Array ( [0] => {{Short description|Study of genes found in the environment}} [1] => {{Use dmy dates|date=November 2019}} [2] => {{good article}} [3] => {{Genetics sidebar}} [4] => [[File:Environmental shotgun sequencing.png|thumb|upright=1.5|In metagenomics, the genetic materials ([[DNA]], '''C''') are [[DNA extraction|extracted]] directly from samples taken from the environment (e.g. soil, sea water, human gut, '''A''') after filtering ('''B'''), and are [[DNA sequencing|sequenced]] ('''E''') after multiplication by [[Molecular cloning|cloning]] ('''D''') in an approach called [[shotgun sequencing]]. These short sequences can then be put together again using [[Sequence assembly|assembly methods]] ('''F''') to deduce the individual genomes or parts of genomes that constitute the original environmental sample. This information can then be used to study the [[species diversity]] and functional potential of the microbial community of the environment.]] [5] => [6] => '''Metagenomics''' is the study of [[genetics|genetic]] material recovered directly from [[Natural environment|environmental]] or clinical samples by a method called [[sequencing]]. The broad field may also be referred to as '''environmental genomics''', '''ecogenomics''', '''community genomics''' or '''microbiomics'''. [7] => [8] => While traditional [[microbiology]] and microbial [[genome sequencing]] and [[genomics]] rely upon cultivated [[clone (genetics)|clonal]] [[microbiological culture|cultures]], early environmental gene sequencing cloned specific genes (often the [[16S ribosomal RNA|16S rRNA]] gene) to produce a profile of diversity in a natural sample. Such work revealed that the vast majority of [[biodiversity|microbial biodiversity]] had been missed by cultivation-based methods. [9] => [10] => 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 [[microbial ecology]] to be investigated at a much greater scale and detail than before. Recent studies use either "[[Shotgun sequencing|shotgun]]" or [[Polymerase chain reaction|PCR]] directed sequencing to get largely unbiased samples of all genes from all the members of the sampled communities. [11] => [12] => ==Etymology== [13] => The term "metagenomics" was first used by [[Jo Handelsman]], [[Robert M. Goodman]], Michelle R. Rondon, [[Jon Clardy]], 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 [[genome]]. In 2005, Kevin Chen and [[Lior Pachter]] (researchers at the [[University of California, Berkeley]]) defined metagenomics as "the application of modern genomics technique without the need for isolation and lab cultivation of individual species". [14] => [15] => ==History== [16] => {{DNA barcoding}} [17] => 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 [[Microbiological culture|cultured]] and thus cannot be sequenced. These early studies focused on 16S [[ribosomal]] [[RNA]] (rRNA) sequences which are relatively short, often [[Conserved sequence|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. These surveys of ribosomal RNA genes taken directly from the environment revealed that [[Microbiological culture|cultivation]] based methods find less than 1% of the bacterial and [[archaea]]l 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. [18] => [19] => In the 1980s early [[molecular biology|molecular work]] in the field was conducted by [[Norman R. Pace]] and colleagues, who used [[Polymerase chain reaction|PCR]] to explore the diversity of ribosomal RNA sequences. The insights gained from these breakthrough studies led Pace to propose the idea of cloning DNA directly from environmental samples as early as 1985.{{cite book | vauthors = Pace NR, Stahl DA, Lane DJ, Olsen GJ | chapter = The Analysis of Natural Microbial Populations by Ribosomal RNA Sequences |date=1986 |pages=1–55| veditors = Marshall KC | title =Advances in Microbial Ecology| volume = 9 |publisher=Springer US |doi=10.1007/978-1-4757-0611-6_1 |isbn=978-1-4757-0611-6 }} This led to the first report of isolating and [[DNA cloning|cloning]] bulk DNA from an environmental sample, published by Pace and colleagues in 1991 while Pace was in the Department of Biology at [[Indiana University]]. Considerable efforts ensured that these were not [[Polymerase chain reaction|PCR]] false positives and supported the existence of a complex community of unexplored species. Although this methodology was limited to exploring highly conserved, [[Noncoding DNA|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 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 [[grass]]es. After leaving the Pace laboratory, [[Edward 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 biology|marine]] samples. [20] => [21] => In 2002, [[Mya Breitbart]], [[Forest Rohwer]], and colleagues used environmental [[shotgun sequencing]] (see below) to show that 200 liters of seawater contains over 5000 different viruses. Subsequent studies showed that there are more than a thousand [[viral species]] in human stool and possibly a million different viruses per kilogram of [[marine sediment]], including many [[bacteriophages]]. Essentially all of the viruses in these studies were new species. In 2004, Gene Tyson, Jill Banfield, and colleagues at the [[University of California, Berkeley]] and the [[Joint Genome Institute]] sequenced DNA extracted from an [[acid mine drainage]] system. This effort resulted in the complete, or nearly complete, genomes for a handful of bacteria and [[archaea]] that had previously resisted attempts to culture them. [22] => [23] => Beginning in 2003, [[Craig Venter]], leader of the privately funded parallel of the [[Human Genome Project]], has led the [[Global Ocean Sampling Expedition]] (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 [[Sargasso Sea]], found DNA from nearly 2000 different [[species]], including 148 types of [[bacteria]] never before seen. Venter thoroughly explored the [[West Coast of the United States]], and completed a two-year expedition in 2006 to explore the [[Baltic Sea|Baltic]], [[Mediterranean Sea|Mediterranean]], and [[Black Sea|Black]] 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 [[plankton]]. [24] => [25] => In 2005 Stephan C. Schuster at [[Penn State University]] and colleagues published the first sequences of an environmental sample generated with [[DNA Sequencing#High-throughput