Predicted known strain in three analyses.

(A) S. aureus known strains predicted and (B) S. epidermidis known strains predicted by Chng et al., the rerunning of PathoScope 2.0 and StrainEst.</p

Strains used in database.

With mutations constantly accumulating in bacterial genomes, it is unclear whether the previously identified bacterial strains are really present in an extant sample. To address this question, we did a case study on the known strains of the bacterial species S. aureus and S. epidermis in 68 atopic dermatitis shotgun metagenomic samples. We evaluated the likelihood of the presence of all sixteen known strains predicted in the original study and by two popular tools in this study. We found that even with the same tool, only two known strains were predicted by the original study and this study. Moreover, none of the sixteen known strains was likely present in these 68 samples. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. It implies the unlikely presence of the previously identified known strains in a current environmental sample. It also called for de novo bacterial strain identification directly from shotgun metagenomic reads.</div

Known strain correlations.

With mutations constantly accumulating in bacterial genomes, it is unclear whether the previously identified bacterial strains are really present in an extant sample. To address this question, we did a case study on the known strains of the bacterial species S. aureus and S. epidermis in 68 atopic dermatitis shotgun metagenomic samples. We evaluated the likelihood of the presence of all sixteen known strains predicted in the original study and by two popular tools in this study. We found that even with the same tool, only two known strains were predicted by the original study and this study. Moreover, none of the sixteen known strains was likely present in these 68 samples. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. It implies the unlikely presence of the previously identified known strains in a current environmental sample. It also called for de novo bacterial strain identification directly from shotgun metagenomic reads.</div

Known strain unique SNP coverages.

With mutations constantly accumulating in bacterial genomes, it is unclear whether the previously identified bacterial strains are really present in an extant sample. To address this question, we did a case study on the known strains of the bacterial species S. aureus and S. epidermis in 68 atopic dermatitis shotgun metagenomic samples. We evaluated the likelihood of the presence of all sixteen known strains predicted in the original study and by two popular tools in this study. We found that even with the same tool, only two known strains were predicted by the original study and this study. Moreover, none of the sixteen known strains was likely present in these 68 samples. Our study thus indicates the limitation of the known-strain-based studies, especially those on rapidly evolving bacterial species. It implies the unlikely presence of the previously identified known strains in a current environmental sample. It also called for de novo bacterial strain identification directly from shotgun metagenomic reads.</div

The correlated unique SNP pairs in the predicted <i>S</i>. <i>aureus</i> known strains.

The correlated unique SNP pairs in the predicted S. aureus known strains.</p

The unique SNPs in the predicted <i>S</i>. <i>aureus</i> known strains.

The unique SNPs in the predicted S. aureus known strains.</p

Comparison of results from our analyses and from two MetaPhlAn based analyses.

<p>Comparison of results from our analyses and from two MetaPhlAn based analyses.</p

When old metagenomic data meet newly sequenced genomes, a case study

<div><p>Dozens of computational methods are developed to identify species present in a metagenomic dataset. Many of these computational methods depend on available sequenced microbial species, which are still far from being representative. To see how newly sequenced genomes affect the analysis results, we re-analyzed a shotgun metagenomic dataset composed of twelve colitis free metagenomic samples and ten colitis-related metagenomic samples. Unexpectedly, we identified at least two new phyla that may relate to colitis development in patients, together with the phylum identified previously. Compared with the previously identified phylum that differed between the two types of samples, the differences associated with the two new phyla are statistically more significant. Moreover, the abundance of the two new phyla correlates more with the severity of colitis. Surprisingly, even by repeating the analyses implemented in the previous study, we found that at least one main conclusion in the previous study is not supported. Our study indicates the importance of re-analysis of the generated metagenomic datasets and the necessity of considering multiple updated tools in metagenomic studies. It also sheds light on the limitations of the popular tools used currently and the importance to infer the presence of taxa without relying upon available sequenced genomes.</p></div

The number of taxa identified based on different criteria.

<p>The number of taxa identified based on different criteria.</p

Lower taxa identified from the phyla <i>Thaumarchaeota</i> and <i>Chlamydiae</i>.

<p>Only taxa from the last column of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198773#pone.0198773.t001" target="_blank">Table 1</a> are shown. Note that no class from these two phyla are identified in the last column of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198773#pone.0198773.t001" target="_blank">Table 1</a>. The phylum <i>Chlamydiae</i> is presented in a dotted box, as it is not identified in the last column of <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0198773#pone.0198773.t001" target="_blank">Table 1</a>.</p