GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor
GEOquery: a bridge between the gene expression omnibus (GEO) and BioConductor
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DOI：
10.1093/bioinformatics/btm254
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发表时间：
2007-07-15
期刊：
影响因子：
5.8
通讯作者：
Meltzer, Paul S.
中科院分区：
文献类型：
--
作者：
Sean, Davis;Meltzer, Paul S.
微阵列技术已经成为标准的分子生物学工具。已经产生了大量生物体、组织类型、治疗条件和疾病状态的实验数据。The Gene Expression Omnibus（Barrett等人，2005），由美国国立卫生研究院的国家生物信息学中心（NCBI）开发，是近14万个基因表达实验的储存库。BioConductor项目（Gentleman等人，2004）是一个开源和开放开发的软件项目，建立在R统计编程环境（R开发核心团队，2005）中，用于分析和理解基因组数据。BioConductor项目中包含的工具代表了许多用于分析微阵列和基因组学数据的最先进方法。我们已经开发了一种软件工具，允许直接从BioConductor访问GEO中的大量信息，消除了过去使此类分析成为劳动密集型的许多格式和解析问题。该软件名为GEOquery，有效地在GEO和BioConductor之间建立了桥梁。从BioConductor轻松获取GEO数据可能会导致使用新颖和严格的统计和生物信息学工具对GEO数据进行新的分析。促进微阵列数据的分析和荟萃分析将提高从已发表的基因组数据中得出生物学重要结论的效率。
Microarray technology has become a standard molecular biology tool. Experimental data have been generated on a huge number of organisms, tissue types, treatment conditions and disease states. The Gene Expression Omnibus ( Barrett et al., 2005), developed by the National Center for Bioinformatics (NCBI) at the National Institutes of Health is a repository of nearly 140 000 gene expression experiments. The BioConductor project (Gentleman et al., 2004) is an open-source and open-development software project built in the R statistical programming environment (R Development core Team, 2005) for the analysis and comprehension of genomic data. The tools contained in the BioConductor project represent many state-of-the-art methods for the analysis of microarray and genemics data. We have developed a software tool that allows access to the wealth of information within GEO directly from BioConductor, eliminating many the formatting and parsing problems that have made such analyses labor-intensive in the past. The software, called GEOquery, effectively establishes a bridge between GEO and BioConductor. Easy access to GEO data from BioConductor will likely lead to new analyses of GEO data using novel and rigorous statistical and bioinformatic tools. Facilitating analyses and meta-analyses of microarray data will increase the efficiency with which biologically important conclusions can be drawn from published genomic data.