Fate of the Tree of Life

Soltis, Douglas E.; Soltis, Pamela S.

doi:10.1016/b978-0-12-812553-3.00006-0

Cited by 1 publication

(1 citation statement)

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“…The inference of evolutionary history from molecular data is a core goal of modern evolutionary biology (Barraclough and Nee, 2001; Soltis and Soltis, 2018). With the increasing availability of large-scale multilocus datasets and advances in computational power, phylogenetic methods have diversified in the past two decades (Delsuc et al, 2005; Liu et al, 2015b).…”

Section: Introductionmentioning

confidence: 99%

TREEasy: an automated workflow to infer gene trees, species trees, and phylogenetic networks from multilocus data

Mao

Hou

Economo

2019

Preprint

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19Multilocus genomic datasets can be used to infer a rich set of information about the 20 evolutionary history of a lineage, including gene trees, species trees, and phylogenetic 21 networks. However, user-friendly tools to run such integrated analyses are lacking, 22and workflows often require tedious reformatting and handling time to shepherd data 23 through a series of individual programs. Here, we present a tool written in Python-24TREEasy-that performs automated sequence alignment (with MAFFT), gene tree 25 inference (with IQ-Tree), species inference from concatenated data (with IQ-Tree), 26 species tree inference from gene trees (with ASTRAL, MP-EST, and STELLS2), and 27 phylogenetic network inference (with SNaQ and PhyloNet). The tool only requires 28 FASTA files and nine parameters as inputs. The Tool can be run as command line or 29 through a Graphical User Interface (GUI). As examples, we reproduced a recent 30 analysis of staghorn coral evolution, and performed a new analysis on the evolution of 31 the WGD clade of yeast. The latter revealed novel inferences that were not identified 32 by previous analyses. TREEasy represents a reliable and simple tool to accelerate 33 research in systematic biology (https://github.com/MaoYafei/TREEasy). 34 35

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