Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data

  • Background Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108–103 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the twoBackground Microbiome analysis is becoming a standard component in many scientific studies, but also requires extensive quality control of the 16S rRNA gene sequencing data prior to analysis. In particular, when investigating low-biomass microbial environments such as human skin, contaminants distort the true microbiome sample composition and need to be removed bioinformatically. We introduce MicrobIEM, a novel tool to bioinformatically remove contaminants using negative controls. Results We benchmarked MicrobIEM against five established decontamination approaches in four 16S rRNA amplicon sequencing datasets: three serially diluted mock communities (108–103 cells, 0.4–80% contamination) with even or staggered taxon compositions and a skin microbiome dataset. Results depended strongly on user-selected algorithm parameters. Overall, sample-based algorithms separated mock and contaminant sequences best in the even mock, whereas control-based algorithms performed better in the two staggered mocks, particularly in low-biomass samples (≤ 106 cells). We show that a correct decontamination benchmarking requires realistic staggered mock communities and unbiased evaluation measures such as Youden’s index. In the skin dataset, the Decontam prevalence filter and MicrobIEM’s ratio filter effectively reduced common contaminants while keeping skin-associated genera. Conclusions MicrobIEM’s ratio filter for decontamination performs better or as good as established bioinformatic decontamination tools. In contrast to established tools, MicrobIEM additionally provides interactive plots and supports selecting appropriate filtering parameters via a user-friendly graphical user interface. Therefore, MicrobIEM is the first quality control tool for microbiome experts without coding experience.show moreshow less

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Metadaten
Author:Claudia Hülpüsch, Luise Rauer, Thomas Nussbaumer, Vera Schwierzeck, Madhumita Bhattacharyya, Veronika Erhart, Claudia Traidl-HoffmannORCiDGND, Matthias Reiger, Avidan U. Neumann
URN:urn:nbn:de:bvb:384-opus4-1095549
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/109554
ISSN:1741-7007OPAC
Parent Title (English):BMC Biology
Publisher:Springer
Place of publication:Berlin
Type:Article
Language:English
Year of first Publication:2023
Publishing Institution:Universität Augsburg
Release Date:2023/11/28
Tag:Cell Biology; Developmental Biology; General Biochemistry, Genetics and Molecular Biology; Physiology; Ecology, Evolution, Behavior and Systematics; Structural Biology; Biotechnology
Volume:21
Issue:1
First Page:269
DOI:https://doi.org/10.1186/s12915-023-01737-5
Institutes:Medizinische Fakultät
Medizinische Fakultät / Universitätsklinikum
Medizinische Fakultät / Lehrstuhl für Umweltmedizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)