sequencing|high-throughput sequencing]], in this case massively parallel [[pyrosequencing]] developed by [[454 Life Sciences]]. Another early paper in this area appeared in 2006 by Robert Edwards, [[Forest Rohwer]], and colleagues at [[San Diego State University]]. [26] => [27] => ==Sequencing== [28] => [[File:Flow diagram of a typical metagenome projects.tiff|thumb|200px|Flow diagram of a typical metagenome project{{cite journal | vauthors = Thomas T, Gilbert J, Meyer F | title = Metagenomics - a guide from sampling to data analysis | journal = Microbial Informatics and Experimentation | volume = 2 | issue = 1 | pages = 3 | date = February 2012 | pmid = 22587947 | pmc = 3351745 | doi = 10.1186/2042-5783-2-3 | doi-access = free }}]] [29] => {{Main|DNA sequencing}} [30] => [31] => Recovery of DNA sequences longer than a few thousand [[base pair]]s from environmental [[Sample (material)|samples]] was very difficult until recent advances in [[molecular biology|molecular biological]] techniques allowed the construction of [[Library (biology)|libraries]] in [[bacterial artificial chromosome]]s (BACs), which provided better [[Vector (molecular biology)|vectors]] for [[molecular cloning]]. [32] => [33] => ===Shotgun metagenomics=== [34] => Advances in [[bioinformatics]], 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 [[shotgun sequencing]] to metagenomic samples (known also as whole metagenome shotgun or WMGS sequencing). The approach, used to sequence many cultured microorganisms and the [[human genome project|human genome]], randomly shears DNA, sequences many short sequences, and [[Sequence assembly|reconstructs]] them into a [[consensus sequence]]. 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. [35] => [36] => ===High-throughput sequencing=== [37] => 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 [[DNA Sequencing#High-throughput sequencing (HTS) methods|high-throughput sequencing]] used massively parallel [[pyrosequencing|454 pyrosequencing]]. Three other technologies commonly applied to environmental sampling are the [[Ion semiconductor sequencing|Ion Torrent Personal Genome Machine]], the [[Illumina (company)|Illumina]] MiSeq or HiSeq and the [[ABI Solid Sequencing|Applied Biosystems SOLiD]] system. These techniques for sequencing DNA generate shorter fragments than [[Sanger sequencing]]; 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. [38] => [39] => An emerging approach combines shotgun sequencing and [[chromosome conformation capture]] (Hi-C), which measures the proximity of any two DNA sequences within the same cell, to guide microbial genome assembly.{{cite journal | vauthors = Stewart RD, Auffret MD, Warr A, Wiser AH, Press MO, Langford KW, Liachko I, Snelling TJ, Dewhurst RJ, Walker AW, Roehe R, Watson M | display-authors = 6 | title = Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen | journal = Nature Communications | volume = 9 | issue = 1 | pages = 870 | date = February 2018 | pmid = 29491419 | pmc = 5830445 | doi = 10.1038/s41467-018-03317-6 | bibcode = 2018NatCo...9..870S | first8 = Maximilian O. | first9 = Andrew H. | first7 = Kyle W. }} Long read sequencing technologies, including PacBio RSII and PacBio Sequel by [[Pacific Biosciences]], and Nanopore MinION, GridION, PromethION by [[Oxford Nanopore Technologies]], is another choice to get long shotgun sequencing reads that should make ease in assembling process.{{cite journal | vauthors = Hiraoka S, Yang CC, Iwasaki W | title = Metagenomics and Bioinformatics in Microbial Ecology: Current Status and Beyond | journal = Microbes and Environments | volume = 31 | issue = 3 | pages = 204–12 | date = September 2016 | pmid = 27383682 | pmc = 5017796 | doi = 10.1264/jsme2.ME16024 }} [40] => [41] => ==Bioinformatics== [42] => {{missing information|section|quality assessment: on assembly (N50, MetaQUAST), on genome (universal single-copy marker genes – CheckM and BUSCO)|date=February 2022}} [43] => [[File:WGS metagenomics analysis steps.gif|thumb|450px|Schematic representation of the main steps necessary for the analysis of whole metagenome shotgun sequencing-derived data.{{cite journal | vauthors = Pérez-Cobas AE, Gomez-Valero L, Buchrieser C | title = Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses | journal = Microbial Genomics | date = 2020 | volume = 6 | issue = 8 | pmid = 32706331 | doi = 10.1099/mgen.0.000409 | pmc = 7641418 | doi-access = free }} The software related to each step is shown in italics.]] [44] => 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 [[rumen]] metagenome generated 279 [[gigabase]]s, or 279 billion base pairs of nucleotide sequence data, while the human gut [[microbiome]] 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.{{cite journal | vauthors = Oulas A, Pavloudi C, Polymenakou P, Pavlopoulos GA, Papanikolaou N, Kotoulas G, Arvanitidis C, Iliopoulos I | display-authors = 6 | title = Metagenomics: tools and insights for analyzing next-generation sequencing data derived from biodiversity studies | journal = Bioinformatics and Biology Insights | volume = 9 | pages = 75–88 | date = 2015 | pmid = 25983555 | pmc = 4426941 | doi = 10.4137/BBI.S12462 }} [45] => [46] => ===Sequence pre-filtering=== [47] => 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 [[eukaryotic]] origin (especially in metagenomes of human origin).{{cite journal | vauthors = Mende DR, Waller AS, Sunagawa S, Järvelin AI, Chan MM, Arumugam M, Raes J, Bork P | display-authors = 6 | title = Assessment of metagenomic assembly using simulated next generation sequencing data | journal = PLOS ONE | volume = 7 | issue = 2 | pages = e31386 | date = 23 February 2012 | pmid = 22384016 | pmc = 3285633 | doi = 10.1371/journal.pone.0031386 | bibcode = 2012PLoSO...731386M | doi-access = free }}{{cite journal | vauthors = Balzer S, Malde K, Grohme MA, Jonassen I | title = Filtering duplicate reads from 454 pyrosequencing data | journal = Bioinformatics | volume = 29 | issue = 7 | pages = 830–6 | date = April 2013 | pmid = 23376350 | pmc = 3605598 | doi = 10.1093/bioinformatics/btt047 }} The methods available for the removal of contaminating eukaryotic genomic DNA sequences include Eu-Detect and DeConseq.