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Metagenomic analyses additionally present their own specific issues, including sequencing errors, chimeric reads, and complex underlying biology many microbial communities have been found to show remarkably high inter-subject variability. Related challenges are associated with high-dimensional data regardless of the data type or experimental platform the number of potential biomarkers, for example, is typically much higher than the number of samples.

Robust statistical tools are needed to ensure the reproducibility of conclusions drawn from metagenomic data, which is crucial for clinical application of the biological findings.

Identifying the most biologically informative features differentiating two or more phenotypes can be challenging in any genomics dataset, and this is particularly true for metagenomic biomarkers. Each of these different types of microbial biomarkers is correlated with disease phenotypes, but few bioinformatic methods exist to explain the class comparisons afforded by metagenomic data. For example, single pathogenic organisms can signal disease if present in a community, and increases and decreases in community complexity have been observed in bacterial vaginosis and Crohn's disease. Analyses of metagenomic data typically seek to identify the specific organisms, clades, operational taxonomic units, or pathways whose relative abundances differ between two or more groups of samples, and several features of microbial communities have been proposed as potential biomarkers for various disease states. More generally, the metagenomic study of microbial communities is an effective approach for identifying the microorganisms or microbial metabolic characteristics of any uncultured sample.

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Changes in microbial abundances in the gut, oral cavity, and skin have been associated with disease states ranging from obesity to psoriasis. The human microbiome, consisting of the total microbial complement associated with human hosts, is an important emerging area for metagenomic biomarker discovery. As sequencing technology continues to develop and makes microbial biomarkers increasingly easily detected, this enables clinical diagnostic and microbiological applications through the comparison of microbial communities. Comparisons between healthy and diseased tissues have highlighted the importance of tasks such as class discovery (detecting novel subtypes of a disease) and class prediction (determining the subtype of a new sample), and recent metagenomic assays have shown that human microbial communities can be used as biomarkers for host factors such as lifestyle and disease. Biomarker discovery has proven to be one of the most broadly applicable and successful means of translating molecular and genomic data into clinical practice.

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