Metagenomics - Knowledge

1067:. Shotgun sequencing reveals genes present in environmental samples. Historically, clone libraries were used to facilitate this sequencing. However, with advances in high throughput sequencing technologies, the cloning step is no longer necessary and greater yields of sequencing data can be obtained without this labour-intensive bottleneck step. Shotgun metagenomics provides information both about which organisms are present and what metabolic processes are possible in the community. 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 under-represented 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, which would otherwise go unnoticed using traditional culturing techniques, will be represented by at least some small sequence segments. 1482:-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. 1969:

critically important for the health of the intestinal tract. There are two types of functions in these range clusters: housekeeping and those specific to the intestine. The housekeeping gene clusters are required in all bacteria and are often major players in the main metabolic pathways including central carbon metabolism and amino acid synthesis. The gut-specific functions include adhesion to host proteins and the harvesting of sugars from globoseries glycolipids. Patients with irritable bowel syndrome were shown to exhibit 25% fewer genes and lower bacterial diversity than individuals not suffering from irritable bowel syndrome indicating that changes in patients' gut biome diversity may be associated with this condition.

1100:; Ion Torrent PGM System and 454 pyrosequencing typically produces ~400 bp reads, Illumina MiSeq produces 400-700bp reads (depending on whether paired end options are used), and SOLiD produce 25–75 bp reads. Historically, these read lengths were significantly shorter than the typical Sanger sequencing read length of ~750 bp, however the Illumina technology is quickly coming close to this benchmark. However, this limitation is compensated for by the much larger number of sequence reads. In 2009, pyrosequenced metagenomes generate 200–500 megabases, and Illumina platforms generate around 20–50 gigabases, but these outputs have increased by orders of magnitude in recent years. 998: 38: 1883: 366: 1164: 1427:

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.

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high-throughput bioinformatic analysis pipelines. The sequence-driven approach to screening is limited by the breadth and accuracy of gene functions present in public sequence databases. In practice, experiments make use of a combination of both functional and sequence-based approaches based upon the function of interest, the complexity of the sample to be screened, and other factors. An example of success using metagenomics as a biotechnology for drug discovery is illustrated with the

7807: 743: 321: 7835: 7795: 559: 1415:(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 1129: 7847: 1698:. 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 1251:. The use of reference genomes allows researchers to improve the assembly of the most abundant microbial species, but this approach is limited by the small subset of microbial phyla for which sequenced genomes are available. After an assembly is created, an additional challenge is "metagenomic deconvolution", or determining which sequences come from which species in the sample. 1980:(HMP), gut microbial communities were assayed using high-throughput DNA sequencing. HMP showed that, unlike individual microbial species, many metabolic processes were present among all body habitats with varying frequencies. Microbial communities of 649 metagenomes drawn from seven primary body sites on 102 individuals were studied as part of the 858:, it did support early microbial morphology-based observations that diversity was far more complex than was known by culturing methods. Soon after that in 1995, Healy reported the metagenomic isolation of functional genes from "zoolibraries" constructed from a complex culture of environmental organisms grown in the laboratory on dried 1861:

approach is limited by availability of a suitable screen and the requirement that the desired trait be expressed in the host cell. Moreover, the low rate of discovery (less than one per 1,000 clones screened) and its labor-intensive nature further limit this approach. In contrast, sequence-driven analysis uses

1396:) a community resource for metagenome data set analysis. As of June 2012 over 14.8 terabases (14x10 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 1890:

Metagenomics can provide valuable insights into the functional ecology of environmental communities. Metagenomic analysis of the bacterial consortia found in the defecations of Australian sea lions suggests that nutrient-rich sea lion faeces may be an important nutrient source for coastal ecosystems.

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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

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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

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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

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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

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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

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Abubucker, Sahar; Segata, Nicola; Goll, Johannes; Schubert, Alyxandria M.; Izard, Jacques; Cantarel, Brandi L.; Rodriguez-Mueller, Beltran; Zucker, Jeremy; Thiagarajan, Mathangi; Henrissat, Bernard; White, Owen; Kelley, Scott T.; Methé, Barbara; Schloss, Patrick D.; Gevers, Dirk; Mitreva, Makedonka;

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shows promise as a sensitive and rapid method to diagnose infection by comparing genetic material found in a patient's sample to databases of all known microscopic human pathogens and thousands of other bacterial, viral, fungal, and parasitic organisms and databases on antimicrobial resistances gene

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While these studies highlight some potentially valuable medical applications, only 31–48.8% of the reads could be aligned to 194 public human gut bacterial genomes and 7.6–21.2% to bacterial genomes available in GenBank which indicates that there is still far more research necessary to capture novel

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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

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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

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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

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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

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while metagenomic data is usually highly non-redundant. Furthermore, the increased use of second-generation sequencing technologies with short read lengths means that much of future metagenomic data will be error-prone. Taken in combination, these factors make the assembly of metagenomic sequence

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The study demonstrated that two bacterial divisions, Bacteroidetes and Firmicutes, constitute over 90% of the known phylogenetic categories that dominate distal gut bacteria. Using the relative gene frequencies found within the gut these researchers identified 1,244 metagenomic clusters that are

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Another medical study as part of the MetaHit (Metagenomics of the Human Intestinal Tract) project consisted of 124 individuals from Denmark and Spain consisting of healthy, overweight, and irritable bowel disease patients. The study attempted to categorize the depth and phylogenetic diversity of

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to screen clones for the sequence of interest. In comparison to cloning-based approaches, using a sequence-only approach further reduces the amount of bench work required. The application of massively parallel sequencing also greatly increases the amount of sequence data generated, which require

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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

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Seas. Analysis of the metagenomic data collected during this journey revealed two groups of organisms, one composed of taxa adapted to environmental conditions of 'feast or famine', and a second composed of relatively fewer but more abundantly and widely distributed taxa primarily composed of

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in which plants grow are inhabited by microbial communities, with one gram of soil containing around 10-10 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

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gastrointestinal bacteria. Using Illumina GA sequence data and SOAPdenovo, a de Bruijn graph-based tool specifically designed for assembly short reads, they were able to generate 6.58 million contigs greater than 500 bp for a total contig length of 10.3 Gb and a N50 length of 2.2 kb.

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Because of its ability to reveal the previously hidden diversity of microscopic life, metagenomics offers a powerful way of understanding the microbial world that might revolutionize understanding of biology. As the price of DNA sequencing continues to fall, metagenomics now allows

929:(GOS), circumnavigating the globe and collecting metagenomic samples throughout the journey. All of these samples were sequenced using shotgun sequencing, in hopes that new genomes (and therefore new organisms) would be identified. The pilot project, conducted in the 1439:

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

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Qin, Junjie; Li, Ruiqiang; Raes, Jeroen; Arumugam, Manimozhiyan; Burgdorf, Kristoffer Solvsten; Manichanh, Chaysavanh; Nielsen, Trine; Pons, Nicolas; Levenez, Florence; Yamada, Takuji; Mende, Daniel R.; Li, Junhua; Xu, Junming; Li, Shaochuan; Li, Dongfang (2010).

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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

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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

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gene catalog identified 3.3 million genes assembled from 567.7 gigabases of sequence data. Collecting, curating, and extracting useful biological information from datasets of this size represent significant computational challenges for researchers.

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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

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An advantage to high throughput sequencing is that this technique does not require cloning the DNA before sequencing, removing one of the main biases and bottlenecks in environmental sampling. The first metagenomic studies conducted using

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and for cleaning up contaminated environments. Increased understanding of how microbial communities cope with pollutants improves assessments of the potential of contaminated sites to recover from pollution and increases the chances of

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DNA sequencing can also be used more broadly to identify species present in a body of water, debris filtered from the air, sample of dirt, or animal's faeces, and even detect diet items from blood meals. This can establish the range of

1949:, but their composition and the mechanism by which they do so remains mysterious. 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 2000:

In animals, metagenomics can be used to profile their gut microbiomes and enable detection of antibiotic-resistant bacteria. This can have implications in monitoring the spread of diseases from wildlife to farmed animals and humans.

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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

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Healy FG, Ray RM, Aldrich HC, Wilkie AC, Ingram LO, Shanmugam KT (1995). "Direct isolation of functional genes encoding cellulases from the microbial consortia in a thermophilic, anaerobic digester maintained on lignocellulose".

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that make assembly especially difficult because of the difference in the relative abundance of species present in the sample. Misassemblies can also involve the combination of sequences from more than one species into chimeric

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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).

522:, and Sean F. Brady, 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 1303:

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.

1798:. 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 1988:

degradation in the gut, as well as phosphate and amino acid transport linked to host phenotype (vaginal pH) in the posterior fornix. The HMP has brought to light the utility of metagenomics in diagnostics and

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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

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This is because the bacteria that are expelled simultaneously with the defecations are adept at breaking down the nutrients in the faeces into a bioavailable form that can be taken up into the food chain.

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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

7730: 5902: 1107:(Hi-C), which measures the proximity of any two DNA sequences within the same cell, to guide microbial genome assembly. Long read sequencing technologies, including PacBio RSII and PacBio Sequel by 3062:

Béjà O, Suzuki MT, Koonin EV, Aravind L, Hadd A, Nguyen LP, et al. (October 2000). "Construction and analysis of bacterial artificial chromosome libraries from a marine microbial assemblage".

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Tyson GW, Chapman J, Hugenholtz P, Allen EE, Ram RJ, Richardson PM, et al. (March 2004). "Community structure and metabolism through reconstruction of microbial genomes from the environment".

1051:, refinements of DNA amplification, and the proliferation of computational power have greatly aided the analysis of DNA sequences recovered from environmental samples, allowing the adaptation of 1400:(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 6335: 1192:

The first step of metagenomic data analysis requires the execution of certain pre-filtering steps, including the removal of redundant, low-quality sequences and sequences of probable

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Sunagawa S, Mende DR, Zeller G, Izquierdo-Carrasco F, Berger SA, Kultima JR, et al. (December 2013). "Metagenomic species profiling using universal phylogenetic marker genes".

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The data generated by metagenomics experiments are both enormous and inherently noisy, containing fragmented data representing as many as 10,000 species. The sequencing of the cow

6732:"A two-step metagenomics approach for the identification and mitochondrial DNA contig assembly of vertebrate prey from the blood meals of common vampire bats (Desmodus rotundus)" 2009:

Differentiating between infectious and non-infectious illness, and identifying the underlying etiology of infection, can be challenging. For example, more than half of cases of

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Schematic representation of the main steps necessary for the analysis of whole metagenome shotgun sequencing-derived data. The software related to each step is shown in italics.

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project. The metagenomic analysis revealed variations in niche specific abundance among 168 functional modules and 196 metabolic pathways within the microbiome. These included

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origin (especially in metagenomes of human origin). The methods available for the removal of contaminating eukaryotic genomic DNA sequences include Eu-Detect and DeConseq.

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species in a sample. Much of the interest in metagenomics comes from these discoveries that showed that the vast majority of microorganisms had previously gone unnoticed.

