Cell-type specificity of ChIP-predicted transcription factor binding sites.
Cell-type specificity of ChIP-predicted transcription factor binding sites.
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DOI：
10.1186/1471-2164-13-372
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发表时间：
2012-08-03
期刊：
影响因子：
4.4
通讯作者：
Sætrom P
中科院分区：
文献类型：
--
作者：
Håndstad T;Rye M;Močnik R;Drabløs F;Sætrom P
背景依赖性转录因子（TF）结合是不同细胞状态之间基因表达模式差异的原因之一。染色质免疫沉淀，然后高通量测序（ChIP-seq）确定一个特定的背景下，在实验中使用的细胞全基因组TF结合位点。但是，这样的ChIP-seq数据能否预测其他细胞环境中的TF结合，以及是否有可能区分环境依赖性和普遍存在的TF结合？我们比较了两种不同细胞类型中多种TF结合的ChIP-seq数据，发现平均只有三分之一的ChIP-seq峰区域是两种细胞类型共有的。预期的是，共同峰在某些基因组环境中更频繁地出现，例如富含CpG的启动子，而染色质差异表征细胞类型特异性TF结合。然而，我们也发现，细胞类型之间的基因型差异可以解释结合的差异。此外，ChIP-seq信号强度和峰聚类是共同峰的最强预测因子。与位于含有多个转录因子峰的区域中的强峰相比，弱峰和孤立峰在细胞类型之间不太常见，并且与指示调节活性的数据不太相关。总之，结果表明，实验噪声在弱峰中普遍存在，而强峰和聚集峰代表了经常发生在其他细胞环境中的高置信度结合事件。然而，30-40%的最强和最聚集的峰显示出上下文依赖性调节。我们表明，通过将信号强度与其他数据相结合，从背景无关的信息，如结合位点保守性和位置权重矩阵得分到背景相关的染色质结构，我们可以预测ChIP-seq峰是否可能存在于其他细胞环境中。
Context-dependent transcription factor (TF) binding is one reason for differences in gene expression patterns between different cellular states. Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) identifies genome-wide TF binding sites for one particular context—the cells used in the experiment. But can such ChIP-seq data predict TF binding in other cellular contexts and is it possible to distinguish context-dependent from ubiquitous TF binding? We compared ChIP-seq data on TF binding for multiple TFs in two different cell types and found that on average only a third of ChIP-seq peak regions are common to both cell types. Expectedly, common peaks occur more frequently in certain genomic contexts, such as CpG-rich promoters, whereas chromatin differences characterize cell-type specific TF binding. We also find, however, that genotype differences between the cell types can explain differences in binding. Moreover, ChIP-seq signal intensity and peak clustering are the strongest predictors of common peaks. Compared with strong peaks located in regions containing peaks for multiple transcription factors, weak and isolated peaks are less common between the cell types and are less associated with data that indicate regulatory activity. Together, the results suggest that experimental noise is prevalent among weak peaks, whereas strong and clustered peaks represent high-confidence binding events that often occur in other cellular contexts. Nevertheless, 30-40% of the strongest and most clustered peaks show context-dependent regulation. We show that by combining signal intensity with additional data—ranging from context independent information such as binding site conservation and position weight matrix scores to context dependent chromatin structure—we can predict whether a ChIP-seq peak is likely to be present in other cellular contexts.
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影响因子：
64.8
作者：
Heintzman, Nathaniel D.;Hon, Gary C.;Hawkins, R. David;Kheradpour, Pouya;Stark, Alexander;Harp, Lindsey F.;Ye, Zhen;Lee, Leonard K.;Stuart, Rhona K.;Ching, Christina W.;Ching, Keith A.;Antosiewicz-Bourget, Jessica E.;Liu, Hui;Zhang, Xinmin;Green, Roland D.;Lobanenkov, Victor V.;Stewart, Ron;Thomson, James A.;Crawford, Gregory E.;Kellis, Manolis;Ren, Bing
通讯作者：
Ren, Bing
DOI：
10.1073/pnas.1016959108
发表时间：
2011-04-05
影响因子：
11.1
作者：
He, Aibin;Kong, Sek Won;Pu, William T.
通讯作者：
Pu, William T.
影响因子：
64.5
作者：
Hallikas, O;Palin, K;Taipale, J
通讯作者：
Taipale, J
影响因子：
3.7
作者：
Conboy, Caitlin M.;Spyrou, Christiana;Thorne, Natalie P.;Wade, Elizabeth J.;Barbosa-Morais, Nuno L.;Wilson, Michael D.;Bhattacharjee, Arindam;Young, Richard A.;Tavare, Simon;Lees, Jacqueline A.;Odom, Duncan T.
通讯作者：
Odom, Duncan T.
影响因子：
14.9
作者：
Jothi, Raja;Cuddapah, Suresh;Barski, Artem;Cui, Kairong;Zhao, Keji
通讯作者：
Zhao, Keji