基因表达条码 3.0：改进的数据处理和挖掘工具。
The Gene Expression Barcode 3.0: improved data processing and mining tools.
机构信息
Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY 14642, USA, Department of Biostatistics, Johns Hopkins University, Baltimore, MD 21205, USA, Informatics and Systems Development, Loyola University Stritch School of Medicine, Maywood, IL 60153, USA, Nutrition, Metabolism & Genomics Group, Division of Human Nutrition, Wageningen University, The Netherlands, Department of Biostatistics and Computational Biology, Dana Farber Cancer Institute, Boston, MA 02215, USA and Center for Biomedical Informatics, Department of Pharmacology and Molecular Therapeutics, Loyola University Stritch School of Medicine, Maywood, IL 60153, USA.
出版信息
Nucleic Acids Res. 2014 Jan;42(Database issue):D938-43. doi: 10.1093/nar/gkt1204. Epub 2013 Nov 22.
The Gene Expression Barcode project, http://barcode.luhs.org, seeks to determine the genes expressed for every tissue and cell type in humans and mice. Understanding the absolute expression of genes across tissues and cell types has applications in basic cell biology, hypothesis generation for gene function and clinical predictions using gene expression signatures. In its current version, this project uses the abundant publicly available microarray data sets combined with a suite of single-array preprocessing, quality control and analysis methods. In this article, we present the improvements that have been made since the previous version of the Gene Expression Barcode in 2011. These include a variety of new data mining tools and summaries, estimated transcriptomes and curated annotations.
基因表达条码项目，http://barcode.luhs.org，旨在确定人类和小鼠中每个组织和细胞类型表达的基因。了解基因在组织和细胞类型中的绝对表达水平，在基础细胞生物学、基因功能假说生成以及使用基因表达谱进行临床预测方面都有应用。在当前版本中，该项目使用了丰富的公开微阵列数据集，并结合了一系列单阵列预处理、质量控制和分析方法。在本文中，我们介绍了自 2011 年基因表达条码的上一个版本以来所做的改进。这些改进包括各种新的数据挖掘工具和摘要、估计的转录组和精心注释。
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