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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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Probability density histogram of profile window sizes in the pool of top-ranked SVM predictors selected after 100 GA runs for the prediction of distal regulatory elements in human CD4+ T cells. The most frequent window size in the top-ranked predictors was 1.0 kb.
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gks149-F6: Probability density histogram of profile window sizes in the pool of top-ranked SVM predictors selected after 100 GA runs for the prediction of distal regulatory elements in human CD4+ T cells. The most frequent window size in the top-ranked predictors was 1.0 kb.

Mentions: Similarly, the probability density histogram of the optimum window size is depicted in Figure 6. The extension and boundaries of the unique epigenetic landscape associated with functional regions in human CD4+ T cells were best encoded in epigenetic profiles spanning 1.0 kb.Figure 6.


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

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

Probability density histogram of profile window sizes in the pool of top-ranked SVM predictors selected after 100 GA runs for the prediction of distal regulatory elements in human CD4+ T cells. The most frequent window size in the top-ranked predictors was 1.0 kb.
© Copyright Policy - creative-commons
Related In: Results  -  Collection

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

gks149-F6: Probability density histogram of profile window sizes in the pool of top-ranked SVM predictors selected after 100 GA runs for the prediction of distal regulatory elements in human CD4+ T cells. The most frequent window size in the top-ranked predictors was 1.0 kb.
Mentions: Similarly, the probability density histogram of the optimum window size is depicted in Figure 6. The extension and boundaries of the unique epigenetic landscape associated with functional regions in human CD4+ T cells were best encoded in epigenetic profiles spanning 1.0 kb.Figure 6.

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