Metagenomics is the study of genetic material recovered directly from
environmental samples. The broad field may also be referred to as
environmental genomics, ecogenomics or community genomics. While
traditional microbiology and microbial genome sequencing and genomics
rely upon cultivated clonal cultures, early environmental gene
sequencing cloned specific genes (often the 16S r
RNA gene) to produce
a profile of diversity in a natural sample. Such work revealed that
the vast majority of microbial biodiversity had been missed by
cultivation-based methods. Recent studies use either "shotgun" or
PCR directed sequencing to get largely unbiased samples of all genes
from all the members of the sampled communities. Because of its
ability to reveal the previously hidden diversity of microscopic life,
metagenomics offers a powerful lens for viewing the microbial world
that has the potential to revolutionize understanding of the entire
living world. 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.
3.1 Shotgun metagenomics
3.2 High-throughput sequencing
4.1 Sequence pre-filtering
4.3 Gene prediction
4.5 Data integration
4.6 Comparative metagenomics
5 Data analysis
5.1 Community metabolism
6.1 Infectious disease diagnosis
6.2 Gut Microbe Characterization
6.4 Environmental remediation
7 See also
9 External links
The term "metagenomics" was first used by Jo Handelsman, Jon Clardy,
Robert M. Goodman, Sean F. Brady, and others, and first appeared in
publication in 1998. The term metagenome referenced the idea that a
collection of genes sequenced from the environment could be analyzed
in a way analogous to the study of a single genome. Recently, Kevin
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
Conventional sequencing begins with a culture of identical cells as a
source of DNA. However, early metagenomic studies revealed that there
are probably large groups of microorganisms in many environments that
cannot be cultured and thus cannot be sequenced. These early studies
16S ribosomal RNA
16S ribosomal RNA sequences which are relatively short,
often conserved within a species, and generally different between
species. Many 16S r
RNA 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 (rRNA) genes
taken directly from the environment revealed that cultivation based
methods find less than 1% of the bacterial and archaeal 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.
Early molecular work in the field was conducted by
Norman R. Pace and
colleagues, who used
PCR to explore the diversity of ribosomal RNA
sequences. The insights gained from these breakthrough studies led
Pace to propose the idea of cloning
DNA directly from environmental
samples as early as 1985. This led to the first report of isolating
and cloning bulk
DNA from an environmental sample, published by Pace
and colleagues in 1991 while Pace was in the Department of Biology
at Indiana University. Considerable efforts ensured that these were
PCR false positives and supported the existence of a complex
community of unexplored species. Although this methodology was limited
to exploring highly conserved, non-protein coding genes, it did
support early microbial morphology-based observations that diversity
was far more complex than was known by culturing methods. Soon after
that, Healy reported the metagenomic isolation of functional genes
from "zoolibraries" constructed from a complex culture of
environmental organisms grown in the laboratory on dried grasses in
1995. After leaving the Pace laboratory,
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
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
University of California, Berkeley and
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.
Flow diagram of a typical metagenome project
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 are
sequenced using shotgun sequencing, in hopes that new genomes (and
therefore new organisms) would be identified. The pilot project,
conducted in the Sargasso Sea, found
DNA from nearly 2000 different
species, including 148 types of bacteria never before seen. Venter
has circumnavigated the globe and thoroughly explored the West Coast
of the United States, and completed a two-year expedition to explore
the Baltic, Mediterranean and 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.
In 2005 Stephan C. Schuster at
Penn State University
Penn State University and colleagues
published the first sequences of an environmental sample generated
with 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.
DNA sequences longer than a few thousand base pairs from
environmental samples was very difficult until recent advances in
molecular biological techniques allowed the construction of libraries
in bacterial artificial chromosomes (BACs), which provided better
vectors for molecular cloning.
Sequencing (ESS). (A) Sampling from habitat; (B)
filtering particles, typically by size; (C) Lysis and
(D) cloning and library construction; (E) sequencing the clones; (F)
sequence assembly into contigs and scaffolds.
