title: Learning chromatin states with factorized information criteria.
abstract: BACKGROUND
  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 genome-wide 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.
authors: Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan, Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology (AIST), 2-41-6, Aomi, Koto-ku, Tokyo 135-0064, Japan.
authors: Michiaki
authors: M
authors: Hamada
authors: Information and Mathematical Science and Bioinformatics Co., Ltd., 4-21-1-601 Higashi-Ikebukuro, Toshima-ku, Tokyo 170-0013, Japan.
authors: Yukiteru
authors: Y
authors: Ono
authors: Department of Media Analytics, NEC Laboratories America, 10080 North Wolfe Road, Suite SW3-350, Cupertino, CA 95014 and.
authors: Ryohei
authors: R
authors: Fujimaki
authors: Department of Electrical Engineering and Bioscience, Faculty of Science and Engineering, Waseda University, 55N-06-10, 3-4-1, Okubo Shinjuku-ku, Tokyo 169-8555, Japan, Graduate School of Frontier Sciences, University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa 277-8562, Japan.
authors: Kiyoshi
authors: K
authors: Asai
mesh_terms: D002843:Chromatin / Q000378:metabolism*; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods*; D059014:High-Throughput Nucleotide Sequencing; D006801:Humans; D008390:Markov Chains; D009707:Nucleosomes / Q000378:metabolism
chemical_list: D002843:Chromatin; D009707:Nucleosomes