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. 2012 Nov;30(11):1036-9.
doi: 10.1038/nbt.2404.

NGS analyses by visualization with Trackster

Jeremy Goecks, Nate Coraor; Galaxy Team; Anton Nekrutenko, James Taylor

NGS analyses by visualization with Trackster

Jeremy Goecks et al. Nat Biotechnol. 2012 Nov.
No abstract available

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Figures

Figure 1
Figure 1
Using Trackster to improve XBP1 transcript assembly results via interactive tool use and parameter space exploration. Top: Interactive tool use in track view: (a) Reference gene annotation for XBP1 locus; (b) mapped RNA-seq reads to be assembled into transcripts; (c) embedded tool interface for Cufflinks, a transcript assembly tool for high-throughput sequencing data, enabling parameter settings to be quickly changed and Cufflinks rerun on data in the visible region to produce a new assembly; (d) initial assembly using default settings; (e), (f), and (g) repeatedly changing settings and rerunning Cufflinks produces new assemblies that are visualized automatically and can be easily compared. Bottom: Tool parameter space exploration: (i) Galaxy’s Cufflinks tool form is used to create and modify a partial tool parameter space tree by setting parameters’ minimum, maximum, and number of samples; (ii) partial Cufflinks parameter tree is displayed, and clicking on a node in the tree runs Cufflinks using all combinations of settings defined by the node’s subtree; (iii) Cufflinks output in the XBP1 region is visualized automatically and can be easily compared. In both Trackster views, Cufflinks is run using only data from the visible or selected region, so each assembly can be created in about a minute. Once good parameter settings have been found, Cufflinks can be run on the complete dataset using the good settings.
Figure 2
Figure 2
Using Trackster to dynamically filtering transcript assemblies for multiple tissues to identify high-quality transcripts and filter out assembly artifacts. Trackster automatically creates filters for attributes values for any genomic inteval dataset, including assembled transcripts. Transcripts that are likely to be assembly artifacts are those with a low Score— a relative measure of transcript expression—or those with a low FPKM—an absolute measure of transcript expression. (a) To compare transcript FPKM values, FPKM is encoded as transparency, making transcripts with high expression darker and easy to see; (b) alternatively, Score is encoded as height and a filter is used to hide low scoring transcripts, so transcript height denotes its Score relative to the filter’s range (224–1000) and transcripts with a high score can be picked out easily; (c) filtering simutaneously by FPKM and Score yields a set of high-quality transcripts.
See this image and copyright information in PMC

References

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