Ewing R M, Claverie J M
Carnegie Institution of Washington, Department of Plant Biology, Stanford, California 94305, USA.
Pac Symp Biocomput. 2000:430-42. doi: 10.1142/9789814447331_0041.
Large-scale expression data, such as that generated by hybridization to microarrays, is potentially a rich source of information on gene function and regulation. By clustering genes according to their expression profiles, groups of genes involved in the same pathways or sharing common regulatory mechanisms may be identified. Publicly-available EST collections are a largely unexplored source of expression data. We previously used a sample of rice ESTs to generate 'digital expression profiles' by counting the frequency of tags for different genes sequenced from different cDNA libraries. A simple statistical test was used to associate genes or cDNA libraries having similar expression profiles. Here we further validate this approach using larger samples of ESTs from the UniGene projects (clustered human, mouse and rat ESTs). Our results show that genes clustered on the basis of expression profile may represent genes implicated in similar pathways or coding for different subunits of multi-component enzyme complexes. In addition we suggest that comparison of clusters from different species, may be useful for confirmation or prediction of orthologs.
大规模表达数据，比如通过与微阵列杂交产生的数据，可能是有关基因功能和调控的丰富信息来源。通过根据基因的表达谱对基因进行聚类，可以识别出参与相同途径或共享共同调控机制的基因群。公开可用的EST文库是一个很大程度上未被探索的表达数据来源。我们之前使用水稻EST样本，通过计算来自不同cDNA文库测序的不同基因标签的频率来生成“数字表达谱”。使用一种简单的统计测试来关联具有相似表达谱的基因或cDNA文库。在这里，我们使用来自UniGene项目（聚类的人类、小鼠和大鼠EST）的更大EST样本进一步验证了这种方法。我们的结果表明，基于表达谱聚类的基因可能代表参与相似途径的基因或编码多组分酶复合物不同亚基的基因。此外，我们建议比较来自不同物种的聚类，可能有助于确认或预测直系同源基因。
