assembled contigs. the first approach is to identify genes based upon homology with genes that are already publicly available in sequence databases, usually by blast searches. this type of approach is implemented in the program megan4. the second, ab initio, uses intrinsic features of the sequence to predict coding regions based upon gene training sets from related organisms. this is the approach taken by programs such as genemark and glimmer. the main advantage of ab initio prediction is that it enables the detection of coding regions that lack homologs in the sequence databases; however, it is most accurate when there are large regions of contiguous genomic dna available for comparison. species diversity gene annotations provide the "what", while measurements of species diversity provide the "who". in order to connect community composition and function in metagenomes, sequences must be binned. binning is the process of associating a particular sequence with an organism. in similarity-based binning, methods such as blast are used to rapidly search for phylogenetic markers or otherwise similar sequences in existing public databases. this approach is implemented in megan. another tool, phymmbl, uses interpolated markov models to assign reads. metaphlan and amphora are methods based on unique clade-specific markers for estimating organismal relative abundances with improved computational performances. other tools, like motus and metaphyler, use universal marker genes to profile prokaryotic species. with the motus profiler is possible to profile species without a reference genome, improving the estimation of microbial community diversity. recent methods, such as slimm, use read coverage landscape of individual reference genomes to minimize false-positive hits and get reliable relative abundances. in composition based binning, methods use intrinsic features of the sequence, such as oligonucleotide frequencies or codon usage bias. once sequences are binned, it is possible to carry out comparative analysis of diversity and richness. data integration the massive amount of exponentially growing sequence data is a daunting challenge that is complicated by the complexity of the metadata associated with metagenomic projects. metadata includes detailed information about the three-dimensional (including depth, or height) geography and environmental features of the sample, physical data about the sample site, and the methodology of the sampling. this information is necessary both to ensure replicability and to enable downstream analysis. because of its importance, metadata and collaborative data review and curation require standardized data formats located in specialized databases, such as the genomes online database (gold). several tools have been developed to integrate metadata and sequence data, allowing downstream comparative analyses of different datasets using a number of ecological indices. in 2007, folker meyer and robert edwards and a team at argonne national laboratory and the university of chicago released the metagenomics rapid annotation using subsystem technology server (mg-rast) a community resource for metagenome data set analysis. as of june 2012 over 14.8 terabases (14x1012 bases) of dna have been analyzed, with more than 10,000 public data sets freely available for comparison within mg-rast. over 8,000 users now have submitted a total of 50,000 metagenomes to mg-rast. the integrated microbial genomes/metagenomes (img/m) system also provides a collection of tools for functional analysis of microbial communities based on their metagenome sequence, based upon reference isolate genomes included from the integrated microbial genomes (img) system and the genomic encyclopedia of bacteria and archaea (geba) project. one of the first standalone tools for analysing high-throughput metagenome shotgun data was megan (meta genome analyzer). a first version of the program was used in 2005 to analyse the metagenomic context of dna sequences obtained from a mammoth bone. based on a blast comparison against a reference database, this tool performs both taxonomic and functional binning, by placing the reads onto the nodes of the ncbi taxonomy using a simple lowest common ancestor (lca) algorithm or onto the nodes of the seed or kegg classifications, respectively. with the advent of fast and inexpensive sequencing instruments, the growth of databases of dna sequences is now exponential (e.g., the ncbi genbank database ). faster and efficient tools are needed to keep pace with the high-throughput sequencing, because the blast-based approaches such as mg-rast or megan run slowly to annotate large samples (e.g., several hours to process a small/medium size dataset/sample ). thus, ultra-fast classifiers have recently emerged, thanks to more affordable powerful servers. these tools can perform the taxonomic annotation at extremely high speed, for example clark (according to clark's authors, it can classify accurately "32 million metagenomic short reads per minute"). at such a speed, a very large dataset/sample of a billion short reads can be processed in about 30 minutes. with the increasing availability of samples containing ancient dna and due to the uncertainty associated with the nature of those samples (ancient dna damage), a fast tool capable of producing conservative similarity estimates has been made available. according to falcon's authors, it can use relaxed thresholds and edit distances without affecting the memory and speed performance. comparative metagenomics comparative analyses between metagenomes can provide additional insight into the function of complex microbial communities and their role in host health. pairwise or multiple comparisons between metagenomes can be made at the level of sequence composition (comparing gc-content or genome size), taxonomic diversity, or functional complement. comparisons of population structure and phylogenetic diversity can be made on the basis of 16s rrna and other phylogenetic marker genes, or—in the case of low-diversity communities—by genome reconstruction from the metagenomic dataset. functional comparisons between metagenomes may be made by comparing sequences against reference databases such as cog or kegg, and tabulating the abundance by category and evaluating any differences for statistical significance. this gene-centric approach emphasizes the functional complement of the community as a whole rather than taxonomic groups, and shows that the functional complements are analogous under similar environmental conditions. consequently, metadata on the environmental context of the metagenomic sample is especially important in comparative analyses, as it provides researchers with the ability to study the effect of habitat upon community structure and function. additionally, several studies have also utilized oligonucleotide usage patterns to identify the differences across diverse microbial communities. examples of such methodologies include the dinucleotide relative abundance approach by willner et al. and the habisign approach of ghosh et al. this latter study also indicated that differences in tetranucleotide usage patterns can be used to identify genes (or metagenomic reads) originating from