1291:, 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 3459:

Hess M, Sczyrba A, Egan R, Kim TW, Chokhawala H, Schroth G, et al. (January 2011). "Metagenomic discovery of biomass-degrading genes and genomes from cow rumen".

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Pratas D, Pinho AJ, Silva RM, Rodrigues JM, Hosseini M, Caetano T, Ferreira PJ (February 2018). "FALCON: a method to infer metagenomic composition of ancient DNA".

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Mohammed MH, Chadaram S, Komanduri D, Ghosh TS, Mande SS (September 2011). "Eu-Detect: an algorithm for detecting eukaryotic sequences in metagenomic data sets".

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Poinar HN, Schwarz C, Qi J, Shapiro B, Macphee RD, Buigues B, et al. (January 2006). "Metagenomics to paleogenomics: large-scale sequencing of mammoth DNA".

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Venter JC, Remington K, Heidelberg JF, Halpern AL, Rusch D, Eisen JA, et al. (April 2004). "Environmental genome shotgun sequencing of the Sargasso Sea".

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false positives and supported the existence of a complex community of unexplored species. Although this methodology was limited to exploring highly conserved,

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Chua, Physilia Y. S.; Carøe, Christian; Crampton-Platt, Alex; Reyes-Avila, Claudia S.; Jones, Gareth; Streicker, Daniel G.; Bohmann, Kristine (4 July 2022).

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are methods based on unique clade-specific markers for estimating organismal relative abundances with improved computational performances. Other tools, like

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Leininger S, Urich T, Schloter M, Schwark L, Qi J, Nicol GW, et al. (August 2006). "Archaea predominate among ammonia-oxidizing prokaryotes in soils".

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or genome size), taxonomic diversity, or functional complement. Comparisons of population structure and phylogenetic diversity can be made on the basis of

2030:(blood-feeding) insects such as mosquitoes and ticks. Metagenomics is routinely used by public health officials and organisations for the surveillance of 417:) to deduce the individual genomes or parts of genomes that constitute the original environmental sample. This information can then be used to study the 1055:

to metagenomic samples (known also as whole metagenome shotgun or WMGS sequencing). The approach, used to sequence many cultured microorganisms and the

6498:"Culture-independent discovery of the malacidins as calcium-dependent antibiotics with activity against multidrug-resistant Gram-positive pathogens" 886:(see below) to show that 200 liters of seawater contains over 5000 different viruses. Subsequent studies showed that there are more than a thousand 818:, indicating that there are numerous non-isolated organisms. These surveys of ribosomal RNA genes taken directly from the environment revealed that 534:) defined metagenomics as "the application of modern genomics technique without the need for isolation and lab cultivation of individual species". 6366: 1330:

are used to rapidly search for phylogenetic markers or otherwise similar sequences in existing public databases. This approach is implemented in

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Pace NR, Stahl DA, Lane DJ, Olsen GJ (1986). "The Analysis of Natural Microbial Populations by Ribosomal RNA Sequences". In Marshall KC (ed.).

2577:"Characterization of uncultivated prokaryotes: isolation and analysis of a 40-kilobase-pair genome fragment from a planktonic marine archaeon" 1354:

is possible to profile species without a reference genome, improving the estimation of microbial community diversity. Recent methods, such as

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Metagenomic sequencing is particularly useful in the study of viral communities. As viruses lack a shared universal phylogenetic marker (as

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Metagenomics allows the study of microbial communities like those present in this stream receiving acid drainage from surface coal mining.

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to measure whole-genome expression and quantification of a microbial community, first employed in analysis of ammonia oxidation in soils.

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Metagenomics has the potential to advance knowledge in a wide variety of fields. It can also be applied to solve practical challenges in

795:. However, early metagenomic studies revealed that there are probably large groups of microorganisms in many environments that cannot be 352: 6596:"High nutrient transport and cycling potential revealed in the microbial metagenome of Australian sea lion (Neophoca cinerea) faeces" 5826:

Kerepesi C, Grolmusz V (June 2017). "The "Giant Virus Finder" discovers an abundance of giant viruses in the Antarctic dry valleys".

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with higher productivity and lower cost. Metagenomic approaches to the analysis of complex microbial communities allow the targeted

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use two approaches in the annotation of coding regions in the assembled contigs. The first approach is to identify genes based upon

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Chua, Physilia Y. S.; Crampton-Platt, Alex; Lammers, Youri; Alsos, Inger G.; Boessenkool, Sanne; Bohmann, Kristine (25 May 2021).

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The massive amount of exponentially growing sequence data is a daunting challenge that is complicated by the complexity of the

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bulk DNA from an environmental sample, published by Pace and colleagues in 1991 while Pace was in the Department of Biology at

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Metagenomics has been an invaluable tool to help characterise the diversity and ecology of pathogens that are vectored by

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DNA sequence data from genomic and metagenomic projects are essentially the same, but genomic sequence data offers higher

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about quality assessment: on assembly (N50, MetaQUAST), on genome (universal single-copy marker genes – CheckM and BUSCO).

7882: 6653: 1866: 1397: 4649:"The metagenomics RAST server - a public resource for the automatic phylogenetic and functional analysis of metagenomes" 1077: 7452: 6984:"PLOS Computational Biology: Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome" 899: 728: 531: 3165:

Rodrigue S, Materna AC, Timberlake SC, Blackburn MC, Malmstrom RR, Alm EJ, Chisholm SW (July 2010). Gilbert JA (ed.).

7263:"Uncovering the Worldwide Diversity and Evolution of the Virome of the Mosquitoes Aedes aegypti and Aedes albopictus" 6206:

Suen G, Scott JJ, Aylward FO, Adams SM, Tringe SG, Pinto-Tomás AA, et al. (September 2010). Sonnenburg J (ed.).

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provide the "who". In order to connect community composition and function in metagenomes, sequences must be binned.

7887: 6149:"Comparative and joint analysis of two metagenomic datasets from a biogas fermenter obtained by 454-pyrosequencing" 1326:

is the process of associating a particular sequence with an organism. In similarity-based binning, methods such as

926: 723: 718: 6805: 4600:"The Genomes OnLine Database (GOLD) v.4: status of genomic and metagenomic projects and their associated metadata" 3680:"Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies" 502:

directed sequencing to get largely unbiased samples of all genes from all the members of the sampled communities.

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Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, et al. (August 2016).

5141:"HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences" 2014: 1104: 1028: 942: 5627:"IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes" 6547:"Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data" 5288:"Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes" 345: 6020:

Charles T (2010). "The Potential for Investigation of Plant-microbe Interactions Using Metagenomics Methods".

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Vogel TM, Simonet P, Jansson JK, Hirsch PR, Tiedje JM, Van Elsas JD, Bailey MJ, Nalin R, Philippot L (2009).

3407:"Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses" 2288:"Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products" 6779:

George I, Stenuit B, Agathos SN (2010). "Application of Metagenomics to Bioremediation". In Marco D (ed.).

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Jaenicke S, Ander C, Bekel T, Bisdorf R, Dröge M, Gartemann KH, et al. (January 2011). Aziz RK (ed.).

1596: 1549: 1112: 1085: 6447:"Isolation of xylose isomerases by sequence- and function-based screening from a soil metagenomic library" 4903:"CLARK: fast and accurate classification of metagenomic and genomic sequences using discriminative k-mers" 2237:"Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes" 1536:

and proteomics) in the quest to determine how metabolites are transferred and transformed by a community.

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gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of

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Nelson KE and White BA (2010). "Metagenomics and Its Applications to the Study of the Human Microbiome".

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new genes, enzymes, and natural products. The application of metagenomics has allowed the development of

1503:), during which the waste products of some organisms are metabolites for others. In one such system, the 1385: 1362:. Once sequences are binned, it is possible to carry out comparative analysis of diversity and richness. 712: 1247:, have been optimized for the shorter reads produced by second-generation sequencing through the use of 910:

system. This effort resulted in the complete, or nearly complete, genomes for a handful of bacteria and

7825: 3997:"MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads" 2960:

Edwards RA, Rodriguez-Brito B, Wegley L, Haynes M, Breitbart M, Peterson DM, et al. (March 2006).

1032: 986: 698: 114: 5675: 1115:, is another choice to get long shotgun sequencing reads that should make ease in assembling process. 7779: 5985:

Committee on Metagenomics: Challenges and Functional Applications, National Research Council (2007).

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Committee on Metagenomics: Challenges and Functional Applications, National Research Council (2007).

1751:. This process is dependent upon microbial consortia (association) that transform the cellulose into 1243:

but nevertheless produce good results when assembling metagenomic data sets. Other programs, such as

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Oulas A, Pavloudi C, Polymenakou P, Pavlopoulos GA, Papanikolaou N, Kotoulas G, et al. (2015).

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Benson DA, Cavanaugh M, Clark K, Karsch-Mizrachi I, Lipman DJ, Ostell J, Sayers EW (January 2013).

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Mende DR, Waller AS, Sunagawa S, Järvelin AI, Chan MM, Arumugam M, et al. (23 February 2012).

1977: 1950: 132: 5188:"TriageTools: tools for partitioning and prioritizing analysis of high-throughput sequencing data" 2013:

remain undiagnosed, despite extensive testing using state-of-the-art clinical laboratory methods.

7872: 7720: 7592: 5995: 3880:"Fast identification and removal of sequence contamination from genomic and metagenomic datasets" 1990: 1216: 997: 819: 796: 587: 475: 2159:"Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity" 1710:

practices which improve crop health by harnessing the relationship between microbes and plants.

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Werner JJ, Knights D, Garcia ML, Scalfone NB, Smith S, Yarasheski K, et al. (March 2011).

4095:"Species-level deconvolution of metagenome assemblies with Hi-C-based contact probability maps" 2808: 2624:

Breitbart M, Salamon P, Andresen B, Mahaffy JM, Segall AM, Mead D, et al. (October 2002).

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Markowitz VM, Chen IM, Chu K, Szeto E, Palaniappan K, Grechkin Y, et al. (January 2012).

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Stewart RD, Auffret MD, Warr A, Wiser AH, Press MO, Langford KW, et al. (February 2018).

1993:. Thus metagenomics is a powerful tool to address many of the pressing issues in the field of 494:

to be investigated at a much greater scale and detail than before. Recent studies use either "

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Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, Szeto E, et al. (January 2017).

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Milanese A, Mende DR, Paoli L, Salazar G, Ruscheweyh HJ, Cuenca M, et al. (March 2019).

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Paez-Espino D, Roux S, Chen IA, Palaniappan K, Ratner A, Chu K, et al. (January 2019).

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One of the first standalone tools for analysing high-throughput metagenome shotgun data was

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Pagani I, Liolios K, Jansson J, Chen IM, Smirnova T, Nosrat B, et al. (January 2012).

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Yooseph S, Nealson KH, Rusch DB, McCrow JP, Dupont CL, Kim M, et al. (November 2010).

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Hover BM, Kim SH, Katz M, Charlop-Powers Z, Owen JG, Ternei MA, et al. (April 2018).

4647:

Meyer F, Paarmann D, D'Souza M, Olson R, Glass EM, Kubal M, et al. (September 2008).

