Seeing the forest for the genes: Using metagenomics to infer the aggregated traits of microbial communities

Noah Fierer, Albert Barberán, Daniel C. Laughlin

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

Most environments harbor large numbers of microbial taxa with ecologies that remain poorly described and characterizing the functional capabilities of whole communities remains a key challenge in microbial ecology. Shotgun metagenomic analyses are increasingly recognized as a powerful tool to understand community-level attributes. However, much of this data is under-utilized due, in part, to a lack of conceptual strategies for linking the metagenomic data to the most relevant community-level characteristics. Microbial ecologists could benefit by borrowing the concept of community-aggregated traits (CATs) from plant ecologists to glean more insight from the ever-increasing amount of metagenomic data being generated. CATs can be used to quantify the mean and variance of functional traits found in a given community. A CAT-based strategy will often yield far more useful information for predicting the functional attributes of diverse microbial communities and changes in those attributes than the more commonly used analytical strategies. A more careful consideration of what CATs to measure and how they can be quantified from metagenomic data, will help build a more integrated understanding of complex microbial communities.

Original languageEnglish (US)
Article number614
JournalFrontiers in Microbiology
Volume5
Issue numberNOV
DOIs
StatePublished - 2014
Externally publishedYes

Keywords

  • Community-aggregated traits
  • Metagenomics
  • Microbial diversity
  • Microbial ecology
  • Traits

ASJC Scopus subject areas

  • Microbiology
  • Microbiology (medical)

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