UEB-VHIR's Metagenomics Training. Session 1. 2013/08/26. An Introduction to Metagenomics Data Analysis. Ferran Briansó (ferran.brianso@vhir.org)

1. An Introduction toAn Introduction to
Metagenomics Data AnalysisMetagenomics Data Analysis
Metagenomics TrainingMetagenomics Training
Ferran BriansóFerran Briansó
VHIR - 26/08/2013
ferran.brianso@vhir.orgferran.brianso@vhir.org

2. OutlineOutline
 Introduction to Metagenomics
 Basic Terminology
 Computational Approaches & Tools
 Whole Genome Shotgun
 16S/ITS Community Surveys
 Recommended Tools
 MEGAN
 mothur
 QIIME
 AXIOME & CloVR

3. Introduction to METAGENOMICSMETAGENOMICS

4. IntroductionIntroduction
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. 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.
“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.”
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
IntroductionIntroduction

6. Source: US Division of Earth & Life Studies of the National Academies
http://dels-old.nas.edu/metagenomics/overview.shtml
IntroductionIntroduction

7. Source: US Division of Earth & Life Studies of the National Academies
http://dels-old.nas.edu/metagenomics/overview.shtml
IntroductionIntroduction

8. Source:
IntroductionIntroduction

9. Source: Feng Chen, JGI
IntroductionIntroduction
Perfomance Comparison for (some) Platforms

10. Basic TERMINOLOGYTERMINOLOGY

11. TerminologyTerminology
 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.

12.  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.
TerminologyTerminology

13.  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).
TerminologyTerminology

14.  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.
TerminologyTerminology

15. Computational APPROACHES & TOOLSAPPROACHES & TOOLS

16. Approaches & ToolsApproaches & Tools

17. Approaches & ToolsApproaches & Tools

18. Approaches & ToolsApproaches & Tools

19. Approaches & ToolsApproaches & Tools

20. Whole Genome SHOTGUNSHOTGUN

21. Whole Genome ShotgunWhole Genome Shotgun

22. WGS WorkflowWGS Workflow

23. WGS WorkflowWGS Workflow

24. WGS WorkflowWGS Workflow

25. WGS WorkflowWGS Workflow

26. Examples of WGS ToolsExamples of WGS Tools

27. Examples of WGS ToolsExamples of WGS Tools

28. Analysis of 16S/ITS16S/ITS Community SurveysCommunity Surveys

29. 16S/ITS community surveys16S/ITS community surveys

30. 16S/ITS issues16S/ITS issues

31. 16S/ITS workflow16S/ITS workflow

32. 16S/ITS workflow16S/ITS workflow

33. 16S/ITS workflow16S/ITS workflow

34. 16S/ITS workflow16S/ITS workflow

35. Some recommended ToolsTools

36. Some (recommended) ToolsSome (recommended) Tools
mothur
MEGAN

37. MEGANMEGAN
2007 →
2011 →
...
...
2012 →

38. MEGAN 4 for 16S rRNAMEGAN 4 for 16S rRNA

39. MEGAN 4 for 16S rRNAMEGAN 4 for 16S rRNA

40. mothurmothur
2009 →

41. mothurmothur
2009 →

42. QIIMEQIIME

43. Integrative Tools/PlatformsTools/Platforms

44. AXIOMEAXIOME

45. AXIOMEAXIOME

46. AXIOMEAXIOME

47. CloVRCloVR
http://www.edgebio.com

48. CloVRCloVR
http://www.edgebio.com
http://clovr.org

49. CloVRCloVR

50. CloVRCloVR

51. CloVRCloVR

52. CloVRCloVR

53. CloVRCloVR

54. Ferran BriansóFerran Briansó
MGTraining 26/08/2013
Thanks for your attentionThanks for your attention
ferran.brianso@vhir.orgferran.brianso@vhir.org
An Introduction toAn Introduction to
Metagenomics Data AnalysisMetagenomics Data Analysis
more info at
http://ueb.vhir.org/MGT