This presentation compares 16S vs metagenomics and walks through two major approaches for taxonomically and functionally annotating.

1. GLBIO/CCBC Microbiome Analysis
Workshop: Metagenomics
Morgan G.I. Langille
Assistant Professor
Dalhousie University
May 16, 2016

2. Learning Objectives
• Contrast 16S and metagenomic sequencing
• Taxonomy from metagenomes
• Function from metagenomes
• Applicability of assembling and gene calling with metagenomic data
• Metagenomic inference and limitations
• Tutorial on processing metagenomic data to determine functional
and taxonomic profiles

3. 16S vs Metagenomics
• 16S is targeted sequencing of a single gene which acts as a
marker for identification
• Pros
– Well established
– Sequencing costs are relatively cheap (~50,000 reads/sample)
– Only amplifies what you want (no host contamination)
• Cons
– Primer choice can bias results towards certain organisms
– Usually not enough resolution to identify to the strain level
– Different primers are needed for archaea & eukaryotes (18S)
– Doesn’t identify viruses

4. 16S vs Metagenomics
• Metagenomics: sequencing all the DNA in a sample
• Pros
– No primer bias
– Can identify all microbes (euks, viruses, etc.)
– Provides functional information (“What are they doing?”)
• Cons
– More expensive (millions of sequences needed)
– Host/site contamination can be significant
– May not be able to sequence “rare” microbes
– Complex bioinformatics

5. TAXONOMIC PROFILES
Who is there?

6. Metagenomics: Who is there?
• Goal: Identify the relative abundance of different
microbes in a sample given using metagenomics
• Problems:
– Reads are all mixed together
– Reads can be short (~100bp)
– Lateral gene transfer
• Two broad approaches
1. Binning Based
2. Marker Based

7. Binning Based
• Attempts to group or “bin” reads into the
genome from which they originated
• Composition-based
– Uses sequence composition such as GC%, k-mers (e.g.
Naïve Bayes Classifier)
– Generally not very precise
• Sequence-based
– Compare reads to large reference database using
BLAST (or some other similarity search method)
– Reads are assigned based on “Best-hit” or “Lowest
Common Ancestor” approach

8. LCA: Lowest Common Ancestor
• Use all BLAST hits above a threshold and assign taxonomy at the
lowest level in the tree which covers these taxa.
• Notable Examples:
– MEGAN: http://ab.inf.uni-
tuebingen.de/software/megan5/
• One of the first metagenomic tools
• Does functional profiling too!
– MG-RAST: https://metagenomics.anl.gov/
• Web-based pipeline (might need to wait awhile for results)
– Kraken: https://ccb.jhu.edu/software/kraken/
• Fastest binning approach to date and very accurate.
• Large computing requirements (e.g. >128GB RAM)

9. Marker Based
• Single Gene
• Identify and extract reads hitting a single marker gene (e.g.
16S, cpn60, or other “universal” genes)
• Use existing bioinformatics pipeline (e.g. QIIME, etc.)
• Multiple Gene
• Several universal genes
– PhyloSift (Darling et al, 2014)
» Uses 37 universal single-copy genes
• Clade specific markers
– MetaPhlAn2 (Truong et al., 2015)

10. Marker or Binning?
• Binning approaches
– Similarity search is computationally intensive
– Varying genome sizes and LGT can bias results
• Marker approaches
– Doesn’t allow functions to be linked directly to
organisms
– Genome reconstruction/assembly is not possible
– Dependent on choice of markers

11. MetaPhlAn2
• Uses “clade-specific” gene markers
• A clade represents a set of genomes that can be
as broad as a phylum or as specific as a species
• Uses ~1 million markers derived from 17,000
genomes
– ~13,500 bacterial and archaeal, ~3,500 viral, and ~110
eukaryotic
• Can identify down to the species level (and
possibly even strain level)
• Can handle millions of reads on a standard
computer within a few minutes

12. MetaPhlAn Marker Selection

13. MetaPhlAn Marker Selection

14. Using MetaPhlan
• MetaPhlan uses Bowtie2 for sequence similarity searching
(nucleotide sequences vs. nucleotide database)
• Paired-end data can be used directly
• Each sample is processed individually and then multiple
sample can be combined together at the last step
• Output is relative abundances at different taxonomic levels

15. Absolute vs. Relative Abundance
• Absolute abundance: Numbers represent real
abundance of thing being measured (e.g. the
actual quantity of a particular gene or organism)
• Relative abundance: Numbers represent
proportion of thing being measured within
sample
• In almost all cases microbiome studies are
measuring relative abundance
– This is due to DNA amplification during sequencing
library preparation not being quantitative

16. Relative Abundance Use Case
• Sample A:
– Has 108 bacterial cells (but we don’t know this from sequencing)
– 25% of the microbiome from this sample is classified as Shigella
• Sample B:
– Has 106 bacterial cells (but we don’t know this from sequencing)
– 50% of the microbiome from this sample is classified as Shigella
• “Sample B contains twice as much Shigella as Sample A”
– WRONG! (If quantified it we would find Sample A has more Shigella)
• “Sample B contains a greater proportion of Shigella compared to
Sample A”
– Correct!

