This document provides an introduction to metagenomics. It defines metagenomics as the study of microbial communities directly in their natural environments using modern genomics techniques. The document outlines the historical context and basic purpose of metagenomics. It describes some of the applications of metagenomics, such as understanding the human microbiome, bioremediation, bioenergy production, and smart farming. Finally, it introduces some basic concepts in metagenomics analysis including binning, OTUs, alpha and beta diversity measurements, and challenges around estimating diversity from samples.

1. Hospital Universitari Vall d’Hebron
Institut de Recerca - VHIR
Institut d’Investigació Sanitària de l’Instituto de Salud Carlos III (ISCIII)
Bioinformatics for
Biological Researchers
http://eib.stat.ub.edu/2014BBR
Ferran Briansó
ferran.brianso@vhir.org
28/05/2014
INTRODUCTION TO METAGENOMICSINTRODUCTION TO METAGENOMICS

2. 1. Introduction
2. Applications
3. Basic Concepts
4. Approaches & Workflows
1. Whole Genome Shotgun
2. 16S/ITS Community Surveys
●
Analysis Tools
1. MEGAN
2. Mothur
3. Qiime
4. Axiome & CloVR
5. MG-RAST
1. More resources
5
1
2
3
4
5
PRESENTATION OUTLINE
6

3. 1 INTRODUCTIONINTRODUCTION

4. Introduction | Metagenomics definition1
4
First use of the term metagenome, referencing 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.
Handelsman, J.; Rondon, M. R.; Brady, S. F.; Clardy, J.; Goodman, R. M. (1998).
"Molecular biological access to the chemistry of unknown soil microbes: A new
frontier for natural products".
Chemistry & Biology 5 (10): R245–R249. doi:10.1016/S1074-5521(98)90108-9.
PMID 9818143

5. 1
First use of the term metagenome, referencing 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.
Handelsman, J.; Rondon, M. R.; Brady, S. F.; Clardy, J.; Goodman, R. M. (1998).
"Molecular biological access to the chemistry of unknown soil microbes: A new
frontier for natural products".
Chemistry & Biology 5 (10): R245–R249. doi:10.1016/S1074-5521(98)90108-9.
PMID 9818143
Chen, K.; Pachter, L. (2005).
"Bioinformatics for Whole-Genome Shotgun Sequencing of Microbial Communities".
PLoS Computational Biology 1 (2): e24. doi:10.1371/journal.pcbi.0010024
Current definition:
“The application of modern genomics techniques to the
study of communities of microbial organisms directly in
their natural environments, bypassing the need for
isolation and lab cultivation of individual species.”
5
Introduction | Metagenomics definition

6. 1
6
Introduction | Historical context

7. 1
Source:
7
Introduction | Historical context

8. 1
Source:
http://howcoolismyresear.ch/#metagenomics
8
Introduction | Historical context

9. 1
9
Introduction | Basic purpose

10. 2 APPLICATIONSAPPLICATIONS

11. 2
11
Applications | What metagenomics can do
● Global Impacts. The role of microbes is critical in maintaining atmospheric
balances, as they are
● the main photosynthetic agents
● responsible for the generation and consumption of greenhouse
gases
● involved at all levels in ecosystems and trophic chains

12. 2
12
Applications | What metagenomics can do
● Global Impacts. The role of microbes is critical in maintaining atmospheric
balances, as they are
● the main photosynthetic agents
● responsible for the generation and consumption of greenhouse
gases
● involved at all levels in ecosystems and trophic chains
● Bioremediation. Cleaning up environmental contamination, such as
● the waste from water treatment facilities
● gasoline leaks on lands or oil spills in the oceans
● toxic chemicals

13. 2
13
Applications | What metagenomics can do
● Bioenergy. We are harnessing microbial power in order to produce
● ethanol (from cellulose), hydrogen, methane, butanol...
● Smart Farming. Microbes help our crops by
● the “supressive soil” phenomenon
(buffer effect against disease-causing organisms)
● soil enrichment and regeneration

14. 2
14
Applications | What metagenomics can do
● Bioenergy. We are harnessing microbial power in order to produce
● ethanol (from cellulose), hydrogen, methane, butanol...
● Smart Farming. Microbes help our crops by
● the “supressive soil” phenomenon
(buffer effect against disease-causing organisms)
● soil enrichment and regeneration
● The World Within. Studying the human microbiome may lead
to valuable new tools and guidelines in
● human and animal nutrition
● better understanding of complex diseases
(obesity, cancer, asthma...)
● drug discovery
● preventative medicine
Grice E.A. & Segre J.A. (2012) The Human Microbiome: Our Second Genome,
Annu. Rev. Genomics Human Genet. 13, 151-170

15. 2
15
Applications | Mapping the Human Microbiome

16. 3 BASIC CONCEPTSBASIC CONCEPTS

17. 3
17
Concepts | Trimming
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.

