title: MAGI: Methylation analysis using genome information.
abstract: By incorporating annotation information into the analysis of next-generation sequencing DNA methylation data, we provide an improvement in performance over current testing procedures. Methylation analysis using genome information (MAGI) is applicable for both unreplicated and replicated data, and provides an effective analysis for studies with low sequencing depth. When compared with current tests, the annotation-informed tests provide an increase in statistical power and offer a significance-based interpretation of differential methylation.
authors.affiliation: Department of Mathematics; University of Wisconsin, La Crosse; La Crosse, WI USA.
authors.forename: Douglas D
authors.initials: DD
authors.lastname: Baumann
authors.affiliation: Department of Statistics; Purdue University; West Lafayette, IN USA.
authors.forename: R W
authors.initials: RW
authors.lastname: Doerge
mesh_terms: D017360:Arabidopsis / Q000235:genetics; D003596:Cytosine / Q000378:metabolism; D019175:DNA Methylation; D016678:Genome; D018745:Genome, Plant; D058977:Molecular Sequence Annotation; D009154:Mutation; D017422:Sequence Analysis, DNA / Q000379:methods
chemical_list: D003596:Cytosine
keywords: annotation informed; differential methylation; epigenetics; epigenomics; statistical bioinformatics; testing methylation