4.7 Article

NetRAX: accurate and fast maximum likelihood phylogenetic network inference

Journal

BIOINFORMATICS
Volume 38, Issue 15, Pages 3725-3733

Publisher

OXFORD UNIV PRESS
DOI: 10.1093/bioinformatics/btac396

Keywords

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Funding

  1. Klaus Tschira Foundation - DFG [STA 860/6-2]
  2. French Agence Nationale de la Recherche program [ANR-19-CE45-0012]
  3. Agence Nationale de la Recherche (ANR) [ANR-19-CE45-0012] Funding Source: Agence Nationale de la Recherche (ANR)

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NetRAX is a tool for maximum likelihood inference of phylogenetic networks in the absence of incomplete lineage sorting. It efficiently computes the phylogenetic likelihood function on trees and extends them to phylogenetic networks. The tool can infer ML phylogenetic networks from partitioned multiple sequence alignments and provides the results in Extended Newick format.
Motivation: Phylogenetic networks can represent non-treelike evolutionary scenarios. Current, actively developed approaches for phylogenetic network inference jointly account for non-treelike evolution and incomplete lineage sorting (ILS). Unfortunately, this induces a very high computational complexity and current tools can only analyze small datasets. Results: We present NetRAX, a tool for maximum likelihood (ML) inference of phylogenetic networks in the absence of ILS. Our tool leverages state-of-the-art methods for efficiently computing the phylogenetic likelihood function on trees, and extends them to phylogenetic networks via the notion of 'displayed trees'. NetRAX can infer ML phylogenetic networks from partitioned multiple sequence alignments and returns the inferred networks in Extended Newick format. On simulated data, our results show a very low relative difference in Bayesian Information Criterion (BIC) score and a near-zero unrooted softwired cluster distance to the true, simulated networks. With NetRAX, a network inference on a partitioned alignment with 8000 sites, 30 taxa and 3 reticulations completes within a few minutes on a standard laptop.

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