ArticleFrontiers in microbiology2019
A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification.
Article in Frontiers in microbiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
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- Aquatic BacteriaInternational journal of molecular sciences · 2023Article
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- Differences in the composition of the bacterial element of the urinary tract microbiome in patients undergoing dialysis and patients after kidney transplantation.Frontiers in microbiology · 2023Article
- A comparative study to determine the association of gut microbiome with schizophrenia in Zhejiang, China.BMC psychiatry · 2022Article
- Combination of Whole Genome Sequencing and Metagenomics for Microbiological Diagnostics.International journal of molecular sciences · 2022Review
- KnotAli: informed energy minimization through the use of evolutionary information.BMC bioinformatics · 2022Article
- Application of microbial analyses to feeds and potential implications for poultry nutrition.Poultry science · 2022Review
- Automated 16S Sequencing Using an R-Based Analysis Module for Bacterial Identification.Microbiology spectrum · 2022Article
- Microbial Identification Using rRNA Operon Region: Database and Tool for Metataxonomics with Long-Read Sequence.Microbiology spectrum · 2022Article
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Authors and funding
11 authors.
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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:
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