8: 7710: 7684: 7542: 7529: 7490: 5676:"Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data" 5329:"Bacterial community structures are unique and resilient in full-scale bioenergy systems" 4750:

Mitra S, Rupek P, Richter DC, Urich T, Gilbert JA, Meyer F, et al. (February 2011).

1843: 1831: 1795: 1672: 1591:

metatranscriptomic studies of microbial communities to date. While originally limited to

1555: 1512: 1266: 1215:

reads into genomes difficult and unreliable. Misassemblies are caused by the presence of

1108: 668: 602: 433: 280: 260: 195: 152: 146: 137: 109: 7329: 7172: 6999: 6941: 6876: 6611: 6164: 5538: 5483: 5344: 5107: 5050: 4975:"Comparative metagenomics revealed commonly enriched gene sets in human gut microbiomes" 4456: 4260: 3895: 3744: 3527: 3472: 3316: 3182: 3075: 2922: 2869: 2804: 2699: 2641: 2404: 2345: 2112: 1142:

Please expand the section to include this information. Further details may exist on the

37: 7664: 7475: 7346: 7313: 7289: 7262: 7238: 7213: 7189: 7156: 7132: 7097: 7078: 7018: 6983: 6958: 6925: 6901: 6860: 6761: 6707: 6680: 6630: 6595: 6571: 6546: 6522: 6497: 6473: 6446: 6422: 6397: 6283: 6258: 6234: 6207: 6183: 6148: 6124: 6097: 6073: 6046: 5861: 5835: 5808: 5782: 5750: 5725: 5706: 5651: 5626: 5602: 5577: 5558: 5503: 5412: 5387: 5363: 5328: 5263: 5236: 5212: 5187: 5163: 5140: 5067: 5034: 4999: 4974: 4929: 4902: 4878: 4851: 4827: 4802: 4778: 4751: 4724: 4699: 4675: 4648: 4624: 4599: 4575: 4548: 4524: 4498:"Accurate and fast estimation of taxonomic profiles from metagenomic shotgun sequences" 4497: 4473: 4440: 4418: 4370: 4345: 4321: 4296: 4220: 4195: 4171: 4146: 4119: 4094: 4070: 4045: 4021: 3996: 3972: 3947: 3914: 3879: 3860: 3812: 3787: 3763: 3728: 3704: 3679: 3599: 3574: 3544: 3511: 3492: 3433: 3406: 3382: 3357: 3333: 3300: 3249: 3201: 3166: 3142: 3117: 3095: 3039: 3012: 2988: 2961: 2942: 2834: 2719: 2557: 2364: 2329: 2263: 2236: 2131: 2096: 1900: 1882: 1862: 1748: 1623: 1608: 1467: 1270: 1089: 1064: 1052: 969:

and colleagues published the first sequences of an environmental sample generated with

907: 883: 847: 807: 693: 677: 657: 615: 495: 406: 275: 270: 245: 167: 7314:"Sensitivity and specificity of metatranscriptomics as an arbovirus surveillance tool" 6354: 4973:

Kurokawa K, Itoh T, Kuwahara T, Oshima K, Toh H, Toyoda A, et al. (August 2007).

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Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. (March 2010).

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Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, et al. (March 2010).

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Schuster SC (January 2008). "Next-generation sequencing transforms today's biology".

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Zakrzewski M, Rašić G, Darbro J, Krause L, Poo YS, Filipović I, et al. (2018).

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and MetaPhyler, use universal marker genes to profile prokaryotic species. With the

7639: 7634: 7387: 7382: 7370: 7341: 7333: 7284: 7274: 7233: 7225: 7184: 7176: 7127: 7109: 7054: 7013: 7003: 6953: 6945: 6896: 6880: 6743: 6702: 6692: 6625: 6615: 6566: 6558: 6517: 6509: 6468: 6458: 6417: 6409: 6350: 6278: 6270: 6229: 6219: 6178: 6168: 6119: 6109: 6068: 6058: 5937: 5903:"Towards "Tera-Terra": Terabase Sequencing of Terrestrial Metagenomes Print E-mail" 5865: 5845: 5792: 5745: 5737: 5710: 5690: 5646: 5638: 5597: 5589: 5562: 5542: 5507: 5487: 5444: 5407: 5399: 5358: 5348: 5299: 5258: 5248: 5207: 5199: 5158: 5148: 5111: 5062: 5054: 4994: 4986: 4924: 4914: 4873: 4863: 4822: 4814: 4773: 4763: 4719: 4711: 4670: 4660: 4619: 4611: 4570: 4560: 4519: 4509: 4468: 4460: 4422: 4402: 4365: 4357: 4316: 4308: 4264: 4215: 4207: 4166: 4158: 4114: 4106: 4065: 4057: 4016: 4008: 3967: 3959: 3909: 3899: 3844: 3807: 3799: 3758: 3748: 3699: 3691: 3642: 3594: 3586: 3539: 3531: 3476: 3428: 3418: 3377: 3369: 3328: 3320: 3279: 3253: 3233: 3196: 3186: 3137: 3129: 3116:

Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C (May 2013).

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directly from samples taken from the environment (e.g. soil, sea water, human gut,

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Lavery TJ, Roudnew B, Seymour J, Mitchell JG, Jeffries T (2012). Steinke D (ed.).

6413: 4700:"IMG/M: the integrated metagenome data management and comparative analysis system" 2592: 1431:

thresholds and edit distances without affecting the memory and speed performance.

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Klitgord N, Segrè D (August 2011). "Ecosystems biology of microbial metabolism".

5403: 5253: 3904: 3753: 3358:"Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond" 3191: 2354: 2253: 2121: 1927: 1574: 1570: 1260: 1248: 891: 5304: 5287: 5153: 4768: 3301:"Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen" 2455: 1519:) working together in order to turn raw resources into fully metabolized waste ( 7839: 7811: 7649: 7552: 7495: 7337: 7180: 7058: 5522: 5333:

Proceedings of the National Academy of Sciences of the United States of America

4752:"Functional analysis of metagenomes and metatranscriptomes using SEED and KEGG" 4514: 4464: 3324: 2630:

Proceedings of the National Academy of Sciences of the United States of America

2479: 2393:

Proceedings of the National Academy of Sciences of the United States of America

2053: 1958: 1931: 1912: 1857: 1835: 1818: 1787: 1651: 1592: 1339: 1228: 1081: 1048: 1006: 974: 895: 868: 834: 390: 378: 265: 190: 7114: 6513: 6274: 5849: 5796: 5741: 4919: 3848: 3638:

The New Science of Metagenomics: Revealing the Secrets of Our Microbial Planet

2389:"Rapid determination of 16S ribosomal RNA sequences for phylogenetic analyses" 1462:

ability to study the effect of habitat upon community structure and function.

1405: 1392:

released the Metagenomics Rapid Annotation using Subsystem Technology server (

1084:. Three other technologies commonly applied to environmental sampling are the 7861: 7624: 7570: 7534: 7123: 7066: 6892: 6757: 6063: 2330:"Bioinformatics for whole-genome shotgun sequencing of microbial communities" 2063: 2027: 1545: 1376: 1211: 1179:, or 279 billion base pairs of nucleotide sequence data, while the human gut 982: 887: 879: 875: 863: 855: 569: 565: 550: 511: 7731:

Matrix-assisted laser desorption ionization-time of flight mass spectrometer

6926:"A human gut microbial gene catalogue established by metagenomic sequencing" 6861:"A human gut microbial gene catalogue established by metagenomic sequencing" 6697: 5578:"IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses" 5353: 4990: 4868: 4665: 4441:"Microbial abundance, activity and population genomic profiling with mOTUs2" 3946:

Kunin V, Copeland A, Lapidus A, Mavromatis K, Hugenholtz P (December 2008).

3512:"A human gut microbial gene catalogue established by metagenomic sequencing" 3480: 2930: 2822: 2413: 2048: 1577:

profiles of complex communities. Because of the technical difficulties (the

1401: 1227:

There are several assembly programs, most of which can use information from

1096:

system. These techniques for sequencing DNA generate shorter fragments than

7614: 7575: 7396: 7355: 7298: 7247: 7214:"Targeted Metagenomics Offers Insights into Potential Tick-Borne Pathogens" 7198: 7141: 7074: 7027: 6967: 6910: 6716: 6639: 6580: 6531: 6482: 6431: 6362: 6292: 6243: 6208:"An insect herbivore microbiome with high plant biomass-degrading capacity" 6192: 6133: 6114: 6082: 5857: 5804: 5759: 5702: 5694: 5660: 5611: 5554: 5499: 5456: 5421: 5372: 5313: 5272: 5221: 5172: 5125: 5076: 5008: 4938: 4887: 4836: 4787: 4733: 4684: 4633: 4584: 4533: 4482: 4414: 4379: 4330: 4278: 4229: 4180: 4128: 4079: 4046:"Velvet: algorithms for de novo short read assembly using de Bruijn graphs" 4030: 3981: 3923: 3856: 3821: 3772: 3713: 3664: 3608: 3553: 3488: 3442: 3391: 3342: 3245: 3210: 3151: 3091: 3048: 2997: 2978: 2938: 2887: 2854:"Genomic and functional adaptation in surface ocean planktonic prokaryotes" 2830: 2777: 2715: 2669: 2650: 2373: 2272: 2140: 2010: 1839: 1450: 1335: 1319: 930: 918: 527: 483: 460: 255: 6731: 6463: 6306:

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Kerepesi C, Grolmusz V (March 2016). "Giant viruses of the Kutch Desert".

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Huson DH, Mitra S, Ruscheweyh HJ, Weber N, Schuster SC (September 2011).

4012: 1772: 1744: 1707: 1688: 1647: 1643: 1533: 1516: 843: 7425:

The “Critical Assessment of Metagenome Interpretation” (CAMI) initiative

6949: 6884: 6098:"Bioprospecting metagenomes: glycosyl hydrolases for converting biomass" 5942: 5925: 5546: 5520: 5491: 4549:"SLIMM: species level identification of microorganisms from metagenomes" 3636: 3535: 3133: 2878: 2853: 2707: 2286:

Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM (October 1998).

890:

in human stool and possibly a million different viruses per kilogram of

7672: 7619: 7609: 7604: 6562: 6398:"Size Does Matter: Application-driven Approaches for Soil Metagenomics" 4565: 4406: 4361: 4312: 3695: 3590: 2545: 2387:

Lane DJ, Pace B, Olsen GJ, Stahl DA, Sogin ML, Pace NR (October 1985).

1768: 1578: 1528: 1524: 1504: 1496: 1441: 1231:

in order to improve the accuracy of assemblies. Some programs, such as

1193: 1180: 981:. Another early paper in this area appeared in 2006 by Robert Edwards, 946: 788: 519: 478:, early environmental gene sequencing cloned specific genes (often the 437: 421:

and functional potential of the microbial community of the environment.

185: 99: 79: 54: 5674:

Paez-Espino D, Pavlopoulos GA, Ivanova NN, Kyrpides NC (August 2017).