17. FUNCTIONAL COMPOSITION
What are they doing?

18. What do we mean by function?
• General categories
– Photosynthesis
– Nitrogen metabolism
– Glycolysis
• Specific gene families
– Nifh
– EC: 1.1.1.1 (alchohol dehydrogenase)
– K00929 (butyrate kinase)

19. Various Functional Databases
• COG
– Well known but original classification (not updated since 2003)
• SEED
– Used by the RAST and MG-RAST systems
• PFAM
– Focused more on protein domains
• EggNOG
– Very comprehensive (~190k groups)
• UniRef
– Has clustering at different levels (e.g. UniRef100, UniRef90, UniRef50)
– Most comprehensive and is constantly updated
• KEGG
– Very popular, each entry is well annotated, and often linked into “Modules” or “Pathways”
– Full access now requires a license fee
• MetaCyc
– Becoming more widely used.
– More microbe focused than KEGG

20. KEGG
• We will focus on using the
KEGG database during this
workshop
• KEGG Orthologs (KOs)
– Most specific. Thought to be
homologs and doing the same
exact “function”
– ~12,000 KOs in the database
– These can be linked into KEGG
Modules and KEGG Pathways,
– Identifiers: K01803, K00231, etc.

21. KEGG (cont.)
• KEGG Modules
– Manually defined functional units
– Small groups of KOs that function together
– ~750 KEGG Modules
– Identified: M00002, M00011, etc.

22. KEGG (cont.)
• KEGG Pathways
– Groups KOs into large pathways (~230)
– Each pathway has a graphical map
– Individual KOs or Modules can be
highlighted within these maps
– Pathways can be collapsed into very
general functional terms (e.g. Amino Acid
Metabolism, Carbohydrate Metabolism,
etc.)

23. Metagenomic Annotation Systems
• Web-based
– Provide functional and taxonomic analysis, plus hosts your data.
– EBI Metagenomics Server
– MG-RAST
– IMG/M
• GUI based
– MEGAN
• Taxonomy and functional annotation
– ClovR
• Virtual Machine based, contains SOP, hasn’t been updated recently
• Command-line based
– MetAMOS
• Built in assembly, highly customizable, some features can be buggy
– Humann
• Functional annotation
– DIY
• Set up your own in-house custom computational pipeline

24. Humann
(Abubucker et al. 2012)

25. Humann Step 1
• Reads are searched against a protein database (e.g. KEGG)
– Can use BLASTX, but much faster methods now available (e.g. BLAT,
USEARCH, RapSearch2, DIAMOND)
Buchfink et al., 2015

26. Humann
(Abubucker et al. 2012)

27. Humann Step 2
• Normalize and weight search results
• The relative abundance of each KO is
calculated:
– Number of reads mapping to a gene sequence in
that KO
– Weighted by the inverse p-value of each mapping
– Normalized by the average length of the KO

28. Humann
(Abubucker et al. 2012)

29. Humann Step 3
• Reduce number of pathways
• A KO can map to one or more KEGG Pathways
– Just because a KO is found in a pathway doesn’t mean
that complete pathway exists in the community
– If a pathway has 20 KOs and only 2 KOs are observed
in the community (but at high abundances) what
should be the abundance of the pathway?
– MinPath (Ye, 2009) attempts to estimate the
abundance of these pathways and remove spurious
noise

30. Humann
(Abubucker et al. 2012)

31. Humann Step 4
• Reduce false positive pathways further and
normalize by KO copy number
• Using the organism information from the KEGG
hits
– Pathways that are not found to be in any of the
observed organisms AND are made up mostly of KOs
mapping to a different pathway are removed
– KO abundance can be divided by the estimated copy
number of that KO as observed from the KEGG
organism database

32. Humann

33. Humann Step 5
• Smoothing pathways by gap filling
– Sequencing depth or poor sequence searches
could lead to some KOs within pathways being
absent or in low abundance
– KOs with 1.5 interquartile ranges below the
pathway median are raised to the pathway
median

34. Humann
(Abubucker et al. 2012)

35. What about assembly?
• Assembly is often
used in genomics to
join raw reads into
longer contigs and
scaffolds
TECHNOLOGY FEATURE
2. Find overlaps between reads
…AGCCTAGACCTACAGGATGCGCGACACGT
GGATGCGCGACACGTCGCATATCCGGT…
3. Assemble overlaps into contigs
1. Fragment DNA and sequence
4. Assemble contigs into scaffolds
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scGenome assembly stitches together a genome
MichaelSchatz,ColdSpringHarbor
rved.

