Publication

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

Peker, N., Garcia-Croes, S., Dijkhuizen, B., Wiersma, H. H., van Zanten, E., Wisselink, G., Friedrich, A. W., Kooistra-Smid, M., Sinha, B., Rossen, J. W. A. & Couto, N., 16-Apr-2019, In : Frontiers in Microbiology. 10, 13 p., 620.

Research output: Contribution to journalArticleAcademicpeer-review

APA

Peker, N., Garcia-Croes, S., Dijkhuizen, B., Wiersma, H. H., van Zanten, E., Wisselink, G., ... Couto, N. (2019). A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification. Frontiers in Microbiology, 10, [620]. https://doi.org/10.3389/fmicb.2019.00620

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. / A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification. In: Frontiers in Microbiology. 2019 ; Vol. 10.

Harvard

Peker, N, Garcia-Croes, S, Dijkhuizen, B, Wiersma, HH, van Zanten, E, Wisselink, G, Friedrich, AW, Kooistra-Smid, M, Sinha, B, Rossen, JWA & Couto, N 2019, 'A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification', Frontiers in Microbiology, vol. 10, 620. https://doi.org/10.3389/fmicb.2019.00620

Standard

A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification. / 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.

In: Frontiers in Microbiology, Vol. 10, 620, 16.04.2019.

Research output: Contribution to journalArticleAcademicpeer-review

Vancouver

Peker N, Garcia-Croes S, Dijkhuizen B, Wiersma HH, van Zanten E, Wisselink G et al. A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification. Frontiers in Microbiology. 2019 Apr 16;10. 620. https://doi.org/10.3389/fmicb.2019.00620


BibTeX

@article{f5777732ed4747a98245703c243bce4b,
title = "A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification",
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 inhouse database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h 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.",
keywords = "clinical microbiology, diagnostics, next-generation sequencing, metagenomics, OTU clustering, mapping, de novo assembly, PROPIONIBACTERIUM-ACNES, CLINICAL MICROBIOLOGY, ENDOCARDITIS, AMPLIFICATION, INFECTION, DISEASE, AGENT",
author = "Nilay Peker and Sharron Garcia-Croes and Brigitte Dijkhuizen and Wiersma, {Henry H.} and {van Zanten}, Evert and Guido Wisselink and Friedrich, {Alex W.} and Mirjam Kooistra-Smid and Bhanu Sinha and Rossen, {John W. A.} and Natacha Couto",
year = "2019",
month = "4",
day = "16",
doi = "10.3389/fmicb.2019.00620",
language = "English",
volume = "10",
journal = "Frontiers in Microbiology",
issn = "1664-302X",
publisher = "Frontiers Media S.A.",

}

RIS

TY - JOUR

T1 - A Comparison of Three Different Bioinformatics Analyses of the 16S-23S rRNA Encoding Region for Bacterial Identification

AU - Peker, Nilay

AU - Garcia-Croes, Sharron

AU - Dijkhuizen, Brigitte

AU - Wiersma, Henry H.

AU - van Zanten, Evert

AU - Wisselink, Guido

AU - Friedrich, Alex W.

AU - Kooistra-Smid, Mirjam

AU - Sinha, Bhanu

AU - Rossen, John W. A.

AU - Couto, Natacha

PY - 2019/4/16

Y1 - 2019/4/16

N2 - 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 inhouse database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h 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.

AB - 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 inhouse database was superior to the other methods, resulting in the shortest turnaround time (2 h and 5 min), approximately 2 h less than OTU clustering and 4.5 h 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.

KW - clinical microbiology

KW - diagnostics

KW - next-generation sequencing

KW - metagenomics

KW - OTU clustering

KW - mapping

KW - de novo assembly

KW - PROPIONIBACTERIUM-ACNES

KW - CLINICAL MICROBIOLOGY

KW - ENDOCARDITIS

KW - AMPLIFICATION

KW - INFECTION

KW - DISEASE

KW - AGENT

U2 - 10.3389/fmicb.2019.00620

DO - 10.3389/fmicb.2019.00620

M3 - Article

VL - 10

JO - Frontiers in Microbiology

JF - Frontiers in Microbiology

SN - 1664-302X

M1 - 620

ER -

ID: 81381448