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Superiority of a mechanistic codon substitution model even for protein sequences in Phylogenetic analysis

Overview of attention for article published in BMC Ecology and Evolution, November 2013
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Title
Superiority of a mechanistic codon substitution model even for protein sequences in Phylogenetic analysis
Published in
BMC Ecology and Evolution, November 2013
DOI 10.1186/1471-2148-13-257
Pubmed ID
Authors

Sanzo Miyazawa

Abstract

Nucleotide and amino acid substitution tendencies are characteristic of each species, organelle, and protein family. Hence, various empirical amino acid substitution rate matrices have needed to be estimated for phylogenetic analysis: JTT, WAG, and LG for nuclear proteins, mtREV for mitochondrial proteins, cpREV10 and cpREV64 for chloroplast-encoded proteins, and FLU for influenza proteins. On the other hand, in a mechanistic codon substitution model, in which each codon substitution rate is proportional to the product of a codon mutation rate and the ratio of fixation depending on the type of amino acid replacement, mutation rates and the strength of selective constraint on amino acids can be tailored to each protein family with additional 11 parameters. As a result, in the evolutionary analysis of codon sequences it outperforms codon substitution models equivalent to empirical amino acid substitution matrices. Is it superior even for amino acid sequences, among which synonymous substitutions cannot be identified?

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Japan 1 2%
Germany 1 2%
Switzerland 1 2%
Brazil 1 2%
Unknown 37 90%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 10 24%
Researcher 8 20%
Student > Bachelor 6 15%
Student > Master 6 15%
Student > Doctoral Student 2 5%
Other 5 12%
Unknown 4 10%
Readers by discipline Count As %
Agricultural and Biological Sciences 17 41%
Computer Science 8 20%
Biochemistry, Genetics and Molecular Biology 5 12%
Immunology and Microbiology 1 2%
Medicine and Dentistry 1 2%
Other 2 5%
Unknown 7 17%