Multipla: Multiscale Pangenomic Locus Analysis

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dc.contributor.authorBrandt, Astrid van denen_US
dc.contributor.authorStåhlbom, Emiliaen_US
dc.contributor.authorWorkum, Fredericus Johannes Maria vanen_US
dc.contributor.authorWetering, Huub van deen_US
dc.contributor.authorLundström, Claesen_US
dc.contributor.authorSmit, Sandraen_US
dc.contributor.authorVilanova, Annaen_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorAndrienko, Nataliaen_US
dc.contributor.editorWang, Beien_US
dc.date.accessioned2025-05-26T06:39:17Z
dc.date.available2025-05-26T06:39:17Z
dc.date.issued2025
dc.description.abstractComparing gene organization across genomic sequences reveals insights into evolutionary and functional diversity among different organisms and varieties. Performing this task across many sequences, such as from a pangenome, is challenging because of the scale, the density of information, and the inherent variation. Often, analyses are centered on a genomic region of interest-a locus that might be associated with a trait or contain genes within the same family or biological pathway. Within these regions, researchers examine the conservation of gene order and orientation across organisms and assess sequence similarity, along with other gene content features such as gene size, to find biological variations or potential errors in the data. Automated methods in comparative genomics struggle to identify meaningful patterns due to varying and often unknown features of interest, leaving manual, time-intensive, and scalability-challenged visualization as the primary alternative. To address these challenges, we present a multiscale design for studying gene organization within pangenomes, developed in close collaboration with domain experts. Our tool, MULTIPLA, enables users to explore organization at multiple levels of detail in a decluttered manner through layout abstractions, semantic zooming, and layouts with flexible distance definitions and feature selections, combining the advantages of manual and automated methods used in practice. We evaluate the design of MULTIPLA through two pangenomic use cases and conclude with lessons learned from designing multiscale views for pangenomic locus analysis.en_US
dc.description.sectionheadersSpatial and Multi-Scale Data Visualization
dc.description.seriesinformationComputer Graphics Forum
dc.identifier.doi10.1111/cgf.70147
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.70147
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf70147
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Human-centered computing → Visual analytics; Visualization design and evaluation methods; Applied computing → Genomics
dc.subjectHuman centered computing → Visual analytics
dc.subjectVisualization design and evaluation methods
dc.subjectApplied computing → Genomics
dc.titleMultipla: Multiscale Pangenomic Locus Analysisen_US
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