A user's guide to the bioinformatic analysis of shotgun metagenomic sequence data for bacterial pathogen detection.

Average nucleotide identity Bioinformatics Foodborne pathogens Limit of detection Metagenome Read recruitment plots Relative abundance

Journal

International journal of food microbiology
ISSN: 1879-3460
Titre abrégé: Int J Food Microbiol
Pays: Netherlands
ID NLM: 8412849

Informations de publication

Date de publication:
17 Nov 2023
Historique:
received: 17 05 2023
revised: 15 09 2023
accepted: 11 11 2023
medline: 30 11 2023
pubmed: 30 11 2023
entrez: 30 11 2023
Statut: aheadofprint

Résumé

Metagenomics, i.e., shotgun sequencing of the total microbial community DNA from a sample, has become a mature technique but its application to pathogen detection in clinical, environmental, and food samples is far from common or standardized. In this review, we summarize ongoing developments in metagenomic sequence analysis that facilitate its wider application to pathogen detection. We examine theoretical frameworks for estimating the limit of detection for a particular level of sequencing effort, current approaches for achieving species and strain analytical resolution, and discuss some relevant modern tools for these tasks. While these recent advances are significant and establish metagenomics as a powerful tool to provide insights not easily attained by culture-based approaches, metagenomics is unlikely to emerge as a widespread, routine monitoring tool in the near future due to its inherently high detection limits, cost, and inability to easily distinguish between viable and non-viable cells. Instead, metagenomics seems best poised for applications involving special circumstances otherwise challenging for culture-based and molecular (e.g., PCR-based) approaches such as the de novo detection of novel pathogens, cases of co-infection by more than one pathogen, and situations where it is important to assess the genomic composition of the pathogenic population(s) and/or its impact on the indigenous microbiome.

Identifiants

pubmed: 38035404
pii: S0168-1605(23)00405-1
doi: 10.1016/j.ijfoodmicro.2023.110488
pii:
doi:

Types de publication

Journal Article Review

Langues

eng

Sous-ensembles de citation

IM

Pagination

110488

Informations de copyright

Copyright © 2023 Elsevier B.V. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare no competing interest.

Auteurs

Blake G Lindner (BG)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Kenji Gerhardt (K)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Dorian J Feistel (DJ)

School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA.

Luis M Rodriguez-R (LM)

Department of Microbiology, Digital Science Center (DiSC), University of Innsbruck, Innsbruck, Austria.

Janet K Hatt (JK)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

Konstantinos T Konstantinidis (KT)

School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, GA, USA; School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA, USA. Electronic address: kostas@ce.gatech.edu.

Classifications MeSH