Dataset Information

Assignment of reference 5'-end 16S rDNA sequences and species-specific sequence polymorphisms improves species identification of Nocardia.

ABSTRACT: 16S rDNA sequence analysis is the most accurate method for definitive species identification of nocardiae. However, conflicting results can be found due to sequence errors in gene databases. This study tested the feasibility of species identification of Nocardia by partial (5'-end 606-bp) 16S rDNA sequencing, based on sequence comparison with "reference" sequences of well-annotated strains. This new approach was evaluated using 96 American Type Culture Collection (n=6), and clinical (n=90) Nocardia isolates. Nucleotide sequence-based polymorphisms within species were indicative of "sequence types" for that species. Sequences were compared with those in the GenBank, Bioinformatics Bacteria Identification and Ribosomal Database Project databases. Compared with the reference sequence set, all 96 isolates were correctly identified using the criterion of ?99% sequence similarity. Seventy-eight (81.3%) were speciated by database comparison; alignment with reference sequences resolved the identity of 14 (15%) isolates whose sequences yielded 100% similarity to sequences in GenBank under >1 species designation. Of 90 clinical isolates, the commonest species was Nocardia nova (33.3%) followed by Nocardia cyriacigeorgica (26.7%). Recently-described or uncommon species included Nocardia veterana (4.4%), Nocarida bejingensis (2.2%) and, Nocardia abscessus and Nocardia arthriditis (each n=1). Nocardia asteroides sensu stricto was rare (n=1). There were nine sequence types of N. nova, three of Nocardia brasiliensis with two each of N. cyriacigeorgica and Nocardia farcinica. Thirteen novel sequences were identified. Alignment of sequences with reference sequences facilitated species identification of Nocardia and allowed delineation of sequence types within species, suggesting that such a barcoding approach can be clinically useful for identification of bacteria.

SUBMITTER: Kong F

PROVIDER: S-EPMC2714554 | biostudies-literature | 2009 Jun

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Assignment of reference 5'-end 16S rDNA sequences and species-specific sequence polymorphisms improves species identification of Nocardia.

Kong Fanrong F Chen Sharon C A SC Chen Xiaoyou X Sintchenko Vitali V Halliday Catriona C Cai Lin L Tong Zhongsheng Z Lee Ok Cha OC Sorrell Tania C TC

The open microbiology journal 20090623

16S rDNA sequence analysis is the most accurate method for definitive species identification of nocardiae. However, conflicting results can be found due to sequence errors in gene databases. This study tested the feasibility of species identification of Nocardia by partial (5'-end 606-bp) 16S rDNA sequencing, based on sequence comparison with "reference" sequences of well-annotated strains. This new approach was evaluated using 96 American Type Culture Collection (n=6), and clinical (n=90) Nocar ...[more]

