Unlocking the potential of metagenomics through replicated experimental design

Abstract

Metagenomics holds enormous promise for discovering novel enzymes and organisms that are biomarkers or drivers of processes relevant to disease, industry and the environment. In the past two years, we have seen a paradigm shift in metagenomics to the application of cross-sectional and longitudinal studies enabled by advances in DNA sequencing and high-performance computing. These technologies now make it possible to broadly assess microbial diversity and function, allowing systematic investigation of the largely unexplored frontier of microbial life. To achieve this aim, the global scientific community must collaborate and agree upon common objectives and data standards to enable comparative research across the Earth's microbiome. Improvements in comparability of data will facilitate the study of biotechnologically relevant processes, such as bioprospecting for new glycoside hydrolases or identifying novel energy sources.

Figure 1

Conceptual diagram of why replicated samples, especially across a gradient or along a time series, are critical for interpretation of results. Structure that is externally imposed via study design greatly improves our ability to recover biologically meaningful relationships rather than simply finding statistical differences between samples (especially important because every pair of biological samples will be different if sequenced deeply enough). In this case, we show the L4 Western English Channel ocean time series samples : Sampling only during the summer, highlighted in blue, would only reveal the tip of the iceberg of variability in this ecosystem, which is driven by seasonal change (the graph shows day on the x-axis; log of the observed number of species on the y-axis). Similar principles apply in other ecosystems that have other major drivers of variation that, when overlooked, can influence the results in ways that are puzzling, or give a misleading picture of variation.

Figure 2

Importance of metadata-enabled studies. Matched-pair diagrams showing visualizations from recently published, high-impact studies with and without metadata, showing the importance of metadata. Examples taken from Costello et al. 2009 (PCoA plot of UniFrac distances between human body habitat associated communities reveals clustering by human body habitat type), Ley et al. 2008 (where a bipartite network diagram shows that the main clustering of mammalian fecal communities is by diet), and Fierer et al. 2010 (where an NMDS plot of UniFrac distances between soil communities shows that the main factor driving variation in these communities is pH). These relationships are immediately and intuitively obvious when the right metadata are applied, but would be essentially impossible to see otherwise.