Efficiencies of different genes and different tree-building methods in recovering a known vertebrate phylogeny |
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Authors: | Russo CA; Takezaki N; Nei M |
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Institution: | Department of Biology, Pennsylvania State University, University Park 16802, USA. |
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Abstract: | The relative efficiencies of different protein-coding genes of the
mitochondrial genome and different tree-building methods in recovering a
known vertebrate phylogeny (two whale species, cow, rat, mouse, opossum,
chicken, frog, and three bony fish species) was evaluated. The
tree-building methods examined were the neighbor joining (NJ), minimum
evolution (ME), maximum parsimony (MP), and maximum likelihood (ML), and
both nucleotide sequences and deduced amino acid sequences were analyzed.
Generally speaking, amino acid sequences were better than nucleotide
sequences in obtaining the true tree (topology) or trees close to the true
tree. However, when only first and second codon positions data were used,
nucleotide sequences produced reasonably good trees. Among the 13 genes
examined, Nd5 produced the true tree in all tree-building methods or
algorithms for both amino acid and nucleotide sequence data. Genes Cytb and
Nd4 also produced the correct tree in most tree-building algorithms when
amino acid sequence data were used. By contrast, Co2, Nd1, and Nd41 showed
a poor performance. In general, large genes produced better results, and
when the entire set of genes was used, all tree-building methods generated
the true tree. In each tree-building method, several distance measures or
algorithms were used, but all these distance measures or algorithms
produced essentially the same results. The ME method, in which many
different topologies are examined, was no better than the NJ method, which
generates a single final tree. Similarly, an ML method, in which many
topologies are examined, was no better than the ML star decomposition
algorithm that generates a single final tree. In ML the best substitution
model chosen by using the Akaike information criterion produced no better
results than simpler substitution models. These results question the
utility of the currently used optimization principles in phylogenetic
construction. Relatively simple methods such as the NJ and ML star
decomposition algorithms seem to produce as good results as those obtained
by more sophisticated methods. The efficiencies of the NJ, ME, MP, and ML
methods in obtaining the correct tree were nearly the same when amino acid
sequence data were used. The most important factor in constructing reliable
phylogenetic trees seems to be the number of amino acids or nucleotides
used.
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