Discover context-specific combinatorial transcription factor interactions by integrating diverse ChIP-Seq data sets.
Discover context-specific combinatorial transcription factor interactions by integrating diverse ChIP-Seq data sets.
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DOI：
10.1093/nar/gkt1105
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发表时间：
2014-02
影响因子：
14.9
通讯作者：
Tan K
中科院分区：
文献类型：
--
作者：
Teng L;He B;Gao P;Gao L;Tan K
转录因子之间的组合相互作用对于整合不同的内在和外在信号、微调调控输出以及增加调控系统的鲁棒性和可塑性至关重要。由于缺乏合适的实验技术和生物信息学工具，目前关于组合调控的知识相当有限。ChIP-Seq数据的快速积累为大量TF和染色质修饰标记提供了全基因组占用图，用于在不知道单个TF结合位点的情况下识别增强子。尚未对这两种数据类型的整合进行广泛研究，导致数据使用不足和错失机会。我们描述了一种新的方法，发现频繁的组合占用模式的多个TF在增强子。我们的方法是基于概率项目集挖掘，并考虑到这两种类型的ChIP-Seq数据的不确定性。通过对四种人类细胞类型中的108个TF的联合分析，我们发现TF之间的细胞类型特异性相互作用是丰富的，并且大多数增强子具有灵活的结构。我们发现，几个家庭的转座因子与组合模式的增强子重叠，这表明这些转座因子家族在组合调控的进化中起着重要的作用。
Combinatorial interactions among transcription factors (TFs) are critical for integrating diverse intrinsic and extrinsic signals, fine-tuning regulatory output and increasing the robustness and plasticity of regulatory systems. Current knowledge about combinatorial regulation is rather limited due to the lack of suitable experimental technologies and bioinformatics tools. The rapid accumulation of ChIP-Seq data has provided genome-wide occupancy maps for a large number of TFs and chromatin modification marks for identifying enhancers without knowing individual TF binding sites. Integration of the two data types has not been researched extensively, resulting in underused data and missed opportunities. We describe a novel method for discovering frequent combinatorial occupancy patterns by multiple TFs at enhancers. Our method is based on probabilistic item set mining and takes into account uncertainty in both types of ChIP-Seq data. By joint analysis of 108 TFs in four human cell types, we found that cell–type-specific interactions among TFs are abundant and that the majority of enhancers have flexible architecture. We show that several families of transposable elements disproportionally overlap with enhancers with combinatorial patterns, suggesting that these transposable element families play an important role in the evolution of combinatorial regulation.
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影响因子：
64.8
作者：
Ernst, Jason;Kheradpour, Pouya;Mikkelsen, Tarjei S.;Shoresh, Noam;Ward, Lucas D.;Epstein, Charles B.;Zhang, Xiaolan;Wang, Li;Issner, Robbyn;Coyne, Michael;Ku, Manching;Durham, Timothy;Kellis, Manolis;Bernstein, Bradley E.
通讯作者：
Bernstein, Bradley E.
影响因子：
30.8
作者：
Kunarso, Galih;Chia, Na-Yu;Bourque, Guillaume
通讯作者：
Bourque, Guillaume
影响因子：
4.6
作者：
Kulkarni, MM;Arnosti, DN
通讯作者：
Arnosti, DN
影响因子：
14.9
作者：
Franceschini A;Szklarczyk D;Frankild S;Kuhn M;Simonovic M;Roth A;Lin J;Minguez P;Bork P;von Mering C;Jensen LJ
通讯作者：
Jensen LJ
影响因子：
30.8
作者：
Chuong, Edward B.;Rumi, M. A. Karim;Soares, Michael J.;Baker, Julie C.
通讯作者：
Baker, Julie C.