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Rauer, L. ; De Tomassi, A.* ; Müller, C.L. ; Hülpüsch, C.* ; Traidl-Hoffmann, C. ; Reiger, M. ; Neumann, A.U.

De-biasing microbiome sequencing data: Bacterial morphology-based correction of extraction bias and correlates of chimera formation.

Microbiome 13:38 (2025)
Verlagsversion Forschungsdaten DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
INTRODUCTION: Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties. METHODS: We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1-V3 16S rRNA gene) together with corresponding DNA mocks. RESULTS: Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions. CONCLUSIONS: Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis. Video Abstract.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter 16s Rrna Gene Sequencing ; Bacterial Mock Community ; Bias Correction ; Cell Lysis ; Chimera Formation ; Contamination ; Extraction Bias ; Kitome ; Positive Control; Halotolerans Gen. Nov.; Sensitivity
ISSN (print) / ISBN 2049-2618
e-ISSN 2049-2618
Zeitschrift Microbiome
Quellenangaben Band: 13, Heft: 1, Seiten: , Artikelnummer: 38 Supplement: ,
Verlag BioMed Central
Verlagsort London
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Environmental Medicine (IEM)
Institute of Computational Biology (ICB)
Förderungen Helmholtz Zentrum Mnchen - Deutsches Forschungszentrum fr Gesundheit und Umwelt (GmbH) (4209)