specific habitats. additionally some methods as triagetools or compareads detect similar reads between two read sets. the similarity measure they apply on reads is based on a number of identical words of length k shared by pairs of reads. a key goal in comparative metagenomics is to identify microbial group(s) which are responsible for conferring specific characteristics to a given environment. however, due to issues in the sequencing technologies artifacts need to be accounted for like in metagenomeseq. others have characterized inter-microbial interactions between the resident microbial groups. a gui-based comparative metagenomic analysis application called community-analyzer has been developed by kuntal et al. which implements a correlation-based graph layout algorithm that not only facilitates a quick visualization of the differences in the analyzed microbial communities (in terms of their taxonomic composition), but also provides insights into the inherent inter-microbial interactions occurring therein. notably, this layout algorithm also enables grouping of the metagenomes based on the probable inter-microbial interaction patterns rather than simply comparing abundance values of various taxonomic groups. in addition, the tool implements several interactive gui-based functionalities that enable users to perform standard comparative analyses across microbiomes. data analysis community metabolism in many bacterial communities, natural or engineered (such as bioreactors), there is significant division of labor in metabolism (syntrophy), during which the waste products of some organisms are metabolites for others. in one such system, the methanogenic bioreactor, functional stability requires the presence of several syntrophic species (syntrophobacterales and synergistia) working together in order to turn raw resources into fully metabolized waste (methane). using comparative gene studies and expression experiments with microarrays or proteomics researchers can piece together a metabolic network that goes beyond species boundaries. such studies require detailed knowledge about which versions of which proteins are coded by which species and even by which strains of which species. therefore, community genomic information is another fundamental tool (with metabolomics and proteomics) in the quest to determine how metabolites are transferred and transformed by a community. metatranscriptomics main article: metatranscriptomics metagenomics allows researchers to access the functional and metabolic diversity of microbial communities, but it cannot show which of these processes are active. the extraction and analysis of metagenomic mrna (the metatranscriptome) provides information on the regulation and expression profiles of complex communities. because of the technical difficulties (the short half-life of mrna, for example) in the collection of environmental rna there have been relatively few in situ metatranscriptomic studies of microbial communities to date. while originally limited to microarray technology, metatranscriptomics studies have made use of transcriptomics technologies to measure whole-genome expression and quantification of a microbial community, first employed in analysis of ammonia oxidation in soils. viruses metagenomic sequencing is particularly useful in the study of viral communities. as viruses lack a shared universal phylogenetic marker (as 16s rna for bacteria and archaea, and 18s rna for eukarya), the only way to access the genetic diversity of the viral community from an environmental sample is through metagenomics. viral metagenomes (also called viromes) should thus provide more and more information about viral diversity and evolution. for example, a metagenomic pipeline called giant virus finder showed the first evidence of existence of giant viruses in a saline desert and in antarctic dry valleys. applications metagenomics has the potential to advance knowledge in a wide variety of fields. it can also be applied to solve practical challenges in medicine, engineering, agriculture, sustainability and ecology. agriculture the soils in which plants grow are inhabited by microbial communities, with one gram of soil containing around 109-1010 microbial cells which comprise about one gigabase of sequence information. the microbial communities which inhabit soils are some of the most complex known to science, and remain poorly understood despite their economic importance. microbial consortia perform a wide variety of ecosystem services necessary for plant growth, including fixing atmospheric nitrogen, nutrient cycling, disease suppression, and sequester iron and other metals. functional metagenomics strategies are being used to explore the interactions between plants and microbes through cultivation-independent study of these microbial communities. by allowing insights into the role of previously uncultivated or rare community members in nutrient cycling and the promotion of plant growth, metagenomic approaches can contribute to improved disease detection in crops and livestock and the adaptation of enhanced farming practices which improve crop health by harnessing the relationship between microbes and plants. biofuel biofuels are fuels derived from biomass conversion, as in the conversion of cellulose contained in corn stalks, switchgrass, and other biomass into cellulosic ethanol. this process is dependent upon microbial consortia(association) that transform the cellulose into sugars, followed by the fermentation of the sugars into ethanol. microbes also produce a variety of sources of bioenergy including methane and hydrogen. the efficient industrial-scale deconstruction of biomass requires novel enzymes with higher productivity and lower cost. metagenomic approaches to the analysis of complex microbial communities allow the targeted screening of enzymes with industrial applications in biofuel production, such as glycoside hydrolases. furthermore, knowledge of how these microbial communities function is required to control them, and metagenomics is a key tool in their understanding. metagenomic approaches allow comparative analyses between convergent microbial systems like biogas fermenters or insect herbivores such as the fungus garden of the leafcutter ants. biotechnology microbial communities produce a vast array of biologically active chemicals that are used in competition and communication. many of the drugs in use today were originally uncovered in microbes; recent progress in mining the rich genetic resource of non-culturable microbes has led to the discovery of new genes, enzymes, and natural products. the application of metagenomics has allowed the development of commodity and fine chemicals, agrochemicals and pharmaceuticals where the benefit of enzyme-catalyzed chiral synthesis is increasingly recognized. two types of analysis are used in the bioprospecting of metagenomic data: function-driven screening for an expressed trait, and sequence-driven screening for dna sequences of interest. function-driven analysis seeks to identify clones expressing a desired trait or useful activity, followed by biochemical characterization and sequence analysis. this approach is limited by availability of a suitable screen and the