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

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 conta

SUBMITTER: Liu Y 

PROVIDER: S-EPMC9649624 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature