Nonparametric Bayesian clustering to detect bipolar methylated genomic loci

BMC Bioinformatics

Hehuang Xie²  Hongxiao Zhu¹  Ming-an Sun³  Xiaowei Wu¹ 

[1]Department of Statistics, Virginia Tech, 250 Drillfield Drive, Blacksburg 24061, VA, USA

[2]Department of Biological Sciences, Virginia Tech, 1405 Perry Street, Blacksburg 24061, VA, USA

[3]Virginia Bioinformatics Institute, Virginia Tech, 1015 Life Science Circle, Blacksburg 24061, VA, USA

关键词: Nonparametric Bayesian;    Epigenetics;    DNA methylation;   

Others  :  1089013
DOI  :  10.1186/s12859-014-0439-2

 received in 2014-07-10, accepted in 2014-12-18,  发布年份 2015

【 摘 要 】

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.

【 授权许可】

   
2015 Wu et al.; licensee BioMed Central.

【 图 表 】

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