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Methylation plotter: a web tool for dynamic visualization of DNA methylation data.

Mallona I, Díez-Villanueva A, Peinado MA - Source Code Biol Med (2014)

Bottom Line: After the data upload, the tool produces different graphical representations of the results following the most commonly used styles to display this type of data.Coupled with this analysis, descriptive statistics and testing for differences at both CpG and group levels are provided.The implementation is based in R/shiny, providing a highly dynamic user interface that generates quality graphics without the need of writing R code.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain ; Health Research Institute Germans Trias i Pujol (IGTP), Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain.

ABSTRACT
Methylation plotter is a Web tool that allows the visualization of methylation data in a user-friendly manner and with publication-ready quality. The user is asked to introduce a file containing the methylation status of a genomic region. This file can contain up to 100 samples and 100 CpGs. Optionally, the user can assign a group for each sample (i.e. whether a sample is a tumoral or normal tissue). After the data upload, the tool produces different graphical representations of the results following the most commonly used styles to display this type of data. They include an interactive plot that summarizes the status of every CpG site and for every sample in lollipop or grid styles. Methylation values ranging from 0 (unmethylated) to 1 (fully methylated) are represented using a gray color gradient. A practical feature of the tool allows the user to choose from different types of arrangement of the samples in the display: for instance, sorting by overall methylation level, by group, by unsupervised clustering or just following the order in which data were entered. In addition to the detailed plot, Methylation plotter produces a methylation profile plot that summarizes the status of the scrutinized region, a boxplot that sums up the differences between groups (if any) and a dendrogram that classifies the data by unsupervised clustering. Coupled with this analysis, descriptive statistics and testing for differences at both CpG and group levels are provided. The implementation is based in R/shiny, providing a highly dynamic user interface that generates quality graphics without the need of writing R code. Methylation plotter is freely available at http://gattaca.imppc.org:3838/methylation_plotter/.

No MeSH data available.


Related in: MedlinePlus

Lollipop-like visualization with Methylation plotter.A, the input data alternates normal and tumor tissue data. B, data visualization after explicitly sorting the samples according to the tissue type; the pattern of tumor hypermethylation is easily detectable.
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Figure 2: Lollipop-like visualization with Methylation plotter.A, the input data alternates normal and tumor tissue data. B, data visualization after explicitly sorting the samples according to the tissue type; the pattern of tumor hypermethylation is easily detectable.

Mentions: The input data consist on beta values, a popular format, that offer an intuitive manner to represent the level of methylation. These beta values are typically generated by the software used to process bead arrays like the Illumina Infinium HumanMethylation450 [15]. Data portals such as the The Cancer Genome Atlas (TCGA) provide beta-values in a comprehensive series of cancer genomics datasets. However, wet lab users oftenly perform bisulfite-treated sequencing of their samples, and therefore require further preprocessing in order to assess the methylation status. For instance, an electrophoregram viewer or even a sequence alignment tool may be necessary. A flowchart of the data acquisition and processing steps is available as Figure 1. An excellent outline of the bisulfite data preprocessing may be found at [11].The methylation plot is interactive: without the need of reuploading the data, the user can customize the plot dimensions on the fly and therefore produce publication- ready figures. Accordingly, the user can select different types of arrangement of the samples in the display: for instance, sorting by overall methylation level, by group, by unsupervised clustering or just as provided. Finally, the lollipop plot allows to select whether to keep the distances between CpGs proportional (that is, disregarding the actual distance) or not. Figure 2 shows a typical lollipop-like output plot, as well the by-group sorting (Figure 2B). For bulky datasets, the user can select a more convenient heatmap-like plot that represents all the scrutinized CpGs in a grid-like manner.Beyond the lollipop or grid-like methylation plots, the tool provides three data representations. First, a heatmap with its associated dendrogram offers the result of the unsupervised clustering of the samples, colouring each dendrogram leaf according to the user-provided group (Figure 3A); this allows an easy checking of coherence between the already established groups and those generated by the unsupervised classification. Second, a profile plot summarizes the methylation panorama according to the sample group, labelling those CpGs that show statistical differences according to the nonparametric test Kruskal-Wallis (Figure 3B). And third, a boxplot depicts the methylation profile for each group highlighting its quartiles, thus simultaneously summarizing the methylation status for each group of samples (Figure 3C).


Methylation plotter: a web tool for dynamic visualization of DNA methylation data.

