DataSheet_2_epiCOLOC: Integrating Large-Scale and Context-Dependent Epigenomics Features for Comprehensive Colocalization Analysis.xlsx

High-throughput genome-wide epigenomic assays, such as ChIP-seq, DNase-seq and ATAC-seq, have profiled a huge number of functional elements across numerous human tissues/cell types, which provide an unprecedented opportunity to interpret human genome and disease in context-dependent manner. Colocalization analysis determines whether genomic features are functionally related to a given search and will facilitate identifying the underlying biological functions characterizing intricate relationships with queries for genomic regions. Existing colocalization methods leveraged diverse assumptions and background models to assess the significance of enrichment, however, they only provided limited and predefined sets of epigenomic features. Here, we comprehensively collected and integrated over 44,385 bulk or single-cell epigenomic assays across 53 human tissues/cell types, such as transcription factor binding, histone modification, open chromatin and transcriptional event. By classifying these profiles into hierarchy of tissue/cell type, we developed a web portal, epiCOLOC (http://mulinlab.org/epicoloc or http://mulinlab.tmu.edu.cn/epicoloc), for users to perform context-dependent colocalization analysis in a convenient way.

高通量全基因组表观基因组检测，如染色质免疫共沉淀测序（ChIP-seq）、脱氧核糖核酸酶I超敏位点测序（DNase-seq）及转座酶可及性测序（ATAC-seq），已在大量人类组织/细胞类型中绘制了海量功能元件图谱，为以情境依赖方式解析人类基因组与疾病提供了前所未有的契机。共定位分析可用于判断基因组特征与给定查询是否存在功能关联，有助于挖掘刻画与基因组区域查询对象间复杂关联的潜在生物学功能。现有共定位方法采用多种假设与背景模型评估富集显著性，但仅能提供有限且预定义的表观基因组特征集合。本研究全面收集并整合了覆盖53种人类组织/细胞类型的超过44385项批量或单细胞表观基因组检测数据，涵盖转录因子结合、组蛋白修饰、开放染色质及转录事件等类型。通过将这些图谱按组织/细胞类型层级进行分类，本研究开发了一款名为epiCOLOC的网络门户（http://mulinlab.org/epicoloc 或 http://mulinlab.tmu.edu.cn/epicoloc），方便用户便捷开展情境依赖式共定位分析。