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Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
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Related Experiment Video

Updated: Aug 28, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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VIVID: A Web Application for Variant Interpretation and Visualization in Multi-dimensional Analyses.

Swapnil Tichkule1,2, Yoochan Myung3,4, Myo T Naung1,2

  • 1Population Health and Immunity, Walter and Eliza Hall Institute of Medical Research, Melbourne, Australia.

Molecular Biology and Evolution
|September 14, 2022
PubMed
Summary
This summary is machine-generated.

VIVID is a new web tool that maps genetic variants to traits in 3D space, aiding evolutionary and genetic load studies. It helps researchers quickly assess genes and prioritize experimental validation.

Keywords:
data visualizationevolutionmulti-dimensional analysispopulation geneticsprotein structurevariant interpretation

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Area of Science:

  • Genomics
  • Population Genetics
  • Bioinformatics

Background:

  • Comparative genomics and population genetics studies generate vast amounts of DNA variant data.
  • Associating genetic variants with phenotypes or fitness is a key goal in these studies.

Purpose of the Study:

  • Introduce VIVID, an interactive web application for encoding genotypic to phenotypic information in 3D space.
  • Facilitate the assessment of genes in adaptive evolution and genetic load studies.
  • Prioritize targets for experimental validation.

Main Methods:

  • Integration of diverse approaches for genotype-phenotype encoding.
  • Mutation mapping, annotation, interaction, and conservation score calculation.
  • Prediction of deleterious variants, diversity and selection analysis.
  • 3D visualization of Variant Call Format data on AlphaFold2 protein models.

Main Results:

  • VIVID enables rapid assessment of genes and prioritization of experimental targets.
  • Demonstrated utility by exploring Plasmodium falciparum evolutionary genetics.
  • Revealed geographic variation in balancing selection signatures in antibody target genes.

Conclusions:

  • VIVID is a user-friendly tool for integrating and visualizing genomic data in a 3D context.
  • It supports the study of adaptive evolution, genetic load, and functional genomics.
  • The tool aids in understanding evolutionary patterns and identifying key genes for further research.