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  3. Saasi: Sampling Aware Ancestral State Inference.
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  3. Saasi: Sampling Aware Ancestral State Inference.

Related Experiment Video

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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SAASI: Sampling Aware Ancestral State Inference.

Yexuan Song1, Ivan Gill2, Ailene MacPherson3,4

  • 1Department of Mathematics, Simon Fraser University, Burnaby, BC, Canada. yexuan_song@sfu.ca.

Nature Communications
|May 11, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Sampling bias can distort viral transmission history. Our new method, sampling-aware ancestral state inference (SAASI), accurately reconstructs viral origins and spread, even with uneven data. This improves phylogeographic analyses.

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Area of Science:

  • Evolutionary biology
  • Computational phylogenetics
  • Epidemiology

Background:

  • Ancestral state inference is crucial for understanding pathogen transmission dynamics.
  • Existing methods are susceptible to sampling bias, potentially leading to inaccurate historical reconstructions.
  • The H5N1 virus presents a complex multi-host transmission network.

Purpose of the Study:

  • To develop a novel method for ancestral state inference that explicitly accounts for sampling bias.
  • To assess the impact of sampling bias on H5N1 virus transmission history in the US.
  • To evaluate the accuracy and scalability of the new method.

Main Methods:

  • Introduction of sampling-aware ancestral state inference (SAASI).
  • Application of SAASI to H5N1 virus transmission data in the United States (2024).
  • Comparative analysis with standard inference methods using simulations.

    • Main Results:

    • SAASI corrects for sampling differences, providing more accurate ancestral state reconstructions.
    • Under-sampling of wild birds in H5N1 data led to an underestimation of the transmission event to cattle.
    • SAASI demonstrates superior accuracy over standard methods when sampling bias is present.

    Conclusions:

    • SAASI is a robust and computationally efficient tool for phylogeographic analysis.
    • Accounting for sampling bias is essential for accurate reconstruction of pathogen transmission histories.
    • The method is scalable to large phylogenetic datasets, facilitating broader applications.