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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

DM regions between neuron and non-neuron samples. (Top left) Methylation profile of the neuron specific enolase (Eno2) – a marker of neuron cells – across frontal cortex samples. (Right) Histogram of log-odds-ratios of DM regions containing at least 10 CpGs. (Bottom left) Histogram of minimum methylation differences of DM regions containing at least 10 CpGs.
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Figure 2: DM regions between neuron and non-neuron samples. (Top left) Methylation profile of the neuron specific enolase (Eno2) – a marker of neuron cells – across frontal cortex samples. (Right) Histogram of log-odds-ratios of DM regions containing at least 10 CpGs. (Bottom left) Histogram of minimum methylation differences of DM regions containing at least 10 CpGs.

Mentions: We compared CpG methylation between neuron and non-neuron samples from mouse frontal cortex published in a recent study of methylation in the mammalian brain [26]. The 6 MethylC-Seq read libraries were processed with MethPipe [14] methylation analysis pipeline using standard parameter cutoffs. The resulting methylome samples had the mean coverage of 12.4 (s.d. 4.7). We computed DM CpGs and DM regions between neuron and non-neuron samples adjusting for baseline differences related to age and sex (12 month and 6 week old females, and 7 week old male). Top-left panel of Figure 2 contains a browser plot [27] with annotated DM regions and hypo methylated regions (HMRs) within a promoter of neuron specific enolase (Eno2), a well known marker of neuron cells [28,29]. The methylation profile of this gene across the frontal cortex samples reveals elongated HMRs upstream and downstream of the unmethylated promoter core in neuron samples compared to the ones in non-neuron samples, which constitute the DM regions.


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

Dolzhenko E, Smith AD - BMC Bioinformatics (2014)

DM regions between neuron and non-neuron samples. (Top left) Methylation profile of the neuron specific enolase (Eno2) – a marker of neuron cells – across frontal cortex samples. (Right) Histogram of log-odds-ratios of DM regions containing at least 10 CpGs. (Bottom left) Histogram of minimum methylation differences of DM regions containing at least 10 CpGs.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: DM regions between neuron and non-neuron samples. (Top left) Methylation profile of the neuron specific enolase (Eno2) – a marker of neuron cells – across frontal cortex samples. (Right) Histogram of log-odds-ratios of DM regions containing at least 10 CpGs. (Bottom left) Histogram of minimum methylation differences of DM regions containing at least 10 CpGs.
Mentions: We compared CpG methylation between neuron and non-neuron samples from mouse frontal cortex published in a recent study of methylation in the mammalian brain [26]. The 6 MethylC-Seq read libraries were processed with MethPipe [14] methylation analysis pipeline using standard parameter cutoffs. The resulting methylome samples had the mean coverage of 12.4 (s.d. 4.7). We computed DM CpGs and DM regions between neuron and non-neuron samples adjusting for baseline differences related to age and sex (12 month and 6 week old females, and 7 week old male). Top-left panel of Figure 2 contains a browser plot [27] with annotated DM regions and hypo methylated regions (HMRs) within a promoter of neuron specific enolase (Eno2), a well known marker of neuron cells [28,29]. The methylation profile of this gene across the frontal cortex samples reveals elongated HMRs upstream and downstream of the unmethylated promoter core in neuron samples compared to the ones in non-neuron samples, which constitute the DM regions.

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