De novo Identification of Microbial Contaminants in Low Microbial Biomass Microbiomes With Squeegee.
Publication Date
11-10-2022
Journal
Nature Communications
DOI
10.1038/s41467-022-34409-z
PMID
36357382
PMCID
PMC9649624
PubMedCentral® Posted Date
11-10-2022
PubMedCentral® Full Text Version
Post-print
Published Open-Access
yes
Keywords
Humans, Biomass, Microbiota, Metagenomics, Metagenome, Specimen Handling, Classification and taxonomy, Metagenomics
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.