Metagenomics is the study of metagenomes, genetic material recovered directly from environmental samples. The broad field may also be referred to as environmental genomics, ecogenomics or community genomics. Traditional microbiology and microbial genome sequencing rely upon cultivated clonal cultures. This relatively new field of genetic research enables studies of organisms that are not easily cultured in a laboratory as well as studies of organisms in their natural environment.
Early environmental gene sequencing cloned specific genes (often the 16S rRNA gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of microbial biodiversity had been missed by cultivation-based methods. Recent studies use "shotgun" Sanger sequencing or massively parallel pyrosequencing to get (mostly) unbiased samples of all genes from all members of sampled communities.
Origin of the term
The term "metagenomics" was first used by Jo Handelsman, Jon Clardy, Robert M. Goodman, and others, and first appeared in publication in 1998.
The term metagenome referenced the idea that a collection of genes sequenced from the environment could be analyzed in a way analogous to the study of a single genome. The exploding interest in environmental genetics, along with the buzzword-like nature of the term, has resulted in the broader use of metagenomics to describe any sequencing of genetic material from environmental (i.e. uncultured) samples, even work that focuses on one organism or gene. Recently, Kevin Chen and Lior Pachter (researchers at the University of California, Berkeley) defined metagenomics as "the application of modern genomics techniques to the study of communities of microbial organisms directly in their natural environments, bypassing the need for isolation and lab cultivation of individual species."
Environmental gene surveys
Conventional sequencing begins with a culture of identical cells as a source of DNA. However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be cultured and thus cannot be sequenced. These early studies focused on 16S ribosomal RNA sequences which are relatively short, often conserved within a species, and generally different between species. Many 16S rRNA sequences have been found which do not belong to any known cultured species, indicating that there are numerous non-isolated organisms out there.
Early molecular work in the field was conducted by Norman R. Pace and colleagues, who used PCR to explore the diversity of ribosomal RNA sequences. The insights gained from these breakthrough studies led Pace to propose the idea of cloning DNA directly from environmental samples as early as 1985. This led to the first report of isolating and cloning bulk DNA from an environmental sample, published by Pace and colleagues in 1991 while Pace was in the Department of Biology at Indiana University. Considerable efforts ensured that these were not PCR false positives and supported the existence of a complex community of unexplored species. Although this methodology was limited to exploring highly conserved, non-protein coding genes, it did support early microbial morphology-based observations that diversity was far more complex than was known by culturing methods.
Soon after that, Healy reported the metagenomic isolation of functional genes from "zoolibraries" constructed from a complex culture of environmental organisms grown in the laboratory on dried grasses in 1995. After leaving the Pace laboratory, Ed DeLong continued in the field and has published work that has largely laid the groundwork for environmental phylogenies based on signature 16S sequences, beginning with his group's construction of libraries from marine samples.
Longer sequences from environmental samples
Recovery of DNA sequences longer than a few thousand base pairs from environmental samples was very difficult until recent advances in molecular biological techniques, particularly related to constructing libraries in bacterial artificial chromosomes (BACs), provided better vectors for molecular cloning.
Advances in bioinformatics, refinements of DNA amplification, and proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples. These advances have enabled the adaptation of shotgun sequencing to metagenomic samples. The approach, used to sequence many cultured microorganisms as well as the human genome, randomly shears DNA, sequences many short sequences, and reconstructs them into a consensus sequence.
In 2002, Mya Breitbart, Forest Rohwer, and colleagues used environmental shotgun sequencing to show that 200 liters of seawater contains over 5000 different viruses. Subsequent studies showed that there are >1000 viral species in human stool and possibly a million different viruses per kilogram of marine sediment, including many bacteriophages. Essentially all of the viruses in these studies were new species. In 2004, Gene Tyson, Jill Banfield, and colleagues at the University of California, Berkeley and the Joint Genome Institute sequenced DNA extracted from an acid mine drainage system. This effort resulted in the complete, or nearly complete, genomes for a handful of bacteria and archaea that had previously resisted attempts to culture them. It was now possible to study entire genomes without the biases associated with laboratory cultures.