{{cite journal | vauthors = Mohammed MH, Chadaram S, Komanduri D, Ghosh TS, Mande SS | title = Eu-Detect: an algorithm for detecting eukaryotic sequences in metagenomic data sets | journal = Journal of Biosciences | volume = 36 | issue = 4 | pages = 709–17 | date = September 2011 | pmid = 21857117 | doi = 10.1007/s12038-011-9105-2 | s2cid = 25857874 }}{{cite journal | vauthors = Schmieder R, Edwards R | title = Fast identification and removal of sequence contamination from genomic and metagenomic datasets | journal = PLOS ONE | volume = 6 | issue = 3 | pages = e17288 | date = March 2011 | pmid = 21408061 | pmc = 3052304 | doi = 10.1371/journal.pone.0017288 | bibcode = 2011PLoSO...617288S | doi-access = free }} [48] => [49] => ===Assembly=== [50] => {{Main|Sequence assembly}} [51] => [52] => DNA sequence data from genomic and metagenomic projects are essentially the same, but genomic sequence data offers higher [[Fold coverage|coverage]] 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 reads into genomes difficult and unreliable. Misassemblies are caused by the presence of [[Repeated sequence (DNA)|repetitive DNA sequences]] 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 [[contig]]s. [53] => [54] => There are several assembly programs, most of which can use information from [[paired-end tag]]s in order to improve the accuracy of assemblies. Some programs, such as [[Phrap]] or [[Celera]] Assembler, were designed to be used to assemble single [[genome]]s but nevertheless produce good results when assembling metagenomic data sets. Other programs, such as [[Velvet assembler]], have been optimized for the shorter reads produced by second-generation sequencing through the use of [[de Bruijn graph]]s.{{cite journal | vauthors = Namiki T, Hachiya T, Tanaka H, Sakakibara Y | title = MetaVelvet: an extension of Velvet assembler to de novo metagenome assembly from short sequence reads | journal = Nucleic Acids Research | volume = 40 | issue = 20 | pages = e155 | date = November 2012 | pmid = 22821567 | pmc = 3488206 | doi = 10.1093/nar/gks678 }}{{cite journal | vauthors = Zerbino DR, Birney E | title = Velvet: algorithms for de novo short read assembly using de Bruijn graphs | journal = Genome Research | volume = 18 | issue = 5 | pages = 821–9 | date = May 2008 | pmid = 18349386 | pmc = 2336801 | doi = 10.1101/gr.074492.107 }} 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. [55] => [56] => ===Gene prediction=== [57] => {{Main|Gene prediction}} [58] => [59] => Metagenomic analysis [[Pipeline (software)|pipelines]] use two approaches in the annotation of coding regions in the assembled contigs. The first approach is to identify genes based upon [[Homology (biology)|homology]] with genes that are already publicly available in [[sequence database]]s, usually by [[BLAST (biotechnology)|BLAST]] searches. This type of approach is implemented in the program [[MEGAN]]4. The second, ''[[ab initio]]'', uses intrinsic features of the sequence to predict coding regions based upon gene training sets from related organisms. This is the approach taken by programs such as [[GeneMark]] and [[GLIMMER]]. The main advantage of ''ab initio'' prediction is that it enables the detection of coding regions that lack homologs in the sequence databases; however, it is most accurate when there are large regions of contiguous genomic DNA available for comparison. [60] => [61] => ===Species diversity=== [62] => {{Main|Species diversity}} [63] => [64] => Gene annotations provide the "what", while measurements of [[Biodiversity|species diversity]] provide the "who". In order to connect community composition and function in metagenomes, sequences must be binned. [[Binning (Metagenomics)|Binning]] is the process of associating a particular sequence with an organism. In similarity-based binning, methods such as [[BLAST (biotechnology)|BLAST]] are used to rapidly search for phylogenetic markers or otherwise similar sequences in existing public databases. This approach is implemented in [[MEGAN]]. Another tool, PhymmBL, uses [[Markov model|interpolated Markov model]]s to assign reads. [http://huttenhower.sph.harvard.edu/metaphlan MetaPhlAn] and [[AMPHORA]] are methods based on unique clade-specific markers for estimating organismal relative abundances with improved computational performances. Other tools, like [https://motu-tool.org/ mOTUs] and MetaPhyler, use universal marker genes to profile prokaryotic species. With the [https://motu-tool.org/ mOTUs profiler] is possible to profile species without a reference genome, improving the estimation of microbial community diversity. Recent methods, such as [https://github.com/seqan/slimm SLIMM], use read coverage landscape of individual reference genomes to minimize false-positive hits and get reliable relative abundances. In composition based binning, methods use intrinsic features of the sequence, such as oligonucleotide frequencies or [[codon usage bias]]. Once sequences are binned, it is possible to carry out comparative analysis of diversity and richness. [65] => [66] => ===Data integration=== [67] => [68] => The massive amount of exponentially growing sequence data is a daunting challenge that is complicated by the complexity of the [[metadata]] associated with metagenomic projects. Metadata includes detailed information about the three-dimensional (including depth, or height) geography and environmental features of the sample, physical data about the sample site, and the methodology of the sampling. This information is necessary both to ensure [[replicability]] and to enable downstream analysis. Because of its importance, metadata and collaborative data review and curation require standardized data formats located in specialized databases, such as the Genomes OnLine Database (GOLD). [69] => [70] => Several tools have been developed to integrate metadata and sequence data, allowing downstream comparative analyses of different datasets using a number of ecological indices. In 2007, Folker Meyer and Robert Edwards and a team at [[Argonne National Laboratory]] and the [[University of Chicago]] released the Metagenomics Rapid Annotation using Subsystem Technology server ([[MG-RAST]]) a community resource for metagenome data set analysis. As of June 2012 over 14.8 terabases (14x1012 bases) of DNA have been analyzed, with more than 10,000 public data sets freely available for comparison within MG-RAST. Over 