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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...
Autores principales: | , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2012
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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. |
format | Online Article Text |
id | pubmed-3315333 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2012 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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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