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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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Autores principales: 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.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2012
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315333/
https://www.ncbi.nlm.nih.gov/pubmed/22121215
http://dx.doi.org/10.1093/nar/gkr902
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author 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.
author_facet 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.
author_sort Hurley, Daniel
collection PubMed
description 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.
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spelling pubmed-33153332012-03-30 Gene network inference and visualization tools for biologists: application to new human transcriptome datasets 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. Nucleic Acids Res Computational Biology 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. Oxford University Press 2012-03 2011-11-24 /pmc/articles/PMC3315333/ /pubmed/22121215 http://dx.doi.org/10.1093/nar/gkr902 Text en © The Author(s) 2011. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/3.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Computational Biology
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.
Gene network inference and visualization tools for biologists: application to new human transcriptome datasets
title Gene network inference and visualization tools for biologists: application to new human transcriptome datasets
title_full Gene network inference and visualization tools for biologists: application to new human transcriptome datasets
title_fullStr Gene network inference and visualization tools for biologists: application to new human transcriptome datasets
title_full_unstemmed Gene network inference and visualization tools for biologists: application to new human transcriptome datasets
title_short Gene network inference and visualization tools for biologists: application to new human transcriptome datasets
title_sort gene network inference and visualization tools for biologists: application to new human transcriptome datasets
topic Computational Biology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3315333/
https://www.ncbi.nlm.nih.gov/pubmed/22121215
http://dx.doi.org/10.1093/nar/gkr902
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