Learning chromatin states with factorized information criteria

Motivation: Recent studies have suggested that both the genome and the genome with epigenetic modifications, the so-called epigenome, play important roles in various biological functions, such as transcription and DNA replication, repair, and recombination. It is well known that specific combinations of histone modifications (e.g. methylations and acetylations) of nucleosomes induce chromatin states that correspond to specific functions of chromatin. Although the advent of next-generation sequencing (NGS) technologies enables measurement of epigenetic information for entire genomes at high-resolution, the variety of chromatin states has not been completely characterized. Results: In this study, we propose a method to estimate the chromatin states indicated by genomewide chromatin marks identified by NGS technologies. The proposed method automatically estimates the number of chromatin states and characterize each state on the basis of a hidden Markov model (HMM) in combination with a recently proposed model selection technique, factorized information criteria. The method is expected to provide an unbiased model because it relies on only two adjustable parameters and avoids heuristic procedures as much as possible. Computational experiments with simulated datasets show that our method automatically learns an appropriate model, even in cases where methods that rely on Bayesian information criteria fail to learn the model structures. In addition, we comprehensively compare our method to ChromHMM on three real datasets and show that our method estimates more chromatin states than ChromHMM for those datasets.