Culture-independent microbiological technologies that interrogate complex microbial populations without prior axenic culture, coupled with high-throughput DNA sequencing, have revolutionized the scale, speed and economics of microbial ecological studies. Their application to the medical realm has led to a highly productive merger of clinical, experimental and environmental microbiology. The functional roles played by members of the human microbiota are being actively explored through experimental manipulation of animal model systems and studies of human populations. In concert, these studies have appreciably expanded our understanding of the composition and dynamics of human-associated microbial communities (microbiota). Of note, several human diseases have been linked to alterations in the composition of resident microbial communities, so-called dysbiosis. However, how changes in microbial communities contribute to disease etiology remains poorly defined. Correlation of microbial composition represents integration of only two datasets (phenotype and microbial composition). This article explores strategies for merging the human microbiome data with multiple additional datasets (e.g. host single nucleotide polymorphisms and host gene expression) and for integrating patient-based data with results from experimental animal models to gain deeper understanding of how host-microbe interactions impact disease.
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