Mining patterns of histone modifications interplay from epigenomic profiles are one of the leading research areas these days. Various methods based on clustering approaches and hidden Markov models have been presented so far with some limitations. Here we present ChromClust, a semi-supervised clustering tool for mining commonly occurring histone modifications at various locations of the genome. Applying our method to 11 chromatin marks in nine human cell types recovered 11 clusters based on distinct chromatin signatures mapping to various elements of the genome. Our approach is efficient in respect to time and space usage along with the added facility of maintaining database at the backend. It outperforms the existing methods with respect to mining patterns in a semi-supervised fashion mapping to various functional elements of the genome. It will aid in future by saving the resources of time and space along with efficiently retrieving the hidden interplay of histone combinations.
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