Global Ocean Sampling Expedition
Main article: Global Ocean Sampling Expedition
Beginning in 2003, Craig Venter, leader of the privately-funded parallel of the Human Genome Project, has led the Global Ocean Sampling Expedition, circumnavigating the globe and collecting metagenomic samples throughout. All of these samples are sequenced using shotgun sequencing, in hopes that new genomes (and therefore new organisms) would be identified. The pilot project, conducted in the Sargasso Sea, found DNA from nearly 2000 different species, including 148 types of bacteria never before seen. As of 2009, Venter has circumnavigated the globe and thoroughly explored the West Coast of the United States, and is currently in the midst of a two-year expedition to explore the Baltic, Mediterranian and Black Seas.
In 2006 Robert Edwards, Forest Rohwer, and colleagues at San Diego State University published the first sequences of environmental samples generated with so-called next generation sequencing, in this case chip-based pyrosequencing developed by 454 Life Sciences. This technique for sequencing DNA generates shorter fragments than conventional techniques, however this limitation is compensated for by the very large number of sequences generated. In addition, this technique does not require cloning the DNA before sequencing, removing one of the main biases in metagenomics.
In 2007, Daniel Huson and Stephan Schuster developed and published the first stand-alone metagenome analysis tool, MEGAN, which can be used to perform a first analysis of a metagenomic shotgun dataset. This tool was originally developed to analyse the metagenome of a mammoth sample. However in a recent study by Monzoorul et al. 2009, it was shown that adopting the LCA approach (of MEGAN) solely based on bit-score of the alignment leads to a number of false positive assignments especially in the context of metagenomic sequences originating from new organisms. This study proposed a new approach called SOrt-ITEMS which used several alignment parameters to increase the accuracy of assignments.
In 2007, Folker Meyer and Robert Edwards and a team at Argonne National Laboratory and the University of Chicago released the Metagenomics RAST server (MG-RAST) a community resource for metagenome data set analysis. The SEED based free, public resource has so far (October 2009) been used for the analysis of over 4000 metagenome data sets. As of October 2009 100+ giga-basepairs of DNA have been analyzed via MG-RAST, more than 350 public data sets are freely available for comparison within MG-RAST.
Metagenomics can improve strategies for monitoring the impact of pollutants on ecosystems and for cleaning up contaminated environments. Increased understanding of how microbial communities cope with pollutants is helping assess the potential of contaminated sites to recover from pollution and increase the chances of bioaugmentation or biostimulation trials to succeed.
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 impact of metagenomics is witnessed in the development of commodity and fine chemicals, agrochemicals and pharmaceuticals where the benefit of enzyme-catalyzed chiral synthesis is increasingly recognized.
Metagenomic sequencing is being used to characterize the microbial communities from 15-18 body sites from at least 250 individuals. This is part of the Human Microbiome initiative with primary goals to determine if there is a core human microbiome, to understand the changes in the human microbiome that can be correlated with human health, and to develop new technological and bioinformatics tools to support these goals.
It is well known that the vast majority of microbes have not been cultivated. Functional metagenomics strategies are being used to explore the interactions between plants and microbes through cultivation-independent study of the microbial communities.
Much of the interest in metagenomics comes from the discovery that the vast majority of microorganisms had previously gone unnoticed. Traditional microbiological methods relied upon laboratory cultures of organisms. Surveys of ribosomal RNA (rRNA) genes taken directly from the environment revealed that cultivation based methods find less than 1% of the bacteria and archaea species in a sample.
Shotgun sequencing and screens of clone libraries reveal genes present in environmental samples. This provides information both on which organisms are present and what metabolic processes are possible in the community. This can be helpful in understanding the ecology of a community, particularly if multiple samples are compared to each other.
