Herrero Javier, Vaquerizas Juan M, Al-Shahrour Fátima, Conde Lucía, Mateos Alvaro, Díaz-Uriarte Javier Santoyo Ramón, Dopazo Joaquín
Bioinformatics Unit, Biotechnology Programme, Centro Nacional de Investigaciones Oncológicas, Melchor Fernández Almagro, 3, E-28029 Madrid, Spain.
Nucleic Acids Res. 2004 Jul 1;32(Web Server issue):W485-91. doi: 10.1093/nar/gkh421.
Since the first papers published in the late nineties, including, for the first time, a comprehensive analysis of microarray data, the number of questions that have been addressed through this technique have both increased and diversified. Initially, interest focussed on genes coexpressing across sets of experimental conditions, implying, essentially, the use of clustering techniques. Recently, however, interest has focussed more on finding genes differentially expressed among distinct classes of experiments, or correlated to diverse clinical outcomes, as well as in building predictors. In addition to this, the availability of accurate genomic data and the recent implementation of CGH arrays has made mapping expression and genomic data on the chromosomes possible. There is also a clear demand for methods that allow the automatic transfer of biological information to the results of microarray experiments. Different initiatives, such as the Gene Ontology (GO) consortium, pathways databases, protein functional motifs, etc., provide curated annotations for genes. Whereas many resources on the web focus mainly on clustering methods, GEPAS has evolved to cope with the aforementioned new challenges that have recently arisen in the field of microarray data analysis. The web-based pipeline for microarray gene expression data, GEPAS, is available at http://gepas.bioinfo.cnio.es.
自九十年代末发表首批论文以来，首次对微阵列数据进行了全面分析，通过该技术解决的问题数量不断增加且日益多样化。最初，研究重点集中在跨实验条件集共同表达的基因上，这实质上意味着使用聚类技术。然而，最近研究重点更多地转向寻找在不同实验类别中差异表达的基因，或与不同临床结果相关的基因，以及构建预测模型。除此之外，准确基因组数据的可用性以及最近CGH阵列的应用使得在染色体上绘制表达和基因组数据成为可能。对于能够将生物信息自动转移到微阵列实验结果的方法也有明确需求。不同的倡议，如基因本体论（GO）联盟、通路数据库、蛋白质功能基序等，为基因提供了经过整理的注释。虽然网络上的许多资源主要侧重于聚类方法，但GEPAS已不断发展以应对微阵列数据分析领域最近出现的上述新挑战。用于微阵列基因表达数据的基于网络的管道GEPAS可在http://gepas.bioinfo.cnio.es获取。
