1. Basic Microbiome Analysis with QIIME
Patricio Jeraldo and Bryan White

2. In this exercise you will
Calculate sample diversity (a-diversity), and test if different sample types have different numbers of OTUs (species)
Calculate differences in microbial community structure (b-diversity): compare OTU composition and abundance between samples and sample types
Compute statistical support for observed differences between sample types
Plot taxonomy composition across samples
Test for potential microbial markers

3. Tools and data We will use QIIME, installed in biocluster
Data set is also located in biocluster
QIIME returns some results as interactive web pages: we will run all commands in biocluster first, then move the results to the desktop and view the results there.

4. Exercise: Interstitial cystitis
Cohort: 15 women (8 with IC, 7 controls)
16S sequencing of stool samples
Hypothesis: IC induces significant changes in gut microbiota
Other questions: is it a change in the community? Is a specific bacteria responsible for the change?

5. Step 0: connect to biocluster
Open the program PuTTY and connect to the cluster with your credentials

6. Step 1: create a directory
To create a directory to store a copy of the data set, type:
And change directory to the newly created one:
mkdir microbiome
cd microbiome

7. Step 2: copy the dataset
The zip file with the data set is in a different directory. Let’s copy it to our own:
Let’s make sure it’s there:
You should see the following:
cp /home/groups/chian_tornado/workshop/*.zip . ls
ICF.microbiome.zip

8. Step 3: unpack the dataset
Let’s unpack the dataset:
And list the files we have so far:
We see 4 files were extracted from the zip file. Let’s go over them…
unzip ICF.microbiome.zip ls
ICF.biom ICF.mapping.txt ICF.microbiome.zip ICF.tree params.txt

9. Step 3a: BIOM file
OTU observation file. It is a matrix of observed OTUs (species) for each sample, annotated with their taxonomy.
Created using our own TORNADO pipeline for 16S reads: quality check, chimera check, align, assign taxonomy and cluster to 97% similarity to find OTUs (pipeline can take hours to days!).

10. Step 3b: mapping file
File with metadata associated with samples. Check its contents:
cat ICF.mapping.txt
#SampleID Barcode Dx SubjectID Description
ICF-1 GGATCGCAGATC Control 1 IC_fecal1
ICF-2 GCTGATGAGCTG Control 2 IC_fecal2
ICF-3 AGCTGTTGTTTG Control 3 IC_fecal3
ICF-4 GGATGGTGTTGC IC 4 IC_fecal4 …

11. Step 3b: mapping file
In our case, the most important column is marked as Dx
In your own analysis, you must supply the metadata!
#SampleID Barcode Dx SubjectID Description
ICF-1 GGATCGCAGATC Control 1 IC_fecal1
ICF-2 GCTGATGAGCTG Control 2 IC_fecal2
ICF-3 AGCTGTTGTTTG Control 3 IC_fecal3
ICF-4 GGATGGTGTTGC IC 4 IC_fecal4 …

12. Step 3c: tree file Newick-formatted phylogenetic tree file
Contains phylogenetic relationships between the different OTUs (species) found in the samples
Another output of the 16S pipelines
Required for some comparison metrics

13. Step 3d: params file File with parameters for QIIME
Needed only when changing default analyses
Let’s see its contents:
It specifies the comparison metrics to use in analyses we will be doing.
cat params.txt
beta_diversity:metrics bray_curtis,unweighted_unifrac,weighted_unifrac
alpha_diversity:metrics chao1,goods_coverage,observed_species,shannon,simpson,PD_whole_tree

14. Step 4: results directory
Last step before diving into the analysis, let’s create a results directory to store our data
mkdir results

15. Step 5: interactive cluster session
Let’s create an interactive session in the cluster: each of us will have our own processor to perform the analyses
Now, change again to our microbiome directory
qsub -I
cd microbiome

16. Step 6: load the QIIME module
Let’s load the qiime module
This makes the QIIME scripts available to us, as well as other software QIIME needs (python, R, etc…)
module add qiime

