Hospital Universitari Vall d’Hebron
Institut de Recerca - VHIR
Institut d’Investigació Sanitària de l’Instituto de Salud C...
1. Introduction
2. Applications
3. Basic Concepts
4. Approaches & Workflows
1. Whole Genome Shotgun
2. 16S/ITS Community S...
1 INTRODUCTIONINTRODUCTION
Introduction | Metagenomics definition1
4
First use of the term metagenome, referencing the idea that a collection of
gene...
1
First use of the term metagenome, referencing the idea that a collection of
genes sequenced from the environment could b...
1
6
Introduction | Historical context
1
Source:
7
Introduction | Historical context
1
Source:
http://howcoolismyresear.ch/#metagenomics
8
Introduction | Historical context
1
9
Introduction | Basic purpose
2 APPLICATIONSAPPLICATIONS
2
11
Applications | What metagenomics can do
● Global Impacts. The role of microbes is critical in maintaining atmospheric...
2
12
Applications | What metagenomics can do
● Global Impacts. The role of microbes is critical in maintaining atmospheric...
2
13
Applications | What metagenomics can do
● Bioenergy. We are harnessing microbial power in order to produce
● ethanol ...
2
14
Applications | What metagenomics can do
● Bioenergy. We are harnessing microbial power in order to produce
● ethanol ...
2
15
Applications | Mapping the Human Microbiome
3 BASIC CONCEPTSBASIC CONCEPTS
3
17
Concepts | Trimming
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes....
18
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA...
19
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA...
20
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated DNA...
3
21
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated D...
3
22
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated D...
3
23
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated D...
3
24
Concepts | Diversity indices (α diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/wat...
3
25
Concepts | Compositional similarity (β diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpage...
3
26
Concepts | Compositional similarity (β diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpage...
3
27
Concepts | Compositional similarity (β diversity)
Mozzarella project, Michele Iacono http://www.science.gov/topicpage...
3
28
Concepts | Compositional similarity (β diversity)
Heat map
Mozzarella project, Michele Iacono http://www.science.gov/...
3
29
● Trimming: is the pre-processing step of cleaning sequence data (primers, multiplexing barcodes...) from
automated D...
4 APPROACHES & WORKFLOWSAPPROACHES & WORKFLOWS
4
31
Workflows | Microbial ecology approaches
4
32
Grice E.A. & Segre J.A. (2012) The Human Microbiome: Our Second Genome,
Annu. Rev. Genomics Human Genet. 13, 151-170
...
4
33
Grice E.A. & Segre J.A. (2012) The Human Microbiome: Our Second Genome,
Annu. Rev. Genomics Human Genet. 13, 151-170
...
4.1 WGS MetagenomicsWGS Metagenomics
4
35
Workflows | Whole Genome Shotgun (WGS)
Sven-Eric Schelhorn https://bioinf.mpi-inf.mpg.de/homepage/research.php?&accou...
4
36
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha...
4
37
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha...
4
38
Workflows | Whole Genome Shotgun (WGS)
Sven-Eric Schelhorn https://bioinf.mpi-inf.mpg.de/homepage/research.php?&accou...
4
39
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha...
4
40
Workflows | Whole Genome Shotgun (WGS)
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha...
4.2 16S/ITS Metagenomics16S/ITS Metagenomics
4
42
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-...
4
43
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-...
4
44
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-...
4
45
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-...
4
46
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-...
4
47
Workflows | 16S/ITS Community Surveys
Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-...
5 METAGENOMICS TOOLSMETAGENOMICS TOOLS
5
49
Tools | “The great quest”
5
50
Tools | “The great quest”
5
51
Tools | “The great quest”
5
52
Tools | “The great quest”
5
53
Tools | MEGAN
http://ab.inf.uni-tuebingen.de/software/megan5/
5
54
Tools | MEGAN
http://ab.inf.uni-tuebingen.de/software/megan5/
5
55
Tools | MEGAN
http://ab.inf.uni-tuebingen.de/software/megan5/
5
56
Tools | Mothur
http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)
5
57
Tools | Mothur
http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)
5
58
Tools | Mothur
http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)
5
59
Tools | Qiime
5
60
Tools | Qiime
5
61
Tools | Qiime
5
62
Tools | Axiome
http://neufeld.github.io/AXIOME
5
63
Tools | Axiome
http://neufeld.github.io/AXIOME
5
64
Tools | Axiome
http://neufeld.github.io/AXIOME
5
65
Tools | CloVR
http://clovr.org
5
66
Tools | CloVR
http://clovr.org
5
67
Tools | CloVR
http://clovr.org
5
68
Tools | MG-RAST
http://http://metagenomics.anl.gov/
5
69
Tools | MG-RAST
http://http://metagenomics.anl.gov/
5
70
Tools | MG-RAST
http://http://metagenomics.anl.gov/
6 MORE RESOURCESMORE RESOURCES
6
72
More resources, courses...
Resources & Projects:
MEGAN DB http://www.megan-db.org/megan-db/ (MEtaGenomics ANalysis)
C...
6
73
More resources, courses...
Courses:
EBI http://www.ebi.ac.uk/training/course/metagenomics2014
EMBO http://cymeandcyst...
Hospital Universitari Vall d’Hebron
Institut de Recerca - VHIR
Institut d’Investigació Sanitària de l’Instituto de Salud C...
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Introduction to Metagenomics. Applications, Approaches and Tools (Bioinformatics for Biological Researchers Course - CSIC, Blanes)

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Course: Bioinformatics for Biologiacl Researchers (2014).
Session: 3.1- Introduction to Metagenomics. Applications, Approaches and Tools.
Statistics and Bioinformatisc Unit (UEB) from Vall d'Hebron Research Institute (www.vhir.org), Barcelona.

