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Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering.

Ucar D, Hu Q, Tan K - Nucleic Acids Res. (2011)

Bottom Line: We identify 843 combinatorial patterns that recur at >0.1% of the genome.A total of 19 chromatin modifications are observed in the combinatorial patterns, 10 of which occur in more than half of the patterns.We also identify combinatorial modification signatures for eight classes of functional DNA elements.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, University of Iowa, Iowa City, 52242 Iowa, USA.

ABSTRACT
Chromatin modifications, such as post-translational modification of histone proteins and incorporation of histone variants, play an important role in regulating gene expression. Joint analyses of multiple histone modification maps are starting to reveal combinatorial patterns of modifications that are associated with functional DNA elements, providing support to the 'histone code' hypothesis. However, due to the lack of analytical methods, only a small number of chromatin modification patterns have been discovered so far. Here, we introduce a scalable subspace clustering algorithm, coherent and shifted bicluster identification (CoSBI), to exhaustively identify the set of combinatorial modification patterns across a given epigenome. Performance comparisons demonstrate that CoSBI can generate biclusters with higher intra-cluster coherency and biological relevance. We apply our algorithm to a compendium of 39 genome-wide chromatin modification maps in human CD4(+) T cells. We identify 843 combinatorial patterns that recur at >0.1% of the genome. A total of 19 chromatin modifications are observed in the combinatorial patterns, 10 of which occur in more than half of the patterns. We also identify combinatorial modification signatures for eight classes of functional DNA elements. Application of CoSBI to epigenome maps of different cells and developmental stages will aid in understanding how chromatin structure helps regulate gene expression.

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Overview of the CoSBI algorithm. In Step 1, for each genomic locus, we identify sets of maximal coherent chromatin marks. In Step 2, using the results of Step 1, we identify sets of biclusters that are coherent in two dimensions.
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Figure 1: Overview of the CoSBI algorithm. In Step 1, for each genomic locus, we identify sets of maximal coherent chromatin marks. In Step 2, using the results of Step 1, we identify sets of biclusters that are coherent in two dimensions.

Mentions: We propose an unsupervised subspace-clustering algorithm for the analysis of chromatin modification data generated using ChIP-chip/seq technology. The algorithm, termed coherent and shifted bi-cluster identification (CoSBI), aims to identify all recurrent combinatorial patterns with coherent signals over the same set of genomic loci and chromatin modifications. Since the patterns we seek are coherent in two dimensions, our problem is similar to finding biclusters in a compendium of gene expression microarray data, which has been extensively studied (26,27). However, the major difference from previous biclustering algorithms is that individual data entries in the 2D data matrix are not scalar values. Instead, they are vectors representing consecutive measurements of a given chromatin modification across a genomic locus (Figure 1). This third dimension of the data presents additional challenges for the design of the clustering algorithm.Figure 1.


Combinatorial chromatin modification patterns in the human genome revealed by subspace clustering.

Ucar D, Hu Q, Tan K - Nucleic Acids Res. (2011)

Overview of the CoSBI algorithm. In Step 1, for each genomic locus, we identify sets of maximal coherent chromatin marks. In Step 2, using the results of Step 1, we identify sets of biclusters that are coherent in two dimensions.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3105409&req=5

Figure 1: Overview of the CoSBI algorithm. In Step 1, for each genomic locus, we identify sets of maximal coherent chromatin marks. In Step 2, using the results of Step 1, we identify sets of biclusters that are coherent in two dimensions.
Mentions: We propose an unsupervised subspace-clustering algorithm for the analysis of chromatin modification data generated using ChIP-chip/seq technology. The algorithm, termed coherent and shifted bi-cluster identification (CoSBI), aims to identify all recurrent combinatorial patterns with coherent signals over the same set of genomic loci and chromatin modifications. Since the patterns we seek are coherent in two dimensions, our problem is similar to finding biclusters in a compendium of gene expression microarray data, which has been extensively studied (26,27). However, the major difference from previous biclustering algorithms is that individual data entries in the 2D data matrix are not scalar values. Instead, they are vectors representing consecutive measurements of a given chromatin modification across a genomic locus (Figure 1). This third dimension of the data presents additional challenges for the design of the clustering algorithm.Figure 1.

Bottom Line: We identify 843 combinatorial patterns that recur at >0.1% of the genome.A total of 19 chromatin modifications are observed in the combinatorial patterns, 10 of which occur in more than half of the patterns.We also identify combinatorial modification signatures for eight classes of functional DNA elements.

View Article: PubMed Central - PubMed

Affiliation: Department of Internal Medicine, University of Iowa, Iowa City, 52242 Iowa, USA.

ABSTRACT
Chromatin modifications, such as post-translational modification of histone proteins and incorporation of histone variants, play an important role in regulating gene expression. Joint analyses of multiple histone modification maps are starting to reveal combinatorial patterns of modifications that are associated with functional DNA elements, providing support to the 'histone code' hypothesis. However, due to the lack of analytical methods, only a small number of chromatin modification patterns have been discovered so far. Here, we introduce a scalable subspace clustering algorithm, coherent and shifted bicluster identification (CoSBI), to exhaustively identify the set of combinatorial modification patterns across a given epigenome. Performance comparisons demonstrate that CoSBI can generate biclusters with higher intra-cluster coherency and biological relevance. We apply our algorithm to a compendium of 39 genome-wide chromatin modification maps in human CD4(+) T cells. We identify 843 combinatorial patterns that recur at >0.1% of the genome. A total of 19 chromatin modifications are observed in the combinatorial patterns, 10 of which occur in more than half of the patterns. We also identify combinatorial modification signatures for eight classes of functional DNA elements. Application of CoSBI to epigenome maps of different cells and developmental stages will aid in understanding how chromatin structure helps regulate gene expression.

Show MeSH
Related in: MedlinePlus