Shotgun metagenomics also is capable of sequencing nearly complete microbial genomes directly from the environment. Because the collection of DNA from an environment is largely uncontrolled, the most abundant organisms in an environmental sample are most highly represented in the resulting sequence data. To achieve the high coverage needed to fully resolve the genomes of underrepresented community members, large samples, often prohibitively so, are needed. On the other hand, the random nature of shotgun sequencing ensures that many of these organisms will be represented by at least some small sequence segments. Due to the limitations of microbial isolation methods, the vast majority of these organisms would go unnoticed using traditional culturing techniques.
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Many bacterial communities show significant division of labor in metabolism. Waste products of some organisms are metabolites for others. Working together they turn raw resources into fully metabolized waste. 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 part (as metabolomics or proteomics) to be able to estimate how metabolites are possibly transferred and transformed through a community.
1.^ Marco, D, ed (2010). Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
2.^ a b Hugenholz, P; Goebel BM, Pace NR (1 September 1998). "Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity". J. Bacteriol 180 (18): 4765–74. PMID 9733676.
3.^ Eisen, JA (2007). "Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes.". PLoS Biology 5 (3): e82. doi:10.1371/journal.pbio.0050082. PMID 17355177.
4.^ Handelsman, J; Rondon MR, Brady SF, Clardy J, Goodman RM (1998). "Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products". Chemistry & Biology 5: 245–249. doi:10.1016/S1074-5521(98)90108-9. .
5.^ Chen, K; Pachter L (2005). "Bioinformatics for whole-genome shotgun sequencing of microbial communities". PLoS Comp Biol 1 (2): 24. doi:10.1371/journal.pcbi.0010024. PMID 16110337. .
6.^ Lane, DJ; Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR (1985). "Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses". Proceedings of the National Academy of Sciences 82 (20): 6955. doi:10.1073/pnas.82.20.6955. PMID 2413450. .
7.^ Pace, NR; DA Stahl, DJ Lane, GJ Olsen (1985). "Analyzing natural microbial populations by rRNA sequences". ASM News 51: 4–12. .
8.^ Pace, NR (1991). "Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing". Journal of Bacteriology 173: 4371–4378. .
9.^ Healy, FG; RM Ray, HC Aldrich, AC Wilkie, LO Ingram, KT Shanmugam (1995). "Direct isolation of functional genes encoding cellulases from the microbial consortia in a thermophilic, anaerobic digester maintained on lignocellulose". Appl. Microbiol Biotechnol. 43 (4): 667. doi:10.1007/BF00164771. PMID 7546604. .
10.^ Stein, JL; TL Marsh, KY Wu, H Shizuya, EF DeLong (1996). "Characterization of uncultivated prokaryotes: isolation and analysis of a 40-kilobase-pair genome fragment from a planktonic marine archaeon". Journal of Bacteriology 178: 591–599. .
11.^ Beja, O; Suzuki MT, Koonin EV, Aravind L, Hadd A, Nguyen LP, Villacorta R, Amjadi M, Garrigues C, Jovanovich SB, Feldman RA, Delong EF (2000). Construction and analysis of bacterial artificial chromosome libraries from a marine microbial assemblage. 2. pp. 516–529.
12.^ Breitbart, M; Salamon P, Andresen B, Mahaffy JM, Segall AM, Mead D, Azam F, Rohwer F (2002). "Genomic analysis of uncultured marine viral communities". Proceedings of the National Academy USA 99 (22): 14250–14255. doi:10.1073/pnas.202488399. PMID 12384570. .
13.^ a b Tyson, GW; Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, Solovyev VV, Rubin EM, Rokhsar DS, Banfield JF (2004). "Insights into community structure and metabolism by reconstruction of microbial genomes from the environment". Nature 428 (6978): 37–43. doi:10.1038/nature02340. PMID 14961025. http://www.nature.com/nature/journal/v428/n6978/full/nature02340.html. .
14.^ Hugenholz, P (2002). "Exploring prokaryotic diversity in the genomic era". Genome Biology 3: 1–8. doi:10.1186/gb-2002-3-2-reviews0003. .
