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bioRxiv · 10.1101/2022.05.11.491507

Geodesics to characterize the phylogenetic landscape

Khodaei, M.·Owen, M.·Beerli, P.
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Abstract

Phylogenetic trees are fundamental for understanding evolutionary history. However, finding maximum likelihood trees is challenging due to the complexity of the likelihood landscape and the size of tree space. Based on the Billera-Holmes-Vogtmann (BHV) distance between trees, we describe a method to generate intermediate trees on the shortest path between two trees, called pathtrees. These pathtrees give a structured way to generate and visualize treespace in an area of interest. They allow investigating intermediate regions between trees of interest, exploring locally optimal trees in topological clusters of treespace, and potentially finding trees of high likelihood unexplored by tree search algorithms. We compared our approach against other tree search tools (PO_SCPLOWAUPC_SCPLOW*, RAxML, and RO_SCPLOWEVC_SCPLOWBO_SCPLOWAYESC_SCPLOW) in terms of generated highest likelihood trees, new topology proportions, and consistency of generated treespace. We assess our method using two datasets. The first consists of 23 primate species (CytB, 1141 bp), leading to well-resolved relationships. The second is a dataset of 182 milksnakes (CytB, 1117 bp), containing many similar sequences and complex relationships among individuals. Our method visualizes the treespace using log likelihood as a fitness function. It finds similarly optimal trees as heuristic methods and presents the likelihood landscape at different scales. It revealed that we could find trees that were not found with MCMC methods. The validation measures indicated that our method performed well mapping treespace into lower dimensions. Our method complements heuristic search analyses, and the visualization allows the inspection of likelihood terraces and exploration of treespace areas not visited by heuristic searches.

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Khodaei, M., Owen, M., Beerli, P.. 2022-05-12. Geodesics to characterize the phylogenetic landscape. https://doi.org/10.1101/2022.05.11.491507

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