3237: 1478:

inter-microbial interactions between the resident microbial groups. A

1470:

they apply on reads is based on a number of identical words of length

7599: 6096:

Li LL, McCorkle SR, Monchy S, Taghavi S, van der Lelie D (May 2009).

5673: 4392: 2031: 1922: 1918: 1871: 1810: 1764: 1736: 1703: 1508: 1500: 1287: 1012: 954: 800: 142: 6259:"Achievements and new knowledge unraveled by metagenomic approaches" 814:

sequences have been found which do not belong to any known cultured

810:

within a species, and generally different between species. Many 16S

7834: 7461: 5988:

Understanding Our Microbial Planet: The New Science of Metagenomics

5926:"TerraGenome: A consortium for the sequencing of a soil metagenome" 5840: 5726:"New dimensions of the virus world discovered through metagenomics" 4958: 4800: 3677: 3646: 3575:"Differential abundance analysis for microbial marker-gene surveys" 2962:"Using pyrosequencing to shed light on deep mine microbial ecology" 2095:

Wooley JC, Godzik A, Friedberg I (February 2010). Bourne PE (ed.).

1639: 1416: 1371: 1292: 1176: 959: 938: 468: 429: 201: 84: 74: 29: 7371:"Metagenomic arbovirus detection using MinION nanopore sequencing" 6729: 5787: 5235:

Maillet N, Lemaitre C, Chikhi R, Lavenier D, Peterlongo P (2012).

3164: 2959: 1523:). Using comparative gene studies and expression experiments with 1507:

bioreactor, functional stability requires the presence of several

7560: 5724:

Kristensen DM, Mushegian AR, Dolja VV, Koonin EV (January 2010).

5139:

Ghosh TS, Mohammed MH, Rajasingh H, Chadaram S, Mande SS (2011).

3404: 2575:

Stein JL, Marsh TL, Wu KY, Shizuya H, DeLong EF (February 1996).

1917:

Metagenomics can improve strategies for monitoring the impact of

1791: 1783: 1760: 1732: 1724: 1719: 1655: 1520: 1393: 1355: 1343: 1296: 934: 911: 823: 815: 799:

and thus cannot be sequenced. These early studies focused on 16S

7369:

Batovska J, Lynch SE, Rodoni BC, Sawbridge TI, Cogan NO (2017).

7041:

Chua, Physilia Ying Shi; Rasmussen, Jacob Agerbo (11 May 2022).

5575: 4343: 2790: 1128: 6681:"Metagenomics: A viable tool for reconstructing herbivore diet" 2623: 1946: 1807: 1803: 1240: 1236: 1221: 523: 94: 69: 7312:

Batovska J, Mee PT, Lynch SE, Sawbridge TI, Rodoni BC (2019).

6678: 5957: 5723: 5624: 5138: 5092:"Metagenomic signatures of 86 microbial and viral metagenomes" 4852:"A comparative evaluation of sequence classification programs" 4147:"Integrative analysis of environmental sequences using MEGAN4" 3945: 3834: 1495:

In many bacterial communities, natural or engineered (such as

1281:

searches. This type of approach is implemented in the program

6545:

Raes J, Letunic I, Yamada T, Jensen LJ, Bork P (March 2011).

5469: 5234: 4646: 3995:

Namiki T, Hachiya T, Tanaka H, Sakakibara Y (November 2012).

3994: 1752: 1740: 1695: 1627: 1412: 1331: 1282: 1232: 1172: 945:, and completed a two-year expedition in 2006 to explore the 859: 409:. These short sequences can then be put together again using 7424: 7368: 6593: 4547:

Dadi TH, Renard BY, Wieler LH, Semmler T, Reinert K (2017).

1794:

with industrial applications in biofuel production, such as

7430: 7154: 6980: 5326: 4092: 3726: 3115: 2685: 2285: 1728: 1699: 1691: 1667: 1562: 1454: 1420: 1318:

Gene annotations provide the "what", while measurements of

1059:, randomly shears DNA, sequences many short sequences, and 811: 6495: 4972: 4900: 3118:"Computational meta'omics for microbial community studies" 7311: 6146: 5923: 4951: 4438: 4144: 3785: 2851: 2626:"Genomic analysis of uncultured marine viral communities" 1595:

technology, metatranscriptomics studies have made use of

1499:), there is significant division of labor in metabolism ( 1479: 1351: 1347: 803: 792: 370: 64: 59: 4901:

Ounit R, Wanamaker S, Close TJ, Lonardi S (March 2015).

4597: 4495: 4196:"Ab initio gene identification in metagenomic sequences" 4093:

Burton JN, Liachko I, Dunham MJ, Shendure J (May 2014).

3788:"Filtering duplicate reads from 454 pyrosequencing data" 791:

begins with a culture of identical cells as a source of

6544: 6444: 6095: 5385: 4546: 3786:

Balzer S, Malde K, Grohme MA, Jonassen I (April 2013).

3573:

Paulson JN, Stine OC, Bravo HC, Pop M (December 2013).

3298: 3061: 3013:"Metagenomics - a guide from sampling to data analysis" 1239:

Assembler, were designed to be used to assemble single

914:

that had previously resisted attempts to culture them.

6829: 5909:. Vol. 6, no. 7. p. 309. Archived from 5386:

McInerney MJ, Sieber JR, Gunsalus RP (December 2009).

5185: 4749: 4496:

Liu B, Gibbons T, Ghodsi M, Treangen T, Pop M (2011).

2908: 7823: 6205: 4697: 4193: 3405:

Pérez-Cobas AE, Gomez-Valero L, Buchrieser C (2020).

2530: 2094: 1103:

An emerging approach combines shotgun sequencing and

1011:

Recovery of DNA sequences longer than a few thousand

822:

based methods find less than 1% of the bacterial and

6778: 5237:"Compareads: comparing huge metagenomic experiments" 5089: 3572: 2742:"Exploring prokaryotic diversity in the genomic era" 2156: 6395: 4295:Huson DH, Auch AF, Qi J, Schuster SC (March 2007). 4294: 2574: 2208:

Metagenomics: Current Innovations and Future Trends

2157:Hugenholtz P, Goebel BM, Pace NR (September 1998). 1953:with primary goals to determine if there is a core 1406:

Genomic Encyclopedia of Bacteria and Archaea (GEBA)