Advances in bioinformatics, refinements of
DNA amplification, and the
proliferation of computational power have greatly aided the analysis
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, randomly
shears DNA, sequences many short sequences, and 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
The first metagenomic studies conducted using high-throughput
sequencing used massively parallel 454 pyrosequencing. Three other
technologies commonly applied to environmental sampling are the Ion
Genome Machine, the Illumina MiSeq or HiSeq and the
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
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. An additional advantage to high
throughput sequencing is that this technique does not require cloning
DNA before sequencing, removing one of the main biases and
bottlenecks in environmental sampling.
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 gigabases, 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.
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). The methods
available for the removal of contaminating eukaryotic genomic DNA
sequences include Eu-Detect and DeConseq.
Main article: Sequence assembly
DNA sequence data from genomic and metagenomic projects are
essentially the same, but genomic sequence data offers higher 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
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 contigs.
There are several assembly programs, most of which can use information
from paired-end tags in order to improve the accuracy of assemblies.
Some programs, such as
Celera Assembler, were designed to be
used to assemble single genomes 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 graphs.
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.
Main article: Gene prediction
Metagenomic analysis pipelines use two approaches in the annotation of
coding regions in the assembled contigs. The first approach is to
identify genes based upon homology with genes that are already
publicly available in sequence databases, usually by
This type of approach is implemented in the program MEGAN4.  The
second, ab initio, uses intrinsic features of the sequence to predict
coding regions based upon gene training sets from related organisms.
This is the approach taken by programs such as GeneMark and
GLIMMER. The main advantage of ab initio prediction is that it enables
the detection of coding regions that lack homologs in the sequence
databases; however, it is most accurate when there are large regions
of contiguous genomic
DNA available for comparison.
A 2016 representation of the tree of life
Gene annotations provide the "what", while measurements of species
diversity provide the "who". In order to connect community
composition and function in metagenomes, sequences must be binned.
Binning is the process of associating a particular sequence with an
organism. In similarity-based binning, methods such as
used to rapidly search for phylogenetic markers or otherwise similar
sequences in existing public databases. This approach is implemented
in MEGAN. Another tool, PhymmBL, uses interpolated Markov models
to assign reads. MetaPhlAn and
AMPHORA are methods based on unique
clade-specific markers for estimating organismal relative abundances
with improved computational performances. Recent methods, such as
SLIMM, use read coverage landscape of individual reference genomes to
minimize false-positive hits and get reliable relative abundances.
In composition based binning, methods use intrinsic features of the
sequence, such as oligonucleotide frequencies or codon usage bias.
Once sequences are binned, it is possible to carry out comparative
analysis of diversity and richness.
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.
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
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
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)
DNA have been analyzed, with more than 10,000 public data sets
freely available for comparison within MG-RAST. Over 8,000 users now
have submitted a total of 50,000 metagenomes to MG-RAST. The
Integrated Microbial Genomes/Metagenomes (IMG/M) system also provides
a collection of tools for functional analysis of microbial communities
based on their metagenome sequence, based upon reference isolate
genomes included from the Integrated Microbial Genomes (IMG) system
and the Genomic Encyclopedia of
One of the first standalone tools for analysing high-throughput
metagenome shotgun data was
Genome ANalyzer). A
first version of the program was used in 2005 to analyse the
metagenomic context of
DNA sequences obtained from a mammoth bone.
Based on a
BLAST comparison against a reference database, this tool
performs both taxonomic and functional binning, by placing the reads
onto the nodes of the NCBI taxonomy using a simple lowest common
ancestor (LCA) algorithm or onto the nodes of the SEED or KEGG
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
MEGAN run slowly to annotate large
samples (e.g., several hours to process a small/medium size
dataset/sample ). Thus, ultra-fast classifiers have recently
emerged, thanks to more affordable powerful servers. These tools can
perform the taxonomic annotation at extremely high speed, for example
CLARK  (according to CLARK's authors, it can classify accurately
"32 million metagenomic short reads per minute"). At such a speed, a
very large dataset/sample of a billion short reads can be processed in
about 30 minutes.
