1. Canadian Bioinformatics Workshops

2. Module #: Title of Module2

3. Module 4 Metagenomic Taxonomic Composition
Morgan Langille
Analysis of Metagenomic Data
June 22-24, 2016

4. Learning Objectives of Module
Contrast 16S and metagenomic sequencing
Be able to describe several approaches for taxonomic composition of a metagenomics sample
Be able to run Metaphlan2 on one or more samples
Be able to determine statistically significant differences in taxonomic abundance across sample groups using STAMP

5. 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
Need different primers usually for archaea & eukaryotes (18S)
Doesn’t identify viruses
Consequtive basepairs

6. 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

7. Who is there?
Taxonomic Profiles

8. 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
Binning Based
Marker Based

9. 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)
Fast
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

10. 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:
One of the first metagenomic tools
Does functional profiling too!
MG-RAST:
Web-based pipeline (might need to wait awhile for results)
Kraken:
Fastest binning approach to date and very accurate.
Large computing requirements (e.g. >128GB RAM)

11. Marker Based Single Gene Multiple 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)

12. Marker or Binning? Binning approaches Marker 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

13. Why MetaPhlAn? Fast (marker database is considerably smaller)
Markers for bacteria, archaea, eukaryotes, and viruses (since MetaPhlAn2 was released)
Being continuously updated and supported
Used by the Human Microbiome Project
Generally accepted as a robust method for taxonomy assignment
Main Disadvantage: not all reads are assigned a taxonomic label

14. MetaPhlAn 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

15. MetaPhlAn Marker Selection

16. MetaPhlAn Marker Selection

17. Using MetaPhlan
MetaPhlan uses Bowtie2 for sequence similarity searching (nucleotide sequences vs. nucleotide database)
Paired-end data can be used directly (but are treated as independent reads)
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

18. 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

19. 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!

20. Visualization and statistics
What is important?
Visualization and statistics

21. Visualization and Statistics
Various tools are available to determine statistically significant taxonomic differences across groups of samples
Excel
SigmaPlot R
MeV (MultiExperiment Viewer)
Python (matplotlib)
LefSe & Graphlan (Huttenhower Group)
STAMP

22. 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

23. STAMP

25. STAMP Plots

26. STAMP
Input
“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
“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

27. Metagenomics Workflow
Putting it all together
Metagenomics Workflow

28. Microbiome Helper
Microbiome Helper is an open resource aimed at helping researchers process and analyze their microbiome data
Combines bioinformatic methods from different groups and is updated as newer and better methods are released
Scripts to wrap and integrate existing tools
Available as an Ubuntu Virtualbox
Tutorials/Walkthroughs

29. Microbiome Helper Wiki

30. Microbiome Helper Vbox

31. Integrated Microbiome Resource
Sequencing and Bioinformatics Support
Integrated Microbiome Resource

32. IMR: Sequencing and bioinformatics service for microbiome projects

33. (16S / 18S amplicons on the Illumina MiSeq)
Microbiome Amplicon Sequencing Workflow
(16S / 18S amplicons on the Illumina MiSeq)
DNA extraction
16S (V6-V8) or 18S (V4) PCR
Gel verification
PCR clean-up & library normalization
Illumina MiSeq sequencing
Method/kit appropriate to specific samples (ex: stool, urine, etc.)
Time = 1-3 d QC
Duplicate with template dilutions
Multiplexing to 384 samples/run
Only 1 PCR w/fusion primers: i5
index F
primer R i7
P5 adapter
P7 adapter
16S/18S sequence
Time = 0.5 d
Invitrogen E-gel 96-well high-throughput method QC
Time = 1 h
Invitrogen SequalPrep 96-well high-throughput method
Time = 1.5 h QC
bp paired-end reads
~25 M reads = ~15 Gb
~65 k reads/sample (for 384)
Time = ~3 d QC
Quality-control check/step
Total Time =
1 week
CGEB-IMR.ca • DalhousieU • March 2015

35. IMR Achievements

36. IMR Achievements

37. IMR Collaborators & Clients

38. Pricing

39. Questions?