Open Access Highly Accessed Research article

Phylogenetic relationships among Staphylococcus species and refinement of cluster groups based on multilocus data

Ryan P Lamers15, Gowrishankar Muthukrishnan1, Todd A Castoe2, Sergio Tafur3, Alexander M Cole1* and Christopher L Parkinson4*

Author Affiliations

1 Burnett School of Biomedical Sciences, University of Central Florida College of Medicine, 4000 Central Florida Boulevard, Orlando, FL, 32816, USA

2 Department of Biochemistry and Molecular Genetics, University of Colorado School of Medicine, 12801 17th Avenue, Aurora, CO, 80045, USA

3 Stokes Advanced Research Computing Center, Institute for Simulation and Training, University of Central Florida, 3100 Technology Parkway, Orlando, FL, 32826, USA

4 Department of Biology, University of Central Florida, 4000 Central Florida Boulevard, Orlando, FL, 32816, USA

5 Current affiliation: Department of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, McMaster University, 1280 Main Street West, Hamilton, ON, L8S 4K1, Canada

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BMC Evolutionary Biology 2012, 12:171  doi:10.1186/1471-2148-12-171

Published: 6 September 2012

Additional files

Additional file 1: Table S1:

GenBank accession numbers for 16S rRNA gene fragments,dnaJ, rpoB, andtufgene fragments analyzed in this study.

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Additional file 2: Table S2:

Evolutionary models for each partition were chosen based on AIC using jModelTest.

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Additional file 3: Figure S1:

Gene trees for individual loci assessed in this study. Shown are Bayesian 50% majority rule phylograms for A) the 16S rRNA, B) dnaJ, C) rpoB, and D) tuf gene fragments. MrBayes was run under the same conditions as those used for concatenated analyses with evolutionary model specified for whole gene fragments in Additional file 2: Table S2. Numbers represent posterior probabilities with grey-filled circles representing a posterior support of 1.00.

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Additional file 4: Figure S2:

Bayesian inferences of phylogeny are highly reproducible, regardless of model employed. Shown are plots of post-burnin generational log likelihoods (lnL) from five representative partitioning strategies across triplicate concatenated BI runs (A); and duplicate BEST runs (B). All runs were highly reproducible regardless of methodology and partitioning strategy.

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Additional file 5: Figure S3:

Tree length (TL) analysis indicates that overparameterization may be occurring within more highly partitioned datasets. Shown are post-burnin generational TL estimates for partitioning strategies assessed in this study. Note that as the complexity of partitioning increases evidence of increased TL and failed convergence is observed.

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Additional file 6: Figure S4:

Model partitioning increases the mean tree length (TL) and run variance. Shown is a box plot indicating the mean TL and 95% confidence interval among partitioning strategies.

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Additional file 7: Figure S5:

Inference of phylogeny using Bayesian estimation of species trees (BEST). Shown is a consensus phylogram of the staphylococcal species tree generated using all four gene fragments under the BEST methodology. Each gene fragment was treated as an individual locus for which individual gene trees were estimated (similar to MB3). Numbers represent posterior probabilities with grey-filled circles representing a posterior probability of 1.00.

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