Visualization is an essential scientific tool that makes it possible to view large amounts of data simultaneously, identify patterns and outliers within data, and communicate findings to others. Data analysis and visualization have traditionally been separate: data are first analyzed, and only then is visualization used to present a graphical overview of the results. This approach breaks down, however, for large genomic data sets, for which visualizing the results of time-consuming, computationally intensive analyses often shows that different analysis settings need to be used. Repeatedly running large analyses and visualizing results is a wasteful and slow way to find good analysis settings. Visualization, then, cannot remain an endpoint for genomic analyses, but instead must be integrated with analysis tools so that it can be used to evaluate intermediate results and incrementally improve an analysis.
Now, a team of researchers from Emory University, Penn State University, and the Galaxy team have developed Trackster, a visual analysis environment for next-generation sequencing data that tightly couples interactive visualization with data analysis. Using Trackster, selected data subsets, rather than complete data sets, can be analyzed, thereby reducing analysis computation time from days to seconds. Trackster takes advantage of this dramatic reduction in analysis computing time to enable an interactive, visual search of analysis settings. Using Trackster, many different analysis settings can be tried quickly and the outputs from different settings visualized together, making it easy to use visual inspection to select the settings that work best—all interactively and within minutes.
- Goecks J, Coraor N, Team TG, Nekrutenko A, Taylor J. (2012) NGS analyses by visualization with Trackster. Nat Biotechnol 30(11), 1036-9. [article]
Also see this document https://main.g2.bx.psu.edu/interactive-rnaseq which specifically describes how Trackster can be used for interactive, visual analysis of high-throughput transcriptome sequencing data.