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PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. Th...

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Autores principales: Feizi, Nikta, Nair, Sisira Kadambat, Smirnov, Petr, Beri, Gangesh, Eeles, Christopher, Esfahani, Parinaz Nasr, Nakano, Minoru, Tkachuk, Denis, Mammoliti, Anthony, Gorobets, Evgeniya, Mer, Arvind Singh, Lin, Eva, Yu, Yihong, Martin, Scott, Hafner, Marc, Haibe-Kains, Benjamin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728279/
https://www.ncbi.nlm.nih.gov/pubmed/34850112
http://dx.doi.org/10.1093/nar/gkab1084
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author Feizi, Nikta
Nair, Sisira Kadambat
Smirnov, Petr
Beri, Gangesh
Eeles, Christopher
Esfahani, Parinaz Nasr
Nakano, Minoru
Tkachuk, Denis
Mammoliti, Anthony
Gorobets, Evgeniya
Mer, Arvind Singh
Lin, Eva
Yu, Yihong
Martin, Scott
Hafner, Marc
Haibe-Kains, Benjamin
author_facet Feizi, Nikta
Nair, Sisira Kadambat
Smirnov, Petr
Beri, Gangesh
Eeles, Christopher
Esfahani, Parinaz Nasr
Nakano, Minoru
Tkachuk, Denis
Mammoliti, Anthony
Gorobets, Evgeniya
Mer, Arvind Singh
Lin, Eva
Yu, Yihong
Martin, Scott
Hafner, Marc
Haibe-Kains, Benjamin
author_sort Feizi, Nikta
collection PubMed
description Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models.
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spelling pubmed-87282792022-01-05 PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis Feizi, Nikta Nair, Sisira Kadambat Smirnov, Petr Beri, Gangesh Eeles, Christopher Esfahani, Parinaz Nasr Nakano, Minoru Tkachuk, Denis Mammoliti, Anthony Gorobets, Evgeniya Mer, Arvind Singh Lin, Eva Yu, Yihong Martin, Scott Hafner, Marc Haibe-Kains, Benjamin Nucleic Acids Res Database Issue Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models. Oxford University Press 2021-11-26 /pmc/articles/PMC8728279/ /pubmed/34850112 http://dx.doi.org/10.1093/nar/gkab1084 Text en © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research. https://creativecommons.org/licenses/by-nc/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://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 Database Issue
Feizi, Nikta
Nair, Sisira Kadambat
Smirnov, Petr
Beri, Gangesh
Eeles, Christopher
Esfahani, Parinaz Nasr
Nakano, Minoru
Tkachuk, Denis
Mammoliti, Anthony
Gorobets, Evgeniya
Mer, Arvind Singh
Lin, Eva
Yu, Yihong
Martin, Scott
Hafner, Marc
Haibe-Kains, Benjamin
PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
title PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
title_full PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
title_fullStr PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
title_full_unstemmed PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
title_short PharmacoDB 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
title_sort pharmacodb 2.0: improving scalability and transparency of in vitro pharmacogenomics analysis
topic Database Issue
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8728279/
https://www.ncbi.nlm.nih.gov/pubmed/34850112
http://dx.doi.org/10.1093/nar/gkab1084
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