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dc.contributor.authorSheik, Cody S.  Concept link
dc.contributor.authorKiel Reese, Brandi  Concept link
dc.contributor.authorTwing, Katrina I.  Concept link
dc.contributor.authorSylvan, Jason B.  Concept link
dc.contributor.authorGrim, Sharon L.  Concept link
dc.contributor.authorSchrenk, Matthew O.  Concept link
dc.contributor.authorSogin, Mitchell L.  Concept link
dc.contributor.authorColwell, Frederick S.  Concept link
dc.identifier.citationFrontiers in Microbiology 9 (2018): 840en_US
dc.description© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Frontiers in Microbiology 9 (2018): 840, doi:10.3389/fmicb.2018.00840.en_US
dc.description.abstractEarth’s subsurface environment is one of the largest, yet least studied, biomes on Earth, and many questions remain regarding what microorganisms are indigenous to the subsurface. Through the activity of the Census of Deep Life (CoDL) and the Deep Carbon Observatory, an open access 16S ribosomal RNA gene sequence database from diverse subsurface environments has been compiled. However, due to low quantities of biomass in the deep subsurface, the potential for incorporation of contaminants from reagents used during sample collection, processing, and/or sequencing is high. Thus, to understand the ecology of subsurface microorganisms (i.e., the distribution, richness, or survival), it is necessary to minimize, identify, and remove contaminant sequences that will skew the relative abundances of all taxa in the sample. In this meta-analysis, we identify putative contaminants associated with the CoDL dataset, recommend best practices for removing contaminants from samples, and propose a series of best practices for subsurface microbiology sampling. The most abundant putative contaminant genera observed, independent of evenness across samples, were Propionibacterium, Aquabacterium, Ralstonia, and Acinetobacter. While the top five most frequently observed genera were Pseudomonas, Propionibacterium, Acinetobacter, Ralstonia, and Sphingomonas. The majority of the most frequently observed genera (high evenness) were associated with reagent or potential human contamination. Additionally, in DNA extraction blanks, we observed potential archaeal contaminants, including methanogens, which have not been discussed in previous contamination studies. Such contaminants would directly affect the interpretation of subsurface molecular studies, as methanogenesis is an important subsurface biogeochemical process. Utilizing previously identified contaminant genera, we found that ∼27% of the total dataset were identified as contaminant sequences that likely originate from DNA extraction and DNA cleanup methods. Thus, controls must be taken at every step of the collection and processing procedure when working with low biomass environments such as, but not limited to, portions of Earth’s deep subsurface. Taken together, we stress that the CoDL dataset is an incredible resource for the broader research community interested in subsurface life, and steps to remove contamination derived sequences must be taken prior to using this dataset.en_US
dc.description.sponsorshipWe wish to acknowledge the support of the Sloan Foundation and the Deep Carbon Observatory and the Department of Energy, Office of Fossil Energy (Colwell).en_US
dc.publisherFrontiers Mediaen_US
dc.rightsAttribution 4.0 International*
dc.subject16S rRNAen_US
dc.subjectMicrobial surveyen_US
dc.subjectCensus of Deep Lifeen_US
dc.subjectDeep subsurfaceen_US
dc.titleIdentification and removal of contaminant sequences from ribosomal gene databases : lessons from the Census of Deep Lifeen_US

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Attribution 4.0 International
Except where otherwise noted, this item's license is described as Attribution 4.0 International