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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.


Visualizing genomic conservation between human and 41 other species. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) primates; (iii) placentals; (iv) vertebrates; and (v) ideograms. The human genome has been divided into 2 MB windows, and for each window the fraction that can be aligned to the corresponding species is plotted as heatmap. The compact non-rectangular layout has been chosen to optimize plotting space utilization for the genome-wide visualization
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Fig3: Visualizing genomic conservation between human and 41 other species. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) primates; (iii) placentals; (iv) vertebrates; and (v) ideograms. The human genome has been divided into 2 MB windows, and for each window the fraction that can be aligned to the corresponding species is plotted as heatmap. The compact non-rectangular layout has been chosen to optimize plotting space utilization for the genome-wide visualization

Mentions: The second example (Fig. 3) shows conservation between the human genome and 41 other species [9]. The human genome is segmented into 2 MB windows and the fraction of each window that that can be aligned to the genome of the respective species is calculated. The 41 species are categorized into primates (6 species), placentals (19 species) and vertebrates (16 species), and each category is visualized as a single track. In each track, the order of species is calculated by hierarchical clustering of the fraction values from all chromosomes. Chromosomes are arranged in a non-rectangular layout for most efficient plotting space utilization, and fraction values are displayed as heatmap. Figure 3 clearly reveals different patterns of conservation between the human genome and the other species. Source code to generate Fig. 3 can be found at Additional file 3.Fig. 3


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

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

Visualizing genomic conservation between human and 41 other species. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) primates; (iii) placentals; (iv) vertebrates; and (v) ideograms. The human genome has been divided into 2 MB windows, and for each window the fraction that can be aligned to the corresponding species is plotted as heatmap. The compact non-rectangular layout has been chosen to optimize plotting space utilization for the genome-wide visualization
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

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

Fig3: Visualizing genomic conservation between human and 41 other species. There are five tracks for each chromosome (from top to bottom): (i) chromosome names; (ii) primates; (iii) placentals; (iv) vertebrates; and (v) ideograms. The human genome has been divided into 2 MB windows, and for each window the fraction that can be aligned to the corresponding species is plotted as heatmap. The compact non-rectangular layout has been chosen to optimize plotting space utilization for the genome-wide visualization
Mentions: The second example (Fig. 3) shows conservation between the human genome and 41 other species [9]. The human genome is segmented into 2 MB windows and the fraction of each window that that can be aligned to the genome of the respective species is calculated. The 41 species are categorized into primates (6 species), placentals (19 species) and vertebrates (16 species), and each category is visualized as a single track. In each track, the order of species is calculated by hierarchical clustering of the fraction values from all chromosomes. Chromosomes are arranged in a non-rectangular layout for most efficient plotting space utilization, and fraction values are displayed as heatmap. Figure 3 clearly reveals different patterns of conservation between the human genome and the other species. Source code to generate Fig. 3 can be found at Additional file 3.Fig. 3

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.