Limits...
GenomeGraphs: integrated genomic data visualization with R.

Durinck S, Bullard J, Spellman PT, Dudoit S - BMC Bioinformatics (2009)

Bottom Line: This allows genomic annotation to be plotted together with experimental data.GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system.GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu

ABSTRACT

Background: Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.

Results: We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system.

Conclusion: GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

Show MeSH
ArrayCGH and exon array data. The first track in this figure shows an ideogram of the human chromosome 3. The red marker highlights the plotted genomic region. The second track shows exon array data, where each data point corresponds to a probe measuring the expression level of an exon. The third track displays copy number data in green and segmented copy number data with dashed blue lines. Note the amplification which can be seen in both the copy number and exon array tracks, suggesting that the amplification event results in higher expression levels of the gene in this region. The bottom track shows the gene annotation data from Ensembl.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2629762&req=5

Figure 1: ArrayCGH and exon array data. The first track in this figure shows an ideogram of the human chromosome 3. The red marker highlights the plotted genomic region. The second track shows exon array data, where each data point corresponds to a probe measuring the expression level of an exon. The third track displays copy number data in green and segmented copy number data with dashed blue lines. Note the amplification which can be seen in both the copy number and exon array tracks, suggesting that the amplification event results in higher expression levels of the gene in this region. The bottom track shows the gene annotation data from Ensembl.

Mentions: In a last step, the gdPlot function is called to plot instances of gdObject that were created above. The objects are given to gdPlot as a list and the order in the list will determine the plotting order from top to bottom. A minimum and maximum base position are also given as arguments to restrict the visualization to this particular genomic region. The plot produced from this example is shown in Figure 1.


GenomeGraphs: integrated genomic data visualization with R.

Durinck S, Bullard J, Spellman PT, Dudoit S - BMC Bioinformatics (2009)

ArrayCGH and exon array data. The first track in this figure shows an ideogram of the human chromosome 3. The red marker highlights the plotted genomic region. The second track shows exon array data, where each data point corresponds to a probe measuring the expression level of an exon. The third track displays copy number data in green and segmented copy number data with dashed blue lines. Note the amplification which can be seen in both the copy number and exon array tracks, suggesting that the amplification event results in higher expression levels of the gene in this region. The bottom track shows the gene annotation data from Ensembl.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2629762&req=5

Figure 1: ArrayCGH and exon array data. The first track in this figure shows an ideogram of the human chromosome 3. The red marker highlights the plotted genomic region. The second track shows exon array data, where each data point corresponds to a probe measuring the expression level of an exon. The third track displays copy number data in green and segmented copy number data with dashed blue lines. Note the amplification which can be seen in both the copy number and exon array tracks, suggesting that the amplification event results in higher expression levels of the gene in this region. The bottom track shows the gene annotation data from Ensembl.
Mentions: In a last step, the gdPlot function is called to plot instances of gdObject that were created above. The objects are given to gdPlot as a list and the order in the list will determine the plotting order from top to bottom. A minimum and maximum base position are also given as arguments to restrict the visualization to this particular genomic region. The plot produced from this example is shown in Figure 1.

Bottom Line: This allows genomic annotation to be plotted together with experimental data.GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system.GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

View Article: PubMed Central - HTML - PubMed

Affiliation: Life Sciences Department, Lawrence Berkeley National Laboratory, 1 Cyclotron Rd, Berkeley, CA 94720, USA. steffen@stat.berkeley.edu

ABSTRACT

Background: Biological studies involve a growing number of distinct high-throughput experiments to characterize samples of interest. There is a lack of methods to visualize these different genomic datasets in a versatile manner. In addition, genomic data analysis requires integrated visualization of experimental data along with constantly changing genomic annotation and statistical analyses.

Results: We developed GenomeGraphs, as an add-on software package for the statistical programming environment R, to facilitate integrated visualization of genomic datasets. GenomeGraphs uses the biomaRt package to perform on-line annotation queries to Ensembl and translates these to gene/transcript structures in viewports of the grid graphics package. This allows genomic annotation to be plotted together with experimental data. GenomeGraphs can also be used to plot custom annotation tracks in combination with different experimental data types together in one plot using the same genomic coordinate system.

Conclusion: GenomeGraphs is a flexible and extensible software package which can be used to visualize a multitude of genomic datasets within the statistical programming environment R.

Show MeSH