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Comprehensive analysis of DNA methylation data with RnBeads.

Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C - Nat. Methods (2014)

Bottom Line: RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/).Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data.Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany.

ABSTRACT
RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

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Related in: MedlinePlus

Analysis of DNA methylation during adult stem cell differentiationRnBeads was used to re-analyze an RRBS dataset comprising 19 cell types of the blood and skin lineages21. All diagrams shown were calculated by RnBeads but have been reformatted according to journal standards.(a) Global distribution of DNA methylation levels among retained and removed CpGs after the preprocessing step.(b) Relative similarity and differences of DNA methylation profiles between cell types. Two maximally informative dimensions were calculated using multi-dimensional scaling (MDS) based on the matrix of average methylation levels in 5kb tiling regions. Samples are color-coded according to cell type.(c) Composite plot of DNA methylation levels in blood (green) and skin (orange) cell types averaged across all genes. Each gene was covered by six equally sized bins and by two flanking regions of the same size. Smoothing was done using cubic splines.(d) Heatmap with hierarchical clustering of DNA methylation levels among lineage marker genes that are specifically expressed in the blood lineage. Clustering used average linkage and Manhattan distance.(e) Scatterplot of groupwise mean DNA methylation levels across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(f) Volcano plot illustrating effect size and statistical significance across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(g) DNA methylation profile of the Hoxb3 gene locus on Chromosome 11 (brown triangle). Heatmaps show DNA methylation levels of single CpGs according to the color scheme in panel d. Smoothing of DNA methylation levels (bottom) was done using cubic splines.
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Figure 2: Analysis of DNA methylation during adult stem cell differentiationRnBeads was used to re-analyze an RRBS dataset comprising 19 cell types of the blood and skin lineages21. All diagrams shown were calculated by RnBeads but have been reformatted according to journal standards.(a) Global distribution of DNA methylation levels among retained and removed CpGs after the preprocessing step.(b) Relative similarity and differences of DNA methylation profiles between cell types. Two maximally informative dimensions were calculated using multi-dimensional scaling (MDS) based on the matrix of average methylation levels in 5kb tiling regions. Samples are color-coded according to cell type.(c) Composite plot of DNA methylation levels in blood (green) and skin (orange) cell types averaged across all genes. Each gene was covered by six equally sized bins and by two flanking regions of the same size. Smoothing was done using cubic splines.(d) Heatmap with hierarchical clustering of DNA methylation levels among lineage marker genes that are specifically expressed in the blood lineage. Clustering used average linkage and Manhattan distance.(e) Scatterplot of groupwise mean DNA methylation levels across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(f) Volcano plot illustrating effect size and statistical significance across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(g) DNA methylation profile of the Hoxb3 gene locus on Chromosome 11 (brown triangle). Heatmaps show DNA methylation levels of single CpGs according to the color scheme in panel d. Smoothing of DNA methylation levels (bottom) was done using cubic splines.

Mentions: The second example focuses on an RRBS dataset describing the DNA methylation dynamics of blood and skin stem cell differentiation in mice21. This dataset comprises 13 blood and 6 skin cell populations with biological replicates and DNA methylation data for slightly more than two million CpGs in each sample. The global distribution of DNA methylation is characteristically bimodal, and discrete peaks at 33%, 50% and 67% DNA methylation disappear after filtering out CpGs with low sequencing coverage (Figure 2a). Exploratory analysis confirms that the difference between blood and skin cell types dominates the analysis (Figure 2b), and DNA methylation levels are generally higher in blood cells than in skin cells when taking regional averages over all annotated genes (Figure 2c). Hierarchical clustering perfectly discriminates between blood and skin cell types (Figure 2d), confirming that DNA methylation patterns tend to be determined more strongly by cellular lineage than by other properties such as cell proliferation or differentiation status.


Comprehensive analysis of DNA methylation data with RnBeads.

