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A multiparameter network reveals extensive divergence between C. elegans bHLH transcription factors.一个多参数网络揭示了秀丽隐杆线虫bHLH转录因子之间的广泛差异。
Cell. 2009 Jul 23;138(2):314-27. doi: 10.1016/j.cell.2009.04.058.
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Diversity and complexity in DNA recognition by transcription factors.转录因子对DNA识别的多样性与复杂性
Science. 2009 Jun 26;324(5935):1720-3. doi: 10.1126/science.1162327. Epub 2009 May 14.
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High-resolution DNA-binding specificity analysis of yeast transcription factors.酵母转录因子的高分辨率DNA结合特异性分析
Genome Res. 2009 Apr;19(4):556-66. doi: 10.1101/gr.090233.108. Epub 2009 Jan 21.
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A library of yeast transcription factor motifs reveals a widespread function for Rsc3 in targeting nucleosome exclusion at promoters.一个酵母转录因子基序文库揭示了Rsc3在靶向启动子处核小体排除方面的广泛功能。
Mol Cell. 2008 Dec 26;32(6):878-87. doi: 10.1016/j.molcel.2008.11.020.
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The DNA-encoded nucleosome organization of a eukaryotic genome.真核生物基因组的DNA编码核小体组织
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Variation in homeodomain DNA binding revealed by high-resolution analysis of sequence preferences.通过对序列偏好的高分辨率分析揭示的同源域DNA结合变异。
Cell. 2008 Jun 27;133(7):1266-76. doi: 10.1016/j.cell.2008.05.024.
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Saccharomyces cerevisiae SFP1: at the crossroads of central metabolism and ribosome biogenesis.酿酒酵母SFP1:处于中心代谢与核糖体生物合成的交叉点
Microbiology (Reading). 2008 Jun;154(Pt 6):1686-1699. doi: 10.1099/mic.0.2008/017392-0.
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Dynamic remodeling of individual nucleosomes across a eukaryotic genome in response to transcriptional perturbation.真核生物基因组中单个核小体响应转录扰动的动态重塑。
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A high-resolution atlas of nucleosome occupancy in yeast.酵母核小体占据情况的高分辨率图谱。
Nat Genet. 2007 Oct;39(10):1235-44. doi: 10.1038/ng2117. Epub 2007 Sep 16.
10
Genome-wide mapping of in vivo protein-DNA interactions.体内蛋白质-DNA相互作用的全基因组图谱绘制。
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区分直接与间接转录因子-DNA 相互作用。

Distinguishing direct versus indirect transcription factor-DNA interactions.

机构信息

Department of Computer Science, Duke University, Durham, North Carolina 27708, USA.

出版信息

Genome Res. 2009 Nov;19(11):2090-100. doi: 10.1101/gr.094144.109. Epub 2009 Aug 3.

DOI:10.1101/gr.094144.109
PMID:19652015
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2775597/
Abstract

Transcriptional regulation is largely enacted by transcription factors (TFs) binding DNA. Large numbers of TF binding motifs have been revealed by ChIP-chip experiments followed by computational DNA motif discovery. However, the success of motif discovery algorithms has been limited when applied to sequences bound in vivo (such as those identified by ChIP-chip) because the observed TF-DNA interactions are not necessarily direct: Some TFs predominantly associate with DNA indirectly through protein partners, while others exhibit both direct and indirect binding. Here, we present the first method for distinguishing between direct and indirect TF-DNA interactions, integrating in vivo TF binding data, in vivo nucleosome occupancy data, and motifs from in vitro protein binding microarray experiments. When applied to yeast ChIP-chip data, our method reveals that only 48% of the data sets can be readily explained by direct binding of the profiled TF, while 16% can be explained by indirect DNA binding. In the remaining 36%, none of the motifs used in our analysis was able to explain the ChIP-chip data, either because the data were too noisy or because the set of motifs was incomplete. As more in vitro TF DNA binding motifs become available, our method could be used to build a complete catalog of direct and indirect TF-DNA interactions. Our method is not restricted to yeast or to ChIP-chip data, but can be applied in any system for which both in vivo binding data and in vitro DNA binding motifs are available.

摘要

转录调控在很大程度上是通过转录因子(TFs)与 DNA 结合来实现的。大量的 TF 结合基序已经通过 ChIP-chip 实验和随后的计算 DNA 基序发现揭示出来。然而,当将 motif 发现算法应用于体内结合的序列(如 ChIP-chip 鉴定的序列)时,其成功受到了限制,因为观察到的 TF-DNA 相互作用不一定是直接的:一些 TF 主要通过蛋白质伴侣间接与 DNA 结合,而另一些 TF 则同时表现出直接和间接结合。在这里,我们提出了一种区分直接和间接 TF-DNA 相互作用的方法,该方法整合了体内 TF 结合数据、体内核小体占据数据和来自体外蛋白质结合微阵列实验的基序。当应用于酵母 ChIP-chip 数据时,我们的方法表明,只有 48%的数据集可以通过所分析的 TF 的直接结合来很好地解释,而 16%可以通过间接 DNA 结合来解释。在其余的 36%中,我们分析中使用的没有一个基序能够解释 ChIP-chip 数据,要么是因为数据太嘈杂,要么是因为基序集不完整。随着更多体外 TF-DNA 结合基序的出现,我们的方法可以用来构建直接和间接 TF-DNA 相互作用的完整目录。我们的方法不仅限于酵母或 ChIP-chip 数据,而是可以应用于任何具有体内结合数据和体外 DNA 结合基序的系统。