O'Connor Timothy R, Bailey Timothy L
Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia.
Institute for Molecular Bioscience, The University of Queensland, Brisbane 4072, Queensland, Australia
Nucleic Acids Res. 2014;42(17):11000-10. doi: 10.1093/nar/gku801. Epub 2014 Sep 8.
Predicting which genomic regions control the transcription of a given gene is a challenge. We present a novel computational approach for creating and validating maps that associate genomic regions (cis-regulatory modules-CRMs) with genes. The method infers regulatory relationships that explain gene expression observed in a test tissue using widely available genomic data for 'other' tissues. To predict the regulatory targets of a CRM, we use cross-tissue correlation between histone modifications present at the CRM and expression at genes within 1 Mbp of it. To validate cis-regulatory maps, we show that they yield more accurate models of gene expression than carefully constructed control maps. These gene expression models predict observed gene expression from transcription factor binding in the CRMs linked to that gene. We show that our maps are able to identify long-range regulatory interactions and improve substantially over maps linking genes and CRMs based on either the control maps or a 'nearest neighbor' heuristic. Our results also show that it is essential to include CRMs predicted in multiple tissues during map-building, that H3K27ac is the most informative histone modification, and that CAGE is the most informative measure of gene expression for creating cis-regulatory maps.
预测哪些基因组区域控制特定基因的转录是一项挑战。我们提出了一种新颖的计算方法，用于创建和验证将基因组区域（顺式调控模块-CRMs）与基因相关联的图谱。该方法利用广泛可用的“其他”组织的基因组数据，推断出能够解释在测试组织中观察到的基因表达的调控关系。为了预测CRM的调控靶点，我们使用CRM处存在的组蛋白修饰与它1 Mbp范围内基因的表达之间的跨组织相关性。为了验证顺式调控图谱，我们表明它们比精心构建的对照图谱能产生更准确的基因表达模型。这些基因表达模型根据与该基因相关的CRM中转录因子的结合情况来预测观察到的基因表达。我们表明，我们的图谱能够识别远程调控相互作用，并且与基于对照图谱或“最近邻”启发式方法构建的将基因与CRM相连的图谱相比有显著改进。我们的结果还表明，在构建图谱时纳入在多个组织中预测的CRM至关重要，H3K27ac是最具信息性的组蛋白修饰，而CAGE是用于创建顺式调控图谱的最具信息性的基因表达测量方法。
