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GDSCTools for mining pharmacogenomic interactions in cancer

Abstract : 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.
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https://hal-pasteur.archives-ouvertes.fr/pasteur-03111932
Contributor : Thomas Cokelaer <>
Submitted on : Friday, January 15, 2021 - 5:41:31 PM
Last modification on : Wednesday, January 20, 2021 - 4:56:02 PM
Long-term archiving on: : Friday, April 16, 2021 - 7:13:51 PM

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Thomas Cokelaer, Elisabeth Chen, Francesco Iorio, Michael Menden, Howard Lightfoot, et al.. GDSCTools for mining pharmacogenomic interactions in cancer. Bioinformatics, Oxford University Press (OUP), 2018, 34 (7), pp.1226-1228. ⟨10.1093/bioinformatics/btx744⟩. ⟨pasteur-03111932⟩

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