Fast and accurate branch lengths estimation for phylogenomic trees

BMC Bioinformatics

Research Article

Celine Scornavacca¹  Emmanuel J. P. Douzery²  Fabio Pardi³  Olivier Gascuel³  Manuel Binet⁴ 

[1] Institut de Biologie Computationnelle, Montpellier, France;Institut des Sciences de l’Evolution de Montpellier, CNRS, IRD, EPHE, Université de Montpellier, France;Institut des Sciences de l’Evolution de Montpellier, CNRS, IRD, EPHE, Université de Montpellier, France;Laboratoire d’Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS, Université de Montpellier, Montpellier, France;Institut de Biologie Computationnelle, Montpellier, France;Laboratoire d’Informatique de Robotique et de Microélectronique de Montpellier (LIRMM), CNRS, Université de Montpellier, Montpellier, France;Institut de Biologie Computationnelle, Montpellier, France;Institut des Sciences de l’Evolution de Montpellier, CNRS, IRD, EPHE, Université de Montpellier, France;

关键词: Phylogenomics;    Supertree;    Branch lengths;    Gene rates;    Distance-based;    Least-squares;   

DOI  :  10.1186/s12859-015-0821-8

 received in 2015-05-19, accepted in 2015-11-02,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundBranch lengths are an important attribute of phylogenetic trees, providing essential information for many studies in evolutionary biology. Yet, part of the current methodology to reconstruct a phylogeny from genomic information — namely supertree methods — focuses on the topology or structure of the phylogenetic tree, rather than the evolutionary divergences associated to it. Moreover, accurate methods to estimate branch lengths — typically based on probabilistic analysis of a concatenated alignment — are limited by large demands in memory and computing time, and may become impractical when the data sets are too large.ResultsHere, we present a novel phylogenomic distance-based method, named ERaBLE (Evolutionary Rates and Branch Length Estimation), to estimate the branch lengths of a given reference topology, and the relative evolutionary rates of the genes employed in the analysis. ERaBLE uses as input data a potentially very large collection of distance matrices, where each matrix is obtained from a different genomic region — either directly from its sequence alignment, or indirectly from a gene tree inferred from the alignment. Our experiments show that ERaBLE is very fast and fairly accurate when compared to other possible approaches for the same tasks. Specifically, it efficiently and accurately deals with large data sets, such as the OrthoMaM v8 database, composed of 6,953 exons from up to 40 mammals.ConclusionsERaBLE may be used as a complement to supertree methods — or it may provide an efficient alternative to maximum likelihood analysis of concatenated alignments — to estimate branch lengths from phylogenomic data sets.

【 授权许可】

CC BY   
© Binet et al. 2015

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