studies of DNA uptake and transformation, we have now applied multiple inference methods to different data sets, including whole genome sequences. Results and Discussion Determining the evolutionary relationships of strains This study used all of the strains whose competence we previously measured and whose seven MLST loci had been sequenced [16]. These strains had been chosen to include both close and distant relatives, according to the phylogeny in Meats et al. [18]. Most do not express a polysaccharide layer surrounding the cell (i.e., are nontypeable); however three serotype d and three serotype e strains were also included. For the present analysis we also included strain 3655 because its genome sequence is now available [22]. Strains and their properties are listed in Table 1. Sequences from these strains were combined into three datasets: a Genome dataset, a MLST dataset, and an Uptake/transforma- tion dataset. Genome dataset: Although strains differ at MLST loci by an average of 2.8% [18], not all of these sites are likely to be ‘informative’ for parsimony analysis. Therefore, we created a new dataset with greatly increased sequence data by aligning the genomes of the 13 strains for which genome sequencing has either been completed or is near completion, giving 1.5 Mb of aligned sequence (Figure 1). DNA uptake and transformation phenotypes have been characterized for 12 of these strains [16]. Sequences in this dataset differ by an average of 3.0%; because some genomes have not been completely assembled, it consists of 101 aligned blocks ranging in size from 150 to 108,151 nt (Figure 1). MLST dataset: To compare our results with previous studies, we also analyzed seven MLST loci from strains whose DNA uptake and transformation are known; strains in this dataset differed by an average of 2.2% (Figure 1). Uptake/transformation dataset: Reconstructing the evolutionary relationships of strains using only data from competence genes would allow us to compare the evolution of competence genes with evolution in housekeeping genes (MLST dataset) and the whole genome (genome dataset). Therefore, the third dataset consisted only of the 30 genes whose products are associated with DNA uptake or transformation (Table 2). This dataset contained the same strains as the Genome dataset; the sequences differed by an average of 2.7% (Figure 1). Before undertaking phylogenetic analysis we first sought to determine the impact of recombination, which is predicted to shuffle the evolutionary histories of different genomic regions, Phylogeny of Competence resulting in conflicting phylogenetic signals [20]. However, recombination between strains does not always preclude the formation of well-defined lineages, as evidenced by the strong population structure in Neisseria meningitidis [23]. The majority of strains in our datasets were nontypeable; these are thought to have had more recombination than typable strains [18,24,25]. To determine whether our datasets contained recombinant regions we scanned each alignment block for signatures of recombination using several recombination detection methods. The first method used PhiTest to search for pairs of polymorphic sites whose evolutionary histories are incompatible [26–28]. The second method used the Recombination Detection Program (RDP) to run six different recombination detection methods, identifying regions as recombinant if all six methods agreed [29]. Because we obtained similar results with both methods we only discuss results from PhiTest. For the MLST dataset, two of the seven aligned blocks had significant evidence for recombination (pgi and recA; 29% of aligned blocks; Table 3). These results are consistent with another study that found high levels of recombination in pgi [25]. For the Genome dataset, 90% (91 of 101) of aligned blocks had significant evidence for recombination; because most of the exceptions were less than 1 kb long this suggests that recombina- tion has been frequent throughout the genome (Table 3). In the uptake/transformation dataset, PhiTest identified recombination in 60% (18 of 30) of aligned blocks (Table 4). These results indicate that the MLST loci have less recombination between strains than the other two datasets; this may be because recombination in these housekeeping genes is deleterious. An example of a putative recombinant region is shown in Figure 2. The next step in our analysis was to determine whether this recombination has diminished phylogenetic signal. We inferred individual MrBayes [30,31] trees for each alignment block to see whether strain relationships varied between trees from different alignment blocks. When calculating a consensus tree from all trees in each dataset, we initially included only clades present in at least 80% of the trees produced from each dataset (7, 101 or 30); this produced star-like trees for all datasets. However the lack of resolution was not caused by this stringent cutoff, as a 60% cut-off gave essentially the same results, with only two clades supported by more than half of the trees (Table 4). Similar results were obtained when Parsimony methods were used for inference (data not shown). This lack of resolution could be due to disagreement between particular trees and/or to an insufficient number of informative Figure 1. The distribution of alignment sizes is shown for each dataset used for phylogenetic analysis. Note the x-axis is a log scale. doi:10.1371/journal.pone.0005854.g001 PLoS ONE | www.plosone.org 3 June 2009 | Volume 4 | Issue 6 | e5854