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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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Illustration of the framework used in identifying CSREs. (A) The data profiles of nine cell types as characterized by nine marks and one control. The raw ChIP-seq reads were mapped to 200 bp bins and the signals were binarized using a Poisson  model. (B) For each bin, the dissimilarity of the resulting binary vectors between two different cell types was measured using hamming distance. For each cell type, the DMS of a bin was the summation of pairwise hamming distance (sPHD) computed between it and other cell types. (C) The DMS profile of each cell type was normalized across the genome. Then, each column of the matrix was multiplied by the corresponding Z-scores to consider the variance in the column. (D) Wavelets smoothing strategy was adopted to smooth the resulting differential profile of each cell type. CSREs were extracted by selecting suitable height and length parameters. Statistical significance (P-value) of each CSRE was calculated by a non-parametric test.
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gkt712-F1: Illustration of the framework used in identifying CSREs. (A) The data profiles of nine cell types as characterized by nine marks and one control. The raw ChIP-seq reads were mapped to 200 bp bins and the signals were binarized using a Poisson model. (B) For each bin, the dissimilarity of the resulting binary vectors between two different cell types was measured using hamming distance. For each cell type, the DMS of a bin was the summation of pairwise hamming distance (sPHD) computed between it and other cell types. (C) The DMS profile of each cell type was normalized across the genome. Then, each column of the matrix was multiplied by the corresponding Z-scores to consider the variance in the column. (D) Wavelets smoothing strategy was adopted to smooth the resulting differential profile of each cell type. CSREs were extracted by selecting suitable height and length parameters. Statistical significance (P-value) of each CSRE was calculated by a non-parametric test.

Mentions: After data preprocessing, we obtained binary modification profiles for all cell types (Figure 1A). We introduce the following steps to identify CSREs. We have implemented this method and made a package called dCMA, which can be easily used for other researchers (Supplementary Methods).Figure 1.


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)

Illustration of the framework used in identifying CSREs. (A) The data profiles of nine cell types as characterized by nine marks and one control. The raw ChIP-seq reads were mapped to 200 bp bins and the signals were binarized using a Poisson  model. (B) For each bin, the dissimilarity of the resulting binary vectors between two different cell types was measured using hamming distance. For each cell type, the DMS of a bin was the summation of pairwise hamming distance (sPHD) computed between it and other cell types. (C) The DMS profile of each cell type was normalized across the genome. Then, each column of the matrix was multiplied by the corresponding Z-scores to consider the variance in the column. (D) Wavelets smoothing strategy was adopted to smooth the resulting differential profile of each cell type. CSREs were extracted by selecting suitable height and length parameters. Statistical significance (P-value) of each CSRE was calculated by a non-parametric test.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gkt712-F1: Illustration of the framework used in identifying CSREs. (A) The data profiles of nine cell types as characterized by nine marks and one control. The raw ChIP-seq reads were mapped to 200 bp bins and the signals were binarized using a Poisson model. (B) For each bin, the dissimilarity of the resulting binary vectors between two different cell types was measured using hamming distance. For each cell type, the DMS of a bin was the summation of pairwise hamming distance (sPHD) computed between it and other cell types. (C) The DMS profile of each cell type was normalized across the genome. Then, each column of the matrix was multiplied by the corresponding Z-scores to consider the variance in the column. (D) Wavelets smoothing strategy was adopted to smooth the resulting differential profile of each cell type. CSREs were extracted by selecting suitable height and length parameters. Statistical significance (P-value) of each CSRE was calculated by a non-parametric test.
Mentions: After data preprocessing, we obtained binary modification profiles for all cell types (Figure 1A). We introduce the following steps to identify CSREs. We have implemented this method and made a package called dCMA, which can be easily used for other researchers (Supplementary Methods).Figure 1.

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