Metagenomics is the study of metagenome, genetics material, recovered directly from environmental sample such as soil, water or faeces. Metagenomics is based on the genomics analysis of microbial DNA directly from the communities present in samples Metagenomics technology – genomics on a large scale will probably lead to great advances in medicine, agriculture, energy production and bioremediation. Metagenomics can unlock the massive uncultured microbial diversity present in the environment for new molecule for therapeutic and biotechnological application. Metagenomic studies have identified many novel microbial genes coding for metabolic pathways such as energy acquisition, carbon and nitrogen metabolism in natural environments that were previously considered to lack such metabolism

1. By
Assist.Prof
Dr. Berciyal Golda.P
VICAS

2. o The term metagenomics first used by Jo
Handelsman, Jon Clarly, Robert M.
Goodman and first appeared in
publication in 1998.
o Metagenomics defined as “the genomics
analysis of microorganism by direct
extraction and cloning DNA from an
assemblage of microorganism.”
o In Greek, meta means “transcendent”
(combination of separate analysis)
Genomics refers to the study of
the genome
Jo Handelsman

3.  Metagenomics is the study of metagenome, genetics material, recovered
directly from environmental sample such as soil, water or faeces.
 Metagenomics is based on the genomics analysis of microbial DNA directly
from the communities present in samples
 Metagenomics technology – genomics on a large scale will probably lead to
great advances in medicine, agriculture, energy production and bioremediation.
 Metagenomics can unlock the massive uncultured microbial diversity present in
the environment for new molecule for therapeutic and biotechnological
application.
 Metagenomic studies have identified many novel microbial genes coding for
metabolic pathways such as energy acquisition, carbon and nitrogen
metabolism in natural environments that were previously considered to lack
such metabolism

4.  The science of metagenomics, only a few years old, will make it
possible to investigate microbes in their natural environments, the
complex communities in which they normally live.
 It will bring about a transformation in biology, medicine, ecology, and
biotechnology that may be as profound as that initiated by the invention
of the microscope.
 All plants and animals have closely associated microbial communities
that make necessary nutrients(carbon, nitrogen, oxygen, and sulfur)
metals, and vitamins available to their hosts.
 We depend on microbes to remediate toxins in the environment—both
the ones that are produced naturally and the ones that are the byproducts
of human activities, such as oil and chemical spills.

5.  In 1985 Pace and coworker introduced the idea a cloning DNA
directly from environmental samples.
 In 1991 Schmidt and coworker cloning of DNA from
Picoplankton in a phase vector subsequent 16S rRNA gene
sequence analyses.
 In 1995, Healy reported first successful function driven
metagenomics library was screened and termed that Zoolibraies.
 In 2002, Mya Breitbart and Forest Rohwer, used shotgun
sequencing to show that 200 liters of seawater contain over 5000
different viruses.

7.  Science of metagenomics make it possible to
investigate resource for the development of novel
genes, enzymes and chemical compounds for use in
biotechnology.
 Microbes, as communities, are key players in
maintaining environmental stability.
 Investigate microbes in their natural environment,
the complex communities in which they normally
live in.
 High-throughput gene-level studies of communities.

10.  Sample processing is the first and most crucial step in metagenomics.
 DNA extracted should be representative of all cells present in the sample and sufficient amounts of
high quality nucleic acids must be obtained for subsequent library production and sequencing.
 Sample fractionation steps should be checked to ensure that sufficient enrichment of the target is
achieved and that minimal contamination of non-target material occurs.
 Physical separation and isolation of cells from the samples might also be important to maximize DNA
yield or avoid co-extraction of enzymatic inhibitors that might interfere with subsequent processing.
 Direct lysis of cells versus indirect lysis has a quantifiable bias in terms of microbial diversity, DNA
yield, and resulting sequence fragment length.
 Some type of sample such as biopsies or ground water often yield very small amounts of DNA but in
library production for most sequencing technologies require high amounts of DNA (ng or µg ), and
hence amplification of starting material might be required.
 Multiple displacement amplification (MDA) using random hexamers and phage phi29 polymerase
is one option employed to increase DNA yields, this method has been widely used in single-cell
genomics and to a certain extent in metagenomics.

