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Application of the Character Compatibility Approach to Generalized Molecular Sequence Data: Branching Order of the Proteobacterial Subdivisions

Overview of attention for article published in Journal of Molecular Evolution, December 2006
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Title
Application of the Character Compatibility Approach to Generalized Molecular Sequence Data: Branching Order of the Proteobacterial Subdivisions
Published in
Journal of Molecular Evolution, December 2006
DOI 10.1007/s00239-006-0082-2
Pubmed ID
Authors

Radhey S. Gupta, Peter H. A. Sneath

Abstract

The character compatibility approach, which removes all homoplasic characters and involves finding the largest clique of compatible characters in a dataset, in principle, provides a powerful means for obtaining correct topology in difficult to resolve cases. However, the usefulness of this approach to generalized molecular sequence data for phylogeny determination has not been studied in the past. We have used this approach to determine the topology of 23 proteobacterial species (6 each of alpha-, beta- and gamma-, 3 delta-, and 2 epsilon-proteobacteria) using sequence data for 10 conserved proteins (Hsp60, Hsp70, EF-Tu, EF-G, alanyl-tRNA synthetase, RecA, GyrA, GyrB, RpoB and RpoC). All sites in the sequence alignments of these proteins where only two amino acids were found, with each amino acid present in at least two species, were selected. Mutual compatibility determination on these binary state sites was carried out by two means. In one case, all of these sites were combined into a large dataset (Set A; 957 characters) prior to compatibility analysis. In the second case, compatibility analysis was carried out on characters from individual proteins and all compatible sites were combined into a large dataset (Set B; 398 characters) for further studies. Upon compatibility analyses, the largest cliques that were obtained from Sets A and B consisted of 337 and 323 compatible characters, respectively. In these cliques, all proteobacterial subgroups were clearly distinguished and branching orders of most of the species were also resolved. The epsilon-proteobacteria exhibited the earliest branching, whereas the beta- and gamma-subgroups were found to have emerged last. The relative placement of the alpha- and delta-subgroups, however, was not resolved. The topology of these species was also determined based on 16S rRNA sequences and a concatenated dataset of sequences for all 10 proteins by means of neighbor-joining, maximum likelihood, and maximum parsimony methods. In the protein trees, all proteobacterial groups were reliably resolved and they branched in the following order: (epsilon(delta(alpha(beta,gamma)))). However, in the rRNA trees, the gamma- and beta-subgroups exhibited polyphyletic branching and many internal nodes were not resolved. These results indicate that the character compatibility analysis using generalized molecular sequence data provides a powerful means for evolutionary studies. Based on molecular sequences, it should be possible to obtain very large datasets of compatible characters that should prove very helpful in clarifying difficult to resolve phylogenetic relationships.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 3%
Brazil 1 3%
Finland 1 3%
Estonia 1 3%
United States 1 3%
Unknown 27 84%

Demographic breakdown

Readers by professional status Count As %
Researcher 9 28%
Student > Ph. D. Student 5 16%
Student > Master 4 13%
Student > Postgraduate 3 9%
Professor > Associate Professor 3 9%
Other 5 16%
Unknown 3 9%
Readers by discipline Count As %
Agricultural and Biological Sciences 24 75%
Biochemistry, Genetics and Molecular Biology 2 6%
Mathematics 1 3%
Computer Science 1 3%
Earth and Planetary Sciences 1 3%
Other 0 0%
Unknown 3 9%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 10 September 2017.
All research outputs
#7,453,479
of 22,786,691 outputs
Outputs from Journal of Molecular Evolution
#450
of 1,438 outputs
Outputs of similar age
#41,503
of 155,738 outputs
Outputs of similar age from Journal of Molecular Evolution
#6
of 7 outputs
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