Metagenomic sequencing of an in vitro-simulated microbial community.
BACKGROUND
Microbial life dominates the earth, but many species are difficult or even impossible to study under laboratory conditions. Sequencing DNA directly from the environment, a technique commonly referred to as metagenomics, is an important tool for cataloging microbial life. This culture-independent approach involves collecting samples that include microbes in them, extracting DNA from the samples, and sequencing the DNA. A sample may contain many different microorganisms, macroorganisms, and even free-floating environmental DNA. A fundamental challenge in metagenomics has been estimating the abundance of organisms in a sample based on the frequency with which the organism's DNA was observed in reads generated via DNA sequencing.


RESULTS
We created mixtures of ten microbial species for which genome sequences are known. Each mixture contained an equal number of cells of each species. We then extracted DNA from the mixtures, sequenced the DNA, and measured the frequency with which genomic regions from each organism was observed in the sequenced DNA. We found that the observed frequency of reads mapping to each organism did not reflect the equal numbers of cells that were known to be included in each mixture. The relative organism abundances varied significantly depending on the DNA extraction and sequencing protocol utilized.


CONCLUSIONS
We describe a new data resource for measuring the accuracy of metagenomic binning methods, created by in vitro-simulation of a metagenomic community. Our in vitro simulation can be used to complement previous in silico benchmark studies. In constructing a synthetic community and sequencing its metagenome, we encountered several sources of observation bias that likely affect most metagenomic experiments to date and present challenges for comparative metagenomic studies. DNA preparation methods have a particularly profound effect in our study, implying that samples prepared with different protocols are not suitable for comparative metagenomics.
Morgan
Jenna L
JL
Department of Medical Microbiology and Immunology, University of California Davis, Davis, California, United States of America.
Darling
Aaron E
AE
Eisen
Jonathan A
JA
D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D004269:DNA, Bacterial / Q000032:analysis*; D016680:Genome, Bacterial; D056186:Metagenomics / Q000379:methods*; D017422:Sequence Analysis, DNA
D004269:DNA, Bacterial