18. 18
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
3 Concepts | Binning, OTUs
http://shuixia100.weebly.com/1/post/2011/12/mothur-tutorial-1.html / Wikipedia: Biological classification

19. 19
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
3 Concepts | Binning, OTUs
http://shuixia100.weebly.com/1/post/2011/12/mothur-tutorial-1.html / Wikipedia: Biological classification

20. 20
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
● Chimeras: Artificial sequences formed during PCR amplification. The majority of them are believed to arise
from incomplete extension. During subsequent cycles of PCR, a partially extended strand can bind to a
template derived from a different but similar sequence. This then acts as a primer that is extended to form a
chimeric sequence (Smith et al. 2010, Thompson et al., 2002, Meyerhans et al., 1990, Judo et al., 1998,
Odelberg, 1995). A chimeric template is created during one round, then amplified by subsequent rounds to
produce chimeric amplicons that are difficult to distinguish from amplicons derived from a single biological
sequence.
3 Concepts | Chimeras
Hass B.J. et al (2011) Chimeric 16S rRNA sequence formation and detection in
Sanger and 454-pyrosequenced PCR amplicons, Genome Res. 21: 494-504.

21. 3
21
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
● Chimeras: Artificial sequences formed during PCR amplification. The majority of them are believed to arise
from incomplete extension. During subsequent cycles of PCR, a partially extended strand can bind to a
template derived from a different but similar sequence. This then acts as a primer that is extended to form a
chimeric sequence (Smith et al. 2010, Thompson et al., 2002, Meyerhans et al., 1990, Judo et al., 1998,
Odelberg, 1995). A chimeric template is created during one round, then amplified by subsequent rounds to
produce chimeric amplicons that are difficult to distinguish from amplicons derived from a single biological
sequence.
● Alpha diversity: the diversity within a particular area or ecosystem; expressed by the number of species (i.e.,
species richness) in that ecosystem, or by one or more diversity indices.
● Beta diversity: a comparison of of diversity between ecosystems, usually measured as the amount of species
change between the ecosystems.
● Gamma diversity: a measure of the overall diversity within a large region. Geographic-scale species diversity
according to Hunter (2002:448).
Concepts | Diversities
Zinger L. et al. (2012) Two decades of describing the unseen majority of
aquatic microbial diversity, Molecular Ecology 21, 1878–1896.

22. 3
22
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
● Chimeras: Artificial sequences formed during PCR amplification. The majority of them are believed to arise
from incomplete extension. During subsequent cycles of PCR, a partially extended strand can bind to a
template derived from a different but similar sequence. This then acts as a primer that is extended to form a
chimeric sequence (Smith et al. 2010, Thompson et al., 2002, Meyerhans et al., 1990, Judo et al., 1998,
Odelberg, 1995). A chimeric template is created during one round, then amplified by subsequent rounds to
produce chimeric amplicons that are difficult to distinguish from amplicons derived from a single biological
sequence.
● Alpha diversity: the diversity within a particular area or ecosystem; expressed by the number of species (i.e.,
species richness) in that ecosystem, or by one or more diversity indices.
● Beta diversity: a comparison of of diversity between ecosystems, usually measured as the amount of species
change between the ecosystems.
● Gamma diversity: a measure of the overall diversity within a large region. Geographic-scale species diversity
according to Hunter (2002:448).
Concepts | Diversity measurement issues
Zhou J. et al. (2010) Random Sampling Process Leads to Overestimation of β-Diversity
of Microbial Communities, mBio 4(3):e00324-13. doi:10.1128/mBio.00324-13.
Diversity can virtually never
be measured directly,
rather it must be estimated
or inferred from available
data. Our estimates are
anchored in the sample
itself.
Magurran (Ed.), Biological Diversity,
Oxford U.P. 2010. Ch. 16 Microbial
Diversity and Ecology

