A Deep Learning Model for Epigenomic Studies

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Abstract

Epigenetics is the study of heritable changes in gene expression that does not involve changes to the underlying DNA sequence, i.e. a change in phenotype not involved by a change in genotype. At least three main factor seems responsible for epigenetic change including DNA methylation, histone modification and non-coding RNA, each one sharing having the same property to affect the dynamic of the chromatin structure by acting on Nucleosomes position. A nucleosome is a DNA-histone complex, where around 150 base pairs of double-stranded DNA is wrapped. The role of nucleosomes is to pack the DNA into the nucleus of the Eukaryote cells, to form the Chromatin. Nucleosome positioning plays an important role in gene regulation and several studies shows that distinct DNA sequence features have been identified to be associated with nucleosome presence. Starting from this suggestion, the identification of nucleosomes on a genomic scale has been successfully performed by DNA sequence features representation and classical supervised classification methods such as Support Vector Machines, Logistic regression and so on. Taking in consideration the successful application of the deep neural networks on several challenging classification problems, in this paper we want to study how deep learning network can help in the identification of nucleosomes.
Original languageEnglish
Publication statusPublished - 2016

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