Exploring thematic structure and predicted functionality of 16S rRNA amplicon data


Autoři: Stephen Woloszynek aff001;  Joshua Chang Mell aff002;  Zhengqiao Zhao aff001;  Gideon Simpson aff003;  Michael P. O’Connor aff004;  Gail L. Rosen aff001
Působiště autorů: Department of Electrical and Computer Engineering, Drexel University, Philadelphia, Pennsylvania, United States of America aff001;  Department of Microbiology and Immunology, Drexel University College of Medicine, Philadelphia, Pennsylvania, United States of America aff002;  Department of Mathematics, Drexel University, Philadelphia, Pennsylvania, United States of America aff003;  Department of Biodiversity, Earth, and Environmental Science, Drexel University, Philadelphia, Pennsylvania, United States of America aff004
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0219235

Souhrn

Analysis of microbiome data involves identifying co-occurring groups of taxa associated with sample features of interest (e.g., disease state). Elucidating such relations is often difficult as microbiome data are compositional, sparse, and have high dimensionality. Also, the configuration of co-occurring taxa may represent overlapping subcommunities that contribute to sample characteristics such as host status. Preserving the configuration of co-occurring microbes rather than detecting specific indicator species is more likely to facilitate biologically meaningful interpretations. Additionally, analyses that use taxonomic relative abundances to predict the abundances of different gene functions aggregate predicted functional profiles across taxa. This precludes straightforward identification of predicted functional components associated with subsets of co-occurring taxa. We provide an approach to explore co-occurring taxa using “topics” generated via a topic model and link these topics to specific sample features (e.g., disease state). Rather than inferring predicted functional content based on overall taxonomic relative abundances, we instead focus on inference of functional content within topics, which we parse by estimating interactions between topics and pathways through a multilevel, fully Bayesian regression model. We apply our methods to three publicly available 16S amplicon sequencing datasets: an inflammatory bowel disease dataset, an oral cancer dataset, and a time-series dataset. Using our topic model approach to uncover latent structure in 16S rRNA amplicon surveys, investigators can (1) capture groups of co-occurring taxa termed topics; (2) uncover within-topic functional potential; (3) link taxa co-occurrence, gene function, and environmental/host features; and (4) explore the way in which sets of co-occurring taxa behave and evolve over time. These methods have been implemented in a freely available R package: https://cran.r-project.org/package=themetagenomics, https://github.com/EESI/themetagenomics.

Klíčová slova:

Crohn's disease – Gene prediction – Metagenomics – Microbial taxonomy – Microbiome – Ribosomal RNA – Shotgun sequencing – Taxonomy


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Článek vyšel v časopise

PLOS One


2019 Číslo 12