IQ-TREE 2: New Models and Efficient Methods for Phylogenetic Inference in the Genomic Era-Reference-Cited by-同舟云学术

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

Published:2020-02-03 Issue:5 Volume:37 Page:1530-1534
ISSN:0737-4038
Container-title:Molecular Biology and Evolution
language:en
Short-container-title:

Author:

Minh Bui Quang¹²^ORCID,Schmidt Heiko A³,Chernomor Olga³,Schrempf Dominik³⁴,Woodhams Michael D⁵,von Haeseler Arndt³⁶,Lanfear Robert²

Affiliation:

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

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

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

4. Department of Biological Physics, Eötvös Lórand University, Budapest, Hungary

5. Discipline of Mathematics, University of Tasmania, Hobart, TAS, Australia

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

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.

Funder

Austrian Science Fund

Australian National University Futures Scheme

European Research Council

European Union's Horizon 2020 research and innovation programme

Publisher

Oxford University Press (OUP)

Subject

Genetics,Molecular Biology,Ecology, Evolution, Behavior and Systematics

Link

http://academic.oup.com/mbe/advance-article-pdf/doi/10.1093/molbev/msaa015/32792673/msaa015.pdf

Reference56 articles.