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Squeegee: A Contamination Detection Tool for Low Microbial Biomass Microbiomes

By Baylor College of Medicine

December 7, 2022

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The Squeegee contamination detection algorithm developed by researchers at Baylor College of Medicine (BCM) and Rice University can instill reproducibility in microbial identification and analysis.

"The premise of Squeegee is that we can use a computer analysis pipeline to help us detect 'breadcrumbs' of contaminants that would be anticipated to be common between the microbiome found in all human [or other mammalian] hosts and the sampling or lab environment," explained BCM's Kjersti Aagaard.

The researchers tested Squeegee on datasets from a large number of persons that are especially low biomass and have many negative controls. BCM's Michael D. Jochum said, "We were able to show that Squeegee was capable of having a high-weighted recall and a very low false-positive rate in these ground truth datasets."

From Baylor College of Medicine
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