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PharmacoGx:用于分析大型药物基因组数据集的 R 包。

PharmacoGx: an R package for analysis of large pharmacogenomic datasets.

机构信息

Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada.

Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada.

出版信息

Bioinformatics. 2016 Apr 15;32(8):1244-6. doi: 10.1093/bioinformatics/btv723. Epub 2015 Dec 9.

Abstract

UNLABELLED

Pharmacogenomics holds great promise for the development of biomarkers of drug response and the design of new therapeutic options, which are key challenges in precision medicine. However, such data are scattered and lack standards for efficient access and analysis, consequently preventing the realization of the full potential of pharmacogenomics. To address these issues, we implemented PharmacoGx, an easy-to-use, open source package for integrative analysis of multiple pharmacogenomic datasets. We demonstrate the utility of our package in comparing large drug sensitivity datasets, such as the Genomics of Drug Sensitivity in Cancer and the Cancer Cell Line Encyclopedia. Moreover, we show how to use our package to easily perform Connectivity Map analysis. With increasing availability of drug-related data, our package will open new avenues of research for meta-analysis of pharmacogenomic data.

AVAILABILITY AND IMPLEMENTATION

PharmacoGx is implemented in R and can be easily installed on any system. The package is available from CRAN and its source code is available from GitHub.

CONTACT

bhaibeka@uhnresearch.ca or benjamin.haibe.kains@utoronto.ca

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

摘要

未加标签

药物基因组学在药物反应生物标志物的开发和新治疗选择的设计方面具有巨大的潜力,这是精准医学的关键挑战。然而,这些数据分散且缺乏有效的访问和分析标准,因此无法充分发挥药物基因组学的潜力。为了解决这些问题,我们实施了 PharmacoGx,这是一个易于使用的、开源的用于综合分析多个药物基因组数据集的软件包。我们演示了我们的软件包在比较大型药物敏感性数据集(如癌症基因组药物敏感性和癌症细胞系百科全书)方面的实用性。此外,我们还展示了如何使用我们的软件包轻松进行连通性图谱分析。随着与药物相关的数据越来越多,我们的软件包将为药物基因组学数据的荟萃分析开辟新的研究途径。

可用性和实施

PharmacoGx 是用 R 语言实现的,可以在任何系统上轻松安装。该软件包可从 CRAN 获得,其源代码可从 GitHub 获得。

联系方式

bhaibeka@uhnresearch.cabenjamin.haibe.kains@utoronto.ca

补充信息

补充数据可在 Bioinformatics 在线获得。

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