15.^ Venter, JC; Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, Wu D, Paulsen I, Nelson KE, Nelson W, Fouts DE, Levy S, Knap AH, Lomas MW, Nealson K, White O, Peterson J, Hoffman J, Parsons R, Baden-Tillson H, Pfannkoch C, Rogers Y, Smith HO (2004). "Environmental Genome Shotgun Sequencing of the Sargasso Sea". Science 304 (5667): 66–74. doi:10.1126/science.1093857. PMID 15001713. .
16.^ Edwards, RA; Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, Peterson DM, Saar MO, Alexander S, Alexander EC, Rohwer F (2006). "Using pyrosequencing to shed light on deep mine microbial ecology". BMC Genomics 7: 57. doi:10.1186/1471-2164-7-57. PMID 16549033. .
17.^ H. N. Poinar, C. Schwarz, Ji Qi, B. Shapiro, R. D. E. MacPhee, B. Buigues, A. Tikhonov, D. H. Huson, L. P. Tomsho, A. Auch, M. Rampp, W. Miller, S. C. Schuster, Metagenomics to Paleogenomics: Large-Scale Sequencing of Mammoth DNA, Science 311:392-394, 2006
18.^ Monzoorul HM, Tarini S, Dinakar K, Sharmila S M (May 2009). "SOrt-ITEMS : Sequence Orthology based approach for Improved Taxonomic Estimation of Metagenomic Sequences". Bioinformatics 25 (14): 1722–30. doi:10.1093/bioinformatics/btp317. PMID 19439565.
19.^ Meyer, F; Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, Paczian T, Rodriguez A, Stevens R, Wilke A, Wilkening J, Edwards RA (2008). "The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes". BMC Bioinformatics 9: 0. doi:10.1186/1471-2105-9-386. PMID 18803844.
20.^ George I et al. (2010). "Application of Metagenomics to Bioremediation". Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
21.^ Wong D (2010). "Applications of Metagenomics for Industrial Bioproducts". Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
22.^ Nelson KE and White BA (2010). "Metagenomics and Its Applications to the Study of the Human Microbiome". Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
23.^ CharlesT (2010). "The Potential for Investigation of Plant-microbe Interactions Using Metagenomics Methods". Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7.
24.^ Allen, EE; Banfield, JF (2005). "Community genomics in microbial ecology and evolution". Nature Reviews Microbiology 3 (6): 489–498. doi:10.1038/nrmicro1157. PMID 15931167.
 Further reading
This article's further reading may not follow Wikipedia's content policies or guidelines. Please improve this article by removing excessive, less relevant or many publications with the same point of view; or by incorporating the relevant publications into the body of the article through appropriate citations. (October 2009)
Edwards RA, Rohwer F (June 2005). "Viral metagenomics". Nat. Rev. Microbiol. 3 (6): 504–10. doi:10.1038/nrmicro1163. PMID 15886693.
Eisen, J. A. (2007). Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes. PLoS Biology 5(3): e82
Green, B. D. & Keller, M. (2006). Capturing the uncultivated majority. Current Opinion in Biotechnology 17, 236-240.
Handelsman J. (2004). Metagenomics: application of genomics to uncultured microorganisms. Microbiology and Molecular Biology Reviews 68:669-685.
Keller, M. & Sengler, K. (2004). Tapping into microbial diversity. Nature Reviews Microbiology 2, 141-150.
Lombard, N. et al. (2006). The metagenomics of microbial communities. Biofutur 24-7.
Riesenfeld, C. S. et al. (2004). Metagenomics: genomic analysis of microbial communities. Annu Rev Genet 38: 525-52.
Rodriguez Valera, F. (2002). Approaches to prokaryotic biodiversity: a population genetics perspective. Environmental Microbiology 4: 628-33.
Rodriguez-Valera. (2004). Environmental genomics, the big picture?. FEMS Microbiology Letters 231:153-158.
Torsvik, V. & Ovreas, L. (2002). Microbial diversity and function in soil: from genes to ecosystems. Current opinion in Microbiology 5: 240-5.
Whitaker, R. J. & Banfield, J. F. (2006). Population genomics in natural microbial communities. Trends in Ecology & Evolution 21: 508-16.
