Genome-Wide Discriminatory Information Patterns of Cytosine DNA Methylation.
Cytosine DNA methylation (CDM) is a highly abundant, heritable but reversible chemical modification to the genome. Herein, a machine learning approach was applied to analyze the accumulation of epigenetic marks in methylomes of 152 ecotypes and 85 silencing mutants of Arabidopsis thaliana. In an information-thermodynamics framework, two measurements were used: (1) the amount of information gained/lost with the CDM changes I R and (2) the uncertainty of not observing a SNP L C R . We hypothesize that epigenetic marks are chromosomal footprints accounting for different ontogenetic and phylogenetic histories of individual populations. A machine learning approach is proposed to verify this hypothesis. Results support the hypothesis by the existence of discriminatory information (DI) patterns of CDM able to discriminate between individuals and between individual subpopulations. The statistical analyses revealed a strong association between the topologies of the structured population of Arabidopsis ecotypes based on I R and on LCR, respectively. A statistical-physical relationship between I R and L C R was also found. Results to date imply that the genome-wide distribution of CDM changes is not only part of the biological signal created by the methylation regulatory machinery, but ensures the stability of the DNA molecule, preserving the integrity of the genetic message under continuous stress from thermal fluctuations in the cell environment.
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68588, USA. robersy@unl.edu.
Robersy
R
Sanchez
Department of Agronomy and Horticulture, University of Nebraska, Lincoln, NE 68588, USA. sally.mackenzie@unl.edu.
Sally A
SA
Mackenzie
D017360:Arabidopsis / Q000235:genetics; D003596:Cytosine / Q000378:metabolism; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D018745:Genome, Plant; D000069550:Machine Learning; D008957:Models, Genetic
D003596:Cytosine
epigenetics; epigenomics; information thermodynamics; linear discriminant analysis; machine learning