8,000 users now have submitted a total of 50,000 metagenomes to MG-RAST. The [[Integrated Microbial Genomes System|Integrated Microbial Genomes/Metagenomes]] (IMG/M) system also provides a collection of tools for functional analysis of microbial communities based on their metagenome sequence, based upon reference isolate genomes included from the [http://img.jgi.doe.gov/cgi-bin/w/main.cgi Integrated Microbial Genomes] (IMG) system and the [http://jgi.doe.gov/programs/GEBA/index.html Genomic Encyclopedia of Bacteria and Archaea (GEBA)] project. [71] => [72] => One of the first standalone tools for analysing high-throughput metagenome shotgun data was [[MEGAN]] (MEta Genome ANalyzer). A first version of the program was used in 2005 to analyse the metagenomic context of DNA sequences obtained from a mammoth bone. Based on a BLAST comparison against a reference database, this tool performs both taxonomic and functional binning, by placing the reads onto the nodes of the NCBI taxonomy using a simple lowest common ancestor (LCA) algorithm or onto the nodes of the [http://www.theseed.org/wiki/Main_Page SEED] or [[KEGG]] classifications, respectively. [73] => [74] => With the advent of fast and inexpensive sequencing instruments, the growth of databases of DNA sequences is now exponential (e.g., the NCBI GenBank database ). Faster and efficient tools are needed to keep pace with the high-throughput sequencing, because the BLAST-based approaches such as MG-RAST or MEGAN run slowly to annotate large samples (e.g., several hours to process a small/medium size dataset/sample ). Thus, ultra-fast classifiers have recently emerged, thanks to more affordable powerful servers. These tools can perform the taxonomic annotation at extremely high speed, for example CLARK (according to CLARK's authors, it can classify accurately "32 million metagenomic short reads per minute"). At such a speed, a very large dataset/sample of a billion short reads can be processed in about 30 minutes. [75] => [76] => With the increasing availability of samples containing ancient DNA and due to the uncertainty associated with the nature of those samples (ancient DNA damage),{{cite bioRxiv | vauthors = Pratas D, Pinho AJ, Silva RM, Rodrigues JM, Hosseini M, Caetano T, Ferreira PJ |title=FALCON: a method to infer metagenomic composition of ancient DNA |date=February 2018 |biorxiv=10.1101/267179 }} a fast tool capable of producing conservative similarity estimates has been made available. According to FALCON's authors, it can use relaxed thresholds and edit distances without affecting the memory and speed performance. [77] => [78] => ===Comparative metagenomics=== [79] => [80] => Comparative analyses between metagenomes can provide additional insight into the function of complex microbial communities and their role in host health. Pairwise or multiple comparisons between metagenomes can be made at the level of sequence composition (comparing [[GC-content]] or genome size), taxonomic diversity, or functional complement. Comparisons of population structure and phylogenetic diversity can be made on the basis of [[16S ribosomal RNA|16S rRNA]] and other phylogenetic marker genes, or—in the case of low-diversity communities—by genome reconstruction from the metagenomic dataset. Functional comparisons between metagenomes may be made by comparing sequences against reference databases such as [[Gene cluster|COG]] or [[KEGG]], and tabulating the abundance by category and evaluating any differences for statistical significance. This gene-centric approach emphasizes the functional complement of the ''community'' as a whole rather than taxonomic groups, and shows that the functional complements are analogous under similar environmental conditions. Consequently, metadata on the environmental context of the metagenomic sample is especially important in comparative analyses, as it provides researchers with the ability to study the effect of habitat upon community structure and function. [81] => [82] => 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.{{cite journal | vauthors = Willner D, Thurber RV, Rohwer F | title = Metagenomic signatures of 86 microbial and viral metagenomes | journal = Environmental Microbiology | volume = 11 | issue = 7 | pages = 1752–66 | date = July 2009 | pmid = 19302541 | doi = 10.1111/j.1462-2920.2009.01901.x | doi-access = free | bibcode = 2009EnvMi..11.1752W }} and the HabiSign approach of Ghosh et al.{{cite journal | vauthors = Ghosh TS, Mohammed MH, Rajasingh H, Chadaram S, Mande SS | title = HabiSign: a novel approach for comparison of metagenomes and rapid identification of habitat-specific sequences | journal = BMC Bioinformatics | volume = 12 Suppl 13 | issue = Supplement 13 | pages = S9 | year = 2011 | pmid = 22373355 | pmc = 3278849 | doi = 10.1186/1471-2105-12-s13-s9 | doi-access = free }} This latter study also indicated that differences in tetranucleotide usage patterns can be used to identify genes (or metagenomic reads) originating from specific habitats. Additionally some methods as TriageTools or Compareads detect similar reads between two read sets. The [[similarity measure]] they apply on reads is based on a number of identical words of length ''k'' shared by pairs of reads. [83] => [84] => A key goal in comparative metagenomics is to identify microbial group(s) which are responsible for conferring specific characteristics to a given environment. However, due to issues in the sequencing technologies artifacts need to be accounted for like in metagenomeSeq. Others have characterized inter-microbial interactions between the resident microbial groups. A [[GUI]]-based comparative metagenomic analysis application called Community-Analyzer has been developed by Kuntal et al. [85] => {{cite journal | vauthors = Kuntal BK, Ghosh TS, Mande SS | title = Community-analyzer: a platform for visualizing and comparing microbial community structure across microbiomes | journal = Genomics | volume = 102 | issue = 4 | pages = 409–18 | date = October 2013 | pmid = 23978768 | doi = 10.1016/j.ygeno.2013.08.004 | doi-access = free }} 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. [86] => [87] => ==Data analysis== [88] => [89] => ===Community metabolism=== [90] => In many bacterial communities, natural or engineered (such as [[bioreactor]]s), there is significant division of labor in metabolism ([[syntrophy]]), during which the waste products of some organisms are metabolites for others. In one such system, the [[methanogen]]ic bioreactor, functional stability requires the presence of several [[syntrophy|syntrophic]] species ([[Syntrophobacterales]] and [[Synergistia]]) working together in order to turn raw resources into fully metabolized waste ([[methane]]). Using comparative gene studies and expression experiments with [[microarray]]s 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 [[metabolomic]]s and proteomics) in the quest to determine how metabolites are transferred and transformed by a community. [91] => [92] => ===Metatranscriptomics=== [93] => {{Further|Transcriptome|Transcriptomics technologies}} [94] => {{Main|Metatranscriptomics}} [95] => [96] => Metagenomics allows researchers to access the functional and metabolic diversity of microbial communities, but it cannot show which of these processes are active. The extraction and analysis of metagenomic [[mRNA]] (the '''metatranscriptome''') provides information on the [[Gene regulation|regulation]] and [[Gene expression|expression]] profiles of complex communities. Because of the technical difficulties (the [[MRNA#Degradation|short half-life]] of mRNA, for example) in the collection of [[environmental RNA]] there have been relatively few ''[[In situ#Biology and biomedical engineering|in situ]]'' metatranscriptomic studies of microbial communities to date. While originally limited to [[DNA microarray|microarray]] technology, metatranscriptomics studies have made use of [[transcriptomics technologies]] to measure whole-genome expression and quantification of a microbial community, first employed in analysis of ammonia oxidation in soils. [97] => [98] => ===Viruses=== [99] => {{Main|Viral metagenomics}} [100] => [101] => Metagenomic sequencing is particularly useful in the study of viral communities. As viruses lack a shared universal phylogenetic marker (as [[16S ribosomal RNA|16S RNA]] for bacteria and archaea, and [[18S ribosomal RNA|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.{{cite journal | vauthors = Paez-Espino D, Eloe-Fadrosh EA, Pavlopoulos GA, Thomas AD, Huntemann M, Mikhailova N, Rubin E, Ivanova NN, Kyrpides NC | display-authors = 6 | title = Uncovering Earth's virome | journal = Nature | volume = 536 | issue = 7617 | pages = 425–30 | date = August 2016 | pmid = 27533034 | doi = 10.1038/nature19094 | url = http://www.escholarship.org/uc/item/4zh090xt | bibcode = 2016Natur.536..425P | s2cid = 4466854 }}{{cite journal | vauthors = Paez-Espino D, Chen IA, Palaniappan K, Ratner A, Chu K, Szeto E, Pillay M, Huang J, Markowitz VM, Nielsen T, Huntemann M, K Reddy TB, Pavlopoulos GA, Sullivan MB, Campbell BJ, Chen F, McMahon K, Hallam SJ, Denef V, Cavicchioli R, Caffrey SM, Streit WR, Webster J, Handley KM, Salekdeh GH, Tsesmetzis N, Setubal JC, Pope PB, Liu WT, Rivers AR, Ivanova NN, Kyrpides NC | display-authors = 6 | title = IMG/VR: a database of cultured and uncultured DNA Viruses and retroviruses | journal = Nucleic Acids Research | volume = 45 | issue = D1 | pages = D457–D465 | date = January 2017 | pmid = 27799466 | pmc = 5210529 | doi = 10.1093/nar/gkw1030 }}{{cite journal | vauthors = Paez-Espino D, Roux S, Chen IA, Palaniappan K, Ratner A, Chu K, Huntemann M, Reddy TB, Pons JC, Llabrés M, Eloe-Fadrosh EA, Ivanova NN, Kyrpides NC | display-authors = 6 | title = IMG/VR v.2.0: an integrated data management and analysis system for cultivated and environmental viral genomes | journal = Nucleic Acids Research | volume = 47 | issue = D1 | pages = D678–D686 | date = January 2019 | pmid = 30407573 | pmc = 6323928 | doi = 10.1093/nar/gky1127 }}{{cite journal | vauthors = Paez-Espino D, Pavlopoulos GA, Ivanova NN, Kyrpides NC | title = Nontargeted virus sequence discovery pipeline and virus clustering for metagenomic data | journal = Nature Protocols | volume = 12 | issue = 8 | pages = 1673–1682 | date = August 2017 | pmid = 28749930 | doi = 10.1038/nprot.2017.063 | s2cid = 2127494 | url = https://escholarship.org/content/qt1549t4d3/qt1549t4d3.pdf?t=p2n6i2 }} For example, a metagenomic pipeline called [[Giant Virus Finder]] showed the first evidence of existence of [[Girus|giant viruses]] in a saline desert [102] => {{cite journal | vauthors = Kerepesi C, Grolmusz V | title = Giant viruses of the Kutch Desert | journal = Archives of Virology | volume = 161 | issue = 3 | pages = 721–4 | date = March 2016 | pmid = 26666442 | doi = 10.1007/s00705-015-2720-8 | arxiv = 1410.1278 | s2cid = 13145926 }} and in Antarctic dry valleys. [103] => {{cite journal | vauthors = Kerepesi C, Grolmusz V | title = The "Giant Virus Finder" discovers an abundance of giant viruses in the Antarctic dry valleys | journal = Archives of Virology | volume = 162 | issue = 6 | pages = 1671–1676 | date = June 2017 | pmid = 28247094 | doi = 10.1007/s00705-017-3286-4 | arxiv = 1503.05575 | s2cid = 1925728 }} [104] => [105] => ==Applications== [106] => [107] => Metagenomics has the potential to advance knowledge in a wide variety of fields. It can also be applied to solve practical challenges in [[medicine]], [[engineering]], [[agriculture]], [[sustainability]] and [[ecology]].{{Cite journal| vauthors = Copeland CS |date=Sep–Oct 2017 |title=The World Within Us |journal=Healthcare Journal of New Orleans|pages=21–26 |url= http://claudiacopeland.com/uploads/3/5/5/6/35560346/_hjno_the_world_within_us.pdf}} [108] => [109] => ===Agriculture=== [110] => The [[soil]]s in which plants grow are inhabited by microbial communities, with one gram of soil containing around 109-1010 microbial cells which comprise about one gigabase of sequence information. The microbial communities which inhabit soils are some of the most complex known to science, and remain poorly understood despite their economic importance. [[Microbial consortia]] perform a wide variety of [[ecosystem service]]s necessary for plant growth, including [[Nitrogen fixation|fixing atmospheric nitrogen]], [[nutrient cycling]], disease suppression, and [[Siderophore|sequester]] [[iron]] and other [[metal]]s. Functional metagenomics strategies are being used to explore the interactions between plants and microbes through cultivation-independent study of these microbial communities.