Learning chromatin states with factorized information criteria | Zendy

Michiaki Hamada | Zendy; Yukiteru Ono | Zendy; Ryohei Fujimaki | Zendy; Kiyoshi Asai | Zendy

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Learning chromatin states with factorized information criteria

Author(s) -

Michiaki Hamada,

Yukiteru Ono,

Ryohei Fujimaki,

Kiyoshi Asai

Publication year - 2015

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btv163

Subject(s) - chromatin , computer science , computational biology , nucleosome , epigenome , epigenetics , genome , bayesian probability , chip sequencing , epigenomics , biology , genetics , artificial intelligence , dna methylation , dna , gene , gene expression

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.

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