11. Dispersing misconceptions and identifying opportunities for the use of 'omics' in soil microbial ecology
James I. Prosser
Nature Reviews Microbiology 13, 439–446 (2015)

12. volcano
Havey metal composition

13.  There are two basic types of
Metagenomics studies
I. Sequence-based Metagenomics-
involves sequencing and analysis of DNA
from environmental samples
II. Function-based Metagenomics
involves screening for a particular
function or activity

14.  Sequence-based metagenomics studies can be used to assemble genomes,
identify genes, find complete metabolic pathways, and compare
organisms of different communities.
 Genome assembly requires lots of computer power but it can lead to a
better understanding of how certain genes help organisms survive in a
particular environment.
 Sequence-based metagenomics can also be used to establish the degree
of diversity and the number of different bacterial species existing in a
particular sample.
 Analyzing microbial diversity is less costly and less computer intensive
than assembling genomes and it can provide valuable information about
the ecology of microbes in a sample.

15.  Whole genome sequencing developed
by J. Craig Venter and Hamilton Smith
in 1995.
 Whole genome sequencing can help to
reconstruct large fragments or even
complete genome from organism in a
community without previous isolation,
allowing the characterization of a large
number of coding and non-coding
sequence can used as phylogenetic
marker.
 Whole genome sequencing provides
information both about which organism
are present & what metabolic processes
are possible in the community. J. Craig Venter

16. WHOLE GENOME
SEQUENCING
 Whole genome sequencing
based on basic four steps
I. Library construction
II. Random sequencing
III. Fragment Alignment and
gap closure
IV. Editing

17.  Carl Woese and coworker started to analyze and sequence
the 16S rDNA genes of various bacteria, using DNA
sequencing, a state-of-the-art technology at that time, and
used the sequences for phylogenetic studies.
 16S rRNA is a part of the ribosomal RNA of prokaryotic cell
which is about 1,542 nucleotide.
 16S gene contain region that are highly conserved between
species and also variable region that are species specific.
I. Conserved region provide excellent amplification targets.
II. Variable region are highly informative for taxonomic
classification.
 Thus is the powerful tool used for classification and
genome analysis

18.  Evidence for horizontal gene transfer
exchange of genetic material between two
genomes without a parental relationship.

19.  DNA sequencing is one of the most important platforms for the
study of biological systems today. (Ronaghi, 2001)
A. Next generation DNAsequencing
I. 454 life sciences or pyrosequencing
II. Solexa/Illumina
III. Sequencing by ligation (SOLiD technology)
IV. Ion Torrent or PGM

20.  Sequence determination is most commonly performed using di-
deoxy chain termination technology, also known as Sanger
sequencing, was developed by Frederick Sanger and collègues
(Sanger et al., 1977).
 Pyrosequencing technology is a novel DNA sequencing technology,
the first alternative to the conventional Sanger method for de novo
DNA sequencing.(Md. Fakruddin et al., 2012)
 Pyrosequencing has the potential advantages of accuracy, flexibility,
parallel processing, and can be easily automated. (Md. Fakruddin et al.,
2012)

21.  Pyrosequencing a DNA sequencing technique that relies on
detection of pyrophosphate release upon nucleotide incorporation
rather than chain termination with dideoxynucleotides.
 In Pyrosequencing (Nyren and Skarpnack, 2001) the sequencing
primer is hybridized to a single-stranded DNA biotin-labeled
template and mixed with the enzymes; DNA polymerase, ATP
sulfurylase, luciferase and apyrase, and the substrates adenosine 5′
phosphosulfate (APS) and luciferin (Gharizadeh et al., 2007).
 Cycles of four deoxynucleotide triphosphates (dNTPs) are
separately added to the reaction mixture iteratively.
 The cascade starts with a nucleic acid polymerization reaction in
which inorganic PPi is released as a result of nucleotide
incorporation by polymerase.

22.  Each nucleotide incorporation event is followed by release of inorganic
pyrophosphate (PPi) in a quantity equimolar to the amount of
incorporated nucleotide.
 The released PPi is quantitatively converted to ATP by ATP sulfurylase
in the presence ofAPS.
 The generated ATP drives the luciferase-mediated conversion of
luciferin to oxyluciferin, producing visible light in amounts that are
proportional to the amount ofATPs.
 The light in the luciferase-catalyzed reaction with a maximum of 560
nm wavelength is then detected by a photon detection device such as a
charge coupled device (CCD) camera or photomultiplier.
 Apyrase is a nucleotide-degrading enzyme, which continuously
degradesATPand non-incorporated dNTPs in the reaction mixture.

