EventsMOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
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Published
with-doi10.3390/MOL2NET-1-e003
This submission belongs to the session 03. USEDAT-01: USA-Europe Data Analysis Training Congress, Cambridge, UK-Bilbao, Spain-Miami, USA, 2015 of the event MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed.
Published date
02 Dec, 2015
Citation
Sally A Mackenzie, Robersy Sanchez, Genome-wide Discriminatory Information Patterns of Cytosine DNA Methylation, in Proceedings of MOL2NET'15, Conference on Molecular, Biomed., Comput. & Network Science and Engineering, 1st ed., 5 December–15 December 2015, MDPI: Basel, Switzerland, doi: 10.3390/MOL2NET-1-e003
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Genome-wide Discriminatory Information Patterns of Cytosine DNA Methylation

Robersy Sanchez 1
Sally A Mackenzie 1
1. University of Nebraska-Lincoln
Abstract

Cytosine DNA methylation (CDM) is a highly abundant epigenetic heritable but reversible chemical modification to the genome. Herein, a machine learning approach, was applied to analyze the accumulation of epigenetic marks in 150 methylomes from Arabidopsis thaliana ecotypes. We hypothesize that these marks are chromosomal footprints that account for different ontogenetic and phylogenetic and histories of individual members of the sampling population. Our results support this hypothesis and suggest a statistical-physical relationship between CDM changes and single nucleotide polymorphism (SNPs). Furthermore, the genome-wide redistribution of CDM changes ensures the thermal stability of the DNA molecule preserving the integrity of the genetic message continuously stressed by thermal fluctuations in the cell environment.

Keywords
Epigenetics
Epigenomics
Information thermodynamics
Linear discriminant analysis
machine learning.
Manuscript
MS.pdf
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