850:. Considerable efforts ensured that these were not 806:(rRNA) sequences which are relatively short, often 6396:Kakirde KS, Parsley LC, Liles MR (November 2010). 3641:. Washington, D.C.: The National Academies Press. 3458: 3355: 3167:"Unlocking short read sequencing for metagenomics" 3010: 2445: 2386: 1273:with genes that are already publicly available in 941:never before seen. Venter thoroughly explored the 6923: 6857: 6654:"What's Swimming in the River? Just Look For DNA" 6333: 3509: 2478: 1630:in a saline desert and in Antarctic dry valleys. 921:, leader of the privately funded parallel of the 7859: 7260: 5825: 5772: 5285: 3877: 3630: 3628: 3626: 3624: 3622: 3620: 3618: 3356:Hiraoka S, Yang CC, Iwasaki W (September 2016). 1763:. Microbes also produce a variety of sources of 5962:TerraGenome international sequencing consortium 5186:Fimereli D, Detours V, Konopka T (April 2013). 4849: 4434: 4432: 2152: 2150: 2018:sequences with associated clinical phenotypes. 2004: 1937: 6832:Metagenomics: Theory, Methods and Applications 6781:Metagenomics: Theory, Methods and Applications 6391: 6389: 6387: 6336:"Biotechnological prospects from metagenomics" 6329: 6327: 6308:Metagenomics: Theory, Methods and Applications 6022:Metagenomics: Theory, Methods and Applications 5994:. The National Academies Press. Archived from 5980: 5978: 5286:Kuntal BK, Ghosh TS, Mande SS (October 2013). 5035:"Metagenomic analyses: past and future trends" 4540: 3568: 3566: 3011:Thomas T, Gilbert J, Meyer F (February 2012). 1671:understood despite their economic importance. 1111:, and Nanopore MinION, GridION, PromethION by 486:had been missed by cultivation-based methods. 7446: 7362: 7305: 7254: 7205: 7148: 7040: 5463: 5434: 5388:"Syntrophy in anaerobic global carbon cycles" 5379: 5090:Willner D, Thurber RV, Rohwer F (July 2009). 4691: 4194:Zhu W, Lomsadze A, Borodovsky M (July 2010). 4043: 3941: 3939: 3937: 3935: 3933: 3615: 3111: 3109: 2904: 2902: 2900: 2733: 2617: 2279: 1070: 768: 440:. The broad field may also be referred to as 346: 7096:Chiu, Charles Y.; Miller, Steven A. 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7459: 7421:journal website 7409: 7404: 7375:J Virol Methods 7367: 7363: 7310: 7306: 7259: 7255: 7210: 7206: 7153: 7149: 7094: 7090: 7039: 7035: 6994:(6): e1002358. 6979: 6975: 6936:(7285): 59–65. 6922: 6918: 6871:(7285): 59–65. 6856: 6849: 6842: 6828: 6824: 6814: 6812: 6802: 6798: 6791: 6777: 6773: 6728: 6724: 6677: 6673: 6663: 6661: 6652: 6651: 6647: 6592: 6588: 6543: 6539: 6494: 6490: 6443: 6439: 6394: 6385: 6375: 6373: 6372:on 4 March 2016 6369: 6338: 6332: 6325: 6318: 6304: 6300: 6255: 6251: 6218:(9): e1001129. 6204: 6200: 6145: 6141: 6094: 6090: 6043: 6039: 6032: 6018: 6014: 6004: 6002: 5998: 5991: 5983: 5976: 5966: 5964: 5956: 5955: 5951: 5922: 5918: 5899: 5895: 5883: 5877: 5873: 5824: 5820: 5771: 5767: 5722: 5718: 5678: 5672: 5668: 5623: 5619: 5574: 5570: 5519: 5515: 5478:(7104): 806–9. 5468: 5464: 5433: 5429: 5384: 5380: 5339:(10): 4158–63. 5325: 5321: 5284: 5280: 5233: 5229: 5184: 5180: 5137: 5133: 5088: 5084: 5031: 5016: 4971: 4967: 4950: 4946: 4899: 4895: 4848: 4844: 4799: 4795: 4748: 4741: 4696: 4692: 4645: 4641: 4596: 4592: 4545: 4541: 4508:(Suppl 2): S4. 4494: 4490: 4437: 4430: 4391: 4387: 4342: 4338: 4301:Genome Research 4293: 4286: 4255:(11): 1223–30. 4241: 4237: 4192: 4188: 4151:Genome Research 4143: 4136: 4091: 4087: 4050:Genome Research 4042: 4038: 3993: 3989: 3944: 3931: 3876: 3872: 3833: 3829: 3784: 3780: 3725: 3721: 3676: 3672: 3657: 3633: 3616: 3571: 3564: 3558: 3522:(7285): 59–65. 3508: 3504: 3467:(6016): 463–7. 3457: 3450: 3403: 3399: 3354: 3350: 3297: 3293: 3266: 3265: 3261: 3222: 3218: 3163: 3159: 3114: 3107: 3060: 3056: 3009: 3005: 2958: 2954: 2917:(5759): 392–4. 2907: 2898: 2892: 2850: 2846: 2814:10.1.1.124.1840 2799:(5667): 66–74. 2789: 2785: 2738: 2734: 2728: 2694:(6978): 37–43. 2684: 2677: 2636:(22): 14250–5. 2622: 2618: 2573: 2569: 2529: 2525: 2477: 2473: 2466: 2444: 2440: 2385: 2381: 2326: 2322: 2284: 2280: 2233: 2229: 2222: 2204: 2200: 2169:(18): 4765–74. 2155: 2148: 2107:(2): e1000667. 2093: 2076: 2072: 2040: 2024: 2007: 1940: 1928:bioaugmentation 1915: 1909: 1880: 1844:pharmaceuticals 1827: 1819:leafcutter ants 1722: 1716: 1664: 1636: 1611: 1605: 1579:short half-life 1558: 1552: 1542: 1493: 1488: 1437: 1368: 1316: 1310: 1285:4. The second, 1263: 1261:Gene prediction 1257: 1255:Gene prediction 1229:paired-end tags 1208: 1202: 1190: 1157: 1151: 1148: 1141: 1132: 1121: 1073: 1045: 1009: 995: 892:marine sediment 781: 740: 733: 724:Diet assessment 715: 703: 689: 673: 664: 648: 638: 622: 574: 572: 564: 540: 508: 359: 318: 311: 310: 301: 293: 292: 291: 290: 239: 231: 230: 222: 200: 181: 173: 172: 128: 120: 119: 106: 105: 104: 48: 17: 12: 11: 5: 7901: 7891: 7890: 7885: 7880: 7875: 7873:Bioinformatics 7870: 7855: 7854: 7842: 7819: 7818: 7816: 7815: 7803: 7790: 7787: 7786: 7784: 7783: 7777: 7771: 7765: 7758: 7756: 7752: 7751: 7749: 7748: 7743: 7738: 7733: 7728: 7723: 7718: 7713: 7707: 7705: 7704:Research tools 7701: 7700: 7698: 7697: 7692: 7687: 7682: 7681: 7680: 7669: 7667: 7661: 7660: 7658: 7657: 7652: 7650:Toxicogenomics 7647: 7642: 7637: 7632: 7627: 7622: 7617: 7612: 7607: 7602: 7597: 7596: 7595: 7585: 7584: 7583: 7573: 7568: 7563: 7557: 7555: 7553:Bioinformatics 7549: 7548: 7546: 7545: 7540: 7532: 7527: 7522: 7517: 7516: 7515: 7505: 7504: 7503: 7496:Genome project 7493: 7488: 7483: 7478: 7472: 7470: 7466: 7465: 7458: 7457: 7450: 7443: 7435: 7429: 7428: 7422: 7408: 7407:External links 7405: 7403: 7402: 7361: 7304: 7267:Microorganisms 7253: 7204: 7147: 7108:(6): 341–355. 7088: 7033: 6973: 6916: 6847: 6840: 6822: 6810:New York Times 6796: 6789: 6771: 6722: 6671: 6660:. 24 July 2013 6645: 6586: 6537: 6508:(4): 415–422. 6488: 6437: 6383: 6323: 6316: 6298: 6249: 6198: 6139: 6088: 6037: 6030: 6012: 5974: 5949: 5916: 5893: 5871: 5818: 5765: 5716: 5666: 5617: 5568: 5513: 5462: 5427: 5378: 5319: 5278: 5227: 5178: 5131: 5102:(7): 1752–66. 5082: 5045:(4): 1153–61. 5014: 4965: 4959:10.1101/267179 4944: 4893: 4842: 4793: 4739: 4690: 4639: 4590: 4539: 4488: 4428: 4401:(12): 1196–9. 4395:Nature Methods 4385: 4350:Nature Methods 4336: 4284: 4235: 4186: 4157:(9): 1552–60. 4134: 4105:(7): 1339–46. 4085: 4036: 3987: 3929: 3870: 3827: 3792:Bioinformatics 3778: 3719: 3670: 3655: 3647:10.17226/11902 3614: 3585:(12): 1200–2. 3579:Nature Methods 3562: 3502: 3448: 3397: 3348: 3291: 3272:Nature Methods 3259: 3226:Nature Methods 3216: 3157: 3105: 3054: 3003: 2952: 2896: 2864:(7320): 60–6. 2844: 2783: 2746:Genome Biology 2732: 2675: 2616: 2567: 2523: 2494:(14): 4371–8. 2471: 2464: 2438: 2399:(20): 6955–9. 2379: 2320: 2298:(10): R245-9. 2278: 2227: 2220: 2198: 2146: 2073: 2071: 2068: 2067: 2066: 2061: 2056: 2054:Metaproteomics 2051: 2046: 2039: 2036: 2023: 2020: 2006: 2003: 1959:bioinformatics 1939: 1936: 1932:biostimulation 1913:Bioremediation 1911:Main article: 1908: 1905: 1879: 1876: 1858:bioprospecting 1836:fine chemicals 1826: 1823: 1806:fermenters or 1718:Main article: 1715: 1712: 1663: 1660: 1652:sustainability 1635: 1632: 1607:Main article: 1604: 1601: 1554:Main article: 1541: 1538: 1492: 1489: 1487: 1484: 1436: 1433: 1367: 1364: 1352:mOTUs profiler 1312:Main article: 1309: 1306: 1259:Main article: 1256: 1253: 1204:Main article: 1201: 1198: 1189: 1186: 1159: 1158: 1135: 1133: 1126: 1120: 1119:Bioinformatics 1117: 1072: 1069: 1049:bioinformatics 1044: 1041: 1007:DNA sequencing 1005:Main article: 994: 991: 975:pyrosequencing 925:, has led the 896:bacteriophages 835:Norman R. Pace 831:molecular work 783: 782: 780: 779: 772: 765: 757: 754: 753: 752: 751: 735: 734: 732: 731: 726: 721: 716: 710: 704: 702: 701: 696: 690: 688: 687: 686: 685: 674: 672: 671: 665: 663: 662: 661: 660: 649: 647: 646: 645: 644: 632: 629: 628: 624: 623: 621: 620: 619: 618: 613: 605: 600: 595: 590: 584: 581: 580: 576: 575: 562: 554: 553: 547: 546: 539: 536: 507: 504: 463:and microbial 361: 360: 358: 357: 350: 343: 335: 332: 331: 330: 329: 313: 312: 309: 308: 302: 299: 298: 295: 294: 289: 288: 283: 278: 273: 268: 266:Immunogenetics 263: 258: 253: 248: 242: 241: 240: 