Table_3_Integrating Peak Colocalization and Motif Enrichment Analysis for the Discovery of Genome-Wide Regulatory Modules and Transcription Factor Recruitment Rules.xlsx

Chromatin immunoprecipitation followed by next-generation sequencing (ChIP-Seq) has opened new avenues of research in the genome-wide characterization of regulatory DNA-protein interactions at the genetic and epigenetic level. As a consequence, it has become the de facto standard for studies on the regulation of transcription, and literally thousands of data sets for transcription factors and cofactors in different conditions and species are now available to the scientific community. However, while pipelines and best practices have been established for the analysis of a single experiment, there is still no consensus on the best way to perform an integrated analysis of multiple datasets in the same condition, in order to identify the most relevant and widespread regulatory modules composed by different transcription factors and cofactors. We present here a computational pipeline for this task, that integrates peak summit colocalization, a novel statistical framework for the evaluation of its significance, and motif enrichment analysis. We show examples of its application to ENCODE data, that led to the identification of relevant regulatory modules composed of different factors, as well as the organization on DNA of the binding motifs responsible for their recruitment.

染色质免疫共沉淀测序（ChIP-Seq）为在遗传与表观遗传层面开展全基因组范围的调控性DNA-蛋白质相互作用特征解析开辟了全新路径。因此，该技术已成为转录调控研究的事实上的标准，目前科学界已拥有数千份来自不同物种、不同实验条件下的转录因子与辅因子数据集。然而，尽管针对单个实验的分析流程与最佳实践已趋于完善，但针对同一实验条件下的多数据集开展整合分析、以鉴定由不同转录因子与辅因子构成的最具相关性与普遍性的调控模块的最优方案，目前仍未达成共识。为此，我们提出一款面向该任务的计算分析流程，其整合了峰位点共定位分析、用于评估其显著性的全新统计框架，以及基序富集分析三个核心模块。我们通过将该流程应用于ENCODE数据集的实例展示了其有效性，该应用成功鉴定出了由不同转录因子构成的相关调控模块，以及负责招募这些因子的DNA结合基序在基因组上的组织形式。