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. Metagenomics offers a powerful lens for viewing the microbial world that has the potential to revolutionize understanding of the entire living world.[1][2]

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.[3] Recent studies use "shotgun" Sanger sequencing or massively parallel pyrosequencing to get largely unbiased samples of all genes from all the members of the sampled communities.[4]



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.[5] 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."[6]

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.[7] The insights gained from these breakthrough studies led Pace to propose the idea of cloning DNA directly from environmental samples as early as 1985.[8] This led to the first report of isolating and cloning bulk DNA from an environmental sample, published by Pace and colleagues in 1991[9] 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.[10] 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.[11]

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.[12]

Shotgun metagenomics

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.[13] 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.[14] 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.[15]

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.[16] 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, Mediterranean 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.[17] 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.


A major problem with metagenomes is binning. Binning is the process of identifying from what organism a particular sequence has originated. Traditionally, BLAST is a method used to rapidly search for similar sequences in existing public databases. More advanced methods have been employed to bin sequences. Big successes have been achieved for a family of methods using intrinsic features of the sequence, such as oligonucleotide frequencies. These methods include TETRA (Teeling et al., 2004),[18] Phylopythia (McHardy et al., 2007), TACOA (Diaz et al., 2009), PCAHIER (Zheng and Wu, 2010),[19] DiScRIBinATE (Ghosh et al., 2010),[20] SPHINX (Mohammed et al., 2011),[21] and Parallel-META (Su et al., 2011).[22] 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.[23] However in a recent study by Monzoorul et al. 2009,[24] 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.[25] As of October 2011 3.7 Terabases (10^12 bases) of DNA have been analyzed by MG-RAST, more than 4300 public data sets are freely available for comparison within MG-RAST. Over 7000 users now have submitted a total of 38,000 metagenomes to MG-RAST. The server also acts as the de-fact repository for metagenomics data.


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.[26]

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.[27]

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.[28]

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.[29]

Finally, metagenomic sequencing is particularly useful in the study of viral communities. As viruses lack a shared universal phylogenetic marker (as are 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.[30]

Microbial diversity

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.[3]

Gene surveys

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.[31]

Environmental genomes

Shotgun metagenomics also is capable of sequencing nearly complete microbial genomes directly from the environment.[14] 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.

Community metabolism

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 tool (with metabolomics and proteomics) in the quest to determine how metabolites are transferred and transformed by a community.[32]

See also

  • Pathogenomics


  1. ^ Marco, D, ed (2010). Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7. 
  2. ^ Marco, D (editor) (2011). Metagenomics: Current Innovations and Future Trends. Caister Academic Press. ISBN 978-1-904455-87-5. 
  3. ^ 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. PMC 107498. PMID 9733676. 
  4. ^ 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. PMC 1821061. PMID 17355177. 
  5. ^ 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. .
  6. ^ Chen, K; Pachter L (2005). "Bioinformatics for whole-genome shotgun sequencing of microbial communities". PLoS Comp Biol 1 (2): 24. Bibcode 2005PLSCB...1...24C. doi:10.1371/journal.pcbi.0010024. PMC 1185649. PMID 16110337. .
  7. ^ 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): 6955–9. Bibcode 1985PNAS...82.6955L. doi:10.1073/pnas.82.20.6955. PMC 391288. PMID 2413450. .
  8. ^ Pace, NR; DA Stahl, DJ Lane, GJ Olsen (1985). "Analyzing natural microbial populations by rRNA sequences". ASM News 51: 4–12. .
  9. ^ Pace, NR; Delong, EF; Pace, NR (1991). "Analysis of a marine picoplankton community by 16S rRNA gene cloning and sequencing". Journal of Bacteriology 173 (14): 4371–4378. PMC 208098. PMID 2066334. .
  10. ^ 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): 667–74. doi:10.1007/BF00164771. PMID 7546604. .
  11. ^ 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 (3): 591–599. PMC 177699. PMID 8550487. 
  12. ^ Beja, O.; Suzuki, MT; Koonin, EV; Aravind, L; Hadd, A; Nguyen, LP; Villacorta, R; Amjadi, M et al. (2000). "Construction and analysis of bacterial artificial chromosome libraries from a marine microbial assemblage". Environmental Microbiology 2 (5): 516–29. doi:10.1046/j.1462-2920.2000.00133.x. PMID 11233160. 
  13. ^ 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. Bibcode 2002PNAS...9914250B. doi:10.1073/pnas.202488399. PMC 137870. PMID 12384570. .
  14. ^ 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. .
  15. ^ Hugenholz, P (2002). "Exploring prokaryotic diversity in the genomic era". Genome Biology 3: 1–8. doi:10.1186/gb-2002-3-2-reviews0003. PMC 139013. PMID 11864374. .
  16. ^ 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. Bibcode 2004Sci...304...66V. doi:10.1126/science.1093857. PMID 15001713. .
  17. ^ 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. PMC 1483832. PMID 16549033. .
  18. ^ Teeling, Hanno; Waldmann, Jost; Lombardot, Thierry; Bauer, Margarete; Oliver, Frank (2004). "TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences". BMC Bioinformatics 5 (163). doi:10.1186/1471-2105-5-163. 
  19. ^ Zheng, Hao; Wu, Hongwei (2010). "Short prokaryotic DNA fragment binning using a hierarchical classifier based on linear discriminant analysis and principal component analysis.". J Bioinform Comput Biol. 8 (6): 995–1011. PMID 21121023. 
  20. ^ Ghosh T S, Monzoorul HM, Mande S S (October 2010). "DiScRIBinATE: a rapid method for accurate taxonomic classification of metagenomic sequences". BMC Bioinformatics 25 (Suppl 7 : S14). doi: PMID 21106121. 
  21. ^ Mohammed MH, Ghosh TS, Dinakar K, Mande SS (October 2010). "SPHINX—an algorithm for taxonomic binning of metagenomic sequences". Bioinformatics 27 (1): 22–30. doi:10.1093/bioinformatics/btq608. PMID 21030462. 
  22. ^
  23. ^ Poinar, HN; Schwarz, C; Qi, J; Shapiro, B; MacPhee, RD; Buigues, B; Tikhonov, A; Huson, DH et al. (2006). "Metagenomics to paleogenomics: large-scale sequencing of mammoth DNA.". Science 311 (5759): 392–4. Bibcode 2006Sci...311..392P. doi:10.1126/science.1123360. PMID 16368896. 
  24. ^ 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. 
  25. ^ 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. PMC 2563014. PMID 18803844. 
  26. ^ George I et al. (2010). "Application of Metagenomics to Bioremediation". Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7. 
  27. ^ Wong D (2010). "Applications of Metagenomics for Industrial Bioproducts". Metagenomics: Theory, Methods and Applications. Caister Academic Press. ISBN 978-1-904455-54-7. 
  28. ^ 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. 
  29. ^ 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. 
  30. ^ Kristensen, DM; Mushegian AR, Dolja VV, Koonin EV (2009). "New dimensions of the virus world discovered through metagenomics". Trends in Microbiology 18 (1): 11–19. doi:10.1016/j.tim.2009.11.003. PMID 19942437. 
  31. ^ 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. 
  32. ^ Klitgord, N.; Segrè, D. (2011). "Ecosystems biology of microbial metabolism". Current Opinion in Biotechnology 22 (4): 541–546. doi:10.1016/j.copbio.2011.04.018. PMID 21592777.  edit

