Guo Yuchun, Gifford David K
MIT, Computer Science and Artificial Intelligence Laboratory, Cambridge, MA, 02139, USA.
BMC Genomics. 2017 Jan 6;18(1):45. doi: 10.1186/s12864-016-3434-3.
The combinatorial binding of trans-acting factors (TFs) to the DNA is critical to the spatial and temporal specificity of gene regulation. For certain regulatory regions, more than one regulatory module (set of TFs that bind together) are combined to achieve context-specific gene regulation. However, previous approaches are limited to either pairwise TF co-association analysis or assuming that only one module is used in each regulatory region.
We present a new computational approach that models the modular organization of TF combinatorial binding. Our method learns compact and coherent regulatory modules from in vivo binding data using a topic model. We found that the binding of 115 TFs in K562 cells can be organized into 49 interpretable modules. Furthermore, we found that tens of thousands of regulatory regions use multiple modules, a structure that cannot be observed with previous hard clustering based methods. The modules discovered recapitulate many published protein-protein physical interactions, have consistent functional annotations of chromatin states, and uncover context specific co-binding such as gene proximal binding of NFY + FOS + SP and distal binding of NFY + FOS + USF. For certain TFs, the co-binding partners of direct binding (motif present) differs from those of indirect binding (motif absent); the distinct set of co-binding partners can predict whether the TF binds directly or indirectly with up to 95% accuracy. Joint analysis across two cell types reveals both cell-type-specific and shared regulatory modules.
Our results provide comprehensive cell-type-specific combinatorial binding maps and suggest a modular organization of combinatorial binding.
反式作用因子（TFs）与DNA的组合结合对于基因调控的时空特异性至关重要。对于某些调控区域，多个调控模块（结合在一起的TFs集合）组合在一起以实现上下文特异性的基因调控。然而，先前的方法仅限于成对的TF共关联分析，或者假设每个调控区域仅使用一个模块。
我们提出了一种新的计算方法，该方法对TF组合结合的模块化组织进行建模。我们的方法使用主题模型从体内结合数据中学习紧凑且连贯的调控模块。我们发现K562细胞中115个TFs的结合可以组织成49个可解释的模块。此外，我们发现数以万计的调控区域使用多个模块，这种结构是以前基于硬聚类的方法无法观察到的。发现的模块概括了许多已发表的蛋白质-蛋白质物理相互作用，具有一致的染色质状态功能注释，并揭示了上下文特异性共结合，例如NFY+FOS+SP的基因近端结合和NFY+FOS+USF的远端结合。对于某些TFs，直接结合（存在基序）的共结合伙伴与间接结合（不存在基序）的共结合伙伴不同；不同的共结合伙伴集可以以高达95%的准确率预测TF是直接结合还是间接结合。对两种细胞类型的联合分析揭示了细胞类型特异性和共享的调控模块。
我们的结果提供了全面的细胞类型特异性组合结合图谱，并表明了组合结合的模块化组织。
