Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their...

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Veröffentlicht in:Nucleic acids research 2012-03, Vol.40 (6), p.2377-2398
Hauptverfasser: Hurley, Daniel, Araki, Hiromitsu, Tamada, Yoshinori, Dunmore, Ben, Sanders, Deborah, Humphreys, Sally, Affara, Muna, Imoto, Seiya, Yasuda, Kaori, Tomiyasu, Yuki, Tashiro, Kosuke, Savoie, Christopher, Cho, Vicky, Smith, Stephen, Kuhara, Satoru, Miyano, Satoru, Charnock-Jones, D Stephen, Crampin, Edmund J, Print, Cristin G
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Sprache:eng
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Zusammenfassung:Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.
ISSN:0305-1048
1362-4962
DOI:10.1093/nar/gkr902