Worden, A. Z. et al. (2006). In-depth analyses of marine microbial community genomics. Trends in Microbiology 14: 331-6.
Xu, J. P. (2006). Microbial ecology in the age of genomics and metagenomics: concepts, tools, and recent advances. Molecular Ecology 15: 1713-31.
Beja, O. et al. (2000). Construction and analysis of bacterial artificial chromosome libraries from a marine microbial assemblage. Environmental Microbiology 2: 516-29.
Sebat, J. L. et al. (2003). Metagenomic profiling: Microarray analysis of an environmental genomic library. Applied and Environmental Microbiology 69: 4927-34.
Suzuki, M. T. et al. (2004). Phylogenetic screening of ribosomal RNA gene-containing clones in bacterial artificial chromosome (BAC) libraries from different depths in Monterey Bay. Microbial Ecology 48: 473-88.
Zhu, W. et al. (2010). Ab initio gene identification in metagenomic sequences. Nucleic Acids Research, Vol. 38, No. 12 e132
Krause L., Diaz N.N., Goesmann A., Kelley S., Nattkemper T.W., Rohwer F., Edwards R.A., Stoye J. Phylogenetic classification of short environmental DNA fragments. Nucleic Acids Res. 36:2230-9, 2008
Huson, D.H., A. Auch, Ji Qi and S.C. Schuster, MEGAN Analysis of Metagenomic Data, Genome Research. 17:377-386, 2007
Krause L, Diaz NN, Bartels D, et al. (July 2006). "Finding novel genes in bacterial communities isolated from the environment". Bioinformatics 22 (14): e281–9. doi:10.1093/bioinformatics/btl247. PMID 16873483. http://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=16873483.
Rodriguez-Brito B, Rohwer F, Edwards RA (2006). "An application of statistics to comparative metagenomics". BMC Bioinformatics 7: 162. doi:10.1186/1471-2105-7-162. PMID 16549025. PMC 1473205. http://www.biomedcentral.com/1471-2105/7/162.
Raes J, Foerstner KU, Bork P (October 2007). "Get the most out of your metagenome: computational analysis of environmental sequence data". Curr. Opin. Microbiol. 10 (5): 490–8. doi:10.1016/j.mib.2007.09.001. PMID 17936679. http://linkinghub.elsevier.com/retrieve/pii/S1369-5274(07)00123-3.
Harrington ED, Singh AH, Doerks T, et al. (August 2007). "Quantitative assessment of protein function prediction from metagenomics shotgun sequences". Proc. Natl. Acad. Sci. U.S.A. 104 (35): 13913–8. doi:10.1073/pnas.0702636104. PMID 17717083. PMC 1955820. http://www.pnas.org/cgi/pmidlookup?view=long&pmid=17717083.
Tress, M. L. et al. (2006). An analysis of the Sargasso Sea resource and the consequences for database composition. Bmc Bioinformatics 7
Foerstner KU, von Mering C, Hooper SD, Bork P (2005) Environments shape the nucleotide composition of genomes. EMBO Rep. 6(12): 1208-13
Raes, J., Korbel, J.O., Lercher, M.J., Von Mering, C. & Bork, P. (2007) Prediction of effective genome size in metagenomic samples. Genome Biology 8, R10 
von Mering, C., Hugenholtz, P., Raes, J., Tringe, S.G., Doerks, T., Jensen, L.J., Ward N. & Bork, P. (2007) Quantitative phylogenetic assessment of microbial communities in diverse environments. Science 315, 1126–1130
Mavromatis K, Ivanova N, Barry K, et al. (June 2007). "Use of simulated data sets to evaluate the fidelity of metagenomic processing methods". Nat. Methods 4 (6): 495–500. doi:10.1038/nmeth1043. PMID 17468765.
Markowitz VM, Ivanova N, Palaniappan K, et al. (July 2006). "An experimental metagenome data management and analysis system". Bioinformatics 22 (14): e359–67. doi:10.1093/bioinformatics/btl217. PMID 16873494. http://bioinformatics.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=16873494.
