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Publication Details
AFRICAN RESEARCH NEXUS
SHINING A SPOTLIGHT ON AFRICAN RESEARCH
Phylogenetic placement of exact amplicon sequences improves associations with clinical information
mSystems, Volume 3, No. 3, Article e00021-18, Year 2018
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Description
Recent algorithmic advances in amplicon-based microbiome studies enable the inference of exact amplicon sequence fragments. These new methods enable the investigation of sub-operational taxonomic units (sOTU) by removing erroneous sequences. However, short (e.g., 150-nucleotide [nt]) DNA sequence fragments do not contain sufficient phylogenetic signal to reproduce a reasonable tree, introducing a barrier in the utilization of critical phylogenetically aware metrics such as Faith's PD or UniFrac. Although fragment insertion methods do exist, those methods have not been tested for sOTUs from high-throughput amplicon studies in insertions against a broad reference phylogeny. We benchmarked the SATé-enabled phylogenetic placement (SEPP) technique explicitly against 16S V4 sequence fragments and showed that it outperforms the conceptually problematic but often-used practice of reconstructing de novo phylogenies. In addition, we provide a BSD-licensed QIIME2 plugin (https://github.com/biocore/q2-fragment-insertion) for SEPP and integration into the microbial study management platform QIITA. IMPORTANCE The move from OTU-based to sOTU-based analysis, while providing additional resolution, also introduces computational challenges. We demonstrate that one popular method of dealing with sOTUs (building a de novo tree from the short sequences) can provide incorrect results in human gut metagenomic studies and show that phylogenetic placement of the new sequences with SEPP resolves this problem while also yielding other benefits over existing methods. © 2018 Ward et al.
Authors & Co-Authors
Janssen, Stefan
United States, La Jolla
University of California, San Diego
McDonald, Daniel T.
United States, La Jolla
University of California, San Diego
González, Antonio
United States, La Jolla
University of California, San Diego
Jiang, Lingjing
United States, La Jolla
University of California, San Diego
Orwoll, Eric S.
United States, Portland
Oregon Health & Science University
Manary, Mark J.
United States, St. Louis
Washington University in St. Louis
Mirarab, Siavash
United States, La Jolla
University of California, San Diego
Knight, Rob D.
United States, La Jolla
University of California, San Diego
Statistics
Citations: 269
Authors: 8
Affiliations: 4
Identifiers
Doi:
10.1128/mSystems.00021-18
ISSN:
23795077
Research Areas
Cancer
Genetics And Genomics