Further reading

Review articles



Marine ecosystems


Extreme environments

Medical sciences and biotechnological applications

Ancient DNA

External links

Wikimedia Foundation. 2010.

Игры ⚽ Нужно решить контрольную?

Look at other dictionaries:

  • Metagenomics — Metagenomik (englisch metagenomics) ist ein Forschungsgebiet der Biowissenschaften, das mit modernen molekularbiologischen Methoden die Gesamtheit der Mikroorganismen eines Biotops zu erfassen versucht. Der Begriff Metagenomik stammt aus einer… …   Deutsch Wikipedia

  • metagenomics — noun The study of genomes recovered from environmental samples; especially the differentiation of genomes from multiple organisms or individuals, either in a symbiotic relationship, or at a crime scene …   Wiktionary

  • Chemical biology — is a scientific discipline spanning the fields of chemistry and biology that involves the application of chemical techniques and tools, often compounds produced through synthetic chemistry, to the study and manipulation of biological systems.… …   Wikipedia

  • 454 Life Sciences — 454 Life Sciences, a Roche company, is a biotechnology company based in Branford, Connecticut specializing in high throughput DNA sequencing using a novel massively parallel sequencing by synthesis approach. 454 has experienced rapid growth since …   Wikipedia

  • Bacterial phyla — …   Wikipedia

  • Craig Venter — J. Craig Venter Craig Venter in 2007 Born October 14, 1946 (194 …   Wikipedia

  • Biological database — Biological databases are libraries of life sciences information, collected from scientific experiments, published literature, high throughput experiment technology, and computational analyses. They contain information from research areas… …   Wikipedia

  • MEGAN — Infobox Software name = MEGAN caption = developer = Daniel Huson et al. latest release version = 2beta8 latest release date = 2008 latest preview version = latest preview date = operating system = Windows, Linux, Mac OS X platform = genre =… …   Wikipedia

  • Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis — CAMERA Content Description Community Cyberinfrastructure for Advanced Marine Microbial Ecology Research and Analysis Contact Research center Univers …   Wikipedia

  • Metagenom — Metagenomik (englisch metagenomics) ist ein Forschungsgebiet der Biowissenschaften, das mit modernen molekularbiologischen Methoden die Gesamtheit der Mikroorganismen eines Biotops zu erfassen versucht. Der Begriff Metagenomik stammt aus einer… …   Deutsch Wikipedia

Share the article and excerpts

Direct link
Do a right-click on the link above
and select “Copy Link”