SDSR: A Spectral Divide-and-Conquer Approach for Species Tree Reconstruction

TL;DR

SDSR is a scalable spectral divide-and-conquer method for species tree reconstruction that offers significant runtime improvements while maintaining accuracy, effectively handling large datasets with complex evolutionary histories.

Contribution

The paper introduces SDSR, a novel spectral graph theory-based divide-and-conquer algorithm for efficient and accurate species tree reconstruction from large genetic datasets.

Findings

SDSR achieves up to 10-fold faster runtimes compared to full-data methods.

SDSR maintains comparable accuracy to traditional methods on synthetic datasets.

Theoretical guarantees support SDSR's recovery performance under the MSC model.

Abstract

Recovering a tree that represents the evolutionary history of a group of species is a key task in phylogenetics. Performing this task using sequence data from multiple genetic markers poses two key challenges. The first is the discordance between the evolutionary history of individual genes and that of the species. The second challenge is computational, as contemporary studies involve thousands of species. Here we present SDSR, a scalable divide-and-conquer approach for species tree reconstruction based on spectral graph theory. The algorithm recursively partitions the species into subsets until their sizes are below a given threshold. The trees of these subsets are reconstructed by a user-chosen species tree algorithm. Finally, these subtrees are merged to form the full tree. On the theoretical front, we derive recovery guarantees for SDSR, under the multispecies coalescent (MSC) model. We also perform a runtime complexity analysis. We show that SDSR, when combined with a species tree reconstruction algorithm as a subroutine, yields substantial runtime savings as compared to applying the same algorithm on the full data. Empirically, we evaluate SDSR on synthetic benchmark datasets with incomplete lineage sorting and horizontal gene transfer. In accordance with our theoretical analysis, the simulations show that combining SDSR with common species tree methods, such as CA-ML or ASTRAL, yields up to 10-fold faster runtimes. In addition, SDSR achieves a comparable tree reconstruction accuracy to that obtained by applying these methods on the full data.