ArticleBMC biology2020
Contaminant DNA in bacterial sequencing experiments is a major source of false genetic variability.
Article in BMC biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers.
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Who cites it
54 citing papers in PubMed.
- Emergence of a Novel, Phenotypically Difficult-to-Detect Vancomycin-Resistant Enterococcus faecium Clone (ST117/CT7799).MicrobiologyOpen · 2026Article
- High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.Genome research · 2026Article
- Article
- Optimizing culture-free approaches to recover high-qualityMicrobial genomics · 2026Article
- Complete genomes reveal a refined map of Mycobacterium tuberculosis genetic diversity across evolutionary scales.Nature communications · 2026Article
- Low-level contamination confounds population genomic analysis.G3 (Bethesda, Md.) · 2026Article
- DNA sequence contamination analyzer (DNASCAN): a supervised analysis toolkit for detecting and removing DNA contaminants.BMC bioinformatics · 2026Article
- BacT-Seq, a Nanopore-Based Whole-Genome Sequencing Workflow Prototype for Rapid and Accurate Pathogen Identification and Resistance Prediction from Positive Blood Cultures: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2026Article
- Methodological bias shapes the interpretation of the urinary microbiome in low-biomass systems.Frontiers in microbiology · 2026Review
- Optimizing culture-free approaches to recover high qualitymedRxiv : the preprint server for health sciences · 2025Article
- Contamination of fungal genomes of Onygenaceae (Phylum Ascomycota) in public databases: incidence, detection, and impact.BMC genomics · 2025Article
- Benchmarking of human read removal strategies for viral and microbial metagenomics.Cell reports methods · 2025Article
- Unraveling Epistatic Interactions Between Sites Under Drug-Dependent Selection in the Mycobacterium tuberculosis Genome.Molecular biology and evolution · 2025Article
- High-accuracy SNV calling for bacterial isolates using deep learning with AccuSNV.bioRxiv : the preprint server for biology · 2025Article
- The new microbiome on the block: challenges and opportunities of using human tumor sequencing data to study microbes.Nature methods · 2025Review
- Genomic analysis of contaminant Stenotrophomonas maltophilia, from placental swab culture, carrying antibiotic resistance: a potential hospital laboratory contaminant.Scientific reports · 2025Article
- Article
- Whole-Genome Sequence-Based Diversity ofPathogens (Basel, Switzerland) · 2025Article
- Efficient and robust search of microbial genomes via phylogenetic compression.Nature methods · 2025Article
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundContaminant DNA is a well-known confounding factor in molecular biology and in genomic repositories. Strikingly, analysis workflows for whole-genome sequencing (WGS) data commonly do not account for errors potentially introduced by contamination, which could lead to the wrong assessment of allele frequency both in basic and clinical research.
resultsWe used a taxonomic filter to remove contaminant reads from more than 4000 bacterial samples from 20 different studies and performed a comprehensive evaluation of the extent and impact of contaminant DNA in WGS. We found that contamination is pervasive and can introduce large biases in variant analysis. We showed that these biases can result in hundreds of false positive and negative SNPs, even for samples with slight contamination. Studies investigating complex biological traits from sequencing data can be completely biased if contamination is neglected during the bioinformatic analysis, and we demonstrate that removing contaminant reads with a taxonomic classifier permits more accurate variant calling. We used both real and simulated data to evaluate and implement reliable, contamination-aware analysis pipelines.
conclusionAs sequencing technologies consolidate as precision tools that are increasingly adopted in the research and clinical context, our results urge for the implementation of contamination-aware analysis pipelines. Taxonomic classifiers are a powerful tool to implement such pipelines.
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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.