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Human Microbiome
For the members of the human microbiome, see human microbiome

The human microbiome consists of about 100 trillion microbial cells, outnumbering human cells 10 to 1. Thus it can significantly affect human physiology. For example, in healthy individuals the microbiota provide a wide range of metabolic functions that humans lack. In diseased inviduals altered microbiota are associated with diseases such as inflammatory bowel disease and vaginosis. Thus studying the human microbiome is an important task that has been undertaken by initiatives such as the Human Microbiome Project and MetaHIT.

The Presence of a Core Microbiome
Aside from simply elucidating the composition of the human microbiome, one of the major questions involving the human microbiome is whether there is a “core”, that is, whether there is a subset of the community that is shared between most humans. If there is a core, then it would be possible to associate certain community compositions with disease states, which is one of the goals of the Human Microbiome Project. It is known that the human microbiome is highly variable both within a single subject and between different individuals. For example, the gut microbiota of humans is markedly dissimilar between individuals, a phenomenon which is also observed in mice. Hamady and Knight show that one can rule out the possibility that any species is found in more than 0.9% of human guts or on 2% of human hands. Although there is very little species level conservation between individuals, it has been shown that this may be a result of functional redundancy as different communities tend to converge on the same functional state.

Studying the Human Microbiome
The problem of elucidating the human microbiome is essentially identifying the members of a microbial community which includes bacteria, eukaryotes and viruses. This is done primarily using DNA-based studies, though RNA, protein and metabolite based studies have also been performed. DNA-based microbiome studies typically can be categorized as either targeted amplicon studies or more recently shotgun metagenomic studies. The former focuses on specific known marker genes and is primarily informative taxonomically, while the latter is an entire metagenomic approach which can also be used to study the functional potential of the community. One of the challenges that is present in human microbiome studies but not in other metagenomic studies is to avoid including the host DNA in the study.

Targeted Amplicon Sequencing
Targeted amplicon sequencing relies on having some expectations about the composition of the community that is being studied. In target amplicon sequencing a phylogenetically informative marker is targeted for sequencing. Such a marker should be present in ideally all the expected organisms. It should also evolve in such a way that it is conserved enough that primers can target genes from a wide range of organisms while evolving quickly enough to allow for finer resolution at the taxonomic level. A common marker for human microbiome studies is the 16S rRNA gene (the sequence of rDNA which encodes for the rRNA molecule). Since ribosomes are present in all living organisms, using the 16S rDNA allows for DNA to be amplified from many more organisms than if another marker were used. The 16S rDNA gene contains both slowly evolving regions and fast evolving regions; the former can be used to design broad primers while the latter allow for finer taxonomic distinction. However, species level resolution is not typically possible using the 16S rDNA. Primer selection is an important step, as anything that cannot be targeted by the primer will not be amplified and thus will not be detected. Different sets of primers have been shown to amplify different taxonomic groups due to sequence variation.

Targeted studies of eukaryotic and viral communities are limited and subject to the challenge of excluding host DNA from amplification and the reduced eukaryotic and viral biomass in the human microbiome.

After the amplicons are sequenced, phylogeny is then used to infer the composition of the microbial community. This is done by clustering the amplicons into operational taxonomic units (OTUs) and inferring phylogenetic relationships between the sequences. An important point is that the scale of data is extensive, and further approaches must be taken to identify patterns from the available information. Tools used to analyze the data include VAMPS, QIIME and mothur.

Metagenomic Sequencing
Metagenomics is also used extensively for studying microbial communities. In metagenomic sequencing DNA is recovered directly from environmental samples in an untargeted manner with the goal of obtaining an unbiased sample from all genes from all members of the community. Recent studies use shotgun Sanger sequencing or pyrosequencing to recover the sequences of the reads. The reads can then be assembled into contigs. To determine the phylogenetic identity of a sequence, it is compared to available full genome sequences using methods such as BLAST. One drawback of this approach is that many members of microbial communities do not have a representative sequenced genome.

Despite the fact that metagenomics is limited by the availability of reference sequences, one significant advantage of metagenomics over targeted amplicon sequencing is that metagenomics data can elucidate the functional potential of the community DNA. Targeted gene surveys cannot do this as they only reveal the phylogenetic relationship between the same gene from different organisms. Functional analysis is done by comparing the recovered sequences to databases of metagenomic annotations such as KEGG. The metabolic pathways that these genes are involved in can then be predicted with tools such as MG-RAST, CAMERA and IMG/M.

RNA and Protein-Based Approaches
Metatranscriptomics studies have been performed to study the gene expression of microbial communities through methods such as the pyrosequencing of extracted RNA. Structure based studies have also identified ncRNAs such as ribozymes from microbiota. . Metaproteomics is a new approach that studies the proteins expressed by microbiota, giving insight into its functional potential