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In this study we began the process of characterizing the microbes we eat. The hypothesis is that the microbes we eat vary both quantitatively and compositionally in a significant way according to dietary pattern. We have selected to characterize the microbiota of three different dietary patterns in order to determine: the average total amount of daily microbes ingested via food and beverages and their composition in the average American adult across three different dietary patterns: 1) the Average American dietary pattern (AA) focused on convenience foods, 2) USDA recommended dietary pattern (USDA) emphasizing fruits and vegetables, lean meat, dairy, and whole grains, and 3) Vegan (VEG) dietary pattern, which excludes all animal products.   Because of the vast, historical effort to make the 16S rRNA gene sequence available for hundreds of thousands of organisms, we are typically able to do a good job of characterizing the taxonomic diversity of most microbial communities. We assume that these organisms have important functional roles to play, and the most straightforward way to predict what these roles are is to use metagenomic sequencing to interrogate the entire genomes of all members of the community. Unfortunately, in many cases, the amount of microbial DNA relative to host or other environmental DNA is small enough to make metagenomic sequencing infeasible. This is the case here, where the plant and animal DNA present in the food we eat is most often much more abundant than the microbial DNA. Some exceptions may exist with respect to fermented foods, but we are equally interested the microbiota associated with a wide variety of food types.  In a case like this for which metagenomic sequencing is infeasible, another approach suggests itself. There is good evidence that a correlation exists between the evolutionary relatedness of two organisms and the similarity of their genomic content. This means that we can leverage the information obtained by sequencing the genome of one organism to predict the functional potential of another, even if that other genome is represented by a single 16S rRNA sequence. The power of this approach is increased when very many, very closely-related genome sequences are available. This predictive approach has recently been implemented in the software package PICRUSt. PICRUSt uses the phylogenetic placement of a 16S rRNA sequence within a phylogeny of sequenced genomes to “reconstruct” what the genome of the organism containing that 16S rRNA sequence might look like.