237: 236: 233: 232: 229: 228: 221: 220: 215: 198: 193: 191:DNA sequencing 188: 182: 179: 178: 175: 174: 171: 170: 165: 160: 155: 150: 140: 135: 129: 126: 125: 122: 121: 118: 117: 112: 103: 102: 97: 92: 87: 82: 77: 72: 67: 62: 57: 51: 50: 49: 47:Key components 46: 45: 42: 41: 33: 32: 26: 25: 15: 9: 6: 4: 3: 2: 7900: 7889: 7886: 7884: 7881: 7879: 7876: 7874: 7871: 7869: 7866: 7865: 7863: 7853: 7843: 7841: 7836: 7831: 7830: 7827: 7814: 7813: 7804: 7802: 7801: 7792: 7791: 7788: 7781: 7778: 7775: 7772: 7769: 7766: 7763: 7760: 7759: 7757: 7755:Organizations 7753: 7747: 7744: 7742: 7739: 7737: 7734: 7732: 7729: 7727: 7724: 7722: 7719: 7717: 7714: 7712: 7709: 7708: 7706: 7702: 7696: 7693: 7691: 7688: 7686: 7683: 7679: 7676: 7675: 7674: 7671: 7670: 7668: 7666: 7662: 7656: 7653: 7651: 7648: 7646: 7643: 7641: 7638: 7636: 7633: 7631: 7628: 7626: 7625:Nutrigenomics 7623: 7621: 7618: 7616: 7613: 7611: 7608: 7606: 7603: 7601: 7598: 7594: 7591: 7590: 7589: 7586: 7582: 7579: 7578: 7577: 7574: 7572: 7571:Chemogenomics 7569: 7567: 7564: 7562: 7559: 7558: 7556: 7554: 7550: 7544: 7541: 7539: 7537: 7533: 7531: 7528: 7526: 7523: 7521: 7518: 7514: 7511: 7510: 7509: 7506: 7502: 7499: 7498: 7497: 7494: 7492: 7489: 7487: 7484: 7482: 7479: 7477: 7474: 7473: 7471: 7467: 7463: 7456: 7451: 7449: 7444: 7442: 7437: 7436: 7433: 7426: 7423: 7420: 7419: 7414: 7411: 7410: 7398: 7394: 7389: 7384: 7380: 7376: 7372: 7365: 7357: 7353: 7348: 7343: 7339: 7335: 7331: 7327: 7323: 7319: 7315: 7308: 7300: 7296: 7291: 7286: 7281: 7276: 7272: 7268: 7264: 7257: 7249: 7245: 7240: 7235: 7231: 7227: 7223: 7219: 7215: 7208: 7200: 7196: 7191: 7186: 7182: 7178: 7174: 7170: 7166: 7162: 7158: 7151: 7143: 7139: 7134: 7129: 7125: 7121: 7116: 7111: 7107: 7103: 7099: 7092: 7084: 7080: 7076: 7072: 7068: 7064: 7060: 7056: 7052: 7048: 7044: 7037: 7029: 7025: 7020: 7015: 7010: 7005: 7001: 6997: 6993: 6989: 6985: 6977: 6969: 6965: 6960: 6955: 6951: 6947: 6943: 6939: 6935: 6931: 6927: 6920: 6912: 6908: 6903: 6898: 6894: 6890: 6886: 6882: 6878: 6874: 6870: 6866: 6862: 6854: 6852: 6843: 6837: 6833: 6826: 6811: 6807: 6800: 6792: 6786: 6782: 6775: 6767: 6763: 6759: 6755: 6750: 6745: 6741: 6737: 6733: 6726: 6718: 6714: 6709: 6704: 6699: 6694: 6690: 6686: 6682: 6675: 6659: 6655: 6649: 6641: 6637: 6632: 6627: 6622: 6617: 6613: 6609: 6606:(5): e36478. 6605: 6601: 6597: 6590: 6582: 6578: 6573: 6568: 6564: 6560: 6556: 6552: 6548: 6541: 6533: 6529: 6524: 6519: 6515: 6511: 6507: 6503: 6499: 6492: 6484: 6480: 6475: 6470: 6465: 6460: 6456: 6452: 6448: 6441: 6433: 6429: 6424: 6419: 6415: 6411: 6407: 6403: 6399: 6392: 6390: 6388: 6368: 6364: 6360: 6356: 6352: 6349:(3): 303–10. 6348: 6344: 6337: 6330: 6328: 6319: 6313: 6309: 6302: 6294: 6290: 6285: 6280: 6276: 6272: 6269:(2): 265–76. 6268: 6264: 6260: 6253: 6245: 6241: 6236: 6231: 6226: 6221: 6217: 6213: 6212:PLOS Genetics 6209: 6202: 6194: 6190: 6185: 6180: 6175: 6170: 6166: 6162: 6159:(1): e14519. 6158: 6154: 6150: 6143: 6135: 6131: 6126: 6121: 6116: 6111: 6107: 6103: 6099: 6092: 6084: 6080: 6075: 6070: 6065: 6060: 6056: 6052: 6048: 6041: 6033: 6027: 6023: 6016: 5997: 5990: 5989: 5981: 5979: 5963: 5959: 5953: 5944: 5939: 5935: 5931: 5927: 5920: 5912: 5908: 5904: 5897: 5889: 5882: 5875: 5867: 5863: 5859: 5855: 5851: 5847: 5842: 5837: 5833: 5829: 5822: 5814: 5810: 5806: 5802: 5798: 5794: 5789: 5784: 5780: 5776: 5769: 5761: 5757: 5752: 5747: 5743: 5739: 5735: 5731: 5727: 5720: 5712: 5708: 5704: 5700: 5696: 5692: 5688: 5684: 5677: 5670: 5662: 5658: 5653: 5648: 5644: 5640: 5636: 5632: 5628: 5621: 5613: 5609: 5604: 5599: 5595: 5591: 5587: 5583: 5579: 5572: 5564: 5560: 5556: 5552: 5548: 5544: 5540: 5536: 5532: 5528: 5524: 5517: 5509: 5505: 5501: 5497: 5493: 5489: 5485: 5481: 5477: 5473: 5466: 5458: 5454: 5450: 5446: 5442: 5438: 5431: 5423: 5419: 5414: 5409: 5405: 5401: 5398:(6): 623–32. 5397: 5393: 5389: 5382: 5374: 5370: 5365: 5360: 5355: 5350: 5346: 5342: 5338: 5334: 5330: 5323: 5315: 5311: 5306: 5301: 5298:(4): 409–18. 5297: 5293: 5289: 5282: 5274: 5270: 5265: 5260: 5255: 5250: 5246: 5242: 5238: 5231: 5223: 5219: 5214: 5209: 5205: 5201: 5197: 5193: 5189: 5182: 5174: 5170: 5165: 5160: 5155: 5150: 5146: 5142: 5135: 5127: 5123: 5118: 5113: 5109: 5105: 5101: 5097: 5093: 5086: 5078: 5074: 5069: 5064: 5060: 5056: 5052: 5048: 5044: 5040: 5036: 5029: 5027: 5025: 5023: 5021: 5019: 5010: 5006: 5001: 4996: 4992: 4988: 4985:(4): 169–81. 4984: 4980: 4976: 4969: 4960: 4955: 4948: 4940: 4936: 4931: 4926: 4921: 4916: 4912: 4908: 4904: 4897: 4889: 4885: 4880: 4875: 4870: 4865: 4861: 4857: 4853: 4846: 4838: 4834: 4829: 4824: 4820: 4816: 4812: 4808: 4804: 4797: 4789: 4785: 4780: 4775: 4770: 4765: 4761: 4757: 4753: 4746: 4744: 4735: 4731: 4726: 4721: 4717: 4713: 4709: 4705: 4701: 4694: 4686: 4682: 4677: 4672: 4667: 4662: 4658: 4654: 4650: 4643: 4635: 4631: 4626: 4621: 4617: 4613: 4609: 4605: 4601: 4594: 4586: 4582: 4577: 4572: 4567: 4562: 4558: 4554: 4550: 4543: 4535: 4531: 4526: 4521: 4516: 4511: 4507: 4503: 4499: 4492: 4484: 4480: 4475: 4470: 4466: 4462: 4458: 4454: 4450: 4446: 4442: 4435: 4433: 4424: 4420: 4416: 4412: 4408: 4404: 4400: 4396: 4389: 4381: 4377: 4372: 4367: 4363: 4359: 4355: 4351: 4347: 4340: 4332: 4328: 4323: 4318: 4314: 4310: 4307:(3): 377–86. 4306: 4302: 4298: 4291: 4289: 4280: 4276: 4271: 4266: 4262: 4258: 4254: 4250: 4246: 4239: 4231: 4227: 4222: 4217: 4213: 4209: 4205: 4201: 4197: 4190: 4182: 4178: 4173: 4168: 4164: 4160: 4156: 4152: 4148: 4141: 4139: 4130: 4126: 4121: 4116: 4112: 4108: 4104: 4100: 4096: 4089: 4081: 4077: 4072: 4067: 4063: 4059: 4055: 4051: 4047: 4040: 4032: 4028: 4023: 4018: 4014: 4010: 4006: 4002: 3998: 3991: 3983: 3979: 3974: 3969: 3965: 3961: 3957: 3953: 3949: 3942: 3940: 3938: 3936: 3934: 3925: 3921: 3916: 3911: 3906: 3901: 3897: 3893: 3890:(3): e17288. 3889: 3885: 3881: 3874: 3866: 3862: 3858: 3854: 3850: 3846: 3843:(4): 709–17. 3842: 3838: 3831: 3823: 3819: 3814: 3809: 3805: 3801: 3797: 3793: 3789: 3782: 3774: 3770: 3765: 3760: 3755: 3750: 3746: 3742: 3739:(2): e31386. 3738: 3734: 3730: 3723: 3715: 3711: 3706: 3701: 3697: 3693: 3689: 3685: 3681: 3674: 3666: 3662: 3658: 3652: 3648: 3644: 3640: 3639: 3631: 3629: 3627: 3625: 3623: 3621: 3619: 3610: 3606: 3601: 3596: 3592: 3588: 3584: 3580: 3576: 3569: 3567: 3555: 3551: 3546: 3541: 3537: 3533: 3529: 3525: 3521: 3517: 3513: 3506: 3498: 3494: 3490: 3486: 3482: 3478: 3474: 3470: 3466: 3462: 3455: 3453: 3444: 3440: 3435: 3430: 3425: 3420: 3416: 3412: 3408: 3401: 3393: 3389: 3384: 3379: 3375: 3371: 3368:(3): 204–12. 3367: 3363: 3359: 3352: 3344: 3340: 3335: 3330: 3326: 3322: 3318: 3314: 3310: 3306: 3302: 3295: 3286: 3281: 3277: 3273: 3269: 3263: 3255: 3251: 3247: 3243: 3239: 3235: 3231: 3227: 3220: 3212: 3208: 3203: 3198: 3193: 3188: 3184: 3180: 3177:(7): e11840. 3176: 3172: 3168: 3161: 3153: 3149: 3144: 3139: 3135: 3131: 3127: 3123: 3119: 3112: 3110: 3101: 3097: 3093: 3089: 3085: 3081: 3077: 3073: 3070:(5): 516–29. 3069: 3065: 3058: 3050: 3046: 3041: 3036: 3031: 3026: 3022: 3018: 3014: 3007: 2999: 2995: 2990: 2985: 2980: 2975: 2971: 2967: 2963: 2956: 2948: 2944: 2940: 2936: 2932: 2928: 2924: 2920: 2916: 2912: 2905: 2903: 2901: 2889: 2885: 2880: 2875: 2871: 2867: 2863: 2859: 2855: 2848: 2840: 2836: 2832: 2828: 2824: 2820: 2815: 2810: 2806: 2802: 2798: 2794: 2787: 2779: 2775: 2770: 2765: 2760: 2755: 2751: 2747: 2743: 2736: 2725: 2721: 2717: 2713: 2709: 2705: 2701: 2697: 2693: 2689: 2682: 2680: 2671: 2667: 2662: 2657: 2652: 2647: 2643: 2639: 2635: 2631: 2627: 2620: 2612: 2608: 2603: 2598: 2594: 2590: 2586: 2582: 2578: 2571: 2563: 2559: 2555: 2551: 2547: 2543: 2540:(4): 667–74. 2539: 2535: 2527: 2519: 2515: 2510: 2505: 2501: 2497: 2493: 2489: 2485: 2481: 2475: 2467: 2461: 2457: 2453: 2449: 2442: 2434: 2430: 2425: 2420: 2415: 2410: 2406: 2402: 2398: 2394: 2390: 2383: 2375: 2371: 2366: 2361: 2356: 2351: 2347: 2343: 2340:(2): 106–12. 