Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit

Lavikka, K.; Oikkonen, J.; Li, Y.; Muranen, T.; Micoli, G.; Marchi, G.; Lahtinen, A.; Huhtinen, K.; Lehtonen, R.; Hietanen, S.; Hynninen, J.; Virtanen, A.; Hautaniemi, S.

doi:10.1101/2023.10.06.561159

bioRxiv · 10.1101/2023.10.06.561159

Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit

Lavikka, K.·Oikkonen, J.·Li, Y.·Muranen, T.·Micoli, G.·Marchi, G.·Lahtinen, A.·Huhtinen, K.·Lehtonen, R.·Hietanen, S.·Hynninen, J.·Virtanen, A.·Hautaniemi, S.

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Abstract

BackgroundVisualization is an indispensable facet of genomic data analysis. Despite the abundance of specialized visualization tools, there remains a distinct need for tailored solutions. However, their implementation typically requires extensive programming expertise from bioinformaticians and software developers, especially when building interactive applications. Toolkits based on visualization grammars offer a more accessible, declarative way to author new visualizations. Nevertheless, current grammar-based solutions fall short in adequately supporting the interactive analysis of large data sets with extensive sample collections, a pivotal task often encountered in cancer research. ResultsWe present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. Users can implement new visualization designs with little effort by using combinatorial building blocks that are put together with a declarative language. These fully customizable visualizations can be embedded in web pages or end-user-oriented applications. The toolkit also includes a fully customizable but user-friendly application for analyzing sample collections, which may comprise genomic and clinical data. Findings can be bookmarked and shared as links that incorporate provenance information. A distinctive element of GenomeSpys architecture is its effective use of the graphics processing unit (GPU) in all rendering. GPU usage enables a high frame rate and smoothly animated interactions, such as navigation within a genome. We demonstrate the utility of GenomeSpy by characterizing the genomic landscape of 753 ovarian cancer samples from patients in the DECIDER clinical trial. Our results expand the understanding of the genomic architecture in ovarian cancer, particularly the diversity of chromosomal instability. We also show how GenomeSpy enabled the discovery of clinically actionable genomic aberrations. ConclusionsGenomeSpy is a visualization toolkit applicable to a wide range of tasks pertinent to genome analysis. It offers high flexibility and exceptional performance in interactive analysis. The toolkit is open source with an MIT license, implemented in JavaScript, and available at https://genomespy.app/.

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Lavikka, K., Oikkonen, J., Li, Y., Muranen, T., Micoli, G., Marchi, G., Lahtinen, A., Huhtinen, K., Lehtonen, R., Hietanen, S., Hynninen, J., Virtanen, A., Hautaniemi, S.. 2023-10-06. Deciphering Cancer Genomes with GenomeSpy: A Grammar-Based Visualization Toolkit. https://doi.org/10.1101/2023.10.06.561159

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