23. 3
23
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
● Chimeras: Artificial sequences formed during PCR amplification. The majority of them are believed to arise
from incomplete extension. During subsequent cycles of PCR, a partially extended strand can bind to a
template derived from a different but similar sequence. This then acts as a primer that is extended to form a
chimeric sequence (Smith et al. 2010, Thompson et al., 2002, Meyerhans et al., 1990, Judo et al., 1998,
Odelberg, 1995). A chimeric template is created during one round, then amplified by subsequent rounds to
produce chimeric amplicons that are difficult to distinguish from amplicons derived from a single biological
sequence.
● Alpha diversity: the diversity within a particular area or ecosystem; expressed by the number of species (i.e.,
species richness) in that ecosystem, or by one or more diversity indices.
● Beta diversity: a comparison of of diversity between ecosystems, usually measured as the amount of species
change between the ecosystems.
● Gamma diversity: a measure of the overall diversity within a large region. Geographic-scale species diversity
according to Hunter (2002:448).
● Rarefaction allows the calculation of species richness for a given number of individual samples, based on the
construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the
number of samples.
Concepts | Rarefaction
most or all species
have been sampled
species rich habitat, only a small
fraction has been sampled
this habitat has not been
exhaustively sampled
Wooley J.C. et al. (2010) A Primer on Metagenomics, PLoS Computational Biology 6 (2) e1000667

24. 3
24
Concepts | Diversity indices (α diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html
Other indices:
berger_parker_d, brillouin_d, dominance,
doubles, esty_ci, fisher_alpha, gini_index,
goods_coverage, margalef, mcintosh_d,
mcintosh_e, menhinick,osd, simpson_reciprocal,
robbins, singles, strong...

25. 3
25
Concepts | Compositional similarity (β diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html

26. 3
26
Concepts | Compositional similarity (β diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html

27. 3
27
Concepts | Compositional similarity (β diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html

28. 3
28
Concepts | Compositional similarity (β diversity)
Heat map
Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html

29. 3
29
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA sequencers prior to sequence assembly and other downstream uses.
● Binning is the process of grouping reads or contigs and assigning them to operational taxonomic units (OTUs).
● OTU (Operational Taxonomic Unit): Taxonomic level of sampling selected by the user to be used in a study.
Typically using a percent sequence similarity threshold for classifying microbes within the same, or different,
OTUs.
● Chimeras: Artificial sequences formed during PCR amplification. The majority of them are believed to arise
from incomplete extension. During subsequent cycles of PCR, a partially extended strand can bind to a
template derived from a different but similar sequence. This then acts as a primer that is extended to form a
chimeric sequence (Smith et al. 2010, Thompson et al., 2002, Meyerhans et al., 1990, Judo et al., 1998,
Odelberg, 1995). A chimeric template is created during one round, then amplified by subsequent rounds to
produce chimeric amplicons that are difficult to distinguish from amplicons derived from a single biological
sequence.
● Alpha diversity: the diversity within a particular area or ecosystem; expressed by the number of species (i.e.,
species richness) in that ecosystem, or by one or more diversity indices.
● Beta diversity: a comparison of of diversity between ecosystems, usually measured as the amount of species
change between the ecosystems.
● Gamma diversity: a measure of the overall diversity within a large region. Geographic-scale species diversity
according to Hunter (2002:448).
● Rarefaction allows the calculation of species richness for a given number of individual samples, based on the
construction of so-called rarefaction curves. This curve is a plot of the number of species as a function of the
number of samples.
● Metadata, reads, fasta/fastq files, counts, OTU tables/networks, .biom files, PCoA, p-values, diversity
metrics, robustness, scores, jackniffed, clustering, UPGMA, trees, bootstrap, Bi-Plots, ...
Concepts | Summary

30. 4 APPROACHES & WORKFLOWSAPPROACHES & WORKFLOWS

31. 4
31
Workflows | Microbial ecology approaches

32. 4
32
Grice E.A. & Segre J.A. (2012) The Human Microbiome: Our Second Genome,
Annu. Rev. Genomics Human Genet. 13, 151-170
Workflows | Overview
Sample collection
DNA extraction
and preparation
Sequencing
Analysis

33. 4
33
Grice E.A. & Segre J.A. (2012) The Human Microbiome: Our Second Genome,
Annu. Rev. Genomics Human Genet. 13, 151-170
Workflows | Overview
Sample collection
DNA extraction
and preparation
Sequencing
Analysis
Experimental design
Sample Quality Controls
Sequence Quality Controls
Biological interpretation

34. 4.1 WGS MetagenomicsWGS Metagenomics

35. 4
35
Workflows | Whole Genome Shotgun (WGS)
Sven-Eric Schelhorn https://bioinf.mpi-inf.mpg.de/homepage/research.php?&account=sven

36. 4
36
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

37. 4
37
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

38. 4
38
Workflows | Whole Genome Shotgun (WGS)
Sven-Eric Schelhorn https://bioinf.mpi-inf.mpg.de/homepage/research.php?&account=sven

39. 4
39
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

40. 4
40
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

41. 4.2 16S/ITS Metagenomics16S/ITS Metagenomics

42. 4
42
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

43. 4
43
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

44. 4
44
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

45. 4
45
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

46. 4
46
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

47. 4
47
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools

48. 5 METAGENOMICS TOOLSMETAGENOMICS TOOLS

49. 5
49
Tools | “The great quest”

50. 5
50
Tools | “The great quest”

51. 5
51
Tools | “The great quest”

52. 5
52
Tools | “The great quest”

53. 5
53
Tools | MEGAN
http://ab.inf.uni-tuebingen.de/software/megan5/

54. 5
54
Tools | MEGAN
http://ab.inf.uni-tuebingen.de/software/megan5/

55. 5
55
Tools | MEGAN
http://ab.inf.uni-tuebingen.de/software/megan5/

56. 5
56
Tools | Mothur
http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)

57. 5
57
Tools | Mothur
http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)

58. 5
58
Tools | Mothur
http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)

59. 5
59
Tools | Qiime

60. 5
60
Tools | Qiime

61. 5
61
Tools | Qiime

62. 5
62
Tools | Axiome
http://neufeld.github.io/AXIOME

63. 5
63
Tools | Axiome
http://neufeld.github.io/AXIOME

64. 5
64
Tools | Axiome
http://neufeld.github.io/AXIOME

65. 5
65
Tools | CloVR
http://clovr.org

66. 5
66
Tools | CloVR
http://clovr.org

67. 5
67
Tools | CloVR
http://clovr.org

68. 5
68
Tools | MG-RAST
http://http://metagenomics.anl.gov/

69. 5
69
Tools | MG-RAST
http://http://metagenomics.anl.gov/

70. 5
70
Tools | MG-RAST
http://http://metagenomics.anl.gov/

71. 6 MORE RESOURCESMORE RESOURCES

72. 6
72
More resources, courses...
Resources & Projects:
MEGAN DB http://www.megan-db.org/megan-db/ (MEtaGenomics ANalysis)
CAMERA http://camera.calit2.net/ (community Cyberinfrastructure for Advanced Microbial Ecology Research and Analysis)
MG-RAST Search http://metagenomics.anl.gov/metagenomics.cgi?page=MetagenomeSearch
IMG http://img.jgi.doe.gov/ (Integrated Microbial Genomes and metagenomes)
MetaBioME http://metasystems.riken.jp/metabiome/ (Comprehensive Metagenomic BioMining Engine)
BOLD http://www.boldsystems.org/ (Barcoding Of Live Database)
GOS Expedition http://www.jcvi.org/cms/research/projects/gos/overview (Global Ocean Sampling)
...

73. 6
73
More resources, courses...
Courses:
EBI http://www.ebi.ac.uk/training/course/metagenomics2014
EMBO http://cymeandcystidium.com/?tag=metagenomics
Coursera https://www.coursera.org/course/genomescience
... and a lot of seminars and workshops everywhere

74. Hospital Universitari Vall d’Hebron
Institut de Recerca - VHIR
Institut d’Investigació Sanitària de l’Instituto de Salud Carlos III (ISCIII)
Thanks for your attentionThanks for your attention
and also thanks to
Josep Gregori (VHIR, ROCHE)
for providing some materials
INTRODUCTION TO METAGENOMICSINTRODUCTION TO METAGENOMICS
Bioinformatics for
Biological Researchers
http://eib.stat.ub.edu/2014BBR
Ferran Briansó
ferran.brianso@vhir.org
28/05/2014