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Many researchers have shown that formal language theory is an appropriate tool in analyzing various biological sequences. Markov model is most closely related to regular grammars, because an n-gram is a subsequence of n items from a given sequence, and language models that are built from n-grams are actually (n-1)-order Markov models.
We investigated whether some subsets of the annotated ENCODE /RoadmapGenomics 15-state model can be predicted by simply creating n-gram models of DNA sequences, in reverse. To achieve this, ChromHMM blocks of human genome were initially dissected into a nucleosome resolution of 200-bp units and, by analyzing the BED files of ChromHMM, each individual unit was assigned one dominant chromatin state.