What is it about?
Species trees help scientists understand how life evolved on a broad scale and support many biological and medical applications. We developed a fully automated tool, called ROADIES, that can build these trees directly from genome data, without needing expert knowledge or heavy computing. This makes species tree inference faster, easier, and accessible to more researchers.
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Why is it important?
ROADIES— which stands for “Reference-free, Orthology-free, Annotation-free, Discordance-aware Estimation of Species Trees”-- stands apart from existing phylogenetic tools in many ways. First, ROADIES is a fully automated pipeline that achieves accuracy comparable to expert-led methods, making high-quality phylogenetic analysis easy, even for non-experts. Second, instead of using predefined genomic regions with specific characteristics, such as protein-coding genes, ROADIES is based on a random sampling of loci from input genomes. This simple approach is effective in generating accurate results and offers unique benefits, including better adherence to models of sequence evolution. Third, ROADIES, unlike many existing methods, is able to take advantage of genes that are present in multiple copies across the genome, without having to establish exact correspondence between them. Finally, ROADIES produces reliable phylogenies for datasets with hundreds of genomes while requiring significantly fewer computational resources. Together, these advances make ROADIES a timely and transformative tool for large-scale evolutionary studies.
Perspectives
With large-scale efforts aiming to sequence all complex species on Earth, we see ROADIES as a timely and transformative tool. It makes species tree inference accessible, which could significantly broaden its use in comparative genomics and biomedical research.
Anshu Gupta
University of California San Diego
Read the Original
This page is a summary of: Accurate, scalable, and fully automated inference of species trees from raw genome assemblies using ROADIES, Proceedings of the National Academy of Sciences, May 2025, Proceedings of the National Academy of Sciences,
DOI: 10.1073/pnas.2500553122.
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