{{cite journal | vauthors = Bringel F, Couée I | title = Pivotal roles of phyllosphere microorganisms at the interface between plant functioning and atmospheric trace gas dynamics | journal = Frontiers in Microbiology | volume = 6 | pages = 486 | date = 22 May 2015 | pmid = 26052316 | pmc = 4440916 | doi = 10.3389/fmicb.2015.00486 | doi-access = free }} 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 [[crop]]s and [[livestock]] and the adaptation of enhanced [[Agriculture|farming]] practices which improve crop health by harnessing the relationship between microbes and plants. [111] => [112] => ===Biofuel=== [113] => {{main|Biofuel}} [114] => [115] => [[Biofuel]]s are [[fuel]]s derived from [[biomass]] conversion, as in the conversion of [[cellulose]] contained in [[Maize|corn]] stalks, [[switchgrass]], and other biomass into [[cellulosic ethanol]]. This process is dependent upon microbial consortia (association) that transform the cellulose into [[sugar]]s, followed by the [[Ethanol fermentation|fermentation]] of the sugars into [[ethanol]]. Microbes also produce a variety of sources of [[bioenergy]] including [[Methanogen|methane]] and [[Biohydrogen|hydrogen]]. [116] => [117] => The [[Issues relating to biofuels#Technical issues|efficient industrial-scale deconstruction]] of biomass requires novel [[enzymes]] with higher productivity and lower cost. Metagenomic approaches to the analysis of complex microbial communities allow the targeted [[Genetic screen|screening]] of [[enzymes]] with industrial applications in biofuel production, such as [[glycoside hydrolase]]s. Furthermore, knowledge of how these microbial communities function is required to control them, and metagenomics is a key tool in their understanding. Metagenomic approaches allow comparative analyses between [[convergent evolution|convergent]] microbial systems like [[biogas]] fermenters or [[insect]] [[herbivore]]s such as the [[ant-fungus mutualism|fungus garden]] of the [[leafcutter ant]]s. [118] => [119] => ===Biotechnology=== [120] => [121] => Microbial communities produce a vast array of biologically active chemicals that are used in competition and communication. Many of the drugs in use today were originally uncovered in microbes; recent progress in mining the rich genetic resource of non-culturable microbes has led to the discovery of new genes, enzymes, and natural products. The application of metagenomics has allowed the development of [[Commodity chemicals|commodity]] and [[fine chemicals]], [[agrochemical]]s and [[Pharmaceutical drug|pharmaceuticals]] where the benefit of [[Enzyme catalysis|enzyme-catalyzed]] [[chiral synthesis]] is increasingly recognized. [122] => [123] => Two types of analysis are used in the [[bioprospecting]] of metagenomic data: function-driven screening for an expressed trait, and sequence-driven screening for DNA sequences of interest. Function-driven analysis seeks to identify clones expressing a desired trait or useful activity, followed by biochemical characterization and sequence analysis. This approach is limited by availability of a suitable screen and the 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 [[Conserved sequence|conserved DNA sequences]] to [[Primer (molecular biology)#PCR primer design|design PCR primers]] 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 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 [[malacidin]] antibiotics.{{cite journal | vauthors = Hover BM, Kim SH, Katz M, Charlop-Powers Z, Owen JG, Ternei MA, Maniko J, Estrela AB, Molina H, Park S, Perlin DS, Brady SF | display-authors = 6 | title = Culture-independent discovery of the malacidins as calcium-dependent antibiotics with activity against multidrug-resistant Gram-positive pathogens | journal = Nature Microbiology | volume = 3 | issue = 4 | pages = 415–422 | date = April 2018 | pmid = 29434326 | pmc = 5874163 | doi = 10.1038/s41564-018-0110-1 }} [124] => [125] => ===Ecology=== [126] => [[File:Iron hydroxide precipitate in stream.jpg|thumb|right|Metagenomics allows the study of microbial communities like those present in this stream receiving acid drainage from surface coal mining.]] [127] => Metagenomics can provide valuable insights into the functional ecology of environmental communities.{{cite journal | vauthors = Raes J, Letunic I, Yamada T, Jensen LJ, Bork P | title = Toward molecular trait-based ecology through integration of biogeochemical, geographical and metagenomic data | journal = Molecular Systems Biology | volume = 7 | pages = 473 | date = March 2011 | pmid = 21407210 | pmc = 3094067 | doi = 10.1038/msb.2011.6 }} 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. 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.{{cite journal | vauthors = Lavery TJ, Roudnew B, Seymour J, Mitchell JG, Jeffries T | title = High nutrient transport and cycling potential revealed in the microbial metagenome of Australian sea lion (Neophoca cinerea) faeces | journal = PLOS ONE | volume = 7 | issue = 5 | pages = e36478 | year = 2012 | pmid = 22606263 | pmc = 3350522 | doi = 10.1371/journal.pone.0036478 | veditors = Steinke D | bibcode = 2012PLoSO...736478L | doi-access = free }} [128] => [129] => DNA sequencing can also be used more broadly to identify species present in a body of water,{{cite web|url=https://www.npr.org/2013/07/24/205178477/whats-swimming-in-the-river-just-look-for-dna|title=What's Swimming in the River? Just Look For DNA|date=24 July 2013|work=NPR.org|access-date=10 October 2014}} debris filtered from the air, sample of dirt, or animal's faeces,{{cite journal |last1=Chua |first1=Physilia Y. S. |last2=Crampton-Platt |first2=Alex |last3=Lammers |first3=Youri |last4=Alsos |first4=Inger G. |last5=Boessenkool |first5=Sanne |last6=Bohmann |first6=Kristine |title=Metagenomics: A viable tool for reconstructing herbivore diet |journal=Molecular Ecology Resources |date=2021-05-25 |volume=21 |issue=7 |pages=1755–0998.13425 |doi=10.1111/1755-0998.13425|pmc=8518049|pmid=33971086 |doi-access=free }} and even detect diet items from blood meals.