Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes

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. Our hypothesis is that contamination from DNA extraction kits or sampling lab environments will leave taxonomic bread crumbs across multiple distinct sample types, allowing for the detection of microbial contaminants when negative controls are unavailable. To test this hypothesis we implemented Squeegee, a de novo contamination detection tool. We tested Squeegee on simulated and real low biomass metagenomic datasets. On the low biomass samples, we compared Squeegee predictions to experimental negative control data and show that Squeegee accurately recovers known contaminants. We also analyzed 749 metagenomic datasets from the Human Microbiome Project and identified likely previously unreported kit contamination. Collectively, our results highlight that Squeegee can identify microbial contaminants with high precision. Squeegee is open-source and available at: https://gitlab.com/treangenlab/squeegee

The Skin Microbiome: Current Techniques, Challenges, and Future Directions

“…However, there are tools that have been developed to identify and remove contaminants originating from reagents. Some of these tools include, but are not limited to, SourceTracker [ 83 ] and Meta-SourceTracker [ 84 ] to predict sources of contamination from amplicon and shotgun metagenomics data, respectively, as well as decontam (amplicon and shotgun metagenomics data) [ 85 ], microDecon (amplicon data) [ 86 ], Recentrifuge (shotgun metagenomic data) [ 87 ], and Squeegee (shotgun metagenomics data with the possibility of application to amplicon data) [ 88 ]. Notably, these tools often require experimental designs with multiple controls to support contaminant removal and still need to be extensively tested in skin microbiome datasets suspected to be contaminated with external sources of microbial DNA.…”

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

“…All sequencing data supporting the findings of this study is publicly available. The simulated datasets generated in this study have been deposited in the Zenodo database with accession 7064705 62 , 7062953 63 , and 7064599 64 . The maternal/infant metagenomic datasets are available for download via NCBI BioProject PRJNA725597.…”