With the increasing availability of samples containing ancient
due to the uncertainty associated with the nature of those samples
DNA damage), FALCON, a fast tool capable of producing
conservative similarity estimates has been made available. According
to FALCON's authors, it can use relaxed thresholds and edit distances
without affecting the memory and speed performance.
Comparative 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
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 and other
phylogenetic marker genes, or—in the case of low-diversity
communities—by genome reconstruction from the metagenomic
dataset. Functional comparisons between metagenomes may be made by
comparing sequences against reference databases such as COG or KEGG,
and tabulating the abundance by category and evaluating any
differences for statistical significance. This gene-centric
approach emphasizes the functional complement of the community as a
whole rather than taxonomic groups, and shows that the functional
complements are analogous under similar environmental conditions.
Consequently, metadata on the environmental context of the metagenomic
sample is especially important in comparative analyses, as it provides
researchers with the ability to study the effect of habitat upon
community structure and function.
Additionally, several studies have also utilized oligonucleotide usage
patterns to identify the differences across diverse microbial
communities. Examples of such methodologies include the dinucleotide
relative abundance approach by Willner et al. and the HabiSign
approach of Ghosh et al. This latter study also indicated that
differences in tetranucleotide usage patterns can be used to identify
genes (or metagenomic reads) originating from specific habitats.
Additionally some methods as TriageTools or Compareads detect
similar reads between two read sets. The similarity measure they apply
on reads is based on a number of identical words of length k shared by
pairs of reads.
A key goal in comparative metagenomics is to identify microbial
group(s) which are responsible for conferring specific characteristics
to a given environment. However, due to issues in the sequencing
technologies artifacts need to be accounted for like in
metagenomeSeq. Others have characterized inter-microbial
interactions between the resident microbial groups. A GUI-based
comparative metagenomic analysis application called Community-Analyzer
has been developed by Kuntal et al.  which implements a
correlation-based graph layout algorithm that not only facilitates a
quick visualization of the differences in the analyzed microbial
communities (in terms of their taxonomic composition), but also
provides insights into the inherent inter-microbial interactions
occurring therein. Notably, this layout algorithm also enables
grouping of the metagenomes based on the probable inter-microbial
interaction patterns rather than simply comparing abundance values of
various taxonomic groups. In addition, the tool implements several
interactive GUI-based functionalities that enable users to perform
standard comparative analyses across microbiomes.
In many bacterial communities, natural or engineered (such as
bioreactors), there is significant division of labor in metabolism
(Syntrophy), during which the waste products of some organisms are
metabolites for others. In one such system, the methanogenic
bioreactor, functional stability requires the presence of several
syntrophic species (
Syntrophobacterales and Synergistia) working
together in order to turn raw resources into fully metabolized waste
(methane). Using comparative gene studies and expression
experiments with microarrays or proteomics researchers can piece
together a metabolic network that goes beyond species boundaries. Such
studies require detailed knowledge about which versions of which
proteins are coded by which species and even by which strains of which
species. Therefore, community genomic information is another
fundamental tool (with metabolomics and proteomics) in the quest to
determine how metabolites are transferred and transformed by a
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
RNA (the metatranscriptome) provides information on the regulation
and expression profiles of complex communities. Because of the
technical difficulties (the short half-life of mRNA, for example) in
the collection of environmental
RNA there have been relatively few in
situ metatranscriptomic studies of microbial communities to date.
While originally limited to microarray technology, metatranscriptomcs
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
Main article: Viral metagenomics
Metagenomic sequencing is particularly useful in the study of viral
communities. As viruses lack a shared universal phylogenetic marker
RNA for bacteria and archaea, and 18S
RNA for eukarya), the
only way to access the genetic diversity of the viral community from
an environmental sample is through metagenomics. Viral metagenomes
(also called viromes) should thus provide more and more information
about viral diversity and evolution. For example, a metagenomic
Giant Virus Finder showed the first evidence of
existence of giant viruses in a saline desert  and in Antarctic
dry valleys .
