Inferring Species Trees from Incongruent Multi-Copy Gene Trees Using the Robinson-Foulds Distance

TL;DR

This paper introduces a novel method for inferring species trees from multi-copy gene trees by generalizing the Robinson-Foulds distance, accommodating incongruences from various biological processes without assuming a specific cause.

Contribution

It develops a new generalized RF distance for mul-trees and formulates the MulRF supertree problem, providing a fast heuristic algorithm that improves accuracy over existing methods.

Findings

MulRF outperforms gene tree parsimony methods in accuracy.

The heuristic algorithm is efficient for large datasets.

The method handles incongruence from multiple biological processes.

Abstract

We present a new method for inferring species trees from multi-copy gene trees. Our method is based on a generalization of the Robinson-Foulds (RF) distance to multi-labeled trees (mul-trees), i.e., gene trees in which multiple leaves can have the same label. Unlike most previous phylogenetic methods using gene trees, this method does not assume that gene tree incongruence is caused by a single, specific biological process, such as gene duplication and loss, deep coalescence, or lateral gene transfer. We prove that it is NP-hard to compute the RF distance between two mul-trees, but it is easy to calculate the generalized RF distance between a mul-tree and a singly-labeled tree. Motivated by this observation, we formulate the RF supertree problem for mul-trees (MulRF), which takes a collection of mul-trees and constructs a species tree that minimizes the total RF distance from the input mul-trees. We present a fast heuristic algorithm for the MulRF supertree problem. Simulation experiments demonstrate that the MulRF method produces more accurate species trees than gene tree parsimony methods when incongruence is caused by gene tree error, duplications and losses, and/or lateral gene transfer. Furthermore, the MulRF heuristic runs quickly on data sets containing hundreds of trees with up to a hundred taxa.