{{Cite journal |last1=Chua |first1=Physilia Y. S. |last2=Carøe |first2=Christian |last3=Crampton-Platt |first3=Alex |last4=Reyes-Avila |first4=Claudia S. |last5=Jones |first5=Gareth |last6=Streicker |first6=Daniel G. |last7=Bohmann |first7=Kristine |date=2022-07-04 |title=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) |url=https://mbmg.pensoft.net/article/78756/ |journal=Metabarcoding and Metagenomics |language=en |volume=6 |pages=e78756 |doi=10.3897/mbmg.6.78756 |s2cid=248041252 |issn=2534-9708|doi-access=free }} This can establish the range of [[invasive species]] and [[endangered species]], and track seasonal populations. [130] => [131] => ===Environmental remediation=== [132] => {{Main|Bioremediation}} [133] => Metagenomics can improve strategies for monitoring the impact of [[pollutant]]s on [[ecosystem]]s 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 [[bioaugmentation]] or [[biostimulation]] trials to succeed. [134] => [135] => ===Gut microbe characterization=== [136] => [[Microbial communities]] play a key role in preserving human [[health]], 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 [[Human Microbiome Project|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. [137] => [138] => 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.{{Cite journal |last1=Qin |first1=Junjie |last2=Li |first2=Ruiqiang |last3=Raes |first3=Jeroen |last4=Arumugam |first4=Manimozhiyan |last5=Burgdorf |first5=Kristoffer Solvsten |last6=Manichanh |first6=Chaysavanh |last7=Nielsen |first7=Trine |last8=Pons |first8=Nicolas |last9=Levenez |first9=Florence |last10=Yamada |first10=Takuji |last11=Mende |first11=Daniel R. |last12=Li |first12=Junhua |last13=Xu |first13=Junming |last14=Li |first14=Shaochuan |last15=Li |first15=Dongfang |date=2010 |title=A human gut microbial gene catalogue established by metagenomic sequencing |journal=Nature |language=en |volume=464 |issue=7285 |pages=59–65 |doi=10.1038/nature08821 |issn=1476-4687 |pmc=3779803 |pmid=20203603|bibcode=2010Natur.464...59. }} The study attempted to categorize the depth and phylogenetic diversity of 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. [139] => [140] => 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 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. [141] => [142] => 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 bacterial genomes.{{cite journal | vauthors = Qin J, Li R, Raes J, Arumugam M, Burgdorf KS, Manichanh C, Nielsen T, Pons N, Levenez F, Yamada T, Mende DR, Li J, Xu J, Li S, Li D, Cao J, Wang B, Liang H, Zheng H, Xie Y, Tap J, Lepage P, Bertalan M, Batto JM, Hansen T, Le Paslier D, Linneberg A, Nielsen HB, Pelletier E, Renault P, Sicheritz-Ponten T, Turner K, Zhu H, Yu C, Li S, Jian M, Zhou Y, Li Y, Zhang X, Li S, Qin N, Yang H, Wang J, Brunak S, Doré J, Guarner F, Kristiansen K, Pedersen O, Parkhill J, Weissenbach J, Bork P, Ehrlich SD, Wang J | display-authors = 6 | title = A human gut microbial gene catalogue established by metagenomic sequencing | journal = Nature | volume = 464 | issue = 7285 | pages = 59–65 | date = March 2010 | pmid = 20203603 | pmc = 3779803 | doi = 10.1038/nature08821 | bibcode = 2010Natur.464...59. }} [143] => [144] => In the [[Human Microbiome Project]] (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 [[human microbiome]] project. The metagenomic analysis revealed variations in niche specific abundance among 168 functional modules and 196 metabolic pathways within the microbiome. These included glycosaminoglycan 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 [[evidence-based medicine]]. Thus metagenomics is a powerful tool to address many of the pressing issues in the field of [[personalized medicine]].{{cite journal|title=PLOS Computational Biology: Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome |journal=PLOS Computational Biology |volume=8 |issue=6 |pages=e1002358 |doi=10.1371/journal.pcbi.1002358 |pmid=22719234 |pmc=3374609 |year = 2012|last1 = Abubucker|first1 = Sahar|last2=Segata |first2=Nicola |last3=Goll |first3=Johannes |last4=Schubert |first4=Alyxandria M. |last5=Izard |first5=Jacques |last6=Cantarel |first6=Brandi L. |last7=Rodriguez-Mueller |first7=Beltran |last8=Zucker |first8=Jeremy |last9=Thiagarajan |first9=Mathangi |last10=Henrissat |first10=Bernard |last11=White |first11=Owen |last12=Kelley |first12=Scott T. |last13=Methé |first13=Barbara |last14=Schloss |first14=Patrick D. |last15=Gevers |first15=Dirk |last16=Mitreva |first16=Makedonka |last17=Huttenhower |first17=Curtis |bibcode=2012PLSCB...8E2358A |doi-access=free }} [145] => [146] => In animals, metagenomics can be used to profile their gut microbiomes and enable detection of antibiotic-resistant bacteria.{{Cite journal |last1=Chua |first1=Physilia Ying Shi |last2=Rasmussen |first2=Jacob Agerbo |date=2022-05-11 |title=Taking metagenomics under the wings |url=https://www.nature.com/articles/s41579-022-00746-5 |journal=Nature Reviews Microbiology |volume=20 |issue=8 |page=447 |language=en |doi=10.1038/s41579-022-00746-5 |pmid=35546350 |s2cid=248739527 |issn=1740-1534}} This can have implications in monitoring the spread of diseases from wildlife to farmed animals and humans. [147] => [148] => ===Infectious disease diagnosis=== [149] => [150] => 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 [[encephalitis]] remain undiagnosed, despite extensive testing using state-of-the-art clinical laboratory methods. [[Clinical metagenomic sequencing]] 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 sequences with associated clinical phenotypes.{{Cite journal |last1=Chiu |first1=Charles Y. |last2=Miller |first2=Steven A. |date=2019 |title=Clinical metagenomics |journal=Nature Reviews Genetics |language=en |volume=20 |issue=6 |pages=341–355 |doi=10.1038/s41576-019-0113-7 |pmid=30918369 |pmc=6858796 |issn=1471-0064|doi-access=free }} [151] => [152] => ===Arbovirus surveillance=== [153] => Metagenomics has been an invaluable tool to help characterise the diversity and ecology of pathogens that are vectored by [[hematophagous]] (blood-feeding) insects such as mosquitoes and ticks.