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Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines.

Fernández M, Miranda-Saavedra D - Nucleic Acids Res. (2012)

Bottom Line: In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells.Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4(+) T cells to predict ∼21,000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%.The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Genomics Laboratory, WPI-Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Osaka, Japan.

ABSTRACT
The chemical modification of histones at specific DNA regulatory elements is linked to the activation, inactivation and poising of genes. A number of tools exist to predict enhancers from chromatin modification maps, but their practical application is limited because they either (i) consider a smaller number of marks than those necessary to define the various enhancer classes or (ii) work with an excessive number of marks, which is experimentally unviable. We have developed a method for chromatin state detection using support vector machines in combination with genetic algorithm optimization, called ChromaGenSVM. ChromaGenSVM selects optimum combinations of specific histone epigenetic marks to predict enhancers. In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells. Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4(+) T cells to predict ∼21,000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%. The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.

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Plot of total predictions versus supported predictions in IFN-γ treated HeLa cells using the H3, H3K4Me1 and H3K4Me3 epigenetic signatures. The dashed line represents an ideal predictor. Evidences of functional regions: square (p300), circle (PReMod), triangle (PhastCons) and cross (any computational).
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gks149-F4: Plot of total predictions versus supported predictions in IFN-γ treated HeLa cells using the H3, H3K4Me1 and H3K4Me3 epigenetic signatures. The dashed line represents an ideal predictor. Evidences of functional regions: square (p300), circle (PReMod), triangle (PhastCons) and cross (any computational).

Mentions: The enhancers predicted both in untreated and in IFN-γ-treated HeLa cells were found to overlap extensively with experimental and computational enhancer marks characteristic of functional regulatory regions. Figures 3 and 4 show the plots of total predictions versus predicted regions overlapping different enhancer marks and functional evidences (supported predictions), both in untreated and in IFN-γ-treated HeLa cells, respectively.Figure 3.


Genome-wide enhancer prediction from epigenetic signatures using genetic algorithm-optimized support vector machines.

Fernández M, Miranda-Saavedra D - Nucleic Acids Res. (2012)

Plot of total predictions versus supported predictions in IFN-γ treated HeLa cells using the H3, H3K4Me1 and H3K4Me3 epigenetic signatures. The dashed line represents an ideal predictor. Evidences of functional regions: square (p300), circle (PReMod), triangle (PhastCons) and cross (any computational).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3378905&req=5

gks149-F4: Plot of total predictions versus supported predictions in IFN-γ treated HeLa cells using the H3, H3K4Me1 and H3K4Me3 epigenetic signatures. The dashed line represents an ideal predictor. Evidences of functional regions: square (p300), circle (PReMod), triangle (PhastCons) and cross (any computational).
Mentions: The enhancers predicted both in untreated and in IFN-γ-treated HeLa cells were found to overlap extensively with experimental and computational enhancer marks characteristic of functional regulatory regions. Figures 3 and 4 show the plots of total predictions versus predicted regions overlapping different enhancer marks and functional evidences (supported predictions), both in untreated and in IFN-γ-treated HeLa cells, respectively.Figure 3.

Bottom Line: In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells.Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4(+) T cells to predict ∼21,000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%.The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.

View Article: PubMed Central - PubMed

Affiliation: Bioinformatics and Genomics Laboratory, WPI-Immunology Frontier Research Center (IFReC), Osaka University, 3-1 Yamadaoka, Suita 565-0871, Osaka, Japan.

ABSTRACT
The chemical modification of histones at specific DNA regulatory elements is linked to the activation, inactivation and poising of genes. A number of tools exist to predict enhancers from chromatin modification maps, but their practical application is limited because they either (i) consider a smaller number of marks than those necessary to define the various enhancer classes or (ii) work with an excessive number of marks, which is experimentally unviable. We have developed a method for chromatin state detection using support vector machines in combination with genetic algorithm optimization, called ChromaGenSVM. ChromaGenSVM selects optimum combinations of specific histone epigenetic marks to predict enhancers. In an independent test, ChromaGenSVM recovered 88% of the experimentally supported enhancers in the pilot ENCODE region of interferon gamma-treated HeLa cells. Furthermore, ChromaGenSVM successfully combined the profiles of only five distinct methylation and acetylation marks from ChIP-seq libraries done in human CD4(+) T cells to predict ∼21,000 experimentally supported enhancers within 1.0 kb regions and with a precision of ∼90%, thereby improving previous predictions on the same dataset by 21%. The combined results indicate that ChromaGenSVM comfortably outperforms previously published methods and that enhancers are best predicted by specific combinations of histone methylation and acetylation marks.

Show MeSH
Related in: MedlinePlus