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Comparative Study
. 2006 Mar 24:6:29.
doi: 10.1186/1471-2148-6-29.

Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified

Thomas M Keane  1 , Christopher J Creevey, Melissa M Pentony, Thomas J Naughton, James O Mclnerney
Affiliations
Comparative Study

Assessment of methods for amino acid matrix selection and their use on empirical data shows that ad hoc assumptions for choice of matrix are not justified

Thomas M Keane et al. BMC Evol Biol. .

Abstract

Background: In recent years, model based approaches such as maximum likelihood have become the methods of choice for constructing phylogenies. A number of authors have shown the importance of using adequate substitution models in order to produce accurate phylogenies. In the past, many empirical models of amino acid substitution have been derived using a variety of different methods and protein datasets. These matrices are normally used as surrogates, rather than deriving the maximum likelihood model from the dataset being examined. With few exceptions, selection between alternative matrices has been carried out in an ad hoc manner.

Results: We start by highlighting the potential dangers of arbitrarily choosing protein models by demonstrating an empirical example where a single alignment can produce two topologically different and strongly supported phylogenies using two different arbitrarily-chosen amino acid substitution models. We demonstrate that in simple simulations, statistical methods of model selection are indeed robust and likely to be useful for protein model selection. We have investigated patterns of amino acid substitution among homologous sequences from the three Domains of life and our results show that no single amino acid matrix is optimal for any of the datasets. Perhaps most interestingly, we demonstrate that for two large datasets derived from the proteobacteria and archaea, one of the most favored models in both datasets is a model that was originally derived from retroviral Pol proteins.

Conclusion: This demonstrates that choosing protein models based on their source or method of construction may not be appropriate.

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Figures

Figure 1
Figure 1
Alternative Trees. Two different trees (with bootstrap support values based on 100 replicates) constructed from a single gene family [34] with different protein models using Phyml v2.4.4 [53]. Tree (a) was produced using the MtREV matrix [15] and Tree (b) was produced using the WAG matrix [18].
Figure 2
Figure 2
Base Tree. The true tree used to generate all of the simulated alignments.
Figure 3
Figure 3
Proteobacteria Dataset. A break-down of the set of best-fit protein models for the proteobacteria dataset.
Figure 4
Figure 4
Vertebrate Dataset. A break-down of the set of best-fit protein models for the vertebrate dataset.
Figure 5
Figure 5
Archaea Dataset. A break-down of the set of best-fit protein models for the archaea dataset.
Figure 6
Figure 6
Pseudo Code. The algorithm used to generate the simulated +F alignments can be described in pseudocode as follows. The function random returns a random number greater than the first argument and less than the second argument.
See this image and copyright information in PMC

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