title: Using Markov chains of nucleotide sequences as a possible precursor to predict functional roles of human genome: a case study on inactive chromatin regions.
abstract: Recent advances in computational epigenetics have provided new opportunities to evaluate n-gram probabilistic language models. In this paper, we describe a systematic genome-wide approach for predicting functional roles in inactive chromatin regions by using a sequence-based Markovian chromatin map of the human genome. We demonstrate that Markov chains of sequences can be used as a precursor to predict functional roles in heterochromatin regions and provide an example comparing two publicly available chromatin annotations of large-scale epigenomics projects: ENCODE project consortium and Roadmap Epigenomics consortium.
authors.affiliation: Bioinformatics Laboratory, Engineering School, Ewha Womans University, Seoul, Korea.
authors.forename: K-E
authors.initials: KE
authors.lastname: Lee
authors.affiliation: Bioinformatics Laboratory, Engineering School, Ewha Womans University, Seoul, Korea.
authors.forename: E-J
authors.initials: EJ
authors.lastname: Lee
authors.affiliation: Bioinformatics Laboratory, Engineering School, Ewha Womans University, Seoul, Korea neo@ewha.ac.kr.|Center for Convergence Research of Advanced Technologies, Ewha Womans University, Seoul, Korea neo@ewha.ac.kr.
authors.forename: H-S
authors.initials: HS
authors.lastname: Park
mesh_terms: D001483:Base Sequence; D002460:Cell Line; D002843:Chromatin / Q000235:genetics*; D002874:Chromosome Mapping; D019295:Computational Biology; D044127:Epigenesis, Genetic; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D015233:Models, Statistical; D058977:Molecular Sequence Annotation; D017422:Sequence Analysis, DNA; D012984:Software
chemical_list: D002843:Chromatin