A Comparison of Three Different Bioinformatics Analyses of the 16S –23S rRNA Encoding Region for Bacterial Identification
Conclusion
The higher resolution at the species level identification provided by 16S–23S rRNA encoding region NGS makes its use in routine diagnostic microbiology potentially attractive. Particularly, data analysis is one of the most important steps of a diagnostic workflow, which requires an optimal pipeline for the interpretation of the sequencing data in a short time. This study demonstrates that de novo assembly and subsequent BLASTN analysis using an in-house developed database compared to OTU clustering and mapping approaches is the most accurate and fastest approach for identification of bacterial pathogens. Yet, OTU clustering should be considered as a second approach if no pathogen species are identified. Although the in-house developed publicly available database has been shown to be robust enough to identify and distinguish relevant bacterial species, it should be continuously updated to represent more currently relevant or emerging pathogens. In conclusion, advancements of the 16S–23S rRNA encoding region NGS-based method along with the subsequent data analysis of de novo assembly and BLAST using a 16S–23S rRNA encoding region database has the potential to be integrated into the routine diagnostic workflow by providing a more accurate and rapid microbial diagnosis.
Author Contributions
NC, JR, BS, MK-S, EZ, and GW conceived and designed the experiments. NP, SG-C, and BD performed the experiments. NP, SG-C, and HW analyzed the data. AF and J...
Source: Frontiers in Microbiology - Category: Microbiology Source Type: research
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