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

Abstract

Contaminant sequences in metagenomic samples can potentially impact the interpretation of findings reported in microbiome studies, especially in low biomass environments. Here the authors describe Squeegee, a computational approach designed to detect microbial contamination within low microbial biomass microbiomes and identify microbial contaminants in publicly available datasets that lack negative controls.