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.