Defining Reference Sequences for Nocardia Species by Similarity and
  Clustering Analyses of 16S rRNA Gene Sequence Data
Manal Helal Fanrong Kong Sharon C. A. Chen Michael Bain Richard Christen Vitali Sintchenko
The intra- and inter-species genetic diversity of bacteria and the absence of
'reference', or the most representative, sequences of individual species
present a significant challenge for sequence-based identification. The aims of
this study were to determine the utility, and compare the performance of
several clustering and classification algorithms to identify the species of 364
sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences
of 16S rRNA gene with no defined species, all within the genus Nocardia. A
total of 364 16S rRNA gene sequences of Nocardia species were studied. In
addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level
at the time of submission to GenBank were used for machine learning
classification experiments. Different clustering algorithms were compared with
a novel algorithm or the linear mapping (LM) of the distance matrix. Principal
Components Analysis was used for the dimensionality reduction and
visualization. Results: The LM algorithm achieved the highest performance and
classified the set of 364 16S rRNA sequences into 80 clusters, the majority of
which (83.52%) corresponded with the original species. The most representative
16S rRNA sequences for individual Nocardia species have been identified as
'centroids' in respective clusters from which the distances to all other
sequences were minimized; 110 16S rRNA gene sequences with identifications
recorded only at the genus level were classified using machine learning
methods. Simple kNN machine learning demonstrated the highest performance and
classified Nocardia species sequences with an accuracy of 92.7% and a mean
frequency of 0.578.