Zhou Yao, Sun Yongzheng, Huang Dandan, Li Mulin Jun
Department of Pharmacology, Tianjin Key Laboratory of Inflammation Biology, School of Basic Medical Sciences, National Clinical Research Center for Cancer, Tianjin Medical University Cancer Institute and Hospital, Tianjin Medical University, Tianjin, China.
Collaborative Innovation Center of Tianjin for Medical Epigenetics, Tianjin Key Laboratory of Medical Epigenetics, Tianjin Medical University, Tianjin, China.
Front Genet. 2020 Feb 12;11:53. doi: 10.3389/fgene.2020.00053. eCollection 2020.
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分析，如ChIP-seq、DNase-seq和ATAC-seq，已对众多人类组织/细胞类型中的大量功能元件进行了分析，这为以依赖于上下文的方式解释人类基因组和疾病提供了前所未有的机会。共定位分析确定基因组特征是否与给定搜索在功能上相关，并将有助于识别与基因组区域查询的复杂关系相关的潜在生物学功能。现有的共定位方法利用了不同的假设和背景模型来评估富集的显著性，然而，它们只提供了有限的和预定义的表观基因组特征集。在这里，我们全面收集并整合了跨越53种人类组织/细胞类型的44,385多个批量或单细胞表观基因组分析，如转录因子结合、组蛋白修饰、开放染色质和转录事件。通过将这些图谱分类到组织/细胞类型的层次结构中，我们开发了一个门户网站epiCOLOC（http://mulinlab.org/epicoloc或http://mulinlab.tmu.edu.cn/epicoloc），供用户以方便的方式进行依赖于上下文的共定位分析。
