Transcription factor binding element detection using functional clustering of mutant expression data.
Transcription factor binding element detection using functional clustering of mutant expression data.
复制标题
使用突变体表达数据的功能聚类检测转录因子结合元件。
DOI：
10.1093/nar/gkh557
复制
发表时间：
2004
期刊：
影响因子：
--
通讯作者：
Zhang,MichaelQ
中科院分区：
文献类型：
--
作者：
Chen,Gengxin;Hata,Naoya;Zhang,MichaelQ
基因突变作为揭示基因功能的有力工具，已广泛应用于分子生物学研究。借助 DNA 微阵列等高通量技术，可以监测突变体中全基因组的基因表达变化。在这里，我们提出了一种简单的方法，使用来自相关转录因子被删除的突变体的微阵列表达数据来检测转录因子结合基序。我们方法的核心部分是基于功能注释对差异表达基因进行聚类，例如基因本体论（GO）。我们使用来自 Rosetta Compendium 的八个微阵列数据集测试了我们的方法，并且能够检测至少四个转录因子的规范结合基序。在染色质 IP 芯片数据的支持下，我们还预测了 Swi4 结合基序的可能变体，并恢复了 Arg80 的核心基序。我们的方法应该很容易适用于使用其他类型的分子生物学技术的微阵列实验，例如条件敲除/过度表达或RNAi介导的“敲低”，以干扰转录因子的表达。我们的方法中包含的功能聚类也可能为相关转录因子的功能提供新的见解。
As a powerful tool to reveal gene functions, gene mutation has been used extensively in molecular biology studies. With high throughput technologies, such as DNA microarray, genome‐wide gene expression changes can be monitored in mutants. Here we present a simple approach to detect the transcription‐factor‐binding motif using microarray expression data from a mutant in which the relevant transcription factor is deleted. A core part of our approach is clustering of differentially expressed genes based on functional annotations, such as Gene Ontology (GO). We tested our method with eight microarray data sets from the Rosetta Compendium and were able to detect canonical binding motifs for at least four transcription factors. With the support of chromatin IP chip data, we also predict a possible variant of the Swi4 binding motif and recover a core motif for Arg80. Our approach should be readily applicable to microarray experiments using other types of molecular biology techniques, such as conditional knockout/overexpression or RNAi‐mediated ‘knockdown’, to perturb the expression of a transcription factor. Functional clustering included in our approach may also provide new insights into the function of the relevant transcription factor.