Abstract [en]

Molecular profiling experiments have become standard in current wet-lab practices. Classically, enrichment analysis has been used to identify biological functions related to these experimental results. Combining molecular profiling results with the wealth of currently available interactomics data, however, offers the opportunity to identify the molecular mechanism behind an observed molecular phenotype. In this paper, we therefore introduce ‘PheNetic’, a user-friendly web server for inferring a sub-network based on probabilistic logical querying. PheNetic extracts from an interactome, the sub-network that best explains genes prioritized through a molecular profiling experiment. Depending on its run mode, PheNetic searches either for a regulatory mechanism that gave explains to the observed molecular phenotype or for the pathways (in)activated in the molecular phenotype. The web server provides access to a large number of interactomes, making sub-network inference readily applicable to a wide variety of organisms. The inferred sub-networks can be interactively visualized in the browser. PheNetic's method and use are illustrated using an example analysis of differential expression results of ampicillin treated Escherichia coli cells. The PheNetic web service is available at http://bioinformatics.intec.ugent.be/phenetic/.

Place, publisher, year, edition, pages
Oxford University Press, 2015. Vol. 43, no W1, p. 244-250
National Category
Computer Sciences Biological Sciences
Identifiers
URN: urn:nbn:se:oru:diva-86350DOI: 10.1093/nar/gkv347ISI: 000359772700038Scopus ID: 2-s2.0-84979852533OAI: oai:DiVA.org:oru-86350DiVA, id: diva2:1474593
Note

Funding Agencies:

Ghent University

FWO G.0329.09 FWO15/PRJ/396

Agentschap voor Innovatie door Wetenschap en Technologie (IWT) [NEMOA]  

KU Leuven PF/10/010

Available from: 2020-10-09 Created: 2020-10-09 Last updated: 2020-12-02Bibliographically approved

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De Raedt, Luc

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