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.
Infectious disease diagnosis
Differentiating between infectious and non-infectious illness, and
identifying the underlying etiology of infection, can be quite
challenging. For example, more than half of cases of encephalitis
remain undiagnosed, despite extensive testing using state-of-the-art
clinical laboratory methods. Metagenomic sequencing shows promise as a
sensitive and rapid method to diagnose infection by comparing genetic
material found in a patient's sample to a database of thousands of
bacteria, viruses, and other pathogens.
Gut Microbe Characterization
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 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.
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 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.
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
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.
Main article: Biofuel
Bioreactors allow the observation of microbial communities as they
convert biomass into cellulosic ethanol.
Biofuels are fuels derived from biomass conversion, as in the
conversion of cellulose contained in corn stalks, switchgrass, and
other biomass into cellulosic ethanol. This process is dependent
upon microbial consortia(association) that transform the cellulose
into sugars, followed by the fermentation of the sugars into ethanol.
Microbes also produce a variety of sources of bioenergy including
methane and hydrogen.
The efficient industrial-scale deconstruction of biomass requires
novel enzymes with higher productivity and lower cost. Metagenomic
approaches to the analysis of complex microbial communities allow the
targeted screening of enzymes with industrial applications in biofuel
production, such as glycoside hydrolases. Furthermore, knowledge
of how these microbial communities function is required to control
them, and metagenomics is a key tool in their understanding.
Metagenomic approaches allow comparative analyses between convergent
microbial systems like biogas fermenters or insect herbivores such
as the fungus garden of the leafcutter ants.
Main article: Bioremediation
Metagenomics can improve strategies for monitoring the impact of
pollutants on ecosystems and for cleaning up contaminated
environments. Increased understanding of how microbial communities
cope with pollutants improves assessments of the potential of
contaminated sites to recover from pollution and increases the chances
of bioaugmentation or biostimulation trials to succeed.
Microbial communities produce a vast array of biologically active
chemicals that are used in competition and communication. Many of
the drugs in use today were originally uncovered in microbes; recent
progress in mining the rich genetic resource of non-culturable
microbes has led to the discovery of new genes, enzymes, and natural
products. The application of metagenomics has allowed the
development of commodity and fine chemicals, agrochemicals and
pharmaceuticals where the benefit of enzyme-catalyzed chiral synthesis
is increasingly recognized.
Two types of analysis are used in the bioprospecting of metagenomic
data: function-driven screening for an expressed trait, and
sequence-driven screening for
DNA sequences of interest.
Function-driven analysis seeks to identify clones expressing a desired
trait or useful activity, followed by biochemical characterization and
sequence analysis. This approach is limited by availability of a
suitable screen and the 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
DNA sequences to 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
The soils in which plants grow are inhabited by microbial communities,
with one gram of soil containing around 109-1010 microbial cells which
comprise about one gigabase of sequence information. The
microbial communities which inhabit soils are some of the most complex
known to science, and remain poorly understood despite their economic
importance. Microbial consortia perform a wide variety of
ecosystem services necessary for plant growth, including fixing
atmospheric nitrogen, nutrient cycling, disease suppression, and
sequester iron and other metals. Functional metagenomics
strategies are being used to explore the interactions between plants
and microbes through cultivation-independent study of these microbial
communities. By allowing insights into the role of previously
uncultivated or rare community members in nutrient cycling and the
promotion of plant growth, metagenomic approaches can contribute to
improved disease detection in crops and livestock and the adaptation
of enhanced farming practices which improve crop health by harnessing
the relationship between microbes and plants.
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. 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.
DNA sequencing can also be used more broadly to identify species
present in a body of water, debris filtered from the air, or
sample of dirt. This can establish the range of invasive species and
endangered species, and track seasonal populations.
Epidemiology and sewage
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