title: Nonparametric Bayesian clustering to detect bipolar methylated genomic loci.
abstract: BACKGROUND
  With recent development in sequencing technology, a large number of genome-wide DNA methylation studies have generated massive amounts of bisulfite sequencing data. The analysis of DNA methylation patterns helps researchers understand epigenetic regulatory mechanisms. Highly variable methylation patterns reflect stochastic fluctuations in DNA methylation, whereas well-structured methylation patterns imply deterministic methylation events. Among these methylation patterns, bipolar patterns are important as they may originate from allele-specific methylation (ASM) or cell-specific methylation (CSM).
  
  
  RESULTS
  Utilizing nonparametric Bayesian clustering followed by hypothesis testing, we have developed a novel statistical approach to identify bipolar methylated genomic regions in bisulfite sequencing data. Simulation studies demonstrate that the proposed method achieves good performance in terms of specificity and sensitivity. We used the method to analyze data from mouse brain and human blood methylomes. The bipolar methylated segments detected are found highly consistent with the differentially methylated regions identified by using purified cell subsets.
  
  
  CONCLUSIONS
  Bipolar DNA methylation often indicates epigenetic heterogeneity caused by ASM or CSM. With allele-specific events filtered out or appropriately taken into account, our proposed approach sheds light on the identification of cell-specific genes/pathways under strong epigenetic control in a heterogeneous cell population.
authors.affiliation: Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg, 24061, VA, USA. xwwu@vt.edu.
authors.forename: Xiaowei
authors.initials: X
authors.lastname: Wu
authors.affiliation: Virginia Bioinformatics Institute, Virginia Tech, 1015 Life Science Circle, Blacksburg, 24061, VA, USA. masun@vt.edu.
authors.forename: Ming-An
authors.initials: MA
authors.lastname: Sun
authors.affiliation: Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg, 24061, VA, USA. hongxiao@vt.edu.
authors.forename: Hongxiao
authors.initials: H
authors.lastname: Zhu
authors.affiliation: Virginia Bioinformatics Institute, Virginia Tech, 1015 Life Science Circle, Blacksburg, 24061, VA, USA. davidxie@vt.edu.|Department of Biological Sciences, Virginia Tech, 1405 Perry Street, Blacksburg, 24061, VA, USA. davidxie@vt.edu.
authors.forename: Hehuang
authors.initials: H
authors.lastname: Xie
mesh_terms: D000483:Alleles; D000818:Animals; D001402:B-Lymphocytes / Q000378:metabolism; D001499:Bayes Theorem; D016000:Cluster Analysis; D019175:DNA Methylation; D044127:Epigenesis, Genetic; D056426:Genetic Loci; D023281:Genomics / Q000379:methods*; D006801:Humans; D051379:Mice; D009457:Neuroglia / Q000378:metabolism; D009474:Neurons / Q000378:metabolism; D009504:Neutrophils / Q000378:metabolism; D017422:Sequence Analysis, DNA; D018709:Statistics, Nonparametric