Polymorphism-aware species trees with advanced mutation models, bootstrap and rate heterogeneity
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Molecular phylogenetics has neglected polymorphisms within present and ancestral populations for a long time. Recently, multispecies coalescent based methods have increased in popularity, however, their application is limited to a small number of species and individuals. We introduced a polymorphism-aware phylogenetic model (PoMo), which overcomes this limitation and scales well with the increasing amount of sequence data while accounting for present and ancestral polymorphisms. PoMo circumvents handling of gene trees and directly infers species trees from allele frequency data. Here, we extend the PoMo implementation in IQ-TREE and integrate search for the statistically best-fit mutation model, the ability to infer mutation rate variation across sites, and assessment of branch support values. We exemplify an analysis of a hundred species with ten haploid individuals each, showing that PoMo can perform inference on large data sets. While PoMo is more accurate than standard substitution models applied to concatenated alignments, it is almost as fast. We also provide bmm-simulate, a software package that allows simulation of sequences evolving under PoMo. The new options consolidate the value of PoMo for phylogenetic analyses with population data.
Schrempf , D , Minh , B Q , von Haeseler , A & Kosiol , C 2019 , ' Polymorphism-aware species trees with advanced mutation models, bootstrap and rate heterogeneity ' , Molecular Biology and Evolution , vol. 36 , no. 6 , pp. 1294-1301 . https://doi.org/10.1093/molbev/msz043
Molecular Biology and Evolution
Copyright © The Author(s) 2019. Published by Oxford University Press on behalf of the Society for Molecular Biology and Evolution. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
DescriptionThis work was funded by the Vienna Science and Technology Fund (WWTF) through project MA16-061. DS was supported by the Austrian Science Fund [FWF-P24551, I-2805-B29] and received funding from the European Research Council under the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 741774. The computational results presented have been achieved in part using the Vienna Scientific Cluster (VSC) and the St Andrews Bioinformatics Unit (StABU) which is funded by a Wellcome Trust ISSF award (grant 105621/Z/14/Z). BQM was supported by the Australian National University Futures grant.
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