ArticleInternational microbiology : the official journal of the Spanish Society for Microbiology2026
Buscogeny: A BUSCO leveraged phylogenomic tree builder.
Article in International microbiology : the official journal of the Spanish Society for Microbiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- First draft genome and genomic analysis ofFrontiers in fungal biology · 2026Article
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2 authors.
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Abstract
Accurately resolving evolutionary relationships among organisms is a cornerstone of evolutionary biology, with phylogenetic trees serving as critical tools for this purpose. While single-gene phylogenies are widely used, they often fail to capture the full complexity of evolutionary processes, particularly in organisms with complex or variable genomes. Concatenated gene alignments, built from multiple orthologous genes, offer a more robust framework but are challenging to generate, especially in taxa with incomplete or poorly annotated genomes such as fungi. To address this challenge, we present Buscogeny (Busco Phylogeny), an open-source command-line tool that automates the construction of concatenated gene alignments and phylogenetic trees using BUSCO-derived single-copy orthologs. Buscogeny integrates key features including genome quality assessment, ortholog extraction, multiple sequence alignment, gap filtering, and recombination filtering, followed by automated phylogenetic inference. By allowing users to select ortholog datasets of varying resolutions and tailor filtering thresholds, Buscogeny is adaptable to both closely related and more distantly related taxa. We demonstrate the utility of Buscogeny in phylogenetic analyses of both bacterial and fungal genomes, including complex groups such as Alternaria, where annotation and genome completeness vary significantly. The streamlined, reproducible workflow provided by Buscogeny makes BUSCO-based phylogenetic inference accessible to researchers across disciplines, enabling robust evolutionary insights from whole-genome data.
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