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gtrellis: an R/Bioconductor package for making genome-level Trellis graphics.

Gu Z, Eils R, Schlesner M - BMC Bioinformatics (2016)

Bottom Line: However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data.In addition, gtrellis provides an extensible framework that allows adding user-defined graphics.The gtrellis package provides an easy and effective way to visualize genomic data and reveal high dimensional relationships on a genome-wide scale. gtrellis can be flexibly extended and thus can also serve as a base package for highly specific purposes. gtrellis makes it easy to produce novel visualizations, which can lead to the discovery of previously unrecognized patterns in genomic data.

View Article: PubMed Central - PubMed

Affiliation: Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.

ABSTRACT

Background: Trellis graphics are a visualization method that splits data by one or more categorical variables and displays subsets of the data in a grid of panels. Trellis graphics are broadly used in genomic data analysis to compare statistics over different categories in parallel and reveal multivariate relationships. However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data.

Results: Here we introduce the gtrellis package which provides an efficient and extensible way to visualize genomic data in a Trellis layout. gtrellis provides highly flexible Trellis layouts which allow efficient arrangement of genomic categories on the plot. It supports multiple-track visualization, which makes it straightforward to visualize several properties of genomic data in parallel to explain complex relationships. In addition, gtrellis provides an extensible framework that allows adding user-defined graphics.

Conclusions: The gtrellis package provides an easy and effective way to visualize genomic data and reveal high dimensional relationships on a genome-wide scale. gtrellis can be flexibly extended and thus can also serve as a base package for highly specific purposes. gtrellis makes it easy to produce novel visualizations, which can lead to the discovery of previously unrecognized patterns in genomic data.

No MeSH data available.


Related in: MedlinePlus

Visualizing differentially methylated regions. Differentially methylated regions (DMRs) between Burkitt lymphomas and germinal center-derived B-cells are illustrated in rainfall plots to visualize the genomic distribution and identify localized clusters. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) rainfall plots for both hyper-methylated DMRs and hypo-methylated DMRs; (iii) genomic density for hyper-methylated DMRs; (iv) genomic density for hypo-methylated DMRs; and (v) ideograms
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Fig2: Visualizing differentially methylated regions. Differentially methylated regions (DMRs) between Burkitt lymphomas and germinal center-derived B-cells are illustrated in rainfall plots to visualize the genomic distribution and identify localized clusters. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) rainfall plots for both hyper-methylated DMRs and hypo-methylated DMRs; (iii) genomic density for hyper-methylated DMRs; (iv) genomic density for hypo-methylated DMRs; and (v) ideograms

Mentions: The first example (Fig. 2) illustrates the distribution of genomic regions which are differentially methylated (DMRs) in Burkitt lymphomas compared to germinal center B cells [8]. A DMR is a genomic interval in which methylation levels at CpG sites are significantly different between disease and control samples. A DMR is designated hyper-methylated if the methylation is higher in disease than in control samples and hypo-methylated if the methylation is lower in disease samples.Fig. 2


gtrellis: an R/Bioconductor package for making genome-level Trellis graphics.

Gu Z, Eils R, Schlesner M - BMC Bioinformatics (2016)

Visualizing differentially methylated regions. Differentially methylated regions (DMRs) between Burkitt lymphomas and germinal center-derived B-cells are illustrated in rainfall plots to visualize the genomic distribution and identify localized clusters. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) rainfall plots for both hyper-methylated DMRs and hypo-methylated DMRs; (iii) genomic density for hyper-methylated DMRs; (iv) genomic density for hypo-methylated DMRs; and (v) ideograms
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4835841&req=5

Fig2: Visualizing differentially methylated regions. Differentially methylated regions (DMRs) between Burkitt lymphomas and germinal center-derived B-cells are illustrated in rainfall plots to visualize the genomic distribution and identify localized clusters. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) rainfall plots for both hyper-methylated DMRs and hypo-methylated DMRs; (iii) genomic density for hyper-methylated DMRs; (iv) genomic density for hypo-methylated DMRs; and (v) ideograms
Mentions: The first example (Fig. 2) illustrates the distribution of genomic regions which are differentially methylated (DMRs) in Burkitt lymphomas compared to germinal center B cells [8]. A DMR is a genomic interval in which methylation levels at CpG sites are significantly different between disease and control samples. A DMR is designated hyper-methylated if the methylation is higher in disease than in control samples and hypo-methylated if the methylation is lower in disease samples.Fig. 2

Bottom Line: However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data.In addition, gtrellis provides an extensible framework that allows adding user-defined graphics.The gtrellis package provides an easy and effective way to visualize genomic data and reveal high dimensional relationships on a genome-wide scale. gtrellis can be flexibly extended and thus can also serve as a base package for highly specific purposes. gtrellis makes it easy to produce novel visualizations, which can lead to the discovery of previously unrecognized patterns in genomic data.

View Article: PubMed Central - PubMed

Affiliation: Division of Theoretical Bioinformatics (B080), German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120, Heidelberg, Germany.

ABSTRACT

Background: Trellis graphics are a visualization method that splits data by one or more categorical variables and displays subsets of the data in a grid of panels. Trellis graphics are broadly used in genomic data analysis to compare statistics over different categories in parallel and reveal multivariate relationships. However, current software packages to produce Trellis graphics have not been designed with genomic data in mind and lack some functionality that is required for effective visualization of genomic data.

Results: Here we introduce the gtrellis package which provides an efficient and extensible way to visualize genomic data in a Trellis layout. gtrellis provides highly flexible Trellis layouts which allow efficient arrangement of genomic categories on the plot. It supports multiple-track visualization, which makes it straightforward to visualize several properties of genomic data in parallel to explain complex relationships. In addition, gtrellis provides an extensible framework that allows adding user-defined graphics.

Conclusions: The gtrellis package provides an easy and effective way to visualize genomic data and reveal high dimensional relationships on a genome-wide scale. gtrellis can be flexibly extended and thus can also serve as a base package for highly specific purposes. gtrellis makes it easy to produce novel visualizations, which can lead to the discovery of previously unrecognized patterns in genomic data.

No MeSH data available.


Related in: MedlinePlus