PanVA: Pangenomic Variant Analysis

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Astrid van den Brandt, Eef M. Jonkheer, Dirk-Jan M. van Workum, Huub van de Wetering, Sandra Smit, Anna Vilanova

 DOI: 10.1109/TVCG.2023.3282364

 Room: 105

2023-10-26T05:33:00ZGMT-0600Change your timezone on the schedule page
2023-10-26T05:33:00Z

Exemplar figure, described by caption below — Overview of PanVA, annotated to show the main components: the Control Panel (A) to select a gene, review peripheral information, and select groups; the Gene Overview (B) to slice an interesting region within the gene for further analysis; and the Locus View (C), the core view to analyze genetic variation in the sliced region (1), and explore association with metadata (2) and hierarchical relations (3).

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Keywords

Visual analytics;design study;pangenomics;comparative genomics;variant analysis

Abstract

Genomics researchers increasingly use multiple reference genomes to comprehensively explore genetic variants underlying differences in detectable characteristics between organisms. Pangenomes allow for an efficient data representation of multiple related genomes and their associated metadata. However, current visual analysis approaches for exploring these complex genotype-phenotype relationships are often based on single reference approaches or lack adequate support for interpreting the variants in the genomic context with heterogeneous (meta)data. This design study introduces PanVA, a visual analytics design for pangenomic variant analysis developed with the active participation of genomics researchers. The design uniquely combines tailored visual representations with interactions such as sorting, grouping, and aggregation, allowing users to navigate and explore different perspectives on complex genotype-phenotype relations. Through evaluation in the context of plants and pathogen research, we show that PanVA helps researchers explore variants in genes and generate hypotheses about their role in phenotypic variation.