ArticlemSystems2019
Quantifying and Understanding Well-to-Well Contamination in Microbiome Research.
Article in mSystems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 110 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
110 citing papers in PubMed, 186 citations in OpenAlex.
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- Identify contaminants with decontam on the QIIME 2 Framework.Microbiology resource announcements · 2026Article
- Analytical validation of a highly accurate and reliable next-generation sequencing-based urine assay.Microbiology spectrum · 2026Article
- CroCoDeEL: accurate control-free detection of cross-sample contamination in metagenomic data.Nature communications · 2026Article
- A Scalable and Cost-Effective In-Line Barcoding Strategy for Standardized 16S rRNA Gene Amplicon Sequencing: Performance Evaluation and Bias Assessment.Molecular ecology resources · 2026Article
- Spatial and temporal patterns of public transit aerobiomes.Microbiome · 2026Article
- Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.The Journal of infectious diseases · 2026Review
- Unveiling pathogens and contaminants: refining metagenomics for clinical diagnostics.Frontiers in microbiology · 2026Article
- Mitigation and detection of putative microbial contaminant reads from long-read metagenomic datasets.Microbial genomics · 2026Article
- Detoxifying and depolymerizing microorganisms reveal intertwined guild collaborations in the gut microbiome of a generalist macro-algivorous fish.bioRxiv : the preprint server for biology · 2025Article
- Streamlined extraction of nucleic acids and metabolites from low- and high-biomass samples using isopropanol and matrix tubes.Microbiology spectrum · 2025Article
- Identification of Sample Processing Errors in Microbiome Studies Using Host Genetic Profiles.bioRxiv : the preprint server for biology · 2025Article
- Metagenomic Next-Generation Sequencing in Infectious Diseases: Clinical Applications, Translational Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2025Review
- Fast-tracking bespoke DNA reference database generation from museum collections for biomonitoring and conservation.Molecular ecology resources · 2025Article
- The respiratory tract virome: unravelling the role of viral dark matter in respiratory health and disease.European respiratory review : an official journal of the European Respiratory Society · 2025Review
- Guidelines for preventing and reporting contamination in low-biomass microbiome studies.Nature microbiology · 2025Review
- Microbiome diversity of low biomass skin sites is captured by metagenomics but not 16S amplicon sequencing.bioRxiv : the preprint server for biology · 2025Article
- Enabling next-generation anaerobic cultivation through biotechnology to advance functional microbiome research.Nature biotechnology · 2025Review
- De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation.Microbiome · 2025Article
50 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Microbial sequences inferred as belonging to one sample may not have originated from that sample. Such contamination may arise from laboratory or reagent sources or from physical exchange between samples. This study seeks to rigorously assess the behavior of this often-neglected between-sample contamination. Using unique bacteria, each assigned a particular well in a plate, we assess the frequency at which sequences from each source appear in other wells. We evaluate the effects of different DNA extraction methods performed in two laboratories using a consistent plate layout, including blanks and low-biomass and high-biomass samples. Well-to-well contamination occurred primarily during DNA extraction and, to a lesser extent, in library preparation, while barcode leakage was negligible. Laboratories differed in the levels of contamination. Extraction methods differed in their occurrences and levels of well-to-well contamination, with plate methods having more well-to-well contamination and single-tube methods having higher levels of background contaminants. Well-to-well contamination occurred primarily in neighboring samples, with rare events up to 10 wells apart. This effect was greatest in samples with lower biomass and negatively impacted metrics of alpha and beta diversity. Our work emphasizes that sample contamination is a combination of cross talk from nearby wells and background contaminants. To reduce well-to-well effects, samples should be randomized across plates, samples of similar biomasses should be processed together, and manual single-tube extractions or hybrid plate-based cleanups should be employed. Researchers should avoid simplistic removals of taxa or operational taxonomic units (OTUs) appearing in negative controls, as many will be microbes from other samples rather than reagent contaminants.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.