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Buscogeny: A BUSCO leveraged phylogenomic tree builder.

John Webster1, Toni A Chapman2

  • 1NSW Department of Primary Industries and Regional Development, Sydney, NSW, Australia. john.webster@dpird.nsw.gov.au.

International Microbiology : the Official Journal of the Spanish Society for Microbiology
|January 23, 2026
PubMed
Summary

Buscogeny is a new tool that automates the creation of phylogenetic trees using BUSCO-derived genes. This method provides robust evolutionary insights from whole-genome data, especially for fungi.

Keywords:
AutomatedBUSCOBioinformaticFungalPhylogeneticPhylogenomicPipelineRecombination filteringTaxonomy

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Area of Science:

  • Evolutionary Biology
  • Genomics
  • Bioinformatics

Background:

  • Phylogenetic trees are crucial for understanding evolutionary relationships.
  • Single-gene phylogenies can be insufficient for complex genomes.
  • Generating concatenated gene alignments is challenging for poorly annotated genomes like fungi.

Purpose of the Study:

  • To present Buscogeny, an open-source tool for automated phylogenetic tree construction.
  • To enable robust evolutionary inference from whole-genome data.
  • To simplify the process for researchers working with diverse or incomplete genomic datasets.

Main Methods:

  • Automated construction of concatenated gene alignments using BUSCO-derived single-copy orthologs.
  • Integration of genome quality assessment, ortholog extraction, and alignment.
  • Inclusion of gap and recombination filtering, followed by automated phylogenetic inference.

Main Results:

  • Demonstrated utility of Buscogeny in phylogenetic analyses of bacterial and fungal genomes.
  • Successful application in complex fungal groups like Alternaria with variable genome completeness.
  • Streamlined and reproducible workflow for BUSCO-based phylogenetic inference.

Conclusions:

  • Buscogeny facilitates robust evolutionary insights from whole-genome data.
  • The tool enhances accessibility of phylogenetic inference for researchers across disciplines.
  • Enables more accurate evolutionary relationship resolution, particularly for challenging taxa.