Markowitz VM, Ivanova NN, Szeto E, Palaniappan K, Chu K, Dalevi D, Chen IM, Grechkin Y, Dubchak I, Anderson I, Lykidis A, Mavromatis K, Hugenholtz P, Kyrpides NC. (2007) IMG/M: a data management and analysis system for metagenomes. Nucleic Acids Res. Epub
Pushker R., D'Auria G., Alba-Casado J.C. and Rodríguez-Valera F. (2005) Micro-Mar: a database for dynamic representation of marine microbial biodiversity. BMC Bioinformatics, 6:222.
F. Meyer, D. Paarmann, M. D'Souza, R. Olson, E. M. Glass, M. Kubal, T. Paczian , A. Rodriguez, R. Stevens, A. Wilke, J. Wilkening, R. A. Edwards, The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes, BMC Bioinformatics 2008, 9:386.
Hingamp P, Brochier C, Talla E, Gautheret D, Thieffry D, Herrmann (2008) Metagenome Annotation Using a Distributed Grid of Undergraduate Students. PLoS Biol 6(11): e296
Sun Y, Cai Y, Liu L, Yu F, Farrell ML, McKendree W, Farmerie W (2009) ESPRIT: Estimating Species Richness Using Large Collections of 16S rRNA Pyrosequences. Nucleic Acids Research, 2009; doi: 10.1093/nar/gkp285.
 Marine ecosystems
Angly, F. E. et al. (2006). The marine viromes of four oceanic regions. PloS Biology 4: 2121-31.
Beja, O. et al. (2000). Bacterial rhodopsin: Evidence for a new type of phototrophy in the sea. Science 289: 1902-6.
Beja, O. et al. (2001). Proteorhodopsin phototrophy in the ocean. Nature 411: 786-9.
Beja, O. et al. (2002). Unsuspected diversity among marine aerobic anoxygenic phototrophs. Nature 415: 630-3.
Culley, A. I. et al. (2006). Metagenomic analysis of coastal RNA virus communities. Science 312: 1795-8.
DeLong, E. F. et al. (2006). Community genomics among stratified microbial assemblages in the ocean's interior. Science 311: 496-503.
Hallam, S. J. et al. (2006). Genomic analysis of the uncultivated marine crenarchaeote Cenarchaeum symbiosum. Proceedings of the National Academy of Sciences of the United States of America 103: 18296-301.
John, D. E. et al. (2006). Gene diversity and organization in rbcL-containing genome fragments from uncultivated Synechococcus in the Gulf of Mexico. Marine Ecology-Progress Series 316: 23-33.
Kannan N. et al. (2007). Structural and Functional Diversity of the Microbibial Kinome. PloS Biology 5: 467-478
Rusch D. B. et al. (2007). The Sorcerer II Global Ocean Sampling Expedition: Northwest Atlantic through Eastern Tropical Pacific. PloS Biology 5: 398-431
Tringe SG, von Mering C, Kobayashi A, et al. (April 2005). "Comparative metagenomics of microbial communities". Science 308 (5721): 554–7. doi:10.1126/science.1107851. PMID 15845853. http://www.sciencemag.org/cgi/pmidlookup?view=long&pmid=15845853.
Woyke, T. et al. (2006). Symbiosis insights through metagenomic analysis of a microbial consortium. Nature 443: 950-5.
Yooseph S. et al. (2007). The Sorcerer II Global Ocean Sampling Expedition: Expanding the Universe of Protein Families. 'PloS Biology 5: 432-466
Yutin, N. & Beja, O. (2005). Putative novel photosynthetic reaction centre organizations in marine aerobic anoxygenic photosynthetic bacteria: insights from metagenomics and environmental genomics. Environmental Microbiology 7: 2027-33.
Mussmann M, Richter M, Lombardot T, Meyerdierks A, Kuever J, Kube M, Glöckner FO, Amann R. (2005). Clustered genes related to sulfate respiration in uncultured prokaryotes support the theory of their concomitant horizontal transfer. J Bacteriol. Oct;187(20):7126-37.
