Run-to-run sequencing variation can introduce taxon-specific bias in the evaluation of fungal microbiomes

Zewei Song, Dan Schlatter, Daryl M. Gohl, Linda L. Kinkel

Research output: Contribution to journalArticlepeer-review

9 Scopus citations

Abstract

The routine use of high-throughput sequencing to profile microbial communities necessitates improved protocols for detecting and adjusting for variation among sequencing runs for marker gene analysis. Although mock communities are widely used as a control among runs, the composition and diversity of mock communities, in most cases, are orders of magnitude lower than the actual samples. We demonstrated that replicated biological samples (“technical replicates”) are superior to a mock community in detecting variation and potential bias among sequencing runs. We present a case in which technical replicates of three soil samples were sequenced in three MiSeq runs containing samples from multiple experiments. The technical replicate samples revealed a potentially biased, outlier sequencing run, from which several Ascomycota taxa were substantially underestimated. Similar bias was seen in the other samples sequenced but was not detected using the mock community. Our study demonstrates that using technical replicates along with traditional mock communities provide additional quality control information and aid in detecting outlier sequencing runs.

Original languageEnglish (US)
Pages (from-to)165-170
Number of pages6
JournalPhytobiomes Journal
Volume2
Issue number3
DOIs
StatePublished - 2018

Bibliographical note

Funding Information:
Funding: This project was supported by University of Minnesota MnDRIVE project funds, Minnesota Agricultural Experiment Station Project MIN-22-018, and NSF Macrosystems Award EF-124189.

Funding Information:
This project was supported by University of Minnesota MnDRIVE project funds, Minnesota Agricultural Experiment Station Project MIN-22-018, and NSF Macrosystems Award EF-124189.

Funding Information:
We thank the sequencing support from the University of Minnesota Genomic Center, and computing support from the Minnesota Supercomputing Institute. L. Hanson provided technical support for the project.

Publisher Copyright:
© 2018 The American Phytopathological Society

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