A Practical Algorithm for Reconstructing Level-1 Phylogenetic Networks

TL;DR

This paper introduces LEV1ATHAN, an efficient polynomial-time algorithm for reconstructing level-1 phylogenetic networks from triplet data, capable of handling noisy data and producing accurate networks or trees.

Contribution

The paper presents a novel practical algorithm combining existing methods with new subroutines for reconstructing level-1 phylogenetic networks efficiently.

Findings

LEV1ATHAN runs in polynomial time and always constructs a level-1 network.

It can reconstruct a phylogenetic tree if data is consistent with one.

It effectively handles noisy triplet data, maintaining high accuracy.

Abstract

Recently much attention has been devoted to the construction of phylogenetic networks which generalize phylogenetic trees in order to accommodate complex evolutionary processes. Here we present an efficient, practical algorithm for reconstructing level-1 phylogenetic networks - a type of network slightly more general than a phylogenetic tree - from triplets. Our algorithm has been made publicly available as the program LEV1ATHAN. It combines ideas from several known theoretical algorithms for phylogenetic tree and network reconstruction with two novel subroutines. Namely, an exponential-time exact and a greedy algorithm both of which are of independent theoretical interest. Most importantly, LEV1ATHAN runs in polynomial time and always constructs a level-1 network. If the data is consistent with a phylogenetic tree, then the algorithm constructs such a tree. Moreover, if the input triplet set is dense and, in addition, is fully consistent with some level-1 network, it will find such a network. The potential of LEV1ATHAN is explored by means of an extensive simulation study and a biological data set. One of our conclusions is that LEV1ATHAN is able to construct networks consistent with a high percentage of input triplets, even when these input triplets are affected by a low to moderate level of noise.