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Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments.

Dolzhenko E, Smith AD - BMC Bioinformatics (2014)

Bottom Line: Several studies have demonstrated the value of this precision: meaningful features that correlate strongly with biological functions can be found associated with only a few CpG sites.Understanding the role of DNA methylation, and more broadly the role of DNA accessibility, requires that methylation differences between populations of cells are identified with extreme precision and in complex experimental designs.The regression-based analysis can handle medium- and large-scale experiments where it becomes critical to accurately model variation in methylation levels between replicates and account for influence of various experimental factors like cell types or batch effects.

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Affiliation: Molecular and Computational Biology Section, Division of Biological Sciences, University of Southern California, Los Angeles, California, USA. andrewds@usc.edu.

ABSTRACT

Background: Whole-genome bisulfite sequencing currently provides the highest-precision view of the epigenome, with quantitative information about populations of cells down to single nucleotide resolution. Several studies have demonstrated the value of this precision: meaningful features that correlate strongly with biological functions can be found associated with only a few CpG sites. Understanding the role of DNA methylation, and more broadly the role of DNA accessibility, requires that methylation differences between populations of cells are identified with extreme precision and in complex experimental designs.

Results: In this work we investigated the use of beta-binomial regression as a general approach for modeling whole-genome bisulfite data to identify differentially methylated sites and genomic intervals.

Conclusions: The regression-based analysis can handle medium- and large-scale experiments where it becomes critical to accurately model variation in methylation levels between replicates and account for influence of various experimental factors like cell types or batch effects.

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Classification of Arabidopsis DM regions. A summary of functional classification of 5,049 DM regions containing 10 CpG or more between 54 inflorescence and 98 leaf samples of Arabidopsis thaliana.
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Figure 3: Classification of Arabidopsis DM regions. A summary of functional classification of 5,049 DM regions containing 10 CpG or more between 54 inflorescence and 98 leaf samples of Arabidopsis thaliana.

Mentions: It is well known that methylation in Aradidopsis plays an important role in silencing of transposable elements (e.g. [32]), which are usually heavily methylated. Interestingly, most of the DM regions we found overlapped transposons (1.781 observed over expected ratio; see also Figure 3). The methylation differences between inflorescence and leaf samples were modest: above 0.1 for 1,271 DM regions and above 0.2 for just 129 regions, indicating relative loss of methylation within transposons in a relatively small fraction of sequenced molecules. Promoter and gene bound DM regions were underrepresented, with 0.19 and 0.28 observed over expected ratios respectively.


Using beta-binomial regression for high-precision differential methylation analysis in multifactor whole-genome bisulfite sequencing experiments.

Dolzhenko E, Smith AD - BMC Bioinformatics (2014)

Classification of Arabidopsis DM regions. A summary of functional classification of 5,049 DM regions containing 10 CpG or more between 54 inflorescence and 98 leaf samples of Arabidopsis thaliana.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230021&req=5

Figure 3: Classification of Arabidopsis DM regions. A summary of functional classification of 5,049 DM regions containing 10 CpG or more between 54 inflorescence and 98 leaf samples of Arabidopsis thaliana.
Mentions: It is well known that methylation in Aradidopsis plays an important role in silencing of transposable elements (e.g. [32]), which are usually heavily methylated. Interestingly, most of the DM regions we found overlapped transposons (1.781 observed over expected ratio; see also Figure 3). The methylation differences between inflorescence and leaf samples were modest: above 0.1 for 1,271 DM regions and above 0.2 for just 129 regions, indicating relative loss of methylation within transposons in a relatively small fraction of sequenced molecules. Promoter and gene bound DM regions were underrepresented, with 0.19 and 0.28 observed over expected ratios respectively.

Bottom Line: Several studies have demonstrated the value of this precision: meaningful features that correlate strongly with biological functions can be found associated with only a few CpG sites.Understanding the role of DNA methylation, and more broadly the role of DNA accessibility, requires that methylation differences between populations of cells are identified with extreme precision and in complex experimental designs.The regression-based analysis can handle medium- and large-scale experiments where it becomes critical to accurately model variation in methylation levels between replicates and account for influence of various experimental factors like cell types or batch effects.

View Article: PubMed Central - HTML - PubMed

Affiliation: Molecular and Computational Biology Section, Division of Biological Sciences, University of Southern California, Los Angeles, California, USA. andrewds@usc.edu.

ABSTRACT

Background: Whole-genome bisulfite sequencing currently provides the highest-precision view of the epigenome, with quantitative information about populations of cells down to single nucleotide resolution. Several studies have demonstrated the value of this precision: meaningful features that correlate strongly with biological functions can be found associated with only a few CpG sites. Understanding the role of DNA methylation, and more broadly the role of DNA accessibility, requires that methylation differences between populations of cells are identified with extreme precision and in complex experimental designs.

Results: In this work we investigated the use of beta-binomial regression as a general approach for modeling whole-genome bisulfite data to identify differentially methylated sites and genomic intervals.

Conclusions: The regression-based analysis can handle medium- and large-scale experiments where it becomes critical to accurately model variation in methylation levels between replicates and account for influence of various experimental factors like cell types or batch effects.

Show MeSH
Related in: MedlinePlus