Evidence map›Paper›PMID 32231882›Full record

ArticlePeerJ2020

Meta-SourceTracker: application of Bayesian source tracking to shotgun metagenomics.

Jordan J McGhee, Nick Rawson, Barbara A Bailey, Antonio Fernandez-Guerra, Laura Sisk-Hackworth, Scott T Kelley

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Article in PeerJ, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

39 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jordan J McGheeBioinformatics and Medical Informatics Program, San Diego State University, San Diego, CA, United States of America.
Nick RawsonDepartment of Mathematics and Statistics, San Diego State University, San Diego, CA, United States of America.
Barbara A BaileyDepartment of Mathematics and Statistics, San Diego State University, San Diego, CA, United States of America.
Antonio Fernandez-GuerraMicrobial Genomics and Bioinformatics Research Group, Max Planck Institute for Marine Microbiology, Bremen, Germany.
Laura Sisk-HackworthDepartment of Biology, San Diego State University, San Diego, CA, United States of America.
Scott T KelleyDepartment of Biology, San Diego State University, San Diego, CA, United States of America.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundMicrobial source tracking methods are used to determine the origin of contaminating bacteria and other microorganisms, particularly in contaminated water systems. The Bayesian SourceTracker approach uses deep-sequencing marker gene libraries (16S ribosomal RNA) to determine the proportional contributions of bacteria from many potential source environments to a given sink environment simultaneously. Since its development, SourceTracker has been applied to an extensive diversity of studies, from beach contamination to human behavior.

methodsHere, we demonstrate a novel application of SourceTracker to work with metagenomic datasets and tested this approach using sink samples from a study of coastal marine environments. Source environment metagenomes were obtained from metagenomics studies of gut, freshwater, marine, sand and soil environments. As part of this effort, we implemented features for determining the stability of source proportion estimates, including precision visualizations for performance optimization, and performed domain-specific source-tracking analyses (i.e., Bacteria, Archaea, Eukaryota and viruses). We also applied SourceTracker to metagenomic libraries generated from samples collected from the International Space Station (ISS).

resultsSourceTracker proved highly effective at predicting the composition of known sources using shotgun metagenomic libraries. In addition, we showed that different taxonomic domains sometimes presented highly divergent pictures of environmental source origins for both the coastal marine and ISS samples. These findings indicated that applying SourceTracker to separate domains may provide a deeper understanding of the microbial origins of complex, mixed-source environments, and further suggested that certain domains may be preferable for tracking specific sources of contamination.

Indexed as

BioinformaticsEnvironmental microbiologySoftware

Identifiers

PMID32231882
PMCPMC7100590

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.