Assenov Y, Müller F, Lutsik P, Walter J, Lengauer T, Bock C - Nat. Methods (2014)

Analysis of DNA methylation during adult stem cell differentiationRnBeads was used to re-analyze an RRBS dataset comprising 19 cell types of the blood and skin lineages21. All diagrams shown were calculated by RnBeads but have been reformatted according to journal standards.(a) Global distribution of DNA methylation levels among retained and removed CpGs after the preprocessing step.(b) Relative similarity and differences of DNA methylation profiles between cell types. Two maximally informative dimensions were calculated using multi-dimensional scaling (MDS) based on the matrix of average methylation levels in 5kb tiling regions. Samples are color-coded according to cell type.(c) Composite plot of DNA methylation levels in blood (green) and skin (orange) cell types averaged across all genes. Each gene was covered by six equally sized bins and by two flanking regions of the same size. Smoothing was done using cubic splines.(d) Heatmap with hierarchical clustering of DNA methylation levels among lineage marker genes that are specifically expressed in the blood lineage. Clustering used average linkage and Manhattan distance.(e) Scatterplot of groupwise mean DNA methylation levels across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(f) Volcano plot illustrating effect size and statistical significance across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(g) DNA methylation profile of the Hoxb3 gene locus on Chromosome 11 (brown triangle). Heatmaps show DNA methylation levels of single CpGs according to the color scheme in panel d. Smoothing of DNA methylation levels (bottom) was done using cubic splines.
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Related In: Results  -  Collection

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Figure 2: Analysis of DNA methylation during adult stem cell differentiationRnBeads was used to re-analyze an RRBS dataset comprising 19 cell types of the blood and skin lineages21. All diagrams shown were calculated by RnBeads but have been reformatted according to journal standards.(a) Global distribution of DNA methylation levels among retained and removed CpGs after the preprocessing step.(b) Relative similarity and differences of DNA methylation profiles between cell types. Two maximally informative dimensions were calculated using multi-dimensional scaling (MDS) based on the matrix of average methylation levels in 5kb tiling regions. Samples are color-coded according to cell type.(c) Composite plot of DNA methylation levels in blood (green) and skin (orange) cell types averaged across all genes. Each gene was covered by six equally sized bins and by two flanking regions of the same size. Smoothing was done using cubic splines.(d) Heatmap with hierarchical clustering of DNA methylation levels among lineage marker genes that are specifically expressed in the blood lineage. Clustering used average linkage and Manhattan distance.(e) Scatterplot of groupwise mean DNA methylation levels across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(f) Volcano plot illustrating effect size and statistical significance across genes, with the 1,000 highest-ranking differentially methylated genes highlighted in red. Point density is shown as blue shading.(g) DNA methylation profile of the Hoxb3 gene locus on Chromosome 11 (brown triangle). Heatmaps show DNA methylation levels of single CpGs according to the color scheme in panel d. Smoothing of DNA methylation levels (bottom) was done using cubic splines.
Mentions: The second example focuses on an RRBS dataset describing the DNA methylation dynamics of blood and skin stem cell differentiation in mice21. This dataset comprises 13 blood and 6 skin cell populations with biological replicates and DNA methylation data for slightly more than two million CpGs in each sample. The global distribution of DNA methylation is characteristically bimodal, and discrete peaks at 33%, 50% and 67% DNA methylation disappear after filtering out CpGs with low sequencing coverage (Figure 2a). Exploratory analysis confirms that the difference between blood and skin cell types dominates the analysis (Figure 2b), and DNA methylation levels are generally higher in blood cells than in skin cells when taking regional averages over all annotated genes (Figure 2c). Hierarchical clustering perfectly discriminates between blood and skin cell types (Figure 2d), confirming that DNA methylation patterns tend to be determined more strongly by cellular lineage than by other properties such as cell proliferation or differentiation status.

Bottom Line: RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/).Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data.Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

View Article: PubMed Central - PubMed

Affiliation: Max Planck Institute for Informatics, Saarbrücken, Germany.

ABSTRACT
RnBeads is a software tool for large-scale analysis and interpretation of DNA methylation data, providing a user-friendly analysis workflow that yields detailed hypertext reports (http://rnbeads.mpi-inf.mpg.de/). Supported assays include whole-genome bisulfite sequencing, reduced representation bisulfite sequencing, Infinium microarrays and any other protocol that produces high-resolution DNA methylation data. Notable applications of RnBeads include the analysis of epigenome-wide association studies and epigenetic biomarker discovery in cancer cohorts.

Show MeSH
Related in: MedlinePlus