Cargando…
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...
Autores principales: | , , , , , , , , , , , , , , , |
---|---|
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 |
_version_ | 1784626703380774912 |
---|---|
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. |
format | Online Article Text |
id | pubmed-8728279 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT feizinikta pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT nairsisirakadambat pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT smirnovpetr pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT berigangesh pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT eeleschristopher pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT esfahaniparinaznasr pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT nakanominoru pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT tkachukdenis pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT mammolitianthony pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT gorobetsevgeniya pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT merarvindsingh pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT lineva pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT yuyihong pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT martinscott pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT hafnermarc pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis AT haibekainsbenjamin pharmacodb20improvingscalabilityandtransparencyofinvitropharmacogenomicsanalysis |