Abstract
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.
Highlights
The epigenome, beyond genome sequence, has been increasingly recognized as key component in the gene regulation to drive certain biological processes and associate with many human diseases (Lawrence et al, 2016; Dor and Cedar, 2018; Feinberg, 2018)
Colocalization analysis was frequently used to study the interplay of various functional elements in different biological processes and conditions, where potential enrichment of a given genomic/epigenomic profile in pre-defined dataset could be drawn from the global perspective (Kanduri et al, 2019)
Integrated with large-scale tissue/cell type-specific epigenomics data, colocalization analysis provides a powerful avenue to investigate biological relations and cell type specificities, such as identifying co-occurrence of transcription regulators (Yan et al, 2013) and inferring causal tissues/cell types from disease-associated variants identified by genome-wide association study (GWAS) (Farh et al, 2015)
Summary
The epigenome, beyond genome sequence, has been increasingly recognized as key component in the gene regulation to drive certain biological processes and associate with many human diseases (Lawrence et al, 2016; Dor and Cedar, 2018; Feinberg, 2018). The International Human Epigenome Consortium (IHEC) project (Bujold et al, 2016) have been initialized, across different countries and consortiums, to coordinate the production of reference maps of human epigenomes for key cellular states relevant to health and diseases These unprecedented growths of epigenetic profiles and following comprehensive analysis of tissue/cell type-specific epigenomes will lead. Comprehensive epigenomics accumulation has motivated novel computational methods of modelling functional elements across many tissues/ cell types, such as ChromHMM (Roadmap Epigenomics et al, 2015) and Segway (Libbrecht et al, 2019) Integrating such large-scale and context-dependent epigenomics features for novel biological findings is in urgent demand (Dozmorov, 2017; Cazaly et al, 2019). Integrated with large-scale tissue/cell type-specific epigenomics data, colocalization analysis provides a powerful avenue to investigate biological relations and cell type specificities, such as identifying co-occurrence of transcription regulators (Yan et al, 2013) and inferring causal tissues/cell types from disease-associated variants identified by genome-wide association study (GWAS) (Farh et al, 2015)
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