title: Differential methylation analysis for BS-seq data under general experimental design.
abstract: DNA methylation is an epigenetic modification with important roles in many biological processes and diseases. Bisulfite sequencing (BS-seq) has emerged recently as the technology of choice to profile DNA methylation because of its accuracy, genome coverage and higher resolution. Current statistical methods to identify differential methylation mainly focus on comparing two treatment groups. With an increasing number of experiments performed under a general and multiple-factor design, particularly in reduced representation bisulfite sequencing, there is a need to develop more flexible, powerful and computationally efficient methods.
  
  
  
  We present a novel statistical model to detect differentially methylated loci from BS-seq data under general experimental design, based on a beta-binomial regression model with 'arcsine' link function. Parameter estimation is based on transformed data with generalized least square approach without relying on iterative algorithm. Simulation and real data analyses demonstrate that our method is accurate, powerful, robust and computationally efficient.
  
  
  
  It is available as Bioconductor package DSS.
  
  
  
  yongpark@pitt.edu or hao.wu@emory.edu
  
  
  
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15261, USA and.
authors.forename: Yongseok
authors.initials: Y
authors.lastname: Park
authors.affiliation: Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA 30322, USA.
authors.forename: Hao
authors.initials: H
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D016678:Genome; D012107:Research Design
references.citation: Genome Res. 2010 Apr;20(4):440-6
references.pmid: 20219944
references.citation: PLoS Genet. 2012;8(6):e1002781
references.pmid: 22737091
references.citation: Science. 2009 May 15;324(5929):929-30
references.pmid: 19372393
references.citation: Nucleic Acids Res. 2014 Apr;42(6):e45
references.pmid: 24423865
references.citation: Bioinformatics. 2015 Jun 15;31(12):1889-96
references.pmid: 25682068
references.citation: Nucleic Acids Res. 2005 Oct 13;33(18):5868-77
references.pmid: 16224102
references.citation: BMC Bioinformatics. 2014 Jun 24;15:215
references.pmid: 24962134
references.citation: Cell Stem Cell. 2014 Oct 2;15(4):447-459
references.pmid: 25220291
references.citation: Front Genet. 2014 Sep 16;5:324
references.pmid: 25278959
references.citation: Cancer Lett. 2007 Jun 28;251(2):311-6
references.pmid: 17223258
references.citation: Lung Cancer. 2010 Aug;69(2):172-9
references.pmid: 19945765
references.citation: Genome Biol. 2008;9(8):231
references.pmid: 18771577
references.citation: PLoS Genet. 2011 Jun;7(6):e1002154
references.pmid: 21731508
references.citation: Nat Genet. 2005 Aug;37(8):853-62
references.pmid: 16007088
references.citation: Genome Biol. 2012 Oct 03;13(10):R83
references.pmid: 23034175
references.citation: Genome Biol. 2014;15(12):550
references.pmid: 25516281
references.citation: PLoS One. 2012;7(2):e31323
references.pmid: 22347464
references.citation: Nature. 2007 May 24;447(7143):425-32
references.pmid: 17522676
references.citation: Nature. 2008 Aug 7;454(7205):766-70
references.pmid: 18600261
references.citation: Bioinformatics. 2013 Jul 01;29(13):1647-53
references.pmid: 23658421
references.citation: Proc Natl Acad Sci U S A. 2009 Feb 24;106(8):2824-8
references.pmid: 19196983
references.citation: Nat Neurosci. 2011 Oct 30;14(12):1607-16
references.pmid: 22037496
references.citation: Genome Biol. 2014 Feb 24;15(2):R38
references.pmid: 24565500
references.citation: Bioinformatics. 2007 Nov 1;23(21):2881-7
references.pmid: 17881408
references.citation: Biochem Biophys Res Commun. 2011 Apr 1;407(1):254-9
references.pmid: 21382345
references.citation: Cell. 2013 Apr 25;153(3):678-91
references.pmid: 23602153
references.citation: Nature. 2009 Nov 19;462(7271):315-22
references.pmid: 19829295
references.citation: Mol Cell. 2012 Dec 28;48(6):849-62
references.pmid: 23219530
references.citation: Biostatistics. 2013 Apr;14(2):232-43
references.pmid: 23001152
references.citation: Genome Biol. 2012 Oct 03;13(10):R87
references.pmid: 23034086
references.citation: Genome Biol. 2014 Feb 03;15(2):R29
references.pmid: 24485249
references.citation: Cell. 1999 Nov 24;99(5):451-4
references.pmid: 10589672
references.citation: Clin Cancer Res. 2014 Feb 15;20(4):814-26
references.pmid: 24334763
references.citation: Bioinformatics. 2014 Sep 1;30(17):2414-22
references.pmid: 24836530
references.citation: Nucleic Acids Res. 2006 Jan 20;34(2):528-42
references.pmid: 16428248
references.citation: Nucleic Acids Res. 2013 Feb 1;41(4):e55
references.pmid: 23268441
references.citation: Bioinformatics. 2014 Jul 1;30(13):1814-22
references.pmid: 24608764
references.citation: Genes Dev. 2002 Jan 1;16(1):6-21
references.pmid: 11782440
references.citation: Nucleic Acids Res. 2014 Apr;42(8):e69
references.pmid: 24561809