When should a DDH experiment be mandatory in microbial taxonomy?
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DNA–DNA hybridizations (DDH) play a key role in microbial species discrimination in cases when 16S rRNA gene sequence similarities are 97 % or higher. Using real-world 16S rRNA gene sequences and DDH data, we here re-investigate whether or not, and in which situations, this threshold value might be too conservative. Statistical estimates of these thresholds are calculated in general as well as more specifically for a number of phyla that are frequently subjected to DDH. Among several methods to infer 16S gene sequence similarities investigated, most of those routinely applied by taxonomists appear well suited for the task. The effects of using distinct DDH methods also seem to be insignificant. Depending on the investigated taxonomic group, a threshold between 98.2 and 99.0 % appears reasonable. In that way, up to half of the currently conducted DDH experiments could safely be omitted without a significant risk for wrongly differentiated species.
KeywordsDNA–DNA hybridization Species concept 16S rRNA gene Generalized linear model BLAST Smith–Waterman Substitution model Microbial taxonomy
We are grateful to Prof. Erko Stackebrandt for providing data and for helpful comments.
Conflict of interest
The authors declare that they have no conflict of interest.
- Ezaki T, Hashimoto Y, Yabuuchi E (1989) Fluorometric deoxyribonucleic acid-deoxyribonucleic acid hybridization in microdilution wells as an alternative to membrane filter hybridization in which radioisotopes are used to determine genetic relatedness among bacterial strains. Int J Syst Bacteriol 39:224–229. doi: 10.1099/00207713-39-3-224 CrossRefGoogle Scholar
- Felsenstein J (2004) Inferring phylogenies. Sinauer Associates, SunderlandGoogle Scholar
- Jukes T, Cantor C (1969) Evolution of protein molecules. Academic Press, New YorkGoogle Scholar
- Lagier J-C, Karkouri K El, Rivet R et al (2013) Non contiguous-finished genome sequence and description of Senegalemassilia anaerobia gen. nov., sp. nov. Stand Genomic Sci. doi: 10.4056/sigs.3246665
- Motulsky H, Christopoulos A (2004) Fitting models to biological data using linear and nonlinear regression: a practical guide to curve fitting. Oxford University Press, OxfordGoogle Scholar
- Nelder JA, Wedderburn RWM (1972) Generalized linear models. J R Stat Soc 135:370–384Google Scholar
- Stackebrandt E, Ebers J (2006) Taxonomic parameters revisited: tarnished gold standards. Microbiol Today 33:152–155Google Scholar
- Swofford DL (2003) PAUP*. Phylogenetic analysis using parsimony (*and other methods). Version 4. Sinauer Associates, SunderlandGoogle Scholar
- R Development Core Team (2011) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://r-project.org/
- Tourova TP, Antonov AS (1988) Identification of microorganisms by rapid DNA–DNA hybridization. In: Colwell RR, Grigorova R (eds) Methods in microbiology. Academic Press, London, pp 333–355Google Scholar