n of G. vaginalis oligotypes in vaginal samples of 24 of 44 women, whose partners also harbored at least one G. vaginalis sequence, were significantly correlated (r≥0.9, p<0.001) with either the penile skin, or urethral, or both samples from their sexual partners (Table 2). Significant correlation in G. vaginalis oligotypes was observed between vaginal and penile skin samples in 19 couples, while for vaginal and urethral samples of only 12 couples had correlation values above 0.9. In 8 couples, there was reduced, but nonetheless high degree of correlation (r≥0.5, p<0.001) between the vaginal and either the penile skin or the urethral samples. In 12 couple no correlation was found between partners (r<0.5). Figure 3 illustrates seven couples whose G. vaginalis compositions are highly correlated (see Figure S3 for stacked bar chart comparison of all samples). Correlation levels between partners did not appear to vary significantly by GS classification, although, the total number of couples in the intermediate and normal categories is small and the total number of G. vaginalis sequences in normal couples is low (Table 1).
Different colors in the pie charts correspond to different oligotypes. In every set, the pie chart on the left represents the sample collected from the female patient.
The oligotype TCCCTCGA was the most abundant overall and it was observed in most of the samples (Table S2). It was the dominant oligotype of 24 of 48 vaginal samples. The TCCCACAG oligotype was the dominant oligotype in 10 vaginal samples. While the TTTTACGA, TTCTACGA and TTCCTCGA were dominant in 3 vaginal samples each, oligotypes TTTTATGA, TTCTTCGA and TCTCACGA were dominant in one vaginal sample each. A complete list of oligotype distribution across genders, anatomical sampling sites, and GS-defined BV and GS-defined normal microbiota is given in Table S2.
UniFrac ,  is a computational method used to compare microbial samples to each other based on their composition with respect to a common phylogeny. After computing a phylogenetic tree for oligotypes using Bayesian inference, we used UniFrac to quantify similarities between samples based on their oligotype composition. Hierarchical clustering analysis on the UniFrac distance matrix of GS-defined BV and GS-defined normal vaginal samples grouped separately based on GS definition (Figure 2). Analogous comparison of urethral and penile skin samples from male partners of women with GS-defined BV or GS-defined normal vaginal flora did not show a similar separation (Figure S2).
It is well known that bacterial species with identical 16S rRNA genes can represent different ecotypes with differences in virulence properties and other phenotypic traits . In this sense, the sensitivity of the 16S rRNA gene is limited, but it is specific; it has been shown that even one nucleotide difference at the level of the 16S rRNA gene may be an indicator of an ecologically distinct strain . Moreover, there is a correlation between 16S rRNA gene divergence and the overall gene content , and the evolutionary distances of 16S rRNA genes can be used to discern genomic differences between species even with short pyrosequencing tag reads .
With the availability and affordability of massively parallel high-throughput sequencing technologies it is now possible to collect vast amounts of sequence data that cover a great deal of bacterial diversity within an environmental sample without the need for cultivation . However, due to the nature of pyrosequencing, sequences contain biologically irrelevant random sequencing errors, rendering them imprecise and noisy for inferring diversity at very low levels of taxonomy with high confidence. For instance, the two 16S rRNA genes of two G. vaginalis strains used in a genomic comparison study  differed by only 6 nucleotides at the 16S rRNA gene level, which was equivalent to 0.38% variation. Nevertheless, these two strains with very low level of variation at the 16S rRNA gene level were significantly different from each other in respect to their whole genomes. However, 0.38% variati