Chang Jeffrey T, Nevins Joseph R
Department of Molecular Genetics and Microbiology, Duke Institute for Genome Sciences and Policy, Duke University Medical Center, Durham, NC 27710, USA.
Bioinformatics. 2006 Dec 1;22(23):2926-33. doi: 10.1093/bioinformatics/btl483. Epub 2006 Sep 25.
Understanding the full meaning of the biology captured in molecular profiles, within the context of the entire biological system, cannot be achieved with a simple examination of the individual genes in the signature. To facilitate such an understanding, we have developed GATHER, a tool that integrates various forms of available data to elucidate biological context within molecular signatures produced from high-throughput post-genomic assays.
Analyzing the Rb/E2F tumor suppressor pathway, we show that GATHER identifies critical features of the pathway. We further show that GATHER identifies common biology in a series of otherwise unrelated gene expression signatures that each predict breast cancer outcome. We quantify the performance of GATHER and find that it successfully predicts 90% of the functions over a broad range of gene groups. We believe that GATHER provides an essential tool for extracting the full value from molecular signatures generated from genome-scale analyses.
GATHER is available at http://gather.genome.duke.edu/
在整个生物系统的背景下，仅通过简单检查特征中的单个基因，无法理解分子谱所反映的生物学的全部意义。为了便于进行这种理解，我们开发了GATHER，这是一种整合各种可用数据的工具，用于阐明高通量后基因组分析产生的分子特征中的生物学背景。
通过分析Rb/E2F肿瘤抑制途径，我们表明GATHER能够识别该途径的关键特征。我们进一步表明，GATHER能在一系列原本不相关但均能预测乳腺癌预后的基因表达特征中识别出共同的生物学特性。我们对GATHER的性能进行了量化，发现它能在广泛的基因组中成功预测90%的功能。我们相信，GATHER为从基因组规模分析产生的分子特征中提取全部价值提供了一个重要工具。
