ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments
ChEA: transcription factor regulation inferred from integrating genome-wide ChIP-X experiments
复制标题
DOI：
10.1093/bioinformatics/btq466
复制
发表时间：
2010-10-01
期刊：
影响因子：
5.8
通讯作者：
Ma'ayan, Avi
中科院分区：
文献类型：
--
作者：
Lachmann, Alexander;Xu, Huilei;Ma'ayan, Avi
动机：实验如ChIP-chip、ChIP-seq、ChIP-PET和DamID（四种方法在本文中称为ChIP-X）用于在全基因组范围内分析转录因子与DNA的结合。这样的实验提供了数百至数千个潜在的结合位点，为一个给定的转录因子在附近的基因coding regions.Results：为了整合这些研究的数据，并利用它为进一步的生物发现，我们收集这些实验的相互作用，构建一个哺乳动物ChIP-X数据库。该数据库包含189 933个相互作用，手动提取87出版物，描述了92个转录因子的结合31 932靶基因。我们使用该数据库分析mRNA表达数据，其中我们使用ChIP-X数据库作为先验生物学知识基因列表库进行基因列表富集分析。该系统作为一个基于网络的交互式应用程序提供，称为ChIP富集分析（ChEA）。使用ChEA，用户可以输入哺乳动物基因符号列表，程序可以计算ChIP-X数据库中转录因子靶的过度表达。ChEA数据库使我们能够重建基于共享重叠靶标和结合位点邻近性连接的转录因子的初始网络。为了证明ChEA的效用，我们提出了三个案例研究。我们展示了如何通过将连接图（CMAP）与ChEA相结合，我们可以对用于靶向癌细胞中特定转录因子活性的化合物进行排序。
Motivation: Experiments such as ChIP-chip, ChIP-seq, ChIP-PET and DamID (the four methods referred herein as ChIP-X) are used to profile the binding of transcription factors to DNA at a genome-wide scale. Such experiments provide hundreds to thousands of potential binding sites for a given transcription factor in proximity to gene coding regions.Results: In order to integrate data from such studies and utilize it for further biological discovery, we collected interactions from such experiments to construct a mammalian ChIP-X database. The database contains 189 933 interactions, manually extracted from 87 publications, describing the binding of 92 transcription factors to 31 932 target genes. We used the database to analyze mRNA expression data where we perform gene-list enrichment analysis using the ChIP-X database as the prior biological knowledge gene-list library. The system is delivered as a web-based interactive application called ChIP Enrichment Analysis (ChEA). With ChEA, users can input lists of mammalian gene symbols for which the program computes over-representation of transcription factor targets from the ChIP-X database. The ChEA database allowed us to reconstruct an initial network of transcription factors connected based on shared overlapping targets and binding site proximity. To demonstrate the utility of ChEA we present three case studies. We show how by combining the Connectivity Map (CMAP) with ChEA, we can rank pairs of compounds to be used to target specific transcription factor activity in cancer cells.