The Effect of Alignment on People's Ability to Judge Event Sequence Similarity

Ruddle, Roy A.; Bernard, Jürgen; Lücke-Tieke, Hendrik; May, Thorsten; Kohlhammer, Jörn

doi:10.1109/tvcg.2021.3050497

Cited by 4 publications

(6 citation statements)

References 42 publications

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“…Classical event sequences are strongly present in the medical domain, particularly in electronic health records (EHR). Here, prevalent analysis tasks include sequential pattern mining [47,83,96], clustering sequences based on similarity characteristics [43,85], comparing individual event sequences [45,51,107], and comparing cohorts of patients [17,69]. Approaches scalable to tens of thousands of events with hundreds of event categories [61] and techniques to reduce the data load using progressive analytics [96] were proposed.…”

Section: Interactive Visual Approaches For Event Sequencesmentioning

confidence: 99%

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IVESA - Visual Analysis of Time-Stamped Event Sequences

Bernard,

Barth,

Cuba

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Overview of IVESA. On the left, the Sequence Overview and Details View primarily enable the analysis of the TSEQs content, i.e., events, event sequences, groups of event sequences, motifs, and features. On the right, the Metadata View supports the analysis of metadata attributes and the TSEQs contextualization, whereas the Summary View includes the entry point to auxiliary views for filtering, motif configuration, feature analysis, and clustering.

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Section: Interactive Visual Approaches For Event Sequencesmentioning

confidence: 99%

“…Alignment (T A ): Similarly, we identified that the dominating sequence alignment (T A ) strategies can often be determined in the design phase. Based on the observed real-world cases, three sequence alignment strategies [84] are necessary to support comparison.…”

Section: Preprocessingmentioning

confidence: 99%

IVESA - Visual Analysis of Time-Stamped Event Sequences

Bernard,

Barth,

Cuba

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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“…In our case, the sets to compare are the different nucleotide sequences in the pangenome, over which an MSA has been calculated by PanTools. Our design assumes a type of sequence alignment has been performed; we opted to display the global alignment because it allows users to judge sequence similarity more accurately than other alignment methods [46].…”

Section: Sequences and Annotationsmentioning

confidence: 99%

PanVA: Pangenomic Variant Analysis

Brandt

Jonkheer

Workum

et al. 2024

IEEE Trans. Visual. Comput. Graphics

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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.

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“…The main channels used to visually encode sequences are color, shape, letters, text and texture [39]. We use color to encode the different nucleotides in the MSA.…”

Section: Sequences and Annotationsmentioning

confidence: 99%

PanVA: Pangenomic Variant Analysis

Brandt¹,

Jonkheer²,

Workum³

et al. 2023

Preprint

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<p>Studying genetic variation underlying phenotypes is an important topic in genomics. In plant genomic research, for example, scientists analyze the variation between cultivars and wild types to develop crops with improved resistance to diseases. This analysis is commonly based on comparison to a single reference genome. Because the number of genomes is growing rapidly and to avoid bias towards a single reference genome, the field is shifting towards the use of pangenomes, i.e., abstract representations of multiple genomes in a species or population. While pangenomes allow for a more complete picture of the genetic variation, their large size and complex data structure hinder analysis. To deal with this, genome scientists need visual analytics tools that support interactive and exploratory analysis of pangenomes to identify relevant information for variant analysis. A major challenge is to handle multiple references together with providing the adequate context of heterogeneous (meta)data, such as annotations, evolutionary relationships, and phenotypes. To address this challenge, we developed PanVA, a visual analytics design for variant analysis in pangenomes. PanVA supports a novel strategy for pangenomic variant analysis that was designed with the active participation of genomics researchers. PanVA uniquely allows researchers to get a complete picture of the variation within genes in a large set of genomes, and identify associations with phenotypes. The design combines tailored visual representations with interactions such as sorting, grouping and aggregation, allowing the user to navigate and explore different perspectives. The realization of the PanVA design is possible through PanTools. Through user evaluation in the context of plants and pathogen research, we demonstrate that PanVA helps researchers explore regions of interest and generate hypotheses about genetic variants and their role in phenotypic variation.</p>

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The Effect of Alignment on People's Ability to Judge Event Sequence Similarity

Cited by 4 publications

References 42 publications

IVESA - Visual Analysis of Time-Stamped Event Sequences

IVESA - Visual Analysis of Time-Stamped Event Sequences

PanVA: Pangenomic Variant Analysis

PanVA: Pangenomic Variant Analysis

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