A statistical toolbox for metagenomics: assessing functional diversity in microbial communities.
BACKGROUND
The 99% of bacteria in the environment that are recalcitrant to culturing have spurred the development of metagenomics, a culture-independent approach to sample and characterize microbial genomes. Massive datasets of metagenomic sequences have been accumulated, but analysis of these sequences has focused primarily on the descriptive comparison of the relative abundance of proteins that belong to specific functional categories. More robust statistical methods are needed to make inferences from metagenomic data. In this study, we developed and applied a suite of tools to describe and compare the richness, membership, and structure of microbial communities using peptide fragment sequences extracted from metagenomic sequence data.


RESULTS
Application of these tools to acid mine drainage, soil, and whale fall metagenomic sequence collections revealed groups of peptide fragments with a relatively high abundance and no known function. When combined with analysis of 16S rRNA gene fragments from the same communities these tools enabled us to demonstrate that although there was no overlap in the types of 16S rRNA gene sequence observed, there was a core collection of operational protein families that was shared among the three environments.


CONCLUSIONS
The results of comparisons between the three habitats were surprising considering the relatively low overlap of membership and the distinctively different characteristics of the three habitats. These tools will facilitate the use of metagenomics to pursue statistically sound genome-based ecological analyses.
Schloss
Patrick D
PD
Department of Microbiology, University of Massachusetts - Amherst, Amherst, MA 01003, USA. pschloss@microbio.umass.edu
Handelsman
Jo
J
D000818:Animals; D001419:Bacteria / Q000235:genetics*; D001842:Bone and Bones / Q000382:microbiology; D019295:Computational Biology / Q000379:methods*; D003627:Data Interpretation, Statistical; D014644:Genetic Variation; D023281:Genomics / Q000379:methods*; D008957:Models, Genetic; D010446:Peptide Fragments / Q000235:genetics; D012336:RNA, Ribosomal, 16S / Q000235:genetics; D012988:Soil Microbiology; D014871:Water Microbiology; D014907:Whales / Q000382:microbiology*
D010446:Peptide Fragments; D012336:RNA, Ribosomal, 16S