PMID: 19639036

Similar Datasets

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:Dinoflagellate species are traditionally defined using morphological characters, but molecular evidence accumulated over the past several decades indicates many morphologically-based descriptions are inaccurate. This recognition led to an increasing reliance on DNA sequence data, particularly rDNA gene segments, in defining species. The validity of this approach assumes the divergence in rDNA or other selected genes parallels speciation events. Another concern is whether single gene rDNA phylogenies by themselves are adequate for delineating species or if multigene phylogenies are required instead. Currently, few studies have directly assessed the relative utility of multigene versus rDNA-based phylogenies for distinguishing species. To address this, the current study examined D1-D3 and ITS/5.8S rDNA gene regions, a multi-gene phylogeny, and morphological characters in Gambierdiscus and other related dinoflagellate genera to determine if they produce congruent phylogenies and identify the same species. Data for the analyses were obtained from previous sequencing efforts and publicly available dinoflagellate transcriptomic libraries as well from the additional nine well-characterized Gambierdiscus species transcriptomic libraries generated in this study. The D1-D3 and ITS/5.8S phylogenies successfully identified the described Gambierdiscus and Alexandrium species. Additionally, the data showed that the D1-D3 and multigene phylogenies were equally capable of identifying the same species. The multigene phylogenies, however, showed different relationships among species and are likely to prove more accurate at determining phylogenetic relationships above the species level. These data indicated that D1-D3 and ITS/5.8S rDNA region phylogenies are generally successful for identifying species of Gambierdiscus, and likely those of other dinoflagellates. To assess how broadly general this finding is likely to be, rDNA molecular phylogenies from over 473 manuscripts representing 232 genera and 863 described species of dinoflagellates were reviewed. Results showed the D1-D3 rDNA and ITS phylogenies in combination are capable of identifying 97% of dinoflagellate species including all the species belonging to the genera Alexandrium, Ostreopsis and Gambierdiscus, although it should be noted that multi-gene phylogenies are preferred for inferring relationships among these species. A protocol is presented for determining when D1-D3, confirmed by ITS/5.8S rDNA sequence data, would take precedence over morphological features when describing new dinoflagellate species. This protocol addresses situations such as: a) when a new species is both morphologically and molecularly distinct from other known species; b) when a new species and closely related species are morphologically indistinguishable, but genetically distinct; and c) how to handle potentially cryptic species and cases where morphotypes are clearly distinct but have the same rDNA sequence. The protocol also addresses other molecular, morphological, and genetic approaches required to resolve species boundaries in the small minority of species where the D1-D3/ITS region phylogenies fail.

Project description:BACKGROUND:Shotgun metagenomic sequencing reveals the potential in microbial communities. However, lower-cost 16S ribosomal RNA (rRNA) gene sequencing provides taxonomic, not functional, observations. To remedy this, we previously introduced Piphillin, a software package that predicts functional metagenomic content based on the frequency of detected 16S rRNA gene sequences corresponding to genomes in regularly updated, functionally annotated genome databases. Piphillin (and similar tools) have previously been evaluated on 16S rRNA data processed by the clustering of sequences into operational taxonomic units (OTUs). New techniques such as amplicon sequence variant error correction are in increased use, but it is unknown if these techniques perform better in metagenomic content prediction pipelines, or if they should be treated the same as OTU data in respect to optimal pipeline parameters. RESULTS:To evaluate the effect of 16S rRNA sequence analysis method (clustering sequences into OTUs vs amplicon sequence variant error correction into amplicon sequence variants (ASVs)) on the ability of Piphillin to predict functional metagenomic content, we evaluated Piphillin-predicted functional content from 16S rRNA sequence data processed through OTU clustering and error correction into ASVs compared to corresponding shotgun metagenomic data. We show a strong correlation between metagenomic data and Piphillin-predicted functional content resulting from both 16S rRNA sequence analysis methods. Differential abundance testing with Piphillin-predicted functional content exhibited a low false positive rate (< 0.05) while capturing a large fraction of the differentially abundant features resulting from corresponding metagenomic data. However, Piphillin prediction performance was optimal at different cutoff parameters depending on 16S rRNA sequence analysis method. Using data analyzed with amplicon sequence variant error correction, Piphillin outperformed comparable tools, for instance exhibiting 19% greater balanced accuracy and 54% greater precision compared to PICRUSt2. CONCLUSIONS:Our results demonstrate that raw Illumina sequences should be processed for subsequent Piphillin analysis using amplicon sequence variant error correction (with DADA2 or similar methods) and run using a 99% ID cutoff for Piphillin, while sequences generated on platforms other than Illumina should be processed via OTU clustering (e.g., UPARSE) and run using a 96% ID cutoff for Piphillin. Piphillin is publicly available for academic users (Piphillin server. http://piphillin.secondgenome.com/.).

Dataset Information

Assignment of reference 5'-end 16S rDNA sequences and species-specific sequence polymorphisms improves species identification of Nocardia.

Publications

Assignment of reference 5'-end 16S rDNA sequences and species-specific sequence polymorphisms improves species identification of Nocardia.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets