Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
Genome-wide signatures of transcription factor activity: connecting transcription factors, disease, and small molecules.
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DOI：
10.1371/journal.pcbi.1003198
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发表时间：
2013
影响因子：
4.3
通讯作者：
Medvedovic M
中科院分区：
文献类型：
--
作者：
Chen J;Hu Z;Phatak M;Reichard J;Freudenberg JM;Sivaganesan S;Medvedovic M
鉴定参与产生全基因组转录谱的转录因子（TF）是建立可以解释观察到的基因表达数据的机制模型的必要步骤。我们开发了一个统计框架，用于构建TF活性的全基因组特征，并用于分析由复杂转录调控程序产生的基因表达数据。我们的框架集成了ChIP-seq数据和适当匹配的基因表达谱，以确定真正的调节（TREG） tf基因相互作用。它提供了调节TF基因相互作用可能性的全基因组量化，可用于识别调节基因，或作为TF活性的全基因组标记。为了有效地利用ChIP-seq数据，我们引入了一种新的统计模型，该模型集成了基因转录起始位点（TSS）周围2 Mb窗口内所有结合“峰”的信息，并提供了基因水平的结合评分和调控相互作用的概率。在第二步中，我们将这些结合得分和调控概率与基因表达数据相结合，以评估真正调控（TREG） tf -基因相互作用的可能性。我们展示了TREG框架在识别两种功能结合事件分布差异很大的tf （ERα和E2f1）调控基因方面的优势。我们还表明，TF活性的TREG特征极大地提高了我们检测ERα参与产生复杂疾病相关转录谱的能力。通过对疾病相关转录特征和药物活性转录特征的大量研究，我们证明与TREG特征的使用相关的统计能力的增加在确定治疗的关键靶点和用于治疗的药物方面具有至关重要的差异。所有的方法都是在一个开源的R包treg中实现的。该包还包含分析中使用的所有数据，包括基于ENCODE ChIP-seq数据的494个TREG绑定配置文件。treg包可以从http://GenomicsPortals.org下载。了解调节差异表达基因表达的转录因子（TF）对于理解导致基因表达变化的信号级联和调控机制至关重要。我们开发了构建基因水平评分（TREG结合评分）的方法，基于所有基因的ChIP-seq数据（TREG结合谱）的生成统计模型来测量基因被调控的可能性。我们还开发了将TREG结合分数与适当匹配的基因表达数据相结合的方法，以创建TF活性的TREG特征。然后，我们使用TREG结合谱和TREG签名来识别参与疾病相关基因表达谱的tf。本研究的两个主要发现是：1)从ChIP-seq数据中得出的TREG结合评分比简单的替代方法更具有信息性，可用于总结ChIP-seq数据；2)结合结合和基因表达数据的TREG特征在检测TF调控活性证据方面比常用的替代方法更敏感。我们表明，TREG特征的这一优势可以区分在复杂转录谱中是否能够推断TF的调控活性。这种增加的敏感性对于建立疾病和药物特征之间的联系至关重要。
Identifying transcription factors (TF) involved in producing a genome-wide transcriptional profile is an essential step in building mechanistic model that can explain observed gene expression data. We developed a statistical framework for constructing genome-wide signatures of TF activity, and for using such signatures in the analysis of gene expression data produced by complex transcriptional regulatory programs. Our framework integrates ChIP-seq data and appropriately matched gene expression profiles to identify True REGulatory (TREG) TF-gene interactions. It provides genome-wide quantification of the likelihood of regulatory TF-gene interaction that can be used to either identify regulated genes, or as genome-wide signature of TF activity. To effectively use ChIP-seq data, we introduce a novel statistical model that integrates information from all binding “peaks” within 2 Mb window around a gene's transcription start site (TSS), and provides gene-level binding scores and probabilities of regulatory interaction. In the second step we integrate these binding scores and regulatory probabilities with gene expression data to assess the likelihood of True REGulatory (TREG) TF-gene interactions. We demonstrate the advantages of TREG framework in identifying genes regulated by two TFs with widely different distribution of functional binding events (ERα and E2f1). We also show that TREG signatures of TF activity vastly improve our ability to detect involvement of ERα in producing complex diseases-related transcriptional profiles. Through a large study of disease-related transcriptional signatures and transcriptional signatures of drug activity, we demonstrate that increase in statistical power associated with the use of TREG signatures makes the crucial difference in identifying key targets for treatment, and drugs to use for treatment. All methods are implemented in an open-source R package treg. The package also contains all data used in the analysis including 494 TREG binding profiles based on ENCODE ChIP-seq data. The treg package can be downloaded at http://GenomicsPortals.org. Knowing transcription factors (TF) that regulate expression of differentially expressed genes is essential for understanding signaling cascades and regulatory mechanisms that lead to changes in gene expression. We developed methods for constructing gene-level scores (TREG binding scores) measuring likelihood that the gene is regulated based on the generative statistical model of ChIP-seq data for all genes (TREG binding profile). We also developed methods for integrating TREG binding scores with appropriately matched gene expression data to create TREG signatures of the TF activity. We then use TREG binding profiles and TREG signatures to identify TFs involved in the disease-related gene expression profiles. Two main findings of our study are: 1) TREG binding scores derived from ChIP-seq data are more informative than simple alternatives that can be used to summarize ChIP-seq data; and 2) TREG signatures that integrate the binding and gene expression data are more sensitive in detecting evidence of TF regulatory activity than commonly used alternatives. We show that this advantage of TREG signatures can make the difference between being able and not being able to infer TF regulatory activity in complex transcriptional profiles. This increased sensitivity was critically important in establishing connections between disease and drug signatures.
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影响因子：
46.9
作者：
Ji, Hongkai;Jiang, Hui;Ma, Wenxiu;Johnson, David S.;Myers, Richard M.;Wong, Wing H.
通讯作者：
Wong, Wing H.
DOI：
10.1006/bbrc.2001.5819
发表时间：
2001-11-02
影响因子：
3.1
作者：
Adhami, VM;Afaq, F;Ahmad, N
通讯作者：
Ahmad, N
影响因子：
5.8
作者：
Freudenberg, Johannes M.;Sivaganesan, Siva;Medvedovic, Mario
通讯作者：
Medvedovic, Mario
影响因子：
4
作者：
McDonnell, Donald P.;Wardell, Suzanne E.
通讯作者：
Wardell, Suzanne E.
影响因子：
11.2
作者：
Dai, HY;van't Veer, L;Friend, S
通讯作者：
Friend, S