ArticleMicrobiome2025
De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation.
Article in Microbiome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Analytic attributes of the 16S rRNA gene sequencing methodology for human gut microbiota characterization.Applied microbiology and biotechnology · 2026Article
- Unleashing the potential of mRNA-seq to uncover the microbiome structure and their crosstalk with host cells: the vulvar ecosystem.Microbiome · 2026Article
- The impact of DNA extraction methods on species quantification and apparent community composition of in vitro oral biofilms.FEMS microbiology letters · 2026Article
- Validation of a long-read 16S rRNA-gene sequencing approach for analysis of clinical samples and bacterial identification in a routine clinical laboratory.Frontiers in microbiology · 2026Article
- Methodological bias shapes the interpretation of the urinary microbiome in low-biomass systems.Frontiers in microbiology · 2026Review
- Converting relative amplicon abundances to absolute abundances via flow cytometry: metagenomic validation and application to long ocean transects.ISME communications · 2026Article
- Metagenomic sequencing enables accurate pathogen and antimicrobial susceptibility profiling in complicated UTIs in approximately four hours.Nature communications · 2025Article
- Impact of DNA Extraction and 16S rRNA Gene Amplification Strategy on Microbiota Profiling of Faecal Samples.International journal of molecular sciences · 2025Article
- Emerging technologies and current challenges in intratumoral microbiota research.Frontiers in cellular and infection microbiology · 2025Review
- A standards perspective on genomic data reusability and reproducibility.Frontiers in bioinformatics · 2025Article
- Benchmarking MicrobIEM - a user-friendly tool for decontamination of microbiome sequencing data.BMC biology · 2023Article
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7 authors.
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Abstract
introductionMicrobiome 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.
methodsWe 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.
resultsMicrobiome 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.
conclusionsOur 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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