26. http://www.molecularecologist.com/next-gen-fieldguide-2014/ (Accessed Aug
17, 2015).

27.  Binning is the process of grouping reads or contigs into individual genomes and
assigning the group to specific species, subspecies or genus.
 More innovative binning approaches include co-abundance gene segregation across
a series of metagenomic sample thus facilating the assembly of microbial genomes
without the need for reference sequences.
 Important considerations for using any binning algorithm are the type of input data
available and the existence of a suitable training dataset or reference genomes.
 Binning methods can be characterized in two different ways depending on
information contained within a given DNA sequence
1. Composition based binning
2. Similarity or homology based binning

28.  Composition based binning is based on the observation that individual
genomes have a unique distribution of k-mer sequence is known as
genomic signatures.
 Binning makes use of this conserved species-specific nucleotide
composition (such as GC) are capable of grouping sequences into their
respective genomes.
 Compositional based binning algorithms include phylopythia, successor
phylopythiaS, S-GSOM, PCAHIER, TACAO, TETRA, ESOM and
ClaMS.
 Composition based binning is not reliable for short reads as they do not
contain enough information.

29.  Similarity based binning refer to the process of using alignment
algorithms such as BLAST or profile hidden markov models
(pHMMs) to obtain similarity information about specific sequences/
genes from publically available databases.
 Similarity based binning algorithms include IMG/M, MG-RAST,
MEGAN, CARMA, Sort-ITEMS and Metaphyler.
 Similarity based binning fail to do so accurately while reads of
short length, the metagenome under consideration consists of
numerous closely related species.

31.  Annotation is the process of assigning functional, positional, and species-
of-origin information to the genes in a database.
 Annotation of metagenome is specifically designed to work with mixtures
of genomes and contig of varying length.
 Annotation is included the four preprocessing steps
A. Trimming of low quality reads–using platform specific tool such as
FASTX-Toolkit, SolexaQA, and Lucy2 the threshold of which depend
on sequencing technology.
B. Masking of low complexity reads-performed using tool such as DUST.
C. A de-replication step that removes sequence that more than 95%
identical.
D. A screening –the pipeline provided the option of removing reads that are
near exact match to the genome of a handful of model organism
including fly, mouse, cow and human.
 Identification of genes within the reads/ assembly contig, a process often
denoted as “gene calling”

32.  Gene are labeled as coding DNA sequence (CDS) identified using a number of
tool including Meta-gene mark, meta-gene or phedia and FragGene scan all of
which utilize ab inito gene prediction algorithms.
 Non-coding RNAs such as t-RNA are predicted using programs like tRNA Scan,
ribosomal RNA (rRNA) gene (5S, 16S and 23S) are predicted using internally
developed rRNA model for IMG/MER and MG-RAST use similarity to compare
known database to predict rRNA.
 Annotation pipeline involves functional assignment to the predicted protein
coding genes. This is currently achieved by homology based searches of query
sequences against databases containing known functional and/or taxonomic
information.
 Both IMG/MER and MGRAST are widely used data management repositories
and comparative genomics environments. They are fully automated pipelines that
provide quality control, gene prediction, and functional annotation.

33. ADVANTAGES OF ASSEMBLING METAGENOMES ARE:
(1) The possibility of analysing the genome context (i.e.,
operons);
(2) Increasing the probability of complete genes and genomes
reconstruction, arising the confidence of sequence
annotation;
(3) Analysis simplification by mapping long contigs instead of
short reads (Thomas et al., 2012; Luo et al., 2013; Segata
et al., 2013).

34.  NCBI is mandated to store all metagenomic data, however, the sheer volume of data being
generated means there is an urgent need for appropriate ways of storing vast amounts of
sequences.
 Tools such as IMG/MER, CAMERA, MGRAST, and EBI metagenomics (which also
incorporates QIIME) provide an integrated environment for analysis, management, storage,
and sharing of metagenome projects.
 The GSC is currently investing heavily toward a widely accepted language that shares
ontologies and nomenclatures thereby providing a common standard for exchange of data
derived from the analysis of metagenomic projects.
 A suite of standard languages for metadata is currently provided by the Minimum
Information about any (x) Sequence checklists (MIxS).
 MIxS is an umbrella term to describe MIMS (Minimum Information about a Metagenome
Sequence) and MIMARKS (Minimum Information about a MARKer Sequence) have been
devised, providing a scheme of standard languages for metadata annotation.

