liu.seSearch for publications in DiVA
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
ModuleDiscoverer: Identification of regulatory modules in protein-protein interaction networks
Leibniz Inst Nat Prod Res and Infect Biol, Germany.
Leibniz Inst Nat Prod Res and Infect Biol, Germany.
Leibniz Inst Nat Prod Res and Infect Biol, Germany.
Linköping University, Department of Physics, Chemistry and Biology, Bioinformatics. Linköping University, Faculty of Science & Engineering.
Show others and affiliations
2018 (English)In: Scientific Reports, ISSN 2045-2322, E-ISSN 2045-2322, Vol. 8, article id 433Article in journal (Refereed) Published
Abstract [en]

The identification of disease-associated modules based on protein-protein interaction networks (PPINs) and gene expression data has provided new insights into the mechanistic nature of diverse diseases. However, their identification is hampered by the detection of protein communities within large-scale, whole-genome PPINs. A presented successful strategy detects a PPINs community structure based on the maximal clique enumeration problem (MCE), which is a non-deterministic polynomial time-hard problem. This renders the approach computationally challenging for large PPINs implying the need for new strategies. We present ModuleDiscoverer, a novel approach for the identification of regulatory modules from PPINs and gene expression data. Following the MCE-based approach, ModuleDiscoverer uses a randomization heuristic-based approximation of the community structure. Given a PPIN of Rattus norvegicus and public gene expression data, we identify the regulatory module underlying a rodent model of non-alcoholic steatohepatitis (NASH), a severe form of non-alcoholic fatty liver disease (NAFLD). The module is validated using single-nucleotide polymorphism (SNP) data from independent genome-wide association studies and gene enrichment tests. Based on gene enrichment tests, we find that ModuleDiscoverer performs comparably to three existing module-detecting algorithms. However, only our NASH-module is significantly enriched with genes linked to NAFLD-associated SNPs. ModuleDiscoverer is available at http://www.hki-jene.de/index.php/0/2/490 (Others/ModuleDiscoverer).

Place, publisher, year, edition, pages
NATURE PUBLISHING GROUP , 2018. Vol. 8, article id 433
National Category
Bioinformatics and Systems Biology
Identifiers
URN: urn:nbn:se:liu:diva-144886DOI: 10.1038/s41598-017-18370-2ISI: 000419940000026PubMedID: 29323246OAI: oai:DiVA.org:liu-144886DiVA, id: diva2:1181675
Note

Funding Agencies|Jena School for Microbial Communication (JSMC); Interdisciplinary Center for Clinical Research - IZKF Jena [J50]; DFG [Transregio 124]

Available from: 2018-02-09 Created: 2018-02-09 Last updated: 2018-03-09

Open Access in DiVA

fulltext(2991 kB)97 downloads
File information
File name FULLTEXT01.pdfFile size 2991 kBChecksum SHA-512
61f08e0e65794f0e64fcf2265f44612d0781d88605a70a20e875e2bc42b5c81dabf8217f8b4af539cd6627a030778e97994502756b56eb502f0c18b30e874470
Type fulltextMimetype application/pdf

Other links

Publisher's full textPubMed

Search in DiVA

By author/editor
Gustafsson, Mika
By organisation
BioinformaticsFaculty of Science & Engineering
In the same journal
Scientific Reports
Bioinformatics and Systems Biology

Search outside of DiVA

GoogleGoogle Scholar
Total: 97 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 159 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf