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Related Concept Videos

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Visualization of large influenza virus sequence datasets using adaptively aggregated trees with sampling-based

Leonid Zaslavsky1, Yiming Bao, Tatiana A Tatusova

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, MD 20894, USA. zaslavsk@ncbi.nlm.nih.gov

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Summary

Researchers can now visualize large influenza phylogenetic trees with an interactive, aggregated approach. This tool aids in exploring vast genomic datasets and retrieving information more efficiently.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • The rapid growth of influenza genome sequence data necessitates advanced computational tools for analysis.
  • Existing visualization methods struggle to represent large-scale phylogenetic datasets effectively.
  • Enhanced web-based visualization is crucial for data exploration and information retrieval.

Purpose of the Study:

  • To develop an efficient method for visualizing large influenza phylogenetic trees.
  • To enable interactive exploration and data retrieval from extensive genomic datasets.
  • To improve the comprehension of complex phylogenetic relationships.

Main Methods:

  • Developed an aggregated visualization approach for large phylogenetic trees.
  • Implemented automatic resolution scaling to fit screen space.
  • Utilized sequence similarity for selecting terminal groups.
  • Incorporated interactive refinement of the aggregated tree representation.
  • Displayed subscale details using small trees and systematic sampling.
  • Enabled annotation with aggregated structured metadata (e.g., seasonal, geographic).

Main Results:

  • Successfully created an aggregated tree representation adaptable to screen size.
  • Demonstrated interactive refinement capabilities for user-driven exploration.
  • Showcased the visualization of subscale details within terminal groups.
  • Integrated metadata aggregation for enhanced contextual understanding.
  • The approach is implemented in JavaScript within the NCBI Influenza Virus Resource.

Conclusions:

  • The developed approach provides an effective solution for visualizing and exploring large influenza phylogenetic datasets.
  • Interactive features and aggregated metadata enhance data comprehension and retrieval.
  • This tool facilitates better understanding of influenza evolution and epidemiology.