35.  Functional metagenomics is a powerful experimental approach for studying gene
function, starting from the extracted DNA of mixed microbial populations.
 A functional approach relies on the construction and screening of metagenomic
libraries—physical libraries that contain DNA cloned from environmental
metagenomes.
 Functional metagenomics begins with the construction of a metagenomic library,
Cosmid- or fosmid-based libraries are often preferred due to their large and consistent
insert size and high cloning efficiency.
 The information obtained from functional metagenomics can help in future
annotation of gene function and serve as a complement to sequence – based
metagenomics.
 Using this function-based approach allows for discovery of novel enzymes whose
functions would not be predicted based on DNA sequence alone.

36. Total metagenomic DNA is extracted from a microbial community
sample, sheared, and ligated into an expression vector and is subsequently
transformed into a suitable library host to create a metagenomic library.
The library is then plated on media containing antibiotics inhibitory to the
wild-type host to select for metagenomic fragments conferring antibiotic
fragments present in colonies growing on antibiotic
resistance.
Metagenomic
selection media are then PCR amplified and sequenced using either
traditional Sanger sequencing or next-generation sequencing methods.
Finally, reads are assembled and annotated in order to identify the
causative antibiotic resistance genes

38. FUNCTIONAL METAGENOME ANALYSIS
 Reconstruction of metabolic pathway from enzyme coding gene is
a relevant matter in the metagenome analysis.
 There are two options to perform functional annotation from
shotgun sequences, one is using sequencing reads directly and
another is read assembly.

39. GENE PREDICTION
 Gene prediction determine which metagenomics reads contain
coding sequence.
 Metagenome assembly, gene prediction and annotation are similar
to the framework followed in whole genome characterization
(Yandell and Ence 2012, Richardson and Watson, 2013)
 Gene prediction by three ways
I. Gene fragments requirements
II. Protein family classification
III. De novo gene prediction

40. METABOLIC PATHWAY RECONSTRUCTION
 Pathway reconstruction of the metagenome data is one of the annotation
goals and the term “inter-organismic meta- routes” or “meta-pathways”
has been proposed for this kind of analysis (De Filippo et al., 2012).
 The concept of metabolic pathway in microbial ecology should be
understood as the flow of information through different species.
 Function annotation has to be used to find each gene in an appropriate
metabolic context, filling missing enzymes in pathways and find optimal
metabolic states to perform the best pathway reconstructions.
 Programs available are MinPath (Ye and Doak, 2009) and MetaPath (Liu
and Pop, 2010). Both use information deposited in KEGG (Ogata et al.,
1999) and MetaCyc (Caspi et al., 2014) repositories.
 Metabolic pathway reconstruction could be completed with information
provided by the data context such as gene function interactions, synteny,
and copy number of annotated genes to integrate the metabolic potential
of consortium.

41. METABOLIC PATHWAY RECONSTRUCTION

42. TOOL USED IN METAGENOMICS

44. APPLICATION OF METAGENOMICS
 Metagenomics has the potential to advance knowledge in
a wide variety of field.
III.
I. Medicine
II. Engineering
Agriculture
IV. Ecology
V. Biotechnology.

45. APPLICATION
 Metagenomics can improve strategies for monitoring the impact of pollutants on ecosystems
and for cleaning up contaminated environments. Increased understanding of bioaugmentation or
biostimulation trials to succeed.
 Recent progress in mining the rich genetic resource of nonculturable microbes has led to the
discovery of new gene, enzymes and natural products. The impact of metagenomics is
witnessed in the development of commodity and fine chemicals, agrochemicals and
pharmaceuticals where the benefit of enzyme catalyzed chiral synthesis is increasingly
recognized.
 Metagenomics libraries are, indeed, an essential tool for the discovery of new enzymatic
activities, facilitating genetic tracking for all biotechnological applications of interest for the
future.
 Metagenomics sequencing is being used to characterize the microbial communities. This is part
of the human micro-biome initiative with primary goals to determine if there is a core human
micro-biome, to understand the changes in the human micro-biome that can be correlated with
human health, and to develop new technological and bioinformatics tools to support these
goals.
 It is well known that the vast majority of microbes have not been cultivated. Functional
metagenomics strategies are being used to explore the interactions between plants and microbes
through cultivation-independent study of the microbial communities.

