in-silico taxonomic classification of 373 genomes reveals species misidentification and new genospecies within the genus pseudomonas

Clicks: 225
ID: 177395
2017
Article Quality & Performance Metrics
Overall Quality Improving Quality
0.0 /100
Combines engagement data with AI-assessed academic quality
AI Quality Assessment
Not analyzed
Abstract
The genus Pseudomonas has one of the largest diversity of species within the Bacteria kingdom. To date, its taxonomy is still being revised and updated. Due to the non-standardized procedure and ambiguous thresholds at species level, largely based on 16S rRNA gene or conventional biochemical assay, species identification of publicly available Pseudomonas genomes remains questionable. In this study, we performed a large-scale analysis of all Pseudomonas genomes with species designation (excluding the well-defined P. aeruginosa) and re-evaluated their taxonomic assignment via in silico genome-genome hybridization and/or genetic comparison with valid type species. Three-hundred and seventy-three pseudomonad genomes were analyzed and subsequently clustered into 145 distinct genospecies. We detected 207 erroneous labels and corrected 43 to the proper species based on Average Nucleotide Identity Multilocus Sequence Typing (MLST) sequence similarity to the type strain. Surprisingly, more than half of the genomes initially designated as Pseudomonas syringae and Pseudomonas fluorescens should be classified either to a previously described species or to a new genospecies. Notably, high pairwise average nucleotide identity (>95%) indicating species-level similarity was observed between P. synxantha-P. libanensis, P. psychrotolerans–P. oryzihabitans, and P. kilonensis- P. brassicacearum, that were previously differentiated based on conventional biochemical tests and/or genome-genome hybridization techniques.
Reference Key
tran2017frontiersin-silico Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Phuong N. Tran;Phuong N. Tran;Michael A. Savka;Han Ming Gan;Han Ming Gan;Han Ming Gan
Journal journal of magnetic resonance (san diego, calif : 1997)
Year 2017
DOI
10.3389/fmicb.2017.01296
URL
Keywords

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

No comments yet. Be the first to comment on this article.