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Discovery of cell-type specific regulatory elements in the human genome using differential chromatin modification analysis.

Chen C, Zhang S, Zhang XS - Nucleic Acids Res. (2013)

Bottom Line: We found cell-type-specific elements unique to each cell type investigated.These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements.These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.

View Article: PubMed Central - PubMed

Affiliation: National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

ABSTRACT
Chromatin modifications have been comprehensively illustrated to play important roles in gene regulation and cell diversity in recent years. Given the rapid accumulation of genome-wide chromatin modification maps across multiple cell types, there is an urgent need for computational methods to analyze multiple maps to reveal combinatorial modification patterns and define functional DNA elements, especially those are specific to cell types or tissues. In this current study, we developed a computational method using differential chromatin modification analysis (dCMA) to identify cell-type-specific genomic regions with distinctive chromatin modifications. We then apply this method to a public data set with modification profiles of nine marks for nine cell types to evaluate its effectiveness. We found cell-type-specific elements unique to each cell type investigated. These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements. These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.

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Functional relevance and cell-type specificity of CSREs. (A) The proportion of CSREs belonging to single, two or more cell types. (B) Overlaps of CSREs between each pair of cell types. The values in the diagonal correspond to the number of identified CSREs in nine cell types and the value in row i column j records the number of CSREs in cell type i overlapped by those in cell type j. (C) CSRE neighboring genes tended to be more significantly connected than was expected, with  indicated by (asterisk). (D) CSRE neighboring genes are more likely to be non-housekeeping genes with the exception of those in K562 and GM12878. Dashed vertical line on the left side represent the P-value threshold (). (E) CSRE neighboring genes show distinct functional enrichments highly relevant to the corresponding cell type contexts. We chose the top five enriched GO terms in each cell type, and  was used to generate the heat map. (F) Enriched phenotypes of SNPs located in the CSREs in three cell types.
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gkt712-F3: Functional relevance and cell-type specificity of CSREs. (A) The proportion of CSREs belonging to single, two or more cell types. (B) Overlaps of CSREs between each pair of cell types. The values in the diagonal correspond to the number of identified CSREs in nine cell types and the value in row i column j records the number of CSREs in cell type i overlapped by those in cell type j. (C) CSRE neighboring genes tended to be more significantly connected than was expected, with indicated by (asterisk). (D) CSRE neighboring genes are more likely to be non-housekeeping genes with the exception of those in K562 and GM12878. Dashed vertical line on the left side represent the P-value threshold (). (E) CSRE neighboring genes show distinct functional enrichments highly relevant to the corresponding cell type contexts. We chose the top five enriched GO terms in each cell type, and was used to generate the heat map. (F) Enriched phenotypes of SNPs located in the CSREs in three cell types.

Mentions: Gene Ontology (GO) terms enrichment analysis was performed using STEM (27) where Fisher’s exact test was used and the Bonferroni corrected q-values were reported. For each cell type, the top five enriched GO terms associating 5–500 genes were selected (Figure 3E). A human protein–protein interaction network consisting of 13 207 proteins and 64 549 interactions was downloaded from the BioGRID website (28). For each cell type, a sub-network was determined by mapping CSRE neighboring genes to the protein–protein interaction, and we let m be the number of interactions observed therein. The expected number of interactions in the sub-network was , where N and n are the numbers of nodes in the whole network and the sub-network, respectively, and M is the number of observed interactions in the whole network. The fold enrichment was . The statistical significance of the fold enrichment was calculated using right-sided Fisher’s exact test.


Discovery of cell-type specific regulatory elements in the human genome using differential chromatin modification analysis.

Chen C, Zhang S, Zhang XS - Nucleic Acids Res. (2013)

Functional relevance and cell-type specificity of CSREs. (A) The proportion of CSREs belonging to single, two or more cell types. (B) Overlaps of CSREs between each pair of cell types. The values in the diagonal correspond to the number of identified CSREs in nine cell types and the value in row i column j records the number of CSREs in cell type i overlapped by those in cell type j. (C) CSRE neighboring genes tended to be more significantly connected than was expected, with  indicated by (asterisk). (D) CSRE neighboring genes are more likely to be non-housekeeping genes with the exception of those in K562 and GM12878. Dashed vertical line on the left side represent the P-value threshold (). (E) CSRE neighboring genes show distinct functional enrichments highly relevant to the corresponding cell type contexts. We chose the top five enriched GO terms in each cell type, and  was used to generate the heat map. (F) Enriched phenotypes of SNPs located in the CSREs in three cell types.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3814353&req=5

gkt712-F3: Functional relevance and cell-type specificity of CSREs. (A) The proportion of CSREs belonging to single, two or more cell types. (B) Overlaps of CSREs between each pair of cell types. The values in the diagonal correspond to the number of identified CSREs in nine cell types and the value in row i column j records the number of CSREs in cell type i overlapped by those in cell type j. (C) CSRE neighboring genes tended to be more significantly connected than was expected, with indicated by (asterisk). (D) CSRE neighboring genes are more likely to be non-housekeeping genes with the exception of those in K562 and GM12878. Dashed vertical line on the left side represent the P-value threshold (). (E) CSRE neighboring genes show distinct functional enrichments highly relevant to the corresponding cell type contexts. We chose the top five enriched GO terms in each cell type, and was used to generate the heat map. (F) Enriched phenotypes of SNPs located in the CSREs in three cell types.
Mentions: Gene Ontology (GO) terms enrichment analysis was performed using STEM (27) where Fisher’s exact test was used and the Bonferroni corrected q-values were reported. For each cell type, the top five enriched GO terms associating 5–500 genes were selected (Figure 3E). A human protein–protein interaction network consisting of 13 207 proteins and 64 549 interactions was downloaded from the BioGRID website (28). For each cell type, a sub-network was determined by mapping CSRE neighboring genes to the protein–protein interaction, and we let m be the number of interactions observed therein. The expected number of interactions in the sub-network was , where N and n are the numbers of nodes in the whole network and the sub-network, respectively, and M is the number of observed interactions in the whole network. The fold enrichment was . The statistical significance of the fold enrichment was calculated using right-sided Fisher’s exact test.

Bottom Line: We found cell-type-specific elements unique to each cell type investigated.These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements.These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.

View Article: PubMed Central - PubMed

Affiliation: National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

ABSTRACT
Chromatin modifications have been comprehensively illustrated to play important roles in gene regulation and cell diversity in recent years. Given the rapid accumulation of genome-wide chromatin modification maps across multiple cell types, there is an urgent need for computational methods to analyze multiple maps to reveal combinatorial modification patterns and define functional DNA elements, especially those are specific to cell types or tissues. In this current study, we developed a computational method using differential chromatin modification analysis (dCMA) to identify cell-type-specific genomic regions with distinctive chromatin modifications. We then apply this method to a public data set with modification profiles of nine marks for nine cell types to evaluate its effectiveness. We found cell-type-specific elements unique to each cell type investigated. These unique features show significant cell-type-specific biological relevance and tend to be located within functional regulatory elements. These results demonstrate the power of a differential comparative epigenomic strategy in deciphering the human genome and characterizing cell specificity.

Show MeSH
Related in: MedlinePlus