Published in

American Society for Microbiology, mSystems, 6(1), 2016

DOI: 10.1128/msystems.00133-16

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SSUnique: Detecting Sequence Novelty in Microbiome Surveys

Journal article published in 2016 by Michael D. J. Lynch ORCID, Josh D. Neufeld ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Preprint: archiving allowed
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Postprint: archiving allowed
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Published version: archiving allowed
Data provided by SHERPA/RoMEO

Abstract

Extensive SSU rRNA gene sequence libraries, constructed from DNA extracts of environmental or host-associated samples, often contain many unclassified sequences, many representing organisms with novel taxonomy (taxonomic “blind spots”) and potentially unique ecology. This novelty is poorly explored in standard workflows, which narrows the breadth and discovery potential of such studies. Here we present the SSUnique analysis pipeline, which will promote the exploration of unclassified diversity in microbiome research and, importantly, enable the discovery of substantial novel taxonomic lineages through the analysis of a large variety of existing data sets.