{{cite journal| vauthors=Zakrzewski M, Rašić G, Darbro J, Krause L, Poo YS, Filipović I | display-authors=etal| title=Mapping the virome in wild-caught Aedes aegypti from Cairns and Bangkok. | journal=Sci Rep | year= 2018 | volume= 8 | issue= 1 | pages= 4690 | pmid=29549363 | doi=10.1038/s41598-018-22945-y | pmc=5856816 | bibcode=2018NatSR...8.4690Z}}{{cite journal| vauthors=Thoendel M| title=Targeted Metagenomics Offers Insights into Potential Tick-Borne Pathogens. | journal=J Clin Microbiol | year= 2020 | volume= 58 | issue= 11 | pages= | pmid=32878948 | doi=10.1128/JCM.01893-20 | pmc=7587107 }}{{cite journal| vauthors=Parry R, James ME, Asgari S| title=Uncovering the Worldwide Diversity and Evolution of the Virome of the Mosquitoes Aedes aegypti and Aedes albopictus. | journal=Microorganisms | year= 2021 | volume= 9 | issue= 8 | page=1653 | pmid=34442732 | doi=10.3390/microorganisms9081653 | pmc=8398489 | doi-access=free }} Metagenomics is{{when|date=December 2022}} routinely used by public health officials and organisations{{where|date=December 2022}} for the surveillance of [[arbovirus]]es.{{cite journal| vauthors=Batovska J, Mee PT, Lynch SE, Sawbridge TI, Rodoni BC| title=Sensitivity and specificity of metatranscriptomics as an arbovirus surveillance tool. | journal=Sci Rep | year= 2019 | volume= 9 | issue= 1 | pages= 19398 | pmid=31852942 | doi=10.1038/s41598-019-55741-3 | pmc=6920425 | bibcode=2019NatSR...919398B }}{{cite journal| vauthors=Batovska J, Lynch SE, Rodoni BC, Sawbridge TI, Cogan NO| title=Metagenomic arbovirus detection using MinION nanopore sequencing. | journal=J Virol Methods | year= 2017 | volume= 249 | issue= | pages= 79–84 | pmid=28855093 | doi=10.1016/j.jviromet.2017.08.019 | pmc= | doi-access=free }} [154] => [155] => == See also == [156] => *[[Binning (Metagenomics)|Binning]] [157] => *[[Sewage#Epidemiology|Epidemiology and sewage]] [158] => *[[Metaproteomics]] [159] => *[[Microbial ecology]] [160] => *[[Pathogenomics]] [161] => [162] => == References == [163] => {{Reflist|refs= [164] => [166] => [167] => [168] => {{cite journal | vauthors = Hugenholtz P, Goebel BM, Pace NR | title = Impact of culture-independent studies on the emerging phylogenetic view of bacterial diversity | journal = Journal of Bacteriology | volume = 180 | issue = 18 | pages = 4765–74 | date = September 1998 | doi = 10.1128/JB.180.18.4765-4774.1998 | pmid = 9733676 | pmc = 107498 }} [169] => [170] => [171] => {{cite journal | vauthors = Eisen JA | title = Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes | journal = PLOS Biology | volume = 5 | issue = 3 | pages = e82 | date = March 2007 | pmid = 17355177 | pmc = 1821061 | doi = 10.1371/journal.pbio.0050082 | doi-access = free }} [172] => [173] => [174] => {{cite book [175] => | editor = Marco, D [176] => | year=2011 [177] => | title=Metagenomics: Current Innovations and Future Trends [178] => | publisher=[[Caister Academic Press]] [179] => | isbn= 978-1-904455-87-5}} [180] => [181] => [182] => {{cite journal | vauthors = Handelsman J, Rondon MR, Brady SF, Clardy J, Goodman RM | title = Molecular biological access to the chemistry of unknown soil microbes: a new frontier for natural products | journal = Chemistry & Biology | volume = 5 | issue = 10 | pages = R245-9 | date = October 1998 | pmid = 9818143 | doi = 10.1016/S1074-5521(98)90108-9 | doi-access = free }}. 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clade-specific marker genes | journal = Nature Methods | volume = 9 | issue = 8 | pages = 811–4 | date = June 2012 | pmid = 22688413 | pmc = 3443552 | doi = 10.1038/nmeth.2066 }} [341] => [342] => [343] => {{cite journal | vauthors = Segata N, Boernigen D, Tickle TL, Morgan XC, Garrett WS, Huttenhower C | title = Computational meta'omics for microbial community studies | journal = Molecular Systems Biology | volume = 9 | issue = 666 | pages = 666 | date = May 2013 | pmid = 23670539 | pmc = 4039370 | doi = 10.1038/msb.2013.22 }} [344] => [345] => [346] => [347] => {{cite journal | vauthors = Dadi TH, Renard BY, Wieler LH, Semmler T, Reinert K | title = SLIMM: species level identification of microorganisms from metagenomes | journal = PeerJ | volume = 5 | pages = e3138 | year = 2017 | pmid = 28367376 | pmc = 5372838 | doi = 10.7717/peerj.3138 | doi-access = free }} [348] => [349] => [350] => {{Cite news | volume = 6 | issue = 7 | page = 309 | last = Jansson | first = Janet | name-list-style = vanc | title = Towards "Tera-Terra": Terabase Sequencing of Terrestrial Metagenomes Print E-mail | work = Microbe | year = 2011 | url = http://www.microbemagazine.org/index.php/07-2011-home/3553-towards-tera-terra-terabase-sequencing-of-terrestrial-metagenomes | url-status = dead | archive-url = https://web.archive.org/web/20120331040553/http://www.microbemagazine.org/index.php/07-2011-home/3553-towards-tera-terra-terabase-sequencing-of-terrestrial-metagenomes | archive-date = 31 March 2012 | df = dmy-all }} [351] => {{Cite journal | vauthors = Vogel TM, Simonet P, Jansson JK, Hirsch PR, Tiedje JM, Van Elsas JD, Bailey MJ, Nalin R, Philippot L | doi = 10.1038/nrmicro2119 | title = TerraGenome: A consortium for the sequencing of a soil metagenome | journal = Nature Reviews Microbiology | volume = 7 | issue = 4 | pages = 252 | year = 2009 | doi-access = free }} [352] => {{cite web | title = TerraGenome Homepage | work = TerraGenome international sequencing consortium | access-date = 30 December 2011 | url = http://www.terragenome.org/ }} [353] => {{cite book | vauthors = Charles T|year=2010|chapter=The Potential for Investigation of Plant-microbe Interactions Using Metagenomics Methods|title=Metagenomics: Theory, Methods and Applications|publisher=Caister Academic Press|isbn= 978-1-904455-54-7}}|2}} [354] => [355] => == External links == [356] => *[http://www.nature.com/nrmicro/focus/metagenomics/index.html Focus on Metagenomics] at ''[[Nature Reviews Microbiology]]'' journal website [357] => *[http://cami-challenge.org The “Critical Assessment of Metagenome Interpretation” (CAMI) initiative] to evaluate methods in metagenomics [358] => {{Prone to spam|date=August 2014}} [359] => [374] => [375] => {{Genomics}} [376] => {{Portal bar|Biology|Medicine}} [377] => [378] => [[Category:Metagenomics| ]] [379] => [[Category:Bioinformatics]] [380] => [[Category:Genomics]] [381] => [[Category:Environmental microbiology]] [382] => [[Category:Microbiology techniques]] [] => )
good wiki

Metagenomics

Metagenomics is the study of genetic material recovered directly from environmental or clinical samples by a method called sequencing. The broad field may also be referred to as environmental genomics, ecogenomics, community genomics or microbiomics.

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