High Resolution Genome Wide Binding Event Finding and Motif Discovery Reveals Transcription Factor Spatial Binding Constraints

An essential component of genome function is the syntax of genomic regulatory elements that determine how diverse transcription factors interact to orchestrate a program of regulatory control. A precise characterization of in vivo spacing constraints between key transcription factors would reveal key aspects of this genomic regulatory language. To discover novel transcription factor spatial binding constraints in vivo, we developed a new integrative computational method, genome wide event finding and motif discovery (GEM). GEM resolves ChIP data into explanatory motifs and binding events at high spatial resolution by linking binding event discovery and motif discovery with positional priors in the context of a generative probabilistic model of ChIP data and genome sequence. GEM analysis of 63 transcription factors in 214 ENCODE human ChIP-Seq experiments recovers more known factor motifs than other contemporary methods, and discovers six new motifs for factors with unknown binding specificity. GEM's adaptive learning of binding-event read distributions allows it to further improve upon previous methods for processing ChIP-Seq and ChIP-exo data to yield unsurpassed spatial resolution and discovery of closely spaced binding events of the same factor. In a systematic analysis of in vivo sequence-specific transcription factor binding using GEM, we have found hundreds of spatial binding constraints between factors. GEM found 37 examples of factor binding constraints in mouse ES cells, including strong distance-specific constraints between Klf4 and other key regulatory factors. In human ENCODE data, GEM found 390 examples of spatially constrained pair-wise binding, including such novel pairs as c-Fos:c-Jun/USF1, CTCF/Egr1, and HNF4A/FOXA1. The discovery of new factor-factor spatial constraints in ChIP data is significant because it proposes testable models for regulatory factor interactions that will help elucidate genome function and the implementation of combinatorial control.基因组功能的核心组成部分，在于基因组调控元件的语法逻辑：其决定了各类转录因子如何相互作用，协同执行基因组的调控程序。精准解析关键转录因子之间的体内间隔约束，将揭示该基因组调控语言的核心内涵。为了在体内发掘全新的转录因子空间结合约束，我们开发了一种全新的整合型计算方法——全基因组事件识别与基序发现（Genome Wide Event Finding and Motif Discovery, GEM）。GEM通过将结合事件识别与基序发现，与基于ChIP数据与基因组序列的生成式概率模型中的位置先验信息进行关联，能够以高空间分辨率将ChIP数据拆解为具有解释性的基序与结合事件。在DNA元件百科全书（Encyclopedia of DNA Elements, ENCODE）计划的214组人类染色质免疫沉淀测序（ChIP-Seq）实验数据中，针对63种转录因子的GEM分析，相较于其他当代方法，能够识别出更多已知的转录因子基序，同时为结合特异性未知的因子发现了6种全新的基序。GEM对结合事件读段分布的自适应学习能力，使其在处理ChIP-Seq与染色质免疫沉淀外切酶测序（ChIP-exo）数据时，能够进一步优化既往方法，实现了无与伦比的空间分辨率，同时可识别同一因子的紧密相邻结合事件。借助GEM对体内序列特异性转录因子结合进行的系统性分析，我们已发现了数百个转录因子间的空间结合约束。GEM在小鼠胚胎干细胞中发现了37例转录因子结合约束实例，包括Klf4与其他关键调控因子之间存在的强距离特异性约束。在人类ENCODE数据中，GEM发现了390例具有空间约束的成对结合实例，包括c-Fos:c-Jun/USF1、CTCF/Egr1以及HNF4A/FOXA1等全新的转录因子对。在ChIP数据中发现全新的转录因子间空间约束具有重要意义，因为其提出了可验证的调控因子相互作用模型，将有助于阐明基因组功能以及组合调控的实现机制。