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ggtreeExtra: Compact Visualization of Richly Annotated Phylogenetic Data.

Shuangbin Xu1, Zehan Dai1, Pingfan Guo1

  • 1Department of Bioinformatics, School of Basic Medical Sciences, Southern Medical University, Guangzhou, China.

Molecular Biology and Evolution
|June 7, 2021
PubMed
Summary
This summary is machine-generated.

The ggtreeExtra package enhances phylogenetic tree visualization by integrating diverse data types and evolutionary statistics. This universal tool offers rich annotations and flexible layouts for broader scientific interpretation.

Keywords:
data integrationdata visualizationphylogenysoftware

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Evolutionary Biology

Background:

  • Phylogenetic trees are crucial for evolutionary studies but visualizing associated heterogeneous data remains challenging.
  • Existing tools often lack comprehensive support for diverse data types and advanced visualization methods.

Purpose of the Study:

  • To introduce ggtreeExtra, a novel R package for advanced phylogenetic tree visualization.
  • To enable the integration and visualization of heterogeneous data alongside phylogenetic information.

Main Methods:

  • Utilizes the grammar of graphics syntax for flexible data layering and annotation.
  • Supports both circular and rectangular tree layouts.
  • Integrates with common evolutionary statistics inference software.

Main Results:

  • ggtreeExtra accommodates a wider range of data types and visualization techniques compared to existing packages.
  • Facilitates the rich annotation of phylogenetic trees with external and evolutionary data.
  • Provides a universal platform for tree data visualization across disciplines.

Conclusions:

  • ggtreeExtra significantly expands the utility of phylogenetic trees for data interpretation.
  • It empowers researchers to visualize and interpret domain-specific data within an evolutionary context.
  • The package serves as a versatile tool for diverse scientific applications requiring tree-based data visualization.