2339: 2335: 2331: 2324: 2315: 2311: 2306: 2301: 2297: 2293: 2289: 2282: 2274: 2270: 2265: 2260: 2255: 2250: 2246: 2242: 2238: 2231: 2223: 2217: 2213: 2209: 2202: 2194: 2190: 2185: 2180: 2176: 2172: 2168: 2164: 2160: 2153: 2151: 2142: 2138: 2133: 2128: 2123: 2118: 2114: 2110: 2106: 2102: 2098: 2091: 2089: 2087: 2085: 2083: 2081: 2079: 2074: 2065: 2064:Pathogenomics 2062: 2060: 2057: 2055: 2052: 2050: 2047: 2045: 2042: 2041: 2035: 2033: 2029: 2028:hematophagous 2019: 2016: 2012: 2002: 1998: 1996: 1992: 1987: 1983: 1979: 1974: 1970: 1966: 1962: 1960: 1956: 1952: 1948: 1944: 1935: 1933: 1929: 1924: 1920: 1914: 1904: 1902: 1898: 1892: 1884: 1875: 1874:antibiotics. 1873: 1868: 1864: 1859: 1854: 1852: 1849: 1845: 1841: 1840:agrochemicals 1837: 1833: 1825:Biotechnology 1822: 1820: 1816: 1815:fungus garden 1812: 1809: 1805: 1801: 1797: 1793: 1789: 1785: 1781: 1776: 1774: 1770: 1766: 1762: 1758: 1754: 1750: 1746: 1742: 1739:contained in 1738: 1734: 1731:derived from 1730: 1726: 1721: 1711: 1709: 1705: 1701: 1697: 1693: 1690: 1686: 1682: 1678: 1674: 1669: 1659: 1657: 1653: 1649: 1645: 1641: 1631: 1629: 1628:giant viruses 1625: 1620: 1616: 1610: 1600: 1598: 1594: 1590: 1589: 1584: 1580: 1576: 1572: 1568: 1564: 1557: 1551: 1547: 1546:Transcriptome 1537: 1535: 1530: 1526: 1522: 1518: 1514: 1510: 1506: 1502: 1498: 1486:Data analysis 1483: 1481: 1475: 1473: 1469: 1463: 1460: 1456: 1452: 1447: 1443: 1432: 1428: 1424: 1422: 1418: 1414: 1409: 1407: 1403: 1399: 1395: 1391: 1387: 1381: 1378: 1377:replicability 1373: 1363: 1361: 1357: 1353: 1349: 1345: 1341: 1337: 1333: 1329: 1325: 1321: 1315: 1305: 1302: 1298: 1294: 1290: 1289: 1284: 1280: 1277:, usually by 1276: 1272: 1268: 1262: 1252: 1250: 1246: 1242: 1238: 1234: 1230: 1225: 1223: 1218: 1213: 1207: 1197: 1195: 1185: 1182: 1178: 1174: 1165: 1155: 1152:February 2022 1145: 1139: 1136:This section 1134: 1130: 1125: 1124: 1116: 1114: 1110: 1106: 1101: 1099: 1095: 1091: 1087: 1083: 1079: 1068: 1066: 1062: 1058: 1054: 1050: 1040: 1038: 1034: 1030: 1026: 1022: 1018: 1014: 1008: 999: 990: 988: 984: 983:Forest Rohwer 980: 977:developed by 976: 972: 968: 963: 961: 956: 952: 951:Mediterranean 948: 944: 940: 936: 932: 928: 924: 920: 915: 913: 909: 905: 901: 897: 893: 889: 888:viral species 885: 881: 880:Forest Rohwer 877: 876:Mya Breitbart 872: 870: 865: 864:Edward DeLong 861: 857: 853: 849: 845: 840: 836: 832: 827: 825: 821: 817: 813: 809: 805: 802: 798: 794: 790: 787:Conventional 778: 773: 771: 766: 764: 759: 758: 756: 755: 749: 739: 738: 737: 736: 730: 727: 725: 722: 720: 717: 714: 711: 709: 706: 705: 700: 697: 695: 692: 691: 684: 681: 680: 679: 678:Amplification 676: 675: 670: 667: 666: 659: 656: 655: 654: 651: 650: 643: 640: 639: 637: 634: 633: 631: 630: 626: 625: 617: 614: 612: 609: 608: 606: 604: 601: 599: 596: 594: 591: 589: 586: 585: 583: 582: 578: 577: 571: 570:Metabarcoding 567: 566:DNA barcoding 560: 556: 555: 552: 551:DNA barcoding 549: 548: 544: 543: 535: 533: 529: 525: 521: 517: 513: 512:Jo Handelsman 503: 501: 497: 493: 487: 485: 481: 477: 474: 470: 466: 462: 457: 455: 451: 447: 443: 439: 435: 434:environmental 431: 427: 420: 416: 412: 408: 404: 400: 396: 392: 388: 384: 380: 376: 372: 367: 356: 351: 349: 344: 342: 337: 336: 334: 333: 327: 317: 316: 315: 314: 307: 304: 303: 297: 296: 287: 284: 282: 279: 277: 274: 272: 269: 267: 264: 262: 259: 257: 254: 252: 249: 247: 244: 243: 235: 234: 227: 224: 223: 219: 216: 212: 203: 199: 197: 194: 192: 189: 187: 184: 183: 177: 176: 169: 166: 164: 161: 159: 156: 154: 151: 148: 144: 141: 139: 136: 134: 131: 130: 124: 123: 116: 113: 111: 108: 107: 101: 98: 96: 93: 91: 88: 86: 83: 81: 78: 76: 73: 71: 68: 66: 63: 61: 58: 56: 53: 52: 44: 43: 39: 35: 34: 31: 28: 27: 23: 22: 19: 7868:Metagenomics 7810: 7798: 7620:Microbiomics 7615:Metabolomics 7576:Connectomics 7535: 7508:Metagenomics 7507: 7416: 7378: 7374: 7364: 7324:(1): 19398. 7321: 7317: 7307: 7270: 7266: 7256: 7221: 7217: 7207: 7164: 7160: 7150: 7105: 7101: 7091: 7050: 7046: 7036: 6991: 6987: 6976: 6933: 6929: 6919: 6868: 6864: 6831: 6825: 6813:. Retrieved 6809: 6799: 6780: 6774: 6739: 6735: 6725: 6688: 6684: 6674: 6662:. Retrieved 6657: 6648: 6603: 6599: 6589: 6554: 6550: 6540: 6505: 6501: 6491: 6454: 6450: 6440: 6405: 6401: 6374:. Retrieved 6367:the original 6346: 6342: 6307: 6301: 6266: 6262: 6252: 6215: 6211: 6201: 6156: 6152: 6142: 6105: 6101: 6091: 6054: 6050: 6040: 6021: 6015: 6003:. Retrieved 5996:the original 5987: 5965:. Retrieved 5961: 5952: 5933: 5929: 5919: 5911:the original 5906: 5896: 5887: 5874: 5831: 5827: 5821: 5781:(3): 721–4. 5778: 5774: 5768: 5733: 5729: 5719: 5686: 5682: 5669: 5634: 5630: 5620: 5585: 5581: 5571: 5530: 5526: 5516: 5475: 5471: 5465: 5443:(4): 541–6. 5440: 5436: 5430: 5395: 5391: 5381: 5336: 5332: 5322: 5295: 5291: 5281: 5244: 5240: 5230: 5195: 5191: 5181: 5144: 5134: 5099: 5095: 5085: 5042: 5038: 4982: 4979:DNA Research 4978: 4968: 4947: 4910: 4907:BMC Genomics 4906: 4896: 4859: 4855: 4845: 4810: 4806: 4796: 4759: 4755: 4707: 4703: 4693: 4656: 4652: 4642: 4607: 4603: 4593: 4556: 4552: 4542: 4505: 4502:BMC Genomics 4501: 4491: 4448: 4444: 4398: 4394: 4388: 4356:(8): 811–4. 4353: 4349: 4339: 4304: 4300: 4252: 4248: 4238: 4206:(12): e132. 4203: 4199: 4189: 4154: 4150: 4102: 4098: 4088: 4056:(5): 821–9. 4053: 4049: 4039: 4007:(20): e155. 4004: 4000: 3990: 3955: 3951: 3887: 3883: 3873: 3840: 3836: 3830: 3798:(7): 830–6. 3795: 3791: 3781: 3736: 3732: 3722: 3687: 3683: 3673: 3637: 3582: 3578: 3519: 3515: 3505: 3464: 3460: 3414: 3410: 3400: 3365: 3361: 3351: 3308: 3304: 3294: 3275: 3271: 3262: 3229: 3225: 3219: 3174: 3170: 3160: 3128:(666): 666. 3125: 3121: 3067: 3063: 3057: 3020: 3016: 3006: 2969: 2966:BMC Genomics 2965: 2955: 2914: 2910: 2861: 2857: 2847: 2796: 2792: 2786: 2749: 2745: 2735: 2691: 2687: 2633: 2629: 2619: 2587:(3): 591–9. 2584: 2580: 2570: 2537: 2533: 2526: 2491: 2487: 2474: 2447: 2441: 2396: 2392: 2382: 2337: 2333: 2323: 2295: 2291: 2281: 2244: 2241:PLOS Biology 2240: 2230: 2207: 2201: 2166: 2162: 2104: 2100: 2025: 2011:encephalitis 2008: 1999: 1975: 1971: 1967: 1963: 1941: 1916: 1893: 1889: 1855: 1828: 1813:such as the 1777: 1757:fermentation 1723: 1665: 1637: 1634:Applications 1612: 1586: 1566: 1559: 1534:metabolomics 1505:methanogenic 1494: 1476: 1471: 1464: 1458: 1438: 1429: 1425: 1410: 1382: 1369: 1317: 1300: 1286: 1264: 1226: 1209: 1191: 1170: 1149: 1137: 1102: 1074: 1063:them into a 1061:reconstructs 1057:human genome 1047:Advances in 1046: 1010: 964: 931:Sargasso Sea 919:Craig Venter 916: 873: 828: 786: 653:Metagenomics 652: 528:Lior Pachter 509: 488: 461:microbiology 458: 454:microbiomics 453: 449: 445: 441: 426:Metagenomics 425: 424: 414: 402: 394: 386: 382: 374: 286:Quantitative 256:Cytogenetics 251:Conservation 133:Introduction 18: 7588:Epigenomics 7520:Pangenomics 7273:(8): 1653. 7167:(1): 4690. 6815:29 December 6005:30 December 5967:30 December 5736:(1): 11–9. 4451:(1): 1014. 3232:(1): 16–8. 2032:arboviruses 1745:switchgrass 1662:Agriculture 1648:agriculture 1644:engineering 1532:tool (with 1525:microarrays 1517:Synergistia 1497:bioreactors 820:cultivation 446:ecogenomics 389:), and are 7862:Categories 7673:Proteomics 7610:Lipidomics 7605:Immunomics 7053:(8): 447. 6742:: e78756. 6664:10 October 6376:20 January 5936:(4): 252. 5841:1503.05575 5198:(7): e86. 4913:(1): 236. 3311:(1): 870. 2480:Schmidt TM 2247:(3): e82. 2070:References 1923:ecosystems 1919:pollutants 1811:herbivores 1800:convergent 1767:including 1694:and other 1593:microarray 1575:expression 1571:regulation 1529:proteomics 1509:syntrophic 1442:GC-content 1194:eukaryotic 1181:microbiome 1013:base pairs 993:Sequencing 789:sequencing 719:Healthcare 520:Jon Clardy 438:sequencing 281:Population 261:Ecological 186:Geneticist 100:Amino acid 80:Nucleotide 55:Chromosome 7600:Glycomics 7381:: 79–84. 7124:1471-0064 7083:248739527 7067:1740-1534 6893:1476-4687 6766:248041252 6758:2534-9708 5788:1410.1278 4803:"GenBank" 4559:: e3138. 3690:: 75–88. 2809:CiteSeerX 1872:malacidin 1832:commodity 1788:screening 1765:bioenergy 1737:cellulose 1704:livestock 1689:sequester 1511:species ( 1501:syntrophy 1459:community 1408:project. 1340:MetaPhlAn 1301:ab initio 1288:ab initio 1267:pipelines 1177:gigabases 1144:talk page 1025:libraries 874:In 2002, 871:samples. 