Deciphering cancer genomes with GenomeSpy: a grammar-based visualization toolkit

Author:

Lavikka Kari1ORCID,Oikkonen Jaana1ORCID,Li Yilin1ORCID,Muranen Taru1ORCID,Micoli Giulia1ORCID,Marchi Giovanni1ORCID,Lahtinen Alexandra1ORCID,Huhtinen Kaisa12ORCID,Lehtonen Rainer3ORCID,Hietanen Sakari4ORCID,Hynninen Johanna4ORCID,Virtanen Anni5ORCID,Hautaniemi Sampsa1ORCID

Affiliation:

1. Research Program in Systems Oncology, Research Programs Unit, Faculty of Medicine, University of Helsinki , 00014 Helsinki , Finland

2. Cancer Research Unit, Institute of Biomedicine and FICAN West Cancer Centre, University of Turku , 20521 Turku , Finland

3. Applied Tumor Genomics Research Program, Research Programs Unit, Faculty of Medicine, University of Helsinki , 00014 Helsinki , Finland

4. Department of Obstetrics and Gynecology, University of Turku and Turku University Hospital , 20521 Turku , Finland

5. Department of Pathology, University of Helsinki and HUS Diagnostic Center, Helsinki University Hospital , 00260 Helsinki , Finland

Abstract

Abstract Background Visualization 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. Yet, current grammar-based solutions fall short in adequately supporting the interactive analysis of large datasets with extensive sample collections, a pivotal task often encountered in cancer research. Findings We present GenomeSpy, a grammar-based toolkit for authoring tailored, interactive visualizations for genomic data analysis. By using combinatorial building blocks and a declarative language, users can implement new visualization designs easily and embed them in web pages or end-user–oriented applications. A distinctive element of GenomeSpy’s architecture is its effective use of the graphics processing unit in all rendering, enabling 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. Conclusions GenomeSpy 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/.

Funder

Horizon 2020 Framework Programme

Publisher

Oxford University Press (OUP)

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