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Published on: August 14, 2018
Annotated Bioinformatic Pipelines for Phylogenomic Placement of Mitochondrial Genomes
Jessica C Winn1, Aletta E Bester-Van Der Merwe1, Simo N Maduna1,2
1Molecular Breeding and Biodiversity Group, Department of Genetics, Stellenbosch University, Stellenbosch, Western Cape, South Africa.
Abstract:
The limited standards for the rigorous and objective use of mitochondrial genomes (mitogenomes) can lead to uncertainties regarding the phylogenetic relationships of taxa under varying evolutionary constraints. The mitogenome exhibits heterogeneity in base composition, and evolutionary rates may vary across different regions, which can cause empirical data to violate assumptions of the applied evolutionary models. Consequently, the unique evolutionary signatures of the dataset must be carefully evaluated before selecting an appropriate approach for phylogenomic inference. Here, we present the bioinformatic pipeline and code used to expand the mitogenome phylogeny of the order Carcharhiniformes (groundsharks), with a focus on houndsharks (Chondrichthyes: Triakidae). We present a rigorous approach for addressing difficult-to-resolve phylogenies, incorporating multi-species coalescent modelling (MSCM) to address gene/species tree discordance. The protocol describes carefully designed approaches for preparing alignments, partitioning datasets, assigning models of evolution, inferring phylogenies based on traditional site-homogenous concatenation approaches as well as under multispecies coalescent and site heterogenous models, and generating statistical data for comparison of different topological outcomes. The datasets required to run our analyses are available on GitHub and Dryad repositories. Key features • An extensive statistical framework to conduct model selection and data partitioning and tackle difficult-to-resolve phylogenies. • Instructions for generating statistical data for comparison of different topological outcomes. • Tips for selecting mitochondrial phylogenomic (mitophylogenomic) approaches to suit unique datasets. • Access to the scripts, data files, and pipelines used to enable replication of all analyses.
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