Mallona I, Díez-Villanueva A, Peinado MA - Source Code Biol Med (2014)

Lollipop-like visualization with Methylation plotter.A, the input data alternates normal and tumor tissue data. B, data visualization after explicitly sorting the samples according to the tissue type; the pattern of tumor hypermethylation is easily detectable.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4066318&req=5

Figure 2: Lollipop-like visualization with Methylation plotter.A, the input data alternates normal and tumor tissue data. B, data visualization after explicitly sorting the samples according to the tissue type; the pattern of tumor hypermethylation is easily detectable.
Mentions: The input data consist on beta values, a popular format, that offer an intuitive manner to represent the level of methylation. These beta values are typically generated by the software used to process bead arrays like the Illumina Infinium HumanMethylation450 [15]. Data portals such as the The Cancer Genome Atlas (TCGA) provide beta-values in a comprehensive series of cancer genomics datasets. However, wet lab users oftenly perform bisulfite-treated sequencing of their samples, and therefore require further preprocessing in order to assess the methylation status. For instance, an electrophoregram viewer or even a sequence alignment tool may be necessary. A flowchart of the data acquisition and processing steps is available as Figure 1. An excellent outline of the bisulfite data preprocessing may be found at [11].The methylation plot is interactive: without the need of reuploading the data, the user can customize the plot dimensions on the fly and therefore produce publication- ready figures. Accordingly, the user can select different types of arrangement of the samples in the display: for instance, sorting by overall methylation level, by group, by unsupervised clustering or just as provided. Finally, the lollipop plot allows to select whether to keep the distances between CpGs proportional (that is, disregarding the actual distance) or not. Figure 2 shows a typical lollipop-like output plot, as well the by-group sorting (Figure 2B). For bulky datasets, the user can select a more convenient heatmap-like plot that represents all the scrutinized CpGs in a grid-like manner.Beyond the lollipop or grid-like methylation plots, the tool provides three data representations. First, a heatmap with its associated dendrogram offers the result of the unsupervised clustering of the samples, colouring each dendrogram leaf according to the user-provided group (Figure 3A); this allows an easy checking of coherence between the already established groups and those generated by the unsupervised classification. Second, a profile plot summarizes the methylation panorama according to the sample group, labelling those CpGs that show statistical differences according to the nonparametric test Kruskal-Wallis (Figure 3B). And third, a boxplot depicts the methylation profile for each group highlighting its quartiles, thus simultaneously summarizing the methylation status for each group of samples (Figure 3C).

Bottom Line: After the data upload, the tool produces different graphical representations of the results following the most commonly used styles to display this type of data.Coupled with this analysis, descriptive statistics and testing for differences at both CpG and group levels are provided.The implementation is based in R/shiny, providing a highly dynamic user interface that generates quality graphics without the need of writing R code.

View Article: PubMed Central - HTML - PubMed

Affiliation: Institute of Predictive and Personalized Medicine of Cancer (IMPPC), Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain ; Health Research Institute Germans Trias i Pujol (IGTP), Ctra. de Can Ruti, camí de les escoles, s/n, 08916 Badalona, Spain.

ABSTRACT
Methylation plotter is a Web tool that allows the visualization of methylation data in a user-friendly manner and with publication-ready quality. The user is asked to introduce a file containing the methylation status of a genomic region. This file can contain up to 100 samples and 100 CpGs. Optionally, the user can assign a group for each sample (i.e. whether a sample is a tumoral or normal tissue). After the data upload, the tool produces different graphical representations of the results following the most commonly used styles to display this type of data. They include an interactive plot that summarizes the status of every CpG site and for every sample in lollipop or grid styles. Methylation values ranging from 0 (unmethylated) to 1 (fully methylated) are represented using a gray color gradient. A practical feature of the tool allows the user to choose from different types of arrangement of the samples in the display: for instance, sorting by overall methylation level, by group, by unsupervised clustering or just following the order in which data were entered. In addition to the detailed plot, Methylation plotter produces a methylation profile plot that summarizes the status of the scrutinized region, a boxplot that sums up the differences between groups (if any) and a dendrogram that classifies the data by unsupervised clustering. Coupled with this analysis, descriptive statistics and testing for differences at both CpG and group levels are provided. The implementation is based in R/shiny, providing a highly dynamic user interface that generates quality graphics without the need of writing R code. Methylation plotter is freely available at http://gattaca.imppc.org:3838/methylation_plotter/.

No MeSH data available.


Related in: MedlinePlus