Abstract

Knowledge of species phylogeny is critical to many fields of biology. In an era of genome data availability, the most common way to make a phylogenetic species tree is by using multiple protein-coding genes, conserved in multiple species. This methodology is composed of several steps: orthology inference, multiple sequence alignment and inference of the phylogeny with dedicated tools. This can be a difficult task, and orthology inference, in particular, is usually computationally intensive and error prone if done ad hoc. This tutorial provides protocols to make use of OMA Orthologous Groups, a set of genes all orthologous to each other, to infer a phylogenetic species tree. It is designed to be user-friendly and computationally inexpensive, by providing two options: (1) Using only precomputed groups with species available on the OMA Browser, or (2) Computing orthologs using OMA Standalone for additional species, with the option of using precomputed orthology relations for those present in OMA. A protocol for downstream analyses is provided as well, including creating a supermatrix, tree inference, and visualization. All protocols use publicly available software, and we provide scripts and code snippets to facilitate data handling. The protocols are accompanied with practical examples.

Highlights

  • Inferring accurate and complete species phylogenies is a fundamental problem in biology[1]

  • With the wealth of genomic data available in an era of high-throughput sequencing, there is much to gain by making phylogenies from multiple gene families, rather than from one single gene

  • This can better represent the evolutionary history of a clade, because the evolutionary history of a single gene can be misrepresentative of a species evolutionary history

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Summary

METHOD ARTICLE

How to build phylogenetic species trees with OMA [version 1; peer review: 2 approved with reservations]. Altenhoff 1,4, Antoine Gürtler[2,3], Christophe Dessimoz 1-3,5,6, Natasha M.

Introduction
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Discussion and conclusion
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Full Text
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