De novo identification of microbial contaminants in low microbial biomass microbiomes with Squeegee

Yunxi Liu, R. A. Leo Elworth, Michael D. Jochum, Kjersti M. Aagaard and Todd J. Treangen ()
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Yunxi Liu: Rice University, Department of Computer Science
R. A. Leo Elworth: Rice University, Department of Computer Science
Michael D. Jochum: Baylor College of Medicine and Texas Children’s Hospital
Kjersti M. Aagaard: Baylor College of Medicine and Texas Children’s Hospital
Todd J. Treangen: Rice University, Department of Computer Science

Nature Communications, 2022, vol. 13, issue 1, 1-14

Abstract: 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.

Date: 2022
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DOI: 10.1038/s41467-022-34409-z

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