Consensus methods for trees of blobs and level-1 phylogenetic networks

L1N consensus produces a level-1 phylogenetic network whose component support values reflect their frequency across input networks, outperforming existing rooted methods in simulated data.

• Why it matters: Many network-inference approaches yield multiple plausible solutions from ties, bootstrap or Bayesian sampling, or tool and parameter differences, and no consensus framework existed for semi-directed networks.
• What they did: The authors build a consensus tree of blobs from networks of any complexity, then extend it to level-1 networks using L1N consensus, implemented in PhyloSummaries.
• The result: In large-scale simulations, L1N consensus improves informativity and accuracy in the rooted setting, with similar gains for semi-directed networks, and applications to Leopardus and HIV datasets support its usefulness.

AI summary of the abstract · may contain errors · not medical advice · read the paper.