Published in: Science, Technology

Introduction to Metagenomics. Applications, Approaches and Tools (Bioinformatics for Biological Researchers Course - CSIC, Blanes)

  1. 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. 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. 3. 1 INTRODUCTIONINTRODUCTION
  4. 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. 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. 6. 1 6 Introduction | Historical context
  7. 7. 1 Source: 7 Introduction | Historical context
  8. 8. 1 Source: http://howcoolismyresear.ch/#metagenomics 8 Introduction | Historical context
  9. 9. 1 9 Introduction | Basic purpose
  10. 10. 2 APPLICATIONSAPPLICATIONS
  11. 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. 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. 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. 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. 15. 2 15 Applications | Mapping the Human Microbiome
  16. 16. 3 BASIC CONCEPTSBASIC CONCEPTS
  17. 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. 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. 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. 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. 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. 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. 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. 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. 25. 3 25 Concepts | Compositional similarity (β diversity) Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html
  26. 26. 3 26 Concepts | Compositional similarity (β diversity) Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html
  27. 27. 3 27 Concepts | Compositional similarity (β diversity) Mozzarella project, Michele Iacono http://www.science.gov/topicpages/w/water+buffalo+mozzarella.html
  28. 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. 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. 30. 4 APPROACHES & WORKFLOWSAPPROACHES & WORKFLOWS
  31. 31. 4 31 Workflows | Microbial ecology approaches
  32. 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. 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. 34. 4.1 WGS MetagenomicsWGS Metagenomics
  35. 35. 4 35 Workflows | Whole Genome Shotgun (WGS) Sven-Eric Schelhorn https://bioinf.mpi-inf.mpg.de/homepage/research.php?&account=sven
  36. 36. 4 36 Workflows | Whole Genome Shotgun (WGS) Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  37. 37. 4 37 Workflows | Whole Genome Shotgun (WGS) Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  38. 38. 4 38 Workflows | Whole Genome Shotgun (WGS) Sven-Eric Schelhorn https://bioinf.mpi-inf.mpg.de/homepage/research.php?&account=sven
  39. 39. 4 39 Workflows | Whole Genome Shotgun (WGS) Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  40. 40. 4 40 Workflows | Whole Genome Shotgun (WGS) Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  41. 41. 4.2 16S/ITS Metagenomics16S/ITS Metagenomics
  42. 42. 4 42 Workflows | 16S/ITS Community Surveys Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  43. 43. 4 43 Workflows | 16S/ITS Community Surveys Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  44. 44. 4 44 Workflows | 16S/ITS Community Surveys Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  45. 45. 4 45 Workflows | 16S/ITS Community Surveys Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  46. 46. 4 46 Workflows | 16S/ITS Community Surveys Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  47. 47. 4 47 Workflows | 16S/ITS Community Surveys Surya Saha & Magdalen Lindeberg http://www.slideshare.net/suryasaha/surya-saha-metagenomics-tools
  48. 48. 5 METAGENOMICS TOOLSMETAGENOMICS TOOLS
  49. 49. 5 49 Tools | “The great quest”
  50. 50. 5 50 Tools | “The great quest”
  51. 51. 5 51 Tools | “The great quest”
  52. 52. 5 52 Tools | “The great quest”
  53. 53. 5 53 Tools | MEGAN http://ab.inf.uni-tuebingen.de/software/megan5/
  54. 54. 5 54 Tools | MEGAN http://ab.inf.uni-tuebingen.de/software/megan5/
  55. 55. 5 55 Tools | MEGAN http://ab.inf.uni-tuebingen.de/software/megan5/
  56. 56. 5 56 Tools | Mothur http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)
  57. 57. 5 57 Tools | Mothur http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)
  58. 58. 5 58 Tools | Mothur http://www.mothur.org/wiki/Main_Page / Kevin R. Theis (Michigan State University)
  59. 59. 5 59 Tools | Qiime
  60. 60. 5 60 Tools | Qiime
  61. 61. 5 61 Tools | Qiime
  62. 62. 5 62 Tools | Axiome http://neufeld.github.io/AXIOME
  63. 63. 5 63 Tools | Axiome http://neufeld.github.io/AXIOME
  64. 64. 5 64 Tools | Axiome http://neufeld.github.io/AXIOME
  65. 65. 5 65 Tools | CloVR http://clovr.org
  66. 66. 5 66 Tools | CloVR http://clovr.org
  67. 67. 5 67 Tools | CloVR http://clovr.org
  68. 68. 5 68 Tools | MG-RAST http://http://metagenomics.anl.gov/
  69. 69. 5 69 Tools | MG-RAST http://http://metagenomics.anl.gov/
  70. 70. 5 70 Tools | MG-RAST http://http://metagenomics.anl.gov/
  71. 71. 6 MORE RESOURCESMORE RESOURCES
  72. 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. 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. 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

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