Comparative annotation of functional regions in the human genome using epigenomic data

TY - JOUR

T1 - Comparative annotation of functional regions in the human genome using epigenomic data

AU - Won, Kyoung-Jae

AU - Zhang, Xian

AU - Wang, Tao

AU - Ding, Bo

AU - Raha, Debasish

AU - Snyder, Michael

AU - Ren, Bing

AU - Wang, Wei

PY - 2013/4

Y1 - 2013/4

N2 - Epigenetic regulation is dynamic and cell-type dependent. The recently available epigenomic data in multiple cell types provide an unprecedented opportunity for a comparative study of epigenetic landscape. We developed a machine-learning method called ChroModule to annotate the epigenetic states in eight ENCyclopedia Of DNA Elements cell types. The trained model successfully captured the characteristic histone-modification patterns associated with regulatory elements, such as promoters and enhancers, and showed superior performance on identifying enhancers compared with the state-of-art methods. In addition, given the fixed number of epigenetic states in the model, ChroModule allows straightforward illustration of epigenetic variability in multiple cell types. Using this feature, we found that invariable and variable epigenetic states across cell types correspond to housekeeping functions and stimulus response, respectively. Especially, we observed that enhancers, but not the other regulatory elements, dictate cell specificity, as similar cell types share common enhancers, and cell-type-specific enhancers are often bound by transcription factors playing critical roles in that cell type. More interestingly, we found some genomic regions are dormant in cell type but primed to become active in other cell types. These observations highlight the usefulness of ChroModule in comparative analysis and interpretation of multiple epigenomes.

AB - Epigenetic regulation is dynamic and cell-type dependent. The recently available epigenomic data in multiple cell types provide an unprecedented opportunity for a comparative study of epigenetic landscape. We developed a machine-learning method called ChroModule to annotate the epigenetic states in eight ENCyclopedia Of DNA Elements cell types. The trained model successfully captured the characteristic histone-modification patterns associated with regulatory elements, such as promoters and enhancers, and showed superior performance on identifying enhancers compared with the state-of-art methods. In addition, given the fixed number of epigenetic states in the model, ChroModule allows straightforward illustration of epigenetic variability in multiple cell types. Using this feature, we found that invariable and variable epigenetic states across cell types correspond to housekeeping functions and stimulus response, respectively. Especially, we observed that enhancers, but not the other regulatory elements, dictate cell specificity, as similar cell types share common enhancers, and cell-type-specific enhancers are often bound by transcription factors playing critical roles in that cell type. More interestingly, we found some genomic regions are dormant in cell type but primed to become active in other cell types. These observations highlight the usefulness of ChroModule in comparative analysis and interpretation of multiple epigenomes.

KW - Artificial Intelligence

KW - Binding Sites

KW - Enhancer Elements, Genetic

KW - Epigenesis, Genetic

KW - Genome, Human

KW - Humans

KW - Molecular Sequence Annotation

KW - Promoter Regions, Genetic

KW - Transcription Factors/metabolism

U2 - 10.1093/nar/gkt143

DO - 10.1093/nar/gkt143

M3 - Journal article

C2 - 23482391

SN - 0305-1048

VL - 41

SP - 4423

EP - 4432

JO - Nucleic Acids Research

JF - Nucleic Acids Research

IS - 8

ER -