MetaAll: integrative bioinformatics workflow for analysing clinical metagenomic data

Abstract Over the past decade, there have been many improvements in the field of metagenomics, including sequencing technologies, advances in bioinformatics and the development of reference databases, but a one-size-fits-all sequencing and bioinformatics pipeline does not yet seem achievable. In thi...

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Veröffentlicht in:Briefings in bioinformatics Jg. 25; H. 6
Hauptverfasser: Bosilj, Martin, Suljič, Alen, Zakotnik, Samo, Slunečko, Jan, Kogoj, Rok, Korva, Misa
Format: Journal Article
Sprache:Englisch
Veröffentlicht: England Oxford University Press 23.09.2024
Oxford Publishing Limited (England)
Schlagworte:
Bioinformatics
Computational Biology - methods
COVID-19 - diagnosis
COVID-19 - virology
Datasets
Humans
Influenza A
Klebsiella
Metagenome
Metagenomics
Metagenomics - methods
Pathogens
Problem Solving Protocol
Rhinovirus
SARS-CoV-2 - genetics
Severe acute respiratory syndrome coronavirus 2
Software
Viral diseases
Workflow
long reads
short PE reads
clinical metagenomics
pathogen detection
bioinformatics workflow
ISSN:1467-5463, 1477-4054, 1477-4054
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Zusammenfassung:Abstract Over the past decade, there have been many improvements in the field of metagenomics, including sequencing technologies, advances in bioinformatics and the development of reference databases, but a one-size-fits-all sequencing and bioinformatics pipeline does not yet seem achievable. In this study, we address the bioinformatics part of the analysis by combining three methods into a three-step workflow that increases the sensitivity and specificity of clinical metagenomics and improves pathogen detection. The individual tools are combined into a user-friendly workflow suitable for analysing short paired-end (PE) and long reads from metagenomics datasets—MetaAll. To demonstrate the applicability of the developed workflow, four complicated clinical cases with different disease presentations and multiple samples collected from different biological sites as well as the CAMI Clinical pathogen detection challenge dataset were used. MetaAll was able to identify putative pathogens in all but one case. In this case, however, traditional microbiological diagnostics were also unsuccessful. In addition, co-infection with Haemophilus influenzae and Human rhinovirus C54 was detected in case 1 and co-infection with SARS-Cov-2 and Influenza A virus (FluA) subtype H3N2 was detected in case 3. In case 2, in which conventional diagnostics could not find a pathogen, mNGS pointed to Klebsiella pneumoniae as the suspected pathogen. Finally, this study demonstrated the importance of combining read classification, contig validation and targeted reference mapping for more reliable detection of infectious agents in clinical metagenome samples.
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ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae597