Understanding microbial community diversity metrics derived from metagenomes: performance evaluation using simulated data sets.

Metagenomics holds the promise of greatly advancing the study of diversity in natural communities, but novel theoretical and methodological approaches must first be developed and adjusted for these data sets. We evaluated widely used macroecological metrics of taxonomic diversity on a simulated set of metagenomic samples, using phylogenetically meaningful protein-coding genes as ecological proxies. To our knowledge, this is the first approach of this kind to evaluate taxonomic diversity metrics derived from metagenomic data sets. We demonstrate that abundance matrices derived from protein-coding marker genes reproduce more faithfully the structure of the original community than those derived from SSU-rRNA gene. We also found that the most commonly used diversity metrics are biased estimators of community structure and differ significantly from their corresponding real parameters and that these biases are most likely caused by insufficient sampling and differences in community phylogenetic composition. Our results suggest that the ranking of samples using multidimensional metrics makes a good qualitative alternative for contrasting community structure and that these comparisons can be greatly improved with the incorporation of metrics for both community structure and phylogenetic diversity. These findings will help to achieve a standardized framework for community diversity comparisons derived from metagenomic data sets.