A nucleosome-guided map of transcription factor binding sites in yeast.
A nucleosome-guided map of transcription factor binding sites in yeast.
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DOI：
10.1371/journal.pcbi.0030215
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发表时间：
2007-11
影响因子：
4.3
通讯作者：
Hartemink, Alexander J.
中科院分区：
文献类型：
--
作者：
Narlikar, Leelavati;Gordan, Raluca;Hartemink, Alexander J.
在整个基因组中寻找转录因子（TF）的功能性DNA结合位点是理解转录调控的关键步骤。不幸的是，这些结合位点通常是短的和简并的，构成了一个重大的统计挑战：在基因组中发生的与已知TF基序的匹配比实际功能要多得多。然而，有关染色质结构的信息可能有助于识别功能位点。特别地，已经显示活性调节区通常耗尽核小体，从而使得TF能够结合那些区域中的DNA。在这里，我们描述了一种新的基序发现算法，该算法采用了一个信息先验的DNA序列位置的基础上的核小体占有率的歧视性观点。当吉布斯采样算法被应用到酵母序列集确定的ChIP芯片，正确的基序被发现在52%以上的情况下，我们的信息比常用的均匀先验。这是第一次证明，核小体占用信息可以用来提高基序发现。尽管我们只使用统计模型来预测核小体占有率，但这种改善是显着的;我们希望随着高分辨率全基因组实验核小体占有率数据的日益可用，我们的结果将进一步改善。在基因组中识别转录因子（TF）结合位点是分子生物学中的一个重要问题。TF结合位点的大规模发现通常是通过搜索通常出现在已知与TF共结合的基因的启动子区域内的短DNA模式来进行的。在这样的问题中，启动子传统上被视为核苷酸碱基串，其中TF结合位点被假定为在任何位置都同样可能出现。然而，在体内，TF定位于DNA结合位点，作为相互之间以及重要的是与称为核小体的DNA包装蛋白的协同性和竞争的复杂热力学过程的一部分。特别地，TF更可能在未被核小体占据的位点处结合DNA。在本文中，我们表明，它是可能的，将知识的核小体景观在整个基因组中，以帮助结合位点的发现，事实上，我们的算法结合核小体占用信息是显着比传统方法更准确。我们使用我们的算法来生成一个条件依赖的，核小体引导的地图结合位点的55个TF在酵母。
Finding functional DNA binding sites of transcription factors (TFs) throughout the genome is a crucial step in understanding transcriptional regulation. Unfortunately, these binding sites are typically short and degenerate, posing a significant statistical challenge: many more matches to known TF motifs occur in the genome than are actually functional. However, information about chromatin structure may help to identify the functional sites. In particular, it has been shown that active regulatory regions are usually depleted of nucleosomes, thereby enabling TFs to bind DNA in those regions. Here, we describe a novel motif discovery algorithm that employs an informative prior over DNA sequence positions based on a discriminative view of nucleosome occupancy. When a Gibbs sampling algorithm is applied to yeast sequence-sets identified by ChIP-chip, the correct motif is found in 52% more cases with our informative prior than with the commonly used uniform prior. This is the first demonstration that nucleosome occupancy information can be used to improve motif discovery. The improvement is dramatic, even though we are using only a statistical model to predict nucleosome occupancy; we expect our results to improve further as high-resolution genome-wide experimental nucleosome occupancy data becomes increasingly available. Identifying transcription factor (TF) binding sites across the genome is an important problem in molecular biology. Large-scale discovery of TF binding sites is usually carried out by searching for short DNA patterns that appear often within promoter regions of genes that are known to be co-bound by a TF. In such problems, promoters have traditionally been treated as strings of nucleotide bases in which TF binding sites are assumed to be equally likely to occur at any position. In vivo, however, TFs localize to DNA binding sites as part of a complicated thermodynamic process of cooperativity and competition, both with one another and, importantly, with DNA packaging proteins called nucleosomes. In particular, TFs are more likely to bind DNA at sites that are not occupied by nucleosomes. In this paper, we show that it is possible to incorporate knowledge of the nucleosome landscape across the genome to aid binding site discovery; indeed, our algorithm incorporating nucleosome occupancy information is significantly more accurate than conventional methods. We use our algorithm to generate a condition-dependent, nucleosome-guided map of binding sites for 55 TFs in yeast.
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