Abulencia, C. B., Wyborski, D. L., Garcia, J. A., Podar, M., Chen, W., Chang, S. H. et al. (2006). Environmental whole-genome amplification to access microbial populations in contaminated sediments. Applied and Environmental Microbiology 72, 3291-3301.
Breitbart et al. (2004). Diversity and population structure of a nearshore marine sediment viral community. Proceedings of the Royal Society B 271: 565-574.
 Extreme environments
Baker, B. J. et al. (2006). Lineages of acidophilic archaea revealed by community genomic analysis. Science 314: 1933-5.
Schoenfeld, T. et al. (2008). Assembly of Viral Metagenomes from Yellowstone Hot Springs. AEM 74: 4166-74
 Medical sciences and biotechnological applications
Breitbart et al. (2003). Metagenomic analyses of an uncultured viral community from human feces. Journal of Bacteriology 185:6220-6223.
Schloss, P. D. & Handelsman, J. (2003). Biotechnological prospects from metagenomics. Current Opinion in Biotechnology 14: 303-310.
Breitbart, M. and Rohwer, F. (2005) Method for discovering novel DNA viruses in blood using viral particle selection and shotgun sequencing. BioTechniques, 39, 729-736.
Mathur, E., Toledo, G., Green, B. D., Podar, M., Richardson, T. H., Kulwiec (2005). A biodiversity-based approach to development of performance enzymes: Applied metagenomics and directed evolution. Industrial Biotechnology, 1, 283-287.
Zengler, K., Paradkar, A., & Keller, M. (2005). New methods to access microbial diversity for small molecule discovery. Natural Products , 275-293.
Zhang, T., Breitbart, M., Lee, W.H., Run, J.Q., Wei, C.L., Soh, S.W., Hibberd, M.L., Liu, E.T., Rohwer, F. and Ruan, Y. (2006) RNA viral community in human feces: prevalence of plant pathogenic viruses. PLoS biology, 4, e3.
Gill, S. R. et al. (2006). Metagenomic analysis of the human distal gut microbiome. Science 312: 1355-1359.
Kurokawa, K. et al. (2007). Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes. DNA Res. 14: 169-181.
 Ancient DNA
H. N. Poinar, C. Schwarz, Ji Qi, B. Shapiro, R. D. E. MacPhee, B. Buigues, A. Tikhonov, D. H. Huson, L. P. Tomsho, A. Auch, M. Rampp, W. Miller, S. C. Schuster, Metagenomics to Paleogenomics: Large-Scale Sequencing of Mammoth DNA, Science 311:392-394, 2006
This article's use of external links may not follow Wikipedia's policies or guidelines. Please improve this article by removing excessive and inappropriate external links. (August 2010)
Wooley JC, Godzik A, Friedberg I (2010). "A primer on metagenomics". PLoS Comput. Biol. 6 (2): e1000667. doi:10.1371/journal.pcbi.1000667. PMID 20195499. PMC 2829047. http://dx.plos.org/10.1371/journal.pcbi.1000667.
MEGAN MEtaGenome ANalyzer. A stand-alone metagenome analysis tool.
Metagenomics and Our Microbial Planet A website on metagenomics and the vital role of microbes on Earth from the National Academies.
The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet A report released by the National Research Council in March 2007. Also, see the Report In Brief.
IMG/M The Integrated Microbial Genomes system, for metagenome analysis by the DOE-JGI.
CAMERA Cyberinfrastructure for Metagenomics, data repository and tools for metagenomics research.
A good overview of metagenomics from the Science Creative Quarterly
list of Metagenome Projects from genomesonline.org
MG-RAST publicly available, free, metagenomics annotation pipeline and repository for pyrosequences, Sanger sequences, and other sequence approaches.
METAREP: JCVI Metagenomics Reports - an open source tool for high-performance comparative metagenomics
Human microbiome project
MetaHIT official website for the EU-funded project : Metagenomics of the Human Intestinal Tract
Annotathon Bioinformatics Training Through Metagenomic Sequence Annotation
Metagenomics Metagenomics research and applications