808:conserved 801:ribosomal 588:Microbial 506:Etymology 391:sequenced 379:extracted 276:Molecular 271:Microbial 246:Classical 147:molecular 143:Evolution 7878:Genomics 7852:Medicine 7812:Category 7538:genomics 7462:Genomics 7397:28855093 7356:31852942 7299:34442732 7248:32878948 7199:29549363 7142:30918369 7075:35546350 7028:22719234 6968:20203603 6911:20203603 6717:33971086 6640:22606263 6600:PLOS ONE 6581:21407210 6532:29434326 6483:21545702 6457:(1): 9. 6432:21076656 6363:12849784 6293:19760178 6244:20885794 6193:21297863 6153:PLOS ONE 6134:19450243 6083:26052316 5890:: 21–26. 5858:28247094 5813:13145926 5805:26666442 5760:19942437 5703:28749930 5661:30407573 5612:27799466 5555:27533034 5500:16915287 5457:21592777 5422:19897353 5373:21368115 5314:23978768 5292:Genomics 5273:23282463 5222:23408855 5173:22373355 5126:19302541 5077:21169428 5009:17916580 4939:25879410 4888:22574964 4837:23193287 4788:21342551 4734:22086953 4685:18803844 4634:22135293 4585:28367376 4534:21989143 4483:30833550 4415:24141494 4380:22688413 4331:17255551 4279:19657372 4230:20403810 4181:21690186 4129:24855317 4080:18349386 4031:22821567 3982:19052320 3924:21408061 3884:PLOS ONE 3865:25857874 3857:21857117 3822:23376350 3773:22384016 3733:PLOS ONE 3714:25983555 3665:21678629 3609:24076764 3554:20203603 3497:36572885 3489:21273488 3443:32706331 3392:27383682 3343:29491419 3246:18165802 3211:20676378 3171:PLOS ONE 3152:23670539 3092:11233160 3049:22587947 3023:(1): 3. 2998:16549033 2947:11238470 2939:16368896 2888:21048761 2831:15001713 2778:11864374 2716:14961025 2670:12384570 2562:31384119 2374:16110337 2273:17355177 2141:20195499 2038:See also 1773:hydrogen 1743:stalks, 1725:Biofuels 1640:medicine 1446:16S rRNA 1388:and the 1372:metadata 1293:GeneMark 1271:homology 1212:coverage 1200:Assembly 1090:Illumina 960:plankton 939:bacteria 902:and the 824:archaeal 797:cultured 748:Category 607:Aquatic 480:16S rRNA 476:cultures 469:genomics 326:Category 211:template 202:Genomics 180:Research 85:Mutation 75:Heredity 30:Genetics 7840:Biology 7826:Portals 7561:Biochip 7347:6920425 7326:Bibcode 7318:Sci Rep 7290:8398489 7239:7587107 7190:5856816 7169:Bibcode 7161:Sci Rep 7133:6858796 7019:3374609 6996:Bibcode 6959:3779803 6938:Bibcode 6902:3779803 6873:Bibcode 6708:8518049 6658:NPR.org 6631:3350522 6608:Bibcode 6572:3094067 6557:: 473. 6523:5874163 6474:3113934 6423:2976544 6284:2773367 6235:2944797 6184:3027613 6161:Bibcode 6125:2694162 6074:4440916 6057:: 486. 5907:Microbe 5866:1925728 5751:3293453 5711:2127494 5652:6323928 5603:5210529 5563:4466854 5535:Bibcode 5508:4380804 5480:Bibcode 5413:2790021 5364:3053989 5341:Bibcode 5264:3526429 5213:3627586 5164:3278849 5104:Bibcode 5068:3067235 5047:Bibcode 5000:2533590 4954:bioRxiv 4930:4428112 4879:3428669 4828:3531190 4779:3044276 4725:3245048 4676:2563014 4659:: 386. 4625:3245063 4576:5372838 4525:3194235 4474:6399450 4453:Bibcode 4423:7728395 4371:3443552 4322:1800929 4257:Bibcode 4221:2896542 4172:3166839 4120:4455782 4071:2336801 4022:3488206 3973:2593568 3915:3052304 3892:Bibcode 3813:3605598 3764:3285633 3741:Bibcode 3705:4426941 3600:4010126 3545:3779803 3524:Bibcode 3469:Bibcode 3461:Science 3434:7641418 3383:5017796 3334:5830445 3313:Bibcode 3254:1465786 3202:2911387 3179:Bibcode 3143:4039370 3100:8267748 3072:Bibcode 3040:3351745 2989:1483832 2919:Bibcode 2911:Science 2866:Bibcode 2839:1454587 2801:Bibcode 2793:Science 2724:4420754 2696:Bibcode 2638:Bibcode 2611:8550487 2554:7546604 2518:2066334 2433:2413450 2401:Bibcode 2365:1185649 2342:Bibcode 2314:9818143 2264:1821061 2193:9733676 2132:2829047 2109:Bibcode 2044:Binning 1976:In the 1878:Ecology 1817:of the 1792:enzymes 1784:enzymes 1769:methane 1761:ethanol 1733:biomass 1720:Biofuel 1714:Biofuel 1708:farming 1656:ecology 1619:18S RNA 1615:16S RNA 1603:Viruses 1588:in situ 1521:methane 1394:MG-RAST 1375:ensure 1344:AMPHORA 1324:Binning 1297:GLIMMER 1241:genomes 1222:contigs 1033:vectors 1017:samples 935:species 912:archaea 860:grasses 844:cloning 816:species 713:Chimera 658:viruses 579:By taxa 563: 538:History 496:shotgun 430:genetic 399:cloning 138:History 110:Outline 7469:Fields 7395: 7354: 7344: 7297: 7287: 7246: 7236: 7224:(11). 7197: 7187: 7140: 7130: 7122: 7081: 7073: 7065: 7026: 7016: 6966: 6956: 6930:Nature 6909: 6899: 6891: 6865:Nature 6838: 6787: 6764: 6756: 6715: 6705: 6638: 6628: 6579: 6569: 6530: 6520: 6481: 6471: 6430: 6420: 6361: 6314: 6291: 6281: 6242: 6232: 6191: 6181: 6132: 6122: 6108:: 10. 6081: 6071: 6028: 5864: 5856: 5811: 5803: 5758: 5748: 5709: 5701: 5659: 5649: 5610: 5600: 5561: 5553: 5527:Nature 5506: 5498: 5472:Nature 5455: 5420: 5410: 5371: 5361: 5312: 5271: 5261: 5220: 5210: 5171: 5161: 5124: 5075: 5065: 5007: 4997: 4956: 4937: 4927: 4886: 4876: 4862:: 92. 4835: 4825: 4786: 4776: 4732: 4722: 4683: 4673: 4632: 4622: 4583: 4573: 4532: 4522: 4481: 4471: 4421: 4413: 4378: 4368: 4329: 4319: 4277: 4228: 4218: 4179: 4169: 4127: 4117: 4078: 4068: 4029: 4019: 3980: 3970: 3922: 3912: 3863: 3855: 3820: 3810: 3771: 3761: 3712: 3702: 3663: 3653: 3607: 3597: 3552: 3542: 3516:Nature 3495: 3487: 3441: 3431: 3390: 3380: 3341: 3331: 3252: 3244: 3209: 3199: 3150: 3140: 3098: 3090: 3047: 3037: 2996: 2986: 2972:: 57. 2945: 2937: 2886: 2858:Nature 2837: 2829: 2811: 2776: 2769:139013 2766: 2722: 2714: 2688:Nature 2668: 2661:137870 2658: 2609: 2602:177699 2599: 2560: 2552: 2516: 2509:208098 2506: 2462: 2431: 2424:391288 2421: 2372: 2362: 2312: 2271: 2261: 2218: 2191: 2184:107498 2181: 2139: 2129: 1947:health 1808:insect 1804:biogas 1753:sugars 1696:metals 1237:Celera 1088:, the 953:, and 947:Baltic 869:marine 746: 598:Pollen 593:Fungal 573: 524:genome 473:clonal 377:) are 324: 238:Fields 95:Allele 70:Genome 7776:(USA) 7536:Socio 7079:S2CID 6762:S2CID 6370:(PDF) 6339:(PDF) 5999:(PDF) 5992:(PDF) 5884:(PDF) 5862:S2CID 5836:arXiv 5809:S2CID 5783:arXiv 5707:S2CID 5679:(PDF) 5559:S2CID 5504:S2CID 4553:PeerJ 4419:S2CID 3861:S2CID 3493:S2CID 3417:(8). 3250:S2CID 3096:S2CID 2943:S2CID 2835:S2CID 2720:S2CID 2558:S2CID 1729:fuels 1700:crops 1668:soils 1565:(the 1413:MEGAN 1356:SLIMM 1348:mOTUs 1332:MEGAN 1328:BLAST 1283:MEGAN 1279:BLAST 1233:Phrap 1173:rumen 955:Black 627:Other 603:Algae 498:" or 115:Index 7800:List 7782:(UK) 7770:(EU) 7764:(JP) 7393:PMID 7352:PMID 7295:PMID 7244:PMID 7195:PMID 7138:PMID 7120:ISSN 7071:PMID 7063:ISSN 7024:PMID 6964:PMID 6907:PMID 6889:ISSN 6836:ISBN 6817:2011 6785:ISBN 6754:ISSN 6713:PMID 6666:2014 6636:PMID 6577:PMID 6528:PMID 6479:PMID 6428:PMID 6378:2012 6359:PMID 6312:ISBN 6289:PMID 6240:PMID 6189:PMID 6130:PMID 6079:PMID 6026:ISBN 6007:2011 5969:2011 5854:PMID 5801:PMID 5756:PMID 5699:PMID 5657:PMID 5608:PMID 5551:PMID 5496:PMID 5453:PMID 5418:PMID 5369:PMID 5310:PMID 5269:PMID 5218:PMID 5169:PMID 5122:PMID 5073:PMID 5005:PMID 4935:PMID 4884:PMID 4833:PMID 4784:PMID 4730:PMID 4681:PMID 4630:PMID 4581:PMID 4530:PMID 4479:PMID 4411:PMID 4376:PMID 4327:PMID 4275:PMID 4226:PMID 4177:PMID 4125:PMID 4076:PMID 4027:PMID 3978:PMID 3920:PMID 3853:PMID 3818:PMID 3769:PMID 3710:PMID 3661:PMID 3651:ISBN 3605:PMID 3550:PMID 3485:PMID 3439:PMID 3388:PMID 3339:PMID 3242:PMID 3207:PMID 3148:PMID 3088:PMID 3045:PMID 2994:PMID 2935:PMID 2884:PMID 2827:PMID 2774:PMID 2712:PMID 2666:PMID 2607:PMID 2550:PMID 2514:PMID 2460:ISBN 2429:PMID 2370:PMID 2310:PMID 2269:PMID 2216:ISBN 2189:PMID 2137:PMID 1899:and 1842:and 1834:and 1778:The 1771:and 1741:corn 1727:are 1702:and 1692:iron 1666:The 1654:and 1573:and 1563:mRNA 1548:and 1515:and 1455:KEGG 1421:KEGG 1417:SEED 1342:and 1295:and 1035:for 812:rRNA 616:fish 467:and 7415:at 7383:doi 7379:249 7342:PMC 7334:doi 7285:PMC 7275:doi 7234:PMC 7226:doi 7185:PMC 7177:doi 7128:PMC 7110:doi 7055:doi 7014:PMC 7004:doi 6954:PMC 6946:doi 6934:464 6897:PMC 6881:doi 6869:464 6744:doi 6703:PMC 6693:doi 6626:PMC 6616:doi 6567:PMC 6559:doi 6518:PMC 6510:doi 6469:PMC 6459:doi 6418:PMC 6410:doi 6351:doi 6279:PMC 6271:doi 6230:PMC 6220:doi 6179:PMC 6169:doi 6120:PMC 6110:doi 6069:PMC 6059:doi 5938:doi 5846:doi 5832:162 5793:doi 5779:161 5746:PMC 5738:doi 5691:doi 5647:PMC 5639:doi 5598:PMC 5590:doi 5543:doi 5531:536 5488:doi 5476:442 5445:doi 5408:PMC 5400:doi 5359:PMC 5349:doi 5337:108 5300:doi 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