From microbial community structure to metabolic inference using paprica

Natalia G Erazo; Avishek Dutta; Jeff S Bowman

doi:10.1016/j.xpro.2021.101005

From microbial community structure to metabolic inference using paprica

STAR Protoc. 2021 Dec 11;2(4):101005. doi: 10.1016/j.xpro.2021.101005. eCollection 2021 Dec 17.

Authors

Natalia G Erazo^{1

2}, Avishek Dutta¹, Jeff S Bowman^{1

2

3}

Affiliations

¹ Scripps Institution of Oceanography, UC San Diego, La Jolla, CA, USA.
² Center for Marine Biodiversity and Conservation, UC San Diego, La Jolla, CA, USA.
³ Center for Microbiome Innovation, UC San Diego, La Jolla, CA, USA.

Abstract

Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from phylogeny using the paprica pipeline. This approach allows resolution at the strain and species level for select regions on the prokaryotic phylogenetic tree and provides an estimate of gene and metabolic pathway abundance. For complete details on the use and execution of this protocol, please refer to Erazo and Bowman (2021).

Keywords: Bioinformatics; Evolutionary biology; Genomics; Metabolism; Microbiology; Systems biology.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Computational Biology / methods*
Genome, Archaeal
Genome, Bacterial
Metabolic Networks and Pathways
Microbiota*
Phylogeny
Sequence Analysis, RNA / methods*