FASTmC: A Suite of Predictive Models for Nonreference-Based Estimations of DNA Methylation

Adam J Bewick; Brigitte T Hofmeister; Kevin Lee; Xiaoyu Zhang; David W Hall; Robert J Schmitz

doi:10.1534/g3.115.025668

FASTmC: A Suite of Predictive Models for Nonreference-Based Estimations of DNA Methylation

G3 (Bethesda). 2015 Dec 17;6(2):447-52. doi: 10.1534/g3.115.025668.

Authors

Adam J Bewick¹, Brigitte T Hofmeister², Kevin Lee¹, Xiaoyu Zhang³, David W Hall¹, Robert J Schmitz⁴

Affiliations

¹ Department of Genetics, University of Georgia, Athens, Georgia 30602.
² Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602.
³ Department of Plant Biology, University of Georgia, Athens, Georgia 30602.
⁴ Department of Genetics, University of Georgia, Athens, Georgia 30602 schmitz@uga.edu.

Abstract

We describe a suite of predictive models, coined FAST(m)C, for nonreference, cost-effective exploration and comparative analysis of context-specific DNA methylation levels. Accurate estimations of true DNA methylation levels can be obtained from as few as several thousand short-reads generated from whole-genome bisulfite sequencing. These models make high-resolution time course or developmental and large diversity studies practical regardless of species, genome size, and availability of a reference genome.

Keywords: DNA methylation; epigenetics; methylome; modeling; whole-genome bisulfite sequencing.

Publication types

Research Support, N.I.H., Extramural
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Animals
Computational Biology / methods*
CpG Islands
DNA Methylation*
Databases, Nucleic Acid
Epigenesis, Genetic*
Epigenomics / methods*
Gene Expression Profiling
Genome-Wide Association Study
Humans
Software*

Abstract

Publication types

MeSH terms

Grants and funding