title: Applications of alignment-free methods in epigenomics.
abstract: Epigenetic mechanisms play an important role in the regulation of cell type-specific gene activities, yet how epigenetic patterns are established and maintained remains poorly understood. Recent studies have supported a role of DNA sequences in recruitment of epigenetic regulators. Alignment-free methods have been applied to identify distinct sequence features that are associated with epigenetic patterns and to predict epigenomic profiles. Here, we review recent advances in such applications, including the methods to map DNA sequence to feature space, sequence comparison and prediction models. Computational studies using these methods have provided important insights into the epigenetic regulatory mechanisms.
authors.affiliation: Dipartimento di Matematica e Informatica, Via Archirafi 34, 90123 Palermo-Italy. giosue.lobosco@unipa.it; Guo-Cheng Yuan, Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, 450 Brookline Ave., Boston, MA 02215, USA. E-mail: gcyuan@jimmy.harvard.edu.
authors.forename: Luca
authors.initials: L
authors.lastname: Pinello
authors.forename: Giosuè
authors.initials: G
authors.lastname: Lo Bosco
authors.forename: Guo-Cheng
authors.initials: GC
authors.lastname: Yuan
mesh_terms: D001185:Artificial Intelligence; D002874:Chromosome Mapping / Q000379:methods / Q000706:statistics & numerical data; D019295:Computational Biology / Q000379:methods; D044127:Epigenesis, Genetic; D057890:Epigenomics / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008957:Models, Genetic; D016415:Sequence Alignment; D017422:Sequence Analysis, DNA / Q000379:methods* / Q000706:statistics & numerical data; D060388:Support Vector Machine
keywords: DNA sequence; alignment-free method; epigenetics; machine learning; nucleosome