Large-scale analysis of the human and mouse transcriptomes

作者：Andrew I. Su, M. Cooke, Keith A. Ching, Yaron Hakak, John Roger Walker, Tim Wiltshire, Anthony P. Orth, Raquel González de Vega, Lisa M. Sapinoso, Aziz Moqrich, Ardem Patapoutian, Garret M. Hampton, Peter G. Schultz, John B. Hogenesch · 发表于：Proceedings of the National Academy of Sciences · 年份：2002 · DOI：10.1073/pnas.012025199 · 被引用次数：1494 · 研究领域：Bioinformatics and Genomic Networks、Gene expression and cancer classification、Machine Learning in Bioinformatics

High-throughput gene expression profiling has become an important tool for investigating transcriptional activity in a variety of biological samples. To date, the vast majority of these experiments have focused on specific biological processes and perturbations. Here, we have generated and analyzed gene expression from a set of samples spanning a broad range of biological conditions. Specifically, we profiled gene expression from 91 human and mouse samples across a diverse array of tissues, organs, and cell lines. Because these samples predominantly come from the normal physiological state in the human and mouse, this dataset represents a preliminary, but substantial, description of the normal mammalian transcriptome. We have used this dataset to illustrate methods of mining these data, and to reveal insights into molecular and physiological gene function, mechanisms of transcriptional regulation, disease etiology, and comparative genomics. Finally, to allow the scientific community to use this resource, we have built a free and publicly accessible website (http://expression.gnf.org) that integrates data visualization and curation of current gene annotations.