Integrated assessment and prediction of transcription factor binding.
Integrated assessment and prediction of transcription factor binding.
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DOI：
10.1371/journal.pcbi.0020070
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发表时间：
2006-06-16
影响因子：
4.3
通讯作者：
Ideker T
中科院分区：
文献类型：
--
作者：
Beyer A;Workman C;Hollunder J;Radke D;Möller U;Wilhelm T;Ideker T
系统性染色质免疫沉淀（ChIP - chip）实验已成为在模式生物和人类中绘制转录相互作用的核心技术。然而，染色质结合的测量并不一定意味着调控，如果结合是条件或辅因子依赖性的，可能难以检测到。为了应对这些挑战，我们提出一种将转录因子（TFs）可靠地分配给靶基因的方法，该方法将许多直接和间接证据整合到一个单一的概率模型中。利用这种方法，我们分析了在标准条件下对酵母转录因子测量的公开可用的ChIP - chip结合图谱，表明我们的模型对这些数据的解释比以前的方法具有显著更高的准确性。汇集高可信度的相互作用揭示了一个包含363个显著的因子组（转录因子模块）的大型网络，这些因子组协同调控共同的靶基因。此外，该方法高可信度地预测了980种新的结合相互作用，这些相互作用可能在迄今未测试的条件下发生。实际上，通过新的ChIP - chip实验，我们表明对因子Rpn4p和Pdr1p预测的相互作用只有在细胞用甲基磺酸甲酯（一种DNA损伤剂）处理后才能观察到。我们概述了第一种将转录因子 - 靶标相互作用的所有可用证据一致整合的方法，并全面确定了由此产生的转录因子模块层次结构。随着在诸如人类等复杂生物中进行越来越多的ChIP - chip分析，而“标准条件”定义不明确，为每个因子确定实验条件的优先级将尤为重要。
转录因子（TFs）在细胞条件依赖的情况下结合在其靶基因附近以调节转录水平。每个基因可能通过特定转录因子组（转录因子模块）的结合而与其他基因有不同的调控方式。最近，关于转录网络的大量各种大规模测量已可获得。在此，作者提出了一个框架，用于一致地整合所有这些证据，以系统地确定每个转录因子直接调控的精确基因集（即转录因子 - 靶标相互作用）。该框架应用于酿酒酵母，使用七种不同的证据来源对该生物中所有可能的转录因子 - 靶标相互作用进行评分。随后，作者采用另一种新开发的算法，基于排名前5000的转录因子 - 靶标相互作用揭示转录因子模块，产生了300多个转录因子模块。转录因子 - 靶标相互作用的新评分方案允许预测在迄今未测试条件下转录因子的结合，这通过对两个转录因子（Pdr1p，Rpn4p）的相互作用进行实验验证得以证明。重要的是，新方法（转录因子 - 靶标相互作用的评分和转录因子模块的识别）可扩展到更大的数据集，使其适用于人类的未来研究，据认为人类有大量得多的转录因子 - 靶标相互作用。
Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF–target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which “standard conditions” are ill defined. Transcription factors (TFs) bind close to their target genes for regulating transcript levels depending on cellular conditions. Each gene may be regulated differently from others through the binding of specific groups of TFs (TF modules). Recently, a wide variety of large-scale measurements about transcriptional networks has become available. Here the authors present a framework for consistently integrating all of this evidence to systematically determine the precise set of genes directly regulated by each TF (i.e., TF–target interactions). The framework is applied to the yeast Saccharomyces cerevisiae using seven distinct sources of evidences to score all possible TF–target interactions in this organism. Subsequently, the authors employ another newly developed algorithm to reveal TF modules based on the top 5,000 TF–target interactions, yielding more than 300 TF modules. The new scoring scheme for TF–target interactions allows predicting the binding of TFs under so-far untested conditions, which is demonstrated by experimentally verifying interactions for two TFs (Pdr1p, Rpn4p). Importantly, the new methods (scoring of TF–target interactions and TF module identification) are scalable to much larger datasets, making them applicable to future studies in humans, which are thought to have substantially larger numbers of TF–target interactions.
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DOI：
10.1073/pnas.0402591101
发表时间：
2004-06-15
影响因子：
11.1
作者：
Fraser, HB;Hirsh, AE;Eisen, MB
通讯作者：
Eisen, MB
DOI：
10.1073/pnas.0407365101
发表时间：
2004-11-16
影响因子：
11.1
作者：
Das, D;Banerjee, N;Zhang, MQ
通讯作者：
Zhang, MQ
影响因子：
5.8
作者：
Leyfer, D;Weng, ZP
通讯作者：
Weng, ZP
DOI：
10.1016/0005-2795(75)90109-9
发表时间：
1975-01-01
期刊：
BIOCHIMICA ET BIOPHYSICA ACTA
影响因子：
--
作者：
MATTHEWS, BW
通讯作者：
MATTHEWS, BW
影响因子：
14.9
作者：
Garten Y;Kaplan S;Pilpel Y
通讯作者：
Pilpel Y