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

Claudia Hülpüsch, Luise Rauer, Thomas Nussbaumer, Vera Schwierzeck, Madhumita Bhattacharyya, Veronika Erhart, Claudia Traidl-Hoffmann, Matthias Reiger, Avidan U. Neumann

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

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 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.

Original languageEnglish
Article number269
JournalBMC Biology
Volume21
Issue number1
DOIs
StatePublished - 23 Nov 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2023, The Author(s).

Funding

We thank Biomap for partially funding this research. Furthermore, we thank the Microbiome Core Facility of ZIEL, Institute for Food and Health of the Technical University of Munich, for sequencing of the samples. We thank Amedeo de Tomassi for supporting the laboratory work, and Dr. Denise Rauer, Jamie Afghani, Johannes Ostner, Mara Stadler, Mathilde Nguyen, and Viet Tran for providing valuable feedback on the tool. Open Access funding enabled and organized by Projekt DEAL. This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking (JU) under grant agreement No 821511. The JU receives support from the European Union’s Horizon 2020 research and innovation programme and EFPIA. This publication/dissemination reflects only the author’s view and the JU is not responsible for any use that may be made of the information it contains.

FundersFunder number
Amedeo de Tomassi
Institute for Food and Health of the Technical University of Munich
Horizon 2020 Framework Programme
European Federation of Pharmaceutical Industries and Associations
Innovative Medicines Initiative821511

    Keywords

    • 16S rRNA gene sequencing
    • Bioinformatic decontamination
    • Decontam
    • Low-biomass microbiome
    • Negative control
    • SourceTracker
    • Youden’s index

    Fingerprint

    Dive into the research topics of 'Benchmarking MicrobIEM – a user-friendly tool for decontamination of microbiome sequencing data'. Together they form a unique fingerprint.

    Cite this