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Epigenome overlap measure (EPOM) for comparing tissue/cell types based on chromatin states
Wei Vivian Li1, Zahra S Razaee2, Jingyi Jessica Li3,4
1Department of Statistics, 8125 Math Sciences Bldg., University of California, Los Angeles, CA, 90095-1554, USA. liw@ucla.edu.
BMC Genomics
|January 29, 2016
Summary
A new Epigenomic Overlap Measure (EPOM) effectively distinguishes and groups diverse tissue and cell types using epigenomic data. This method aids in identifying cell-type-specific disease variants and understanding gene regulation.
Area of Science:
- Epigenomics
- Genomics
- Computational Biology
Background:
- Epigenomic marks regulate gene expression and phenotypic variation.
- High-throughput sequencing generates vast epigenomic data for comparative analysis.
- Existing methods struggle to accurately distinguish and group diverse tissue and cell types based on epigenomic features.
Purpose of the Study:
- To develop a novel method for analyzing epigenomic data to accurately group and distinguish tissue and cell types.
- To identify tissue/cell-type-associated enhancers and promoters.
- To explore the potential of identified elements in understanding cell-type-specific diseases.
Main Methods:
- Utilized a three-step testing procedure: ANOVA, t-test, and overlap test.
- Calculated a new Epigenomic Overlap Measure (EPOM).
- EPOM generates a correspondence map of biological samples based on epigenomic mark comparisons.
Main Results:
- EPOM successfully distinguished and grouped various tissue and cell types, revealing biologically meaningful similarities (e.g., Heart/Muscle, Blood/T-cell).
- Gene ontology enrichment analysis supported EPOM findings, indicating distinct functions for associated enhancers and promoters.
- Identified enhancers and promoters were enriched in disease-related SNPs, suggesting potential for identifying causal variants in cell-type-specific diseases.
Conclusions:
- The EPOM measure effectively groups biological samples and creates clear correspondence maps.
- The identified enhancers and promoters offer a catalog for studying biological processes and disease variants.
- Associated promoters showed more cell-type-specific functions than enhancers, while non-associated elements exhibited more housekeeping functions.
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