36. Assembly for Metagenomics?
• Pros
– Less computation time for similarity search (sequences are collapsed)
– Can allow annotation when reads are too short (<100bp)
– Can sometimes (partially) reconstruct genomes
• Cons
– Assembly is computationally intensive (high memory machines
needed)
– Collapsed reads must be added back to get relative abundances (not
all assemblers do this natively)
– Low read depth and high diversity can cause assemblers to fail
– Reads are not all from the same genome so chimeras are possible
– Some organisms/genes will assemble easier (e.g. more abundant)
which could lead to annotation bias

37. What about gene calling?
• In genomics, normally you would predict the start and stop
positions of genes using a gene prediction program before
annotating the genes
• In metagenomics:
– Pros:
• May result in less false positives from annotating “non-real” genes
• Lowers the number of similarity searches
– Cons
• Computationally intensive
• No good learning dataset
• Raw reads will not cover an entire gene
• Often requires assembled data
– Possible tools: FragGeneScan, MetaGeneAnnotator
– Alternative: Do 6 frame-translation (e.g. BLASTX)

38. Community Function Potential
• Important that this is metagenomics, not
metatranscriptomics, and not metaproteomics
• These annotations suggest the functional
potential of the community
• The presence of these genes/functions does not
mean that they are biologically active (e.g. may
not be transcribed)

39. PICRUST
Predicting function from 16S profiles

40. Sample 1 Sample 2 Sample 3
OTU 1 4 0 2
OTU 2 1 0 0
OTU 3 2 4 2
16S rRNA gene
QIIME
Shotgun Metagenomics
HUMAnN
Sample 1 Sample 2 Sample 3
K00001 20 15 18
K00002 1 2 0
K00003 4 5 4
MetaPhlAn
PICRUSt
STAMPSTAMP

41. 41
PICRUSt
• Phylogenetic Investigation of Communities by
Reconstruction of Unobserved States
• http://picrust.github.com

42. PICRUSt: How does it work?

43. Predicting the abundance of a
single function
Known gene abundance
Ancestral gene abundance
Predicted gene abundance

44. Predicting the abundance of a
single function
Known gene abundance
Ancestral gene
abundance
Predicted gene
abundance
Repeat for each function (~8000X)
Repeat for all unknown tips (>100,000)

45. PICRUSt: Predicting Metagenomes
S1 S2 S3
12345 10 0 5
67890 1 0 0
66666 4 8 2
16S Copy
Number
12345 5
67890 1
66666 2
S1 S2 S3
12345 2 0 1
67890 1 0 0
66666 2 4 1
Normalized OTU Table
PICRUST 16S Predictions
OTU Table

46. PICRUSt: Predicting Metagenomes
S1 S2 S3
12345 10 0 5
67890 1 0 0
66666 4 8 2
16S Copy
Number
12345 5
67890 1
66666 2
K0001 K0002 K0003
12345 4 0 2
67890 1 0 0
66666 2 4 2
S1 S2 S3
12345 2 0 1
67890 1 0 0
66666 2 4 1
S1 S2 S3
12345 2 0 1
67890 1 0 0
66666 2 4 1
S1 S2 S3
K0001 13 8 6
K0002 8 16 4
K0003 8 8 4
Normalized OTU Table
Metagenome Prediction
PICRUST 16S Predictions
PICRUST KEGG Predictions
OTU Table

47. PICRUSt predictions across body sites
47
Langille et al., 2013, Nature Biotechnology

48. 48

49. 49

50. 50

51. VISUALIZATION AND STATISTICS
What is important?

52. Visualization and Statistics
• Various tools are available to determine
statistically significant taxonomic differences
across groups of samples
– Excel
– SigmaPlot
– Past
– R (many libraries)
– Python (matplotlib)
– STAMP

53. STAMP

55. STAMP Plots

56. STAMP
• Input
1. “Profile file”: Table of features (samples by OTUs,
samples by functions, etc.)
• Features can form a heirarchy (e.g. Phylum, Order, Class,
etc) to allow data to be collapsed within the program
2. “Group file”: Contains different metadata for
grouping samples
• Can be two groups: (e.g. Healthy vs Sick) or multiple groups
(e.g. Water depth at 2M, 4M, and 6M)
• Output
– PCA, heatmap, box, and bar plots
– Tables of significantly different features

57. METAGENOMICS WORKFLOW
Putting it all together

58. Microbiome Helper
• Standard Operating Procedures (SOPs)
– 16S
– Shotgun Metagenomics
• Scripts to wrap and integrate existing tools
– Available as an Ubuntu Virtualbox
• Tutorials/Walkthroughs
• https://github.com/mlangill/microbiome_helper/wiki

59. IMR: Integrated Microbiome Resource
• Offers sequencing and bioinformatics for
microbiome projects (http://cgeb-imr.ca)

60. QUESTIONS?

61. Tutorial