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GDSCTools for mining pharmacogenomic interactions in cancer

MOTIVATION: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computation...

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Autores principales: Cokelaer, Thomas, Chen, Elisabeth, Iorio, Francesco, Menden, Michael P, Lightfoot, Howard, Saez-Rodriguez, Julio, Garnett, Mathew J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031019/
https://www.ncbi.nlm.nih.gov/pubmed/29186349
http://dx.doi.org/10.1093/bioinformatics/btx744
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author Cokelaer, Thomas
Chen, Elisabeth
Iorio, Francesco
Menden, Michael P
Lightfoot, Howard
Saez-Rodriguez, Julio
Garnett, Mathew J
author_facet Cokelaer, Thomas
Chen, Elisabeth
Iorio, Francesco
Menden, Michael P
Lightfoot, Howard
Saez-Rodriguez, Julio
Garnett, Mathew J
author_sort Cokelaer, Thomas
collection PubMed
description MOTIVATION: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. RESULTS: We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-60310192018-07-10 GDSCTools for mining pharmacogenomic interactions in cancer Cokelaer, Thomas Chen, Elisabeth Iorio, Francesco Menden, Michael P Lightfoot, Howard Saez-Rodriguez, Julio Garnett, Mathew J Bioinformatics Applications Notes MOTIVATION: Large pharmacogenomic screenings integrate heterogeneous cancer genomic datasets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces. RESULTS: We have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic datasets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (analysis of variance) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC and to implement new analytical methods. In addition, non-GDSC data resources can also be analysed since drug responses and genomic features can be encoded as CSV files. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2018-04-01 2017-11-24 /pmc/articles/PMC6031019/ /pubmed/29186349 http://dx.doi.org/10.1093/bioinformatics/btx744 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com
spellingShingle Applications Notes
Cokelaer, Thomas
Chen, Elisabeth
Iorio, Francesco
Menden, Michael P
Lightfoot, Howard
Saez-Rodriguez, Julio
Garnett, Mathew J
GDSCTools for mining pharmacogenomic interactions in cancer
title GDSCTools for mining pharmacogenomic interactions in cancer
title_full GDSCTools for mining pharmacogenomic interactions in cancer
title_fullStr GDSCTools for mining pharmacogenomic interactions in cancer
title_full_unstemmed GDSCTools for mining pharmacogenomic interactions in cancer
title_short GDSCTools for mining pharmacogenomic interactions in cancer
title_sort gdsctools for mining pharmacogenomic interactions in cancer
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6031019/
https://www.ncbi.nlm.nih.gov/pubmed/29186349
http://dx.doi.org/10.1093/bioinformatics/btx744
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