A graph-based approach to systematically reconstruct human transcriptional regulatory modules
A graph-based approach to systematically reconstruct human transcriptional regulatory modules
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DOI：
10.1093/bioinformatics/btm227
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发表时间：
2007-07-01
期刊：
影响因子：
5.8
通讯作者：
Zhou, Xianghong Jasmine
中科院分区：
文献类型：
--
作者：
Yan, Xifeng;Mehan, Michael R.;Zhou, Xianghong Jasmine
动机：研究基因调控的一个主要挑战是系统地重建转录调控模块，转录调控模块被定义为由一组共同的转录因子调控的基因组。一种常用的转录模块重建方法是从微阵列数据集获得共表达簇。然而，这样的结果通常包含假阳性，因为来自许多转录模块的基因可能在给定类型的条件下同时受到干扰。在这项研究中，我们提出并验证了在不同条件下的多个微阵列数据集中形成共表达簇的基因更有可能形成转录模块。然而，识别基因共表达的许多微阵列dataset的一个子集是不是一个微不足道的计算problem.Results：我们提出了一个基于图形的数据挖掘方法，有效地和系统地识别频繁的共表达集群。给定m个微阵列数据集，我们将每个微阵列数据集建模为一个共表达图，并在[theta m]个数据集（0
Motivation: A major challenge in studying gene regulation is to systematically reconstruct transcription regulatory modules, which are defined as sets of genes that are regulated by a common set of transcription factors. A commonly used approach for transcription module reconstruction is to derive coexpression clusters from a microarray dataset. However, such results often contain false positives because genes from many transcription modules may be simultaneously perturbed upon a given type of conditions. In this study, we propose and validate that genes, which form a coexpression cluster in multiple microarray datasets across diverse conditions, are more likely to form a transcription module. However, identifying genes coexpressed in a subset of many microarray datasets is not a trivial computational problem.Results: We propose a graph-based data-mining approach to efficiently and systematically identify frequent coexpression clusters. Given m microarray datasets, we model each microarray dataset as a coexpression graph, and search for vertex sets which are frequently densely connected across [theta m] datasets (0