AUTHOR=Peker Nilay , Garcia-Croes Sharron , Dijkhuizen Brigitte , Wiersma Henry H. , van Zanten Evert , Wisselink Guido , Friedrich Alex W. , Kooistra-Smid Mirjam , Sinha Bhanu , Rossen John W. A. , Couto Natacha 

TITLE=A Comparison of Three Different Bioinformatics Analyses of the 16S–23S rRNA Encoding Region for Bacterial Identification

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 10 - 2019

YEAR=2019

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2019.00620

DOI=10.3389/fmicb.2019.00620

ISSN=1664-302X

ABSTRACT=Rapid and reliable identification of bacterial pathogens directly from patient samples is required for optimizing antimicrobial therapy. Although Sanger sequencing of the 16S ribosomal RNA (rRNA) gene is used as a molecular method, species identification and discrimination is not always achievable for bacteria as their 16S rRNA genes have sometimes high sequence homology. Recently, next generation sequencing (NGS) of the 16S-23S rRNA encoding region has been proposed for reliable identification of pathogens directly from patient samples. However, data analysis is laborious and time-consuming and a database for the complete 16S-23S rRNA encoding region is not available. Therefore, a better, faster, and stronger approach is needed for NGS data analysis of the 16S-23S rRNA encoding region. We compared speed and diagnostic accuracy of different data analysis approaches: de novo assembly followed by Basic Local Alignment Search Tool (BLAST), Operational Taxonomic Unit (OTU) clustering, or mapping using an in-house developed 16S-23S rRNA encoding region database for the identification of bacterial species. De novo assembly followed by BLAST using the in-house database was superior to the other methods, resulting in the shortest turnaround time (2 hours and 5 minutes), approximately 2 hours less than OTU clustering and 4.5 hours less than mapping, and a sensitivity of 80%. Mapping was the slowest and most laborious data analysis approach with a sensitivity of 60%, whereas OTU clustering was the least laborious approach with 70% sensitivity. Although the in-house database requires more sequence entries to improve the sensitivity, the combination of de novo assembly and BLAST currently appears to be the optimal approach for data analysis.