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Evaluation of 16S rRNA sequencing and reevaluation of a short biochemical scheme for identification of clinically significant Bacteroides species.

ABSTRACT: Sequence analysis of the 16S rRNA gene represents a highly accurate and versatile method for bacterial classification and identification, even when the species in question is notoriously difficult to identify by phenotypic means. In this study, we evaluated the utility of 16S rRNA gene sequencing as a means of identifying clinically important Bacteroides species. We sequenced 231 clinical isolates that had been identified by a short biochemical scheme. Based on the sequence analysis, 192 clinical isolates were assigned to an established species, with the other 39 clinical strains revealing five unique sequences that may represent five novel species. This is in contrast to identification obtained from a short biochemical scheme, by which only 73.5% (172 of 231) of isolates were correctly identified to species level. Based on the solid identification obtained from 16S rRNA gene sequencing, the short biochemical scheme was modified and improved to provide clinical laboratories with an inexpensive and simple alternative for the identification of isolates of clinically significant Bacteroides species.

SUBMITTER: Song Y

PROVIDER: S-EPMC1081359 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature

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Evaluation of 16S rRNA sequencing and reevaluation of a short biochemical scheme for identification of clinically significant Bacteroides species.

Song Yuli Y Liu Chengxu C Bolanos Mauricio M Lee Julia J McTeague Maureen M Finegold Sydney M SM

Journal of clinical microbiology 20050401 4

Sequence analysis of the 16S rRNA gene represents a highly accurate and versatile method for bacterial classification and identification, even when the species in question is notoriously difficult to identify by phenotypic means. In this study, we evaluated the utility of 16S rRNA gene sequencing as a means of identifying clinically important Bacteroides species. We sequenced 231 clinical isolates that had been identified by a short biochemical scheme. Based on the sequence analysis, 192 clinica ...[more]

PMID: 15814962

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Project description:BackgroundIn the last few years, 16S rRNA gene sequencing (16S rDNA-seq) has seen a surprisingly rapid increase in election rate as a methodology to perform microbial community studies. Despite the considerable popularity of this technique, an exiguous number of specific tools are currently available for proper 16S rDNA-seq count data preprocessing and simulation. Indeed, the great majority of tools have been developed adapting methodologies previously used for bulk RNA-seq data, with poor assessment of their applicability in the metagenomics field. For such tools and the few ones specifically developed for 16S rDNA-seq data, performance assessment is challenging, mainly due to the complex nature of the data and the lack of realistic simulation models. In fact, to the best of our knowledge, no software thought for data simulation are available to directly obtain synthetic 16S rDNA-seq count tables that properly model heavy sparsity and compositionality typical of these data.ResultsIn this paper we present metaSPARSim, a sparse count matrix simulator intended for usage in development of 16S rDNA-seq metagenomic data processing pipelines. metaSPARSim implements a new generative process that models the sequencing process with a Multivariate Hypergeometric distribution in order to realistically simulate 16S rDNA-seq count table, resembling real experimental data compositionality and sparsity. It provides ready-to-use count matrices and comes with the possibility to reproduce different pre-coded scenarios and to estimate simulation parameters from real experimental data. The tool is made available at http://sysbiobig.dei.unipd.it/?q=Software#metaSPARSimand https://gitlab.com/sysbiobig/metasparsim.ConclusionmetaSPARSim is able to generate count matrices resembling real 16S rDNA-seq data. The availability of count data simulators is extremely valuable both for methods developers, for which a ground truth for tools validation is needed, and for users who want to assess state of the art analysis tools for choosing the most accurate one. Thus, we believe that metaSPARSim is a valuable tool for researchers involved in developing, testing and using robust and reliable data analysis methods in the context of 16S rRNA gene sequencing.

Project description:Short-amplicon 16S rRNA gene sequencing is currently the method of choice for studies investigating microbiomes. However, comparative studies on differences in procedures are scarce. We sequenced human stool samples and mock communities with increasing complexity using a variety of commonly used protocols. Short amplicons targeting different variable regions (V-regions) or ranges thereof (V1-V2, V1-V3, V3-V4, V4, V4-V5, V6-V8, and V7-V9) were investigated for differences in the composition outcome due to primer choices. Next, the influence of clustering (operational taxonomic units [OTUs], zero-radius OTUs [zOTUs], and amplicon sequence variants [ASVs]), different databases (GreenGenes, the Ribosomal Database Project, Silva, the genomic-based 16S rRNA Database, and The All-Species Living Tree), and bioinformatic settings on taxonomic assignment were also investigated. We present a systematic comparison across all typically used V-regions using well-established primers. While it is known that the primer choice has a significant influence on the resulting microbial composition, we show that microbial profiles generated using different primer pairs need independent validation of performance. Further, comparing data sets across V-regions using different databases might be misleading due to differences in nomenclature (e.g., Enterorhabdus versus Adlercreutzia) and varying precisions in classification down to genus level. Overall, specific but important taxa are not picked up by certain primer pairs (e.g., Bacteroidetes is missed using primers 515F-944R) or due to the database used (e.g., Acetatifactor in GreenGenes and the genomic-based 16S rRNA Database). We found that appropriate truncation of amplicons is essential and different truncated-length combinations should be tested for each study. Finally, specific mock communities of sufficient and adequate complexity are highly recommended.IMPORTANCE In 16S rRNA gene sequencing, certain bacterial genera were found to be underrepresented or even missing in taxonomic profiles when using unsuitable primer combinations, outdated reference databases, or inadequate pipeline settings. Concerning the last, quality thresholds as well as bioinformatic settings (i.e., clustering approach, analysis pipeline, and specific adjustments such as truncation) are responsible for a number of observed differences between studies. Conclusions drawn by comparing one data set to another (e.g., between publications) appear to be problematic and require independent cross-validation using matching V-regions and uniform data processing. Therefore, we highlight the importance of a thought-out study design including sufficiently complex mock standards and appropriate V-region choice for the sample of interest. The use of processing pipelines and parameters must be tested beforehand.

Dataset Information

Evaluation of 16S rRNA sequencing and reevaluation of a short biochemical scheme for identification of clinically significant Bacteroides species.

Publications

Evaluation of 16S rRNA sequencing and reevaluation of a short biochemical scheme for identification of clinically significant Bacteroides species.

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