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

ABSTRACT: 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 identificati

SUBMITTER: Woloszynek S 

PROVIDER: S-EPMC6905537 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature