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Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination.

ABSTRACT: Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while between-species contamination generally does not. Contaminant reads mapping to references or becoming incorporated into chimeric sequences during assembly are the sources of those errors. Contamination sufficient to influence clustering analyses is present in public sequence databases.

SUBMITTER: Pightling AW

PROVIDER: S-EPMC6918607 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination.

Pightling Arthur W AW Pettengill James B JB Wang Yu Y Rand Hugh H Strain Errol E

Genome biology 20191218 1

Although it is assumed that contamination in bacterial whole-genome sequencing causes errors, the influences of contamination on clustering analyses, such as single-nucleotide polymorphism discovery, phylogenetics, and multi-locus sequencing typing, have not been quantified. By developing and analyzing 720 Listeria monocytogenes, Salmonella enterica, and Escherichia coli short-read datasets, we demonstrate that within-species contamination causes errors that confound clustering analyses, while b ...[more]

PMID: 31849328

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Project description:BACKGROUND: 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. METHODS: 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. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra-species variability.

Project description:Modern isothermal microcalorimeters (IMC) are able to detect the metabolic heat of bacteria with an accuracy sufficient to recognize even the smallest traces of bacterial contamination of water, food, and medical samples. The modern IMC techniques are often superior to conventional detection methods in terms of the detection time, reliability, labor, and technical effort. What is missing is a systematic analysis of the influence of the cultivation conditions on calorimetric detection. For the acceptance of IMC techniques, it is advantageous if already standardized cultivation techniques can be combined with calorimetry. Here we performed such a systematic analysis using Lactobacillus plantarum as a model bacterium. Independent of the cultivation techniques, IMC detections were much faster for high bacterial concentrations (>102 CFU?mL-1) than visual detections. At low bacterial concentrations (<102 CFU?mL-1), detection times were approximately the same. Our data demonstrate that all kinds of traditional cultivation techniques like growth on agar (GOA) or in agar (GIA), in liquid media (GL) or on agar after enrichment via membrane filtration (GF) can be combined with IMC. The order of the detection times was GF < GIA ? GL ? GOA. The observed linear relationship between the calorimetric detection times and the initial bacterial concentrations can be used to quantify the bacterial contamination. Further investigations regarding the correlation between the filling level (in mm) of the calorimetric vessel and the specific maximum heat flow (in ?W?g-1) illustrated two completely different results for liquid and solid media. Due to the better availability of substrates and the homogeneous distribution of bacteria growing in a liquid medium, the volume-related maximum heat flow was independent on the filling level of the calorimetric vessels. However, in a solid medium, the volume-related maximum heat flow approached a threshold and achieved a maximum at low filling levels. This fundamentally different behavior can be explained by the spatial limitation of the growth of bacterial colonies and the reduced substrate supply due to diffusion.

Dataset Information

Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination.

Publications

Within-species contamination of bacterial whole-genome sequence data has a greater influence on clustering analyses than between-species contamination.

Similar Datasets

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