Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression
Genome-wide computational prediction of transcriptional regulatory modules reveals new insights into human gene expression
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DOI：
10.1101/gr.4866006
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发表时间：
2006-05-01
期刊：
影响因子：
7
通讯作者：
Robert, FO
中科院分区：
文献类型：
--
作者：
Blanchette, M;Bataille, AR;Robert, FO
调控区域的鉴定是人类基因组功能注释中最重要且最具挑战性的问题之一。在高等真核生物中，转录因子（TF）结合位点常常成簇组织，称为顺式调控模块（CRM）。虽然单个转录因子结合位点的预测是一个众所周知的难题，但已证明顺式调控模块的预测在一定程度上更可靠。从Transfac中记录的200多个转录因子家族的一组预测结合位点出发，我们描述了一种算法，该算法基于这样的原理：顺式调控模块通常包含几个对于少数不同转录因子在系统发育上保守的结合位点。该方法能够预测人类基因组内超过118,000个顺式调控模块。利用染色质免疫沉淀芯片（ChIP - chip）表明其中一部分在体内被转录因子结合。对它们的分析揭示，除其他方面外，顺式调控模块密度在基因组中差异很大，富含顺式调控模块的区域常常位于编码参与发育的转录因子的基因附近。预测的顺式调控模块在基因3'端附近以及远离基因的区域呈现出令人惊讶的富集。我们记录了某些转录因子结合位于相对于其靶基因特定区域的模块的趋势，并确定了可能参与组织特异性调控的转录因子。预测的顺式调控模块集作为一个名为PReMod的公共数据库（http://genomequebec.mcgill.ca/PReMod）可供使用，这将有助于分析特定生物系统中的调控机制。
The identification of regulatory regions is one of the most important and challenging problems toward the functional annotation of the human genome. In higher eukaryotes, transcription-factor (TF) binding sites are often organized in clusters called cis-regulatory modules (CRM). While the prediction of individual TF-binding sites is a notoriously difficult problem, CRM prediction has proven to be somewhat more reliable. Starting from a set of predicted binding sites for more than 200 TF families documented in Transfac, we describe an algorithm relying on the principle that CRMs generally contain several phylogenetically conserved binding sites for a few different TFs. The method allows the prediction of more than 118,000 CRMs within the human genome. A subset of these is shown to be bound in vivo by TFs using ChIP-chip. Their analysis reveals, among other things, that CRM density varies widely across the genome, with CRM-rich regions often being located near genes encoding transcription factors involved in development. Predicted CRMs show a surprising enrichment near the 3' end of genes and in regions far from genes. We document the tendency for certain TFs to bind modules located in specific regions with respect to their target genes and identify TFs likely to be involved in tissue-specific regulation. The set of predicted CRMs, which is made available as a public database called PReMod (http://genomequebec.mcgill.ca/PReMod), will help analyze regulatory mechanisms in specific biological systems.