Metagenomic signatures of 86 microbial and viral metagenomes.
Previous studies have shown that dinucleotide abundances capture the majority of variation in genome signatures and are useful for quantifying lateral gene transfer and building molecular phylogenies. Metagenomes contain a mixture of individual genomes, and might be expected to lack compositional signatures. In many metagenomic data sets the majority of sequences have no significant similarities to known sequences and are effectively excluded from subsequent analyses. To circumvent this limitation, di-, tri- and tetranucleotide abundances of 86 microbial and viral metagenomes consisting of short pyrosequencing reads were analysed to provide a method which includes all sequences that can be used in combination with other analysis to increase our knowledge about microbial and viral communities. Both principal component analysis and hierarchical clustering showed definitive groupings of metagenomes drawn from similar environments. Together these analyses showed that dinucleotide composition, as opposed to tri- and tetranucleotides, defines a metagenomic signature which can explain up to 80% of the variance between biomes, which is comparable to that obtained by functional genomics. Metagenomes with anomalous content were also identified using dinucleotide abundances. Subsequent analyses determined that these metagenomes were contaminated with exogenous DNA, suggesting that this approach is a useful metric for quality control. The predictive strength of the dinucleotide composition also opens the possibility of assigning ecological classifications to unknown fragments. Environmental selection may be responsible for this dinucleotide signature through direct selection of specific compositional signals; however, simulations suggest that the environment may select indirectly by promoting the increased abundance of a few dominant taxa.
Willner
Dana
D
Department of Biology, San Diego State University, 5500 Campanile Dr., San Diego, CA 92182, USA. willner9@aol.com
Thurber
Rebecca Vega
RV
Rohwer
Forest
F
D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D004783:Environmental Microbiology; D054892:Metagenome; D056186:Metagenomics / Q000379:methods*; D008828:Microbiological Techniques / Q000379:methods*; D010802:Phylogeny