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

View ORCID ProfileYunxi Liu, View ORCID ProfileR. A. Leo Elworth, View ORCID ProfileMichael D. Jochum, View ORCID ProfileKjersti M. Aagaard, View ORCID ProfileTodd J. Treangen
doi: https://doi.org/10.1101/2021.05.06.442815
Yunxi Liu
1Rice University, Department of Computer Science, Houston, 77005, USA
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R. A. Leo Elworth
1Rice University, Department of Computer Science, Houston, 77005, USA
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Michael D. Jochum
2Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas 77030, USA
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Kjersti M. Aagaard
2Department of Obstetrics and Gynecology, Baylor College of Medicine and Texas Children’s Hospital, Houston, Texas 77030, USA
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Todd J. Treangen
1Rice University, Department of Computer Science, Houston, 77005, USA
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  • For correspondence: treangen@rice.edu
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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

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • https://gitlab.com/treangenlab/squeegee

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-ND 4.0 International license.
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Posted May 07, 2021.
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Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes
Yunxi Liu, R. A. Leo Elworth, Michael D. Jochum, Kjersti M. Aagaard, Todd J. Treangen
bioRxiv 2021.05.06.442815; doi: https://doi.org/10.1101/2021.05.06.442815
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Squeegee: de-novo identification of reagent and laboratory induced microbial contaminants in low biomass microbiomes
Yunxi Liu, R. A. Leo Elworth, Michael D. Jochum, Kjersti M. Aagaard, Todd J. Treangen
bioRxiv 2021.05.06.442815; doi: https://doi.org/10.1101/2021.05.06.442815

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