IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era

Bui Quang Minh ^{1, 2}

Olga Chernomor ³

Michael D. Woodhams ⁴

H.A. Schmidt ³

Nicolas Lartillot ^{3, 5}

Arndt von Haeseler ^{3, 6}

Robert Lanfear ²

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Research School of Computer Science, Australian National University, Canberra, ACT, Australia |

Department of Ecology and Evolution, Research School of Biology, Australian National University, Canberra, ACT, Australia |

Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna, Austria |

⁴

Discipline of Mathematics, University of Tasmania, Hobart, TAS, Australia |

⁵

Department of Biological Physics, Eötvös Lórand University, Budapest, Hungary |

⁶

Bioinformatics and Computational Biology, Faculty of Computer Science, University of Vienna, Vienna, Austria |

Publication type: Journal Article

Publication date: 2020-02-03

Oxford University Press

Journal: Molecular Biology and Evolution

scimago Q1

SJR: 4.061

CiteScore: 19.7

Impact factor: 11

ISSN: 07374038, 15371719

DOI: 10.1093/molbev/msaa015

Copy DOI

PubMed ID: 32011700

Molecular Biology

Genetics

Ecology, Evolution, Behavior and Systematics

Abstract

Abstract IQ-TREE (http://www.iqtree.org, last accessed February 6, 2020) is a user-friendly and widely used software package for phylogenetic inference using maximum likelihood. Since the release of version 1 in 2014, we have continuously expanded IQ-TREE to integrate a plethora of new models of sequence evolution and efficient computational approaches of phylogenetic inference to deal with genomic data. Here, we describe notable features of IQ-TREE version 2 and highlight the key advantages over other software.

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