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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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Related in: MedlinePlus

Comparison of DM detection methods. The Jaccard indexes comparing the truly differentially methylated CpGs to the CpGs identified as differentially methylated by each method. The panels are labeled according to the distributions of methylation levels of non-differntially methylated CpGs.
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Figure 1: Comparison of DM detection methods. The Jaccard indexes comparing the truly differentially methylated CpGs to the CpGs identified as differentially methylated by each method. The panels are labeled according to the distributions of methylation levels of non-differntially methylated CpGs.

Mentions: With RADMeth, CpGs with FDR corrected p-values below 0.01 were identified as differentially methylated. The correlation parameter was set to compute correlation between p-values of CpGs up to 200 bp from one another.The Jaccard indexes corresponding to each method applied to each dataset are described in Figure 1.


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

Dolzhenko E, Smith AD - BMC Bioinformatics (2014)

Comparison of DM detection methods. The Jaccard indexes comparing the truly differentially methylated CpGs to the CpGs identified as differentially methylated by each method. The panels are labeled according to the distributions of methylation levels of non-differntially methylated CpGs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Comparison of DM detection methods. The Jaccard indexes comparing the truly differentially methylated CpGs to the CpGs identified as differentially methylated by each method. The panels are labeled according to the distributions of methylation levels of non-differntially methylated CpGs.
Mentions: With RADMeth, CpGs with FDR corrected p-values below 0.01 were identified as differentially methylated. The correlation parameter was set to compute correlation between p-values of CpGs up to 200 bp from one another.The Jaccard indexes corresponding to each method applied to each dataset are described in Figure 1.

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