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Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
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PanFP: pangenome-based functional profiles for microbial communities.

Se-Ran Jun1,2, Michael S Robeson3,4, Loren J Hauser5

  • 1Comparative Genomics Group, Bioscience Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA. junse@ornl.gov.

BMC Research Notes
|September 28, 2015
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Summary
This summary is machine-generated.

We developed PanFP, a cost-effective computational method to infer microbial community functions from 16S rRNA gene surveys. This tool enables functional profiling of diverse ecosystems, enhancing microbiome research.

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Area of Science:

  • Microbial Ecology
  • Bioinformatics
  • Genomics

Background:

  • Understanding microbial community-ecosystem function relationships is crucial.
  • Current DNA sequencing methods (rRNA gene surveys, shotgun metagenomics) have limitations in cost and scope for large studies.
  • 16S rRNA gene surveys are cost-effective for estimating microbial diversity but lack direct functional information.

Purpose of the Study:

  • To develop a computational method for inferring microbial community functional profiles from 16S rRNA gene survey data.
  • To provide a cost-effective alternative to shotgun metagenomics for functional analysis of microbial communities.

Main Methods:

  • Developed pangenome-based functional profiles (PanFP) for Bacteria and Archaea.
  • PanFP reconstructs pangenomes from known genes and genomes within an operational taxonomic unit's (OTU) lineage.
  • Functional profiles are derived by weighting pangenome profiles with observed OTU abundance in samples.

Main Results:

  • PanFP accurately infers functional profiles, validated against shotgun metagenomic data (Spearman correlations of 0.8-0.9).
  • Method performance is comparable to existing tools like PICRUSt.
  • PanFP uniquely supports any OTU-building method, unlike closed-reference strategies.

Conclusions:

  • PanFP is an automated computational method for inferring microbial functional profiles from 16S rRNA gene surveys.
  • Provides a cost-effective approach for studying complex ecosystems via predicted functional metagenomes.
  • Source code and documentation are publicly available on GitHub.