Content area
Abstract
We describe a suite of predictive models, coined FASTmC, 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.
Details
Title
FASTmC: A Suite of Predictive Models for Nonreference-Based Estimations of DNA Methylation
Author
Bewick, Adam J 1 ; Hofmeister, Brigitte T 2 ; Lee, Kevin 1 ; Zhang, Xiaoyu 3 ; Hall, David W 1 ; Schmitz, Robert J 4
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; Department of Genetics, University of Georgia, Athens, Georgia 30602
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; Department of Genetics, University of Georgia, Athens, Georgia 30602
Pages
447-452
Publication year
2016
Publication date
Feb 1, 2016
e-ISSN
21601836
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
3169750764
Copyright
© 2016 Bewick et al..