Esteban and Hutchinson BMC Genomics 2011, 12:261 http://www.biomedcentral.com/1471-2164/12/261 molecular evolution. Understanding the mechanisms of poxvirus evolution may shed light on important aspects of poxvirus biology such as adaptation of monkeypox virus to become more sustainable in human populations. Combined with experimental data, identification of sites under diversifying selection may be important in guiding future mutagenesis studies, as these residues may be important in host specific interactions such as protein- protein contacts or immune epitopes. Further, many genes with unknown or poorly defined functions were shown to have sites under diversifying selection. These data may provide a basis for investigating the function of these as host-interacting proteins or virulence factors, and may identify highly conserved functional residues or diverse host-specific residues that are of particular interest. Methods Sequence selection and alignment A subset of viruses of the Chordopoxvirus subfamily was selected based on close phylogenetic relationships [4]. Fully sequenced genomes of the Orthopoxvirus, Lepori- poxvirus, Yatapoxvirus, Capripoxvirus, Suipoxvirus and Mule Deer pox genera were used (Additional file 1, Table S1). Other Chordopoxvirus genera were excluded because their sequences were typically too divergent to generate reliable alignments and have very different % GC content compared to other poxviruses. Gene families were defined by the Viral Orthologous Clusters (VOCs) database [2]. CPXV-BR virus was used as a reference genome; all gene positions used are those in CPXV-BR, and only gene families with a member in CPXV-BR were used in the analysis. Gene families with duplicated genes (paralogs) and gene families that con- tained less than 6 genes were excluded. All protein and nucleotide sequences were acquired from the VOCs database. Using perl scripts, identical nucleotide sequences and their corresponding amino acid sequences were removed and then parsed into their respective gene families. Codon based alignments of nucleic acid sequences of gene families were generated using PRANK [52]. PRANK has been shown to generate more reliable alignments resulting in fewer false posi- tives than other alignment programs [53]. Tree construction Maximum likelihood trees were constructed from the nucleotide alignments using the DNAml executable within the PHYLIP program. The trees were used as starting points for subsequent analysis using PAML4. Detection of evolutionary pressures with PAML4 Each gene family was analyzed using the codeml pro- gram within the PAML4 package [54] to assess the Page 10 of 12 selective forces acting on the genes. Codon based models were used since they have the greatest power to detect differing selective pressures acting on a pro- tein [10,11]. To identify the presence of a codon site class under diversifying selection, two separate site model comparisons were used. In the first, the model M1a, which allows for two site categories, ω = 0 and ω = 1, was compared to the alternative model M2a, that has an additional site class in which ω is allowed to exceed 1. In the second comparison, the model (M7) that allows for sites with 0 < ω < 1, was compared to the alternative model (M8) that allows for an additional site class in which ω may exceed 1. Each gene family was analyzed on a 156 core cluster composed of 2.2GHz AMD Opteron cores. The computational time taken for each gene family varied by the number of sequences per gene family and the length of the sequences and ranged from several minutes to 31 days. Perl scripts were then used to compile results. Differences in log likelihood values between and alternative hypotheses were analyzed using the Likeli- hood Ratio Test (LRT) and the resulting values com- pared to appropriate chi squared values to determine significance, using a confidence threshold of p < 0.05 and df = 1. Bayes empirical Bayes For genes in which the alternative model showed a signifi- cantly better fit of the data, Bayes theorem [18] was applied to calculate the posterior probability that each site belongs to a particular ω class. This was performed through the codeml executable of the PAML4 package [54]. Additional material Additional File 1: Viral genomes used in this analysis. Additional File 2: Gene families excluded from analysis. Additional File 3: Basic statistics of genes used in analysis. Additional File 4: Genes under diversifying selection identified by model M8 only. Additional File 5: Proportions and omega values of significant genes. Additional File 6: Sites in significant genes (models M2a and M8) under diversifying selection determined by Bayes empirical Bayes analysis. Additional File 7: Sites in significant genes (model M8 only) under diversifying selection determined by Bayes Empirical Bayes analysis. Acknowledgements The authors would like to acknowledge Greg Priest-Dorman, Phil Tully and Marc Smith (Vassar College) for assistance with the server and for writing perl scripts, and Brian Trapp for writing perl scripts. This work was supported by start-up funds from Vassar College.