RT Journal Article
SR Electronic
T1 Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.05.06.442815
DO 10.1101/2021.05.06.442815
A1 Yunxi Liu
A1 R. A. Leo Elworth
A1 Michael D. Jochum
A1 Kjersti M. Aagaard
A1 Todd J. Treangen
YR 2021
UL http://biorxiv.org/content/early/2021/05/07/2021.05.06.442815.abstract
AB 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/squeegeeCompeting Interest StatementThe authors have declared no competing interest.