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Mammalian Genome

, Volume 25, Issue 11–12, pp 636–647 | Cite as

Mitochondrial data are not suitable for resolving placental mammal phylogeny

  • Claire C. Morgan
  • Christopher J. Creevey
  • Mary J. O’Connell
Article

Abstract

Mitochondrial data have traditionally been used in reconstructing a variety of species phylogenies. The low rates of recombination and thorough characterization of mitochondrial data across vertebrate species make it a particularly attractive phylogenetic marker. The relatively low number of fully sequenced mammal genomes and the lack of extensive sampling within Superorders have posed a serious problem for reaching agreement on the placement mammal species. The use of mitochondrial data sequences from large numbers of mammals could serve to circumvent the taxon-sampling deficit. Here we assess the suitability of mitochondrial data as a phylogenetic marker in mammal phylogenetics. MtDNA datasets of mammal origin have been filtered as follows: (i) we have sampled sparsely across the phylogenetic tree, (ii) we have constrained our sampling to genes with high taxon coverage, (iii) we have categorised rates across sites in a phylogeny independent manner and have removed fast evolving sites, and (iv), we have sampled from very shallow divergence times to reduce phylogenetic conflict. However, topologies obtained using these filters are not consistent with previous studies and are discordant across different genes. Individual mitochondrial genes, and indeed all mitochondrial genes analysed as a supermatrix, resulted in poor resolution of the species phylogeny. Overall, our study highlights the limitations of mitochondrial data, not only for resolving deep divergences and but also for shallow divergences in the mammal phylogeny.

Keywords

Mammal Mitochondrial DNA Data quality Site-rate categorization Site-stripping Phylogeny 

Notes

Acknowledgments

We would like to thank the Irish Research Council for Science, Engineering and Technology for the Embark Initiative Postgraduate Scholarship to CCM: RS2000172 and Science Foundation Ireland (SFI) for funding to Dr. Mary J. O’Connell (EOB: 2673). We would like to thank the SFI/Higher Education authority (HEA) Irish Centre for High-End Computing (ICHEC: dclif023b) and SCI-SYM DCU for processor time. We would like to thank Paul Kilroy-Glynn for initial discussion, Dr. Davide Pisani and Prof James McInerney for their helpful comments and the Orla Benson travel award (DCU) for funding.

Conflict of interest

Authors declare no conflict of interest.

Supplementary material

335_2014_9544_MOESM1_ESM.xls (93 kb)
Supplementary Table 1: Taxon coverage across mtGenes. The species name is given along with whether or not it is represented in each of the 13 mtGenes. The final column shows the total number of times the species is represented across all mtGenes (XLS 93 kb)
335_2014_9544_MOESM2_ESM.nex (4.5 mb)
Supplementary Table 2: MSA for untreated mtGene and SM datasets. All alignments used in this study are supplied in this file (NEX 4594 kb)
335_2014_9544_MOESM3_ESM.xls (2.4 mb)
Supplementary Table 3: Phylogenetic trees obtained for all Datasets. The dataset is listed along with the phylogenetic tree and its associated lnL score. The Γ parameter is denoted as +G throughout (XLS 2469 kb)
335_2014_9544_MOESM4_ESM.xls (95 kb)
Supplementary Table 4: Summary of Likelihood Mapping for all Datasets For each dataset, the number of taxa and amino acids are given along with the scores for regions 1–7 from LM analysis. The phylogenetic conflict score is the sum of values from regions 4–7 and this is given in the final column (XLS 95 kb)
335_2014_9544_MOESM5_ESM.xls (102 kb)
Supplementary Table 5: Robinson-Fould distances between topologies (XLS 101 kb)

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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Claire C. Morgan
    • 1
    • 2
    • 3
  • Christopher J. Creevey
    • 4
  • Mary J. O’Connell
    • 1
    • 2
  1. 1.Bioinformatics and Molecular Evolution Group, School of BiotechnologyDublin City UniversityDublin 9Ireland
  2. 2.Centre for Scientific Computing & Complex Systems Modelling (SCI-SYM)Dublin City UniversityDublin 9Ireland
  3. 3.National Heart and Lung InstituteImperial College LondonLondonUK
  4. 4.Institute of Biological, Environmental and Rural SciencesAberystwyth UniversityWalesUK

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