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

Daniel Hurley, Hiromitsu Araki, Yoshinori Tamada, Ben Dunmore, Deborah Sanders, Sally Humphreys, Muna Affara, Seiya Imoto, Kaori Yasuda, Yuki Tomiyasu, Kosuke Tashiro, Christopher Savoie, Vicky Cho, Stephen Smith, Satoru Kuhara, Satoru Miyano, D. Stephen Charnock-Jones, Edmund J. Crampin, Cristin G. Print

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)2377-2398
Number of pages22
JournalNucleic acids research
Volume40
Issue number6
DOIs
Publication statusPublished - Mar 1 2012

All Science Journal Classification (ASJC) codes

  • Genetics

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    Hurley, D., Araki, H., Tamada, Y., Dunmore, B., Sanders, D., Humphreys, S., Affara, M., Imoto, S., Yasuda, K., Tomiyasu, Y., Tashiro, K., Savoie, C., Cho, V., Smith, S., Kuhara, S., Miyano, S., Charnock-Jones, D. S., Crampin, E. J., & Print, C. G. (2012). Gene network inference and visualization tools for biologists: Application to new human transcriptome datasets. Nucleic acids research, 40(6), 2377-2398. https://doi.org/10.1093/nar/gkr902