47. • New enzymes, antibiotics, and other reagents identified
• More exotic habitats can be intently studied
• Can only progress as library technology progresses, including
sequencing technology
• Improved bioinformatics will quicken analysis for library
profiling
• Investigating ancient DNAremnants
• Discoveries such as phylogenic tags (rRNAgenes, etc) will give
momentum to the growing field
• Learning novel pathways will lead to knowledge about the
current nonculturable bacteria to then culture these systems

48. LIMITATION
 To much data.
 Most gene are not identifiable
 Contamination, chimeric clone sequences
 Extraction problem
 Requires proteomics or expression studies to demonstrate phenotypic characteristics
 Need a standard method for annotating genomes
 Can only progress as library technology progresses, including sequencing technology.
 Requires high throughput instrumentation not readily available to most institutions.

49. CONCLUSION
 Metagenomics has benefited in the past few years from many
visionary investments in both financial and intellectual terms.
 The science of metagenomics is currently in its pioneering stages
of development as a field, and many tools and technologies are
undergoing rapid evolution.
 The best use of the metagenomics as a tool to address
fundamental question of microbial ecology, evolution and
diversity and to derive and test new hypothesis.
 As datasets more
analysis,
complex and
storage, and
comprehensive,
become increasingly
novel tools for
visualization will be required.

50.  Metagenomics allows us to discover new genes and proteins or
even the complete genomes of non-cultivable organisms in less
time and with better accuracy than classical microbiology or
molecular methods.
 In addition to the phenotypic dimension of human biology, such as
gene expression profiling, proteomics, and metabolomics, perhaps
we need to extend our concept of the human genome to include
the more comprehensive and plastic human metagenome in
laboratory medicine.

51. REFERENCE
 Jo Handelsman (2004) Metagenomics: Application of Genomics to Uncultured
Microorganisms Microbiology and Molecular Biology Review, 68: 4, 669-685
 Alejandra Escobar-Zepeda, Arturo Vera-Poncede León and Alejandro Sanchez-
Flores (2015) The Road to Metagenomics: From Microbiology to DNA
Sequencing Technologies and Bioinformatics, Frontiers in Genetics, 6, 348: 1-15.
 Asiya Nazir (2016) Review on Metagenomics and its Applications: Imperial
Journal of Interdisciplinary Research 2: 3, 277-286.
 Thomas J. Sharpton (2014) An introduction to the analysis of shotgun
metagenomic data Frontier in Plant Science, 5, 209, 1-14.
 Torsten Thomas, Jack Gilbert, and Folker Meyer (2012) Metagenomics a guide
from sampling to data analysis Microb Inform Exp. 2: 3. 1-15.
 Anastasis Oulas et. al. (2015) Metagenomics: Tools and Insights for Analyzing
Next Generation Sequencing Data Derived from Biodiversity Studies, Bioinform
Biol Insights. 9: 75–88.
 Joseph F. Petrosino et. al. (2019) Metagenomic Pyrosequencing and Microbial
Identification Clinical Chemistry 55: 5, 856–866.

52. CONT..
 Patake R. S. and Patake G. R (2011) A Mini Review on Metagenomics and its
Implications in Ecological and Environmental Biotechnology Universal Journal
of Environmental Research and Technology, 1: 1-6.
 Wolfgang R Streit and Ruth A Schmitz (2004) Metagenomics – the key to the
uncultured microbes Current Opinion in Microbiology, 7: 492–498.
 Jianping Xu (2006) Microbial ecology in the age of genomics and metagenomics:
concepts, tools, and recent advances Molecular Ecology 15, 1713–1731.
 R.D. Sleator, C. Shortall and C. Hill (2008) Metagenomics Letters in Applied
Microbiology 47: 361–366.
 Md. Fakruddin (2012) Pyrosequencing- principles and applications, International
Journal of Life Science & Pharma Research, 2: 2, 65-76.
 Ludmila Chistoserdova (2009) Functional Metagenomics: Recent Advances and
Future Challenges, Biotechnology and Genetic Engineering Reviews, 26 335-352.