ArticleNature communications2022
De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers.
What it found
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Who cites it
33 citing papers in PubMed, 40 citations in OpenAlex.
- Advancing the Deciphering of Host-Microbe Crosstalk with Spatial Omics: A Mini-Review.Current microbiology · 2026Review
- Spatial Heterogeneity of Intratumoral Microbiota and Its Roles in Tumor-Microbiota Interactions and Therapeutic Implications.Pathogens (Basel, Switzerland) · 2026Review
- Invasion dynamics of antimicrobial-resistant E. coli in river biofilms: impacts on the resistome, microbiomes, and horizontal gene transfer.npj antimicrobials and resistance · 2026Article
- Intratumoral microbiota in cancer: molecular mechanism and therapeutic strategies.Molecular biomedicine · 2026Review
- DNA sequence contamination analyzer (DNASCAN): a supervised analysis toolkit for detecting and removing DNA contaminants.BMC bioinformatics · 2026Article
- Setting higher standards for reports of microbial species in human cancers.Nature cancer · 2026Review
- krepp: a k-mer-based maximum pseudo-likelihood method for estimating read distances and genome-wide phylogenetic placement.Genome biology · 2026Article
- Planning and Analyzing a Low-Biomass Microbiome Study: A Data Analysis Perspective.The Journal of infectious diseases · 2026Review
- Mitigation and detection of putative microbial contaminant reads from long-read metagenomic datasets.Microbial genomics · 2026Article
- Seminal dysbiosis in idiopathic male infertility: toward a seminal dysbiosis-inflammatory/oxidative endophenotype-SDF axis.Frontiers in endocrinology · 2026Review
- Development of a multiplex ddPCR assay for simultaneous absolute quantification of bacterial, fungal, and human DNA.PloS one · 2026Article
- Article
- Acute Respiratory Infections (ARIs): Current Etiological Perspectives and Advances in Viral Metagenomics-A Review.Viruses · 2025Review
- The Association Between Maternal Diet and the Human Milk Microbiome: A Review of Evidence and Methodological Challenges.Microorganisms · 2025Review
- Challenges and Opportunities in Analyzing Cancer-Associated Microbiomes.Cancer research · 2025Review
- Multi-kingdom microbiota analysis reveals bacteria-viral interplay in IBS with depression and anxiety.NPJ biofilms and microbiomes · 2025Article
- Guidelines for preventing and reporting contamination in low-biomass microbiome studies.Nature microbiology · 2025Review
- Gut and Joint Microbiomes: Implications in Osteoarthritis.Rheumatic diseases clinics of North America · 2025Review
- Bioinformatic approaches to blood and tissue microbiome analyses: challenges and perspectives.Briefings in bioinformatics · 2025Review
- Common microbial signatures in blood and their amplification in clinical disorders.Gut microbes reports · 2025Review
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
Computational analysis of host-associated microbiomes has opened the door to numerous discoveries relevant to human health and disease. However, contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low-biomass environments. Contamination from DNA extraction kits or sampling lab environments leaves taxonomic "bread crumbs" across multiple distinct sample types. Here we describe Squeegee, a de novo contamination detection tool that is based upon this principle, allowing the detection of microbial contaminants when negative controls are unavailable. On the low-biomass samples, we compare Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers putative contaminants. We analyze samples of varying biomass from the Human Microbiome Project and identify likely, previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision and thus represents a computational approach for contaminant detection when negative controls are unavailable.
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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.