17. Step 7: library stats Let’s do a quick check on our BIOM file
Note the minimum number of seqs in the library. We will use this number to better compare the different samples…
per_library_stats.py –I ICF.biom
Num samples: 15
Num otus: 260
Num observations (sequences):
Table density (fraction of non-zero values):
Seqs/sample summary:
Min:
Max: …

18. Step 8: a-diversity
Let’s measure the diversity of the samples. We will use the number from the previous slide so that, for comparison purposes, all samples will have the same number of sequences…
The results will be stored in the results/alpha_diversity directory as interactive web pages and other files.
alpha_rarefaction.py –I ICF.biom –t ICF.tree –m ICF.mapping.txt –o results/alpha_diversity –p params.txt –e 10267
This calculation will take from 5 to 7 minutes to complete

19. Step 9: b-diversity
Now let’s compare all samples using their composition, also specifying that we’re interested in the Dx column.
The results will be stored in the results/beta_diversity directory as interactive web pages and other files. We will be using some of those files as input for further analysis.
beta_diversity_through_plots.py –I ICF.biom –t ICF.tree –m ICF.mapping.txt –o results/beta_diversity –p params.txt –e –c Dx
This calculation will take about 5 minutes to complete

20. Step 9: taxonomy
Let’s create a graphical summary of the taxonomical composition of the samples
Also, let’s do the same but merging the control and the IC samples (using the Dx column)
The results will be stored in the results/taxonomy directory as interactive web pages and other files.
summarize_taxa_through_plots.py –I ICF.biom –m ICF.mapping.txt –o results/taxonomy
summarize_taxa_through_plots.py –I ICF.biom –m ICF.mapping.txt –o results/taxonomy_Dx –c Dx

21. Step 10: ANOVA tests
Let’s see if there are OTUs (species) that explains the differences between the sample categories. We will do that using an ANOVA test…
The resulting file, ANOVA.txt, sorts the OTUs in the data according to how likely they are driving the differences between samples. The file includes probabilities (uncorrected and corrected), as well as abundance information and lineage of the OTU.
otu_category_significance.py –i ICF.biom –m ICF.mapping.txt –o results/ANOVA.txt –s ANOVA –c Dx

22. Statistical tests
If the control and IC samples cluster together, the following tests will measure the significance of such clustering based on the metrics that we just calculated…

23. Step 11: a-diversity significance
Let’s see if control and IC cases differ significantly in number of observed OTUs, using our previous a-diversity calculation…
Let’s look at the output:
It seems that the categories are very different… we will confirm this later when looking at diversity plots.
compare_alpha_diversity.py –i results/alpha_diversity/alpha_div_collated/observed_species.txt –c Dx –o results/species_significance.txt –d 10260
cat results/species_significance.txt
Comparison tval pval
Control,IC

24. Step 12: b-diversity significance
Let’s compare the categories again, this time using the output from the b-diversity calculations. In particular we will use the UniFrac matrix… Let’s perform an ANOSIM test.
Now let’s take a look at those results…
Although the p-value is significant, the R statistic says that the clustering is only moderately strong.
compare_categories.py –-method anosim –i results/beta_diversity/unweighted_unifrac_dm.txt –m ICF.mapping.txt –c Dx –o results/anosim –n 9999
cat results/anosim/anosim_results.txt
Method name R statistic p-value Number of permutations
ANOSIM

25. Packing the results Now let’s pack the results directory
The zip file now can be transferred to your computer. Do so, and then unpack it. We will explore the results through the interactive web pages QIIME created for us.
zip –r results.zip results

26. Results: a-diversity
Inside the results directory, navigate to alpha_diversity -> alpha_rarefaction_plots and open rarefaction_plots.html
Select observed_species as metric, and Dx as category. A graph will be displayed.

28. Control significantly more diverse than IC

29. Results: b-diversity
Now let’s look at the ordination plots for the samples. Go to beta_diversity -> unweighted_unifrac_2d_discrete and open the HTML file
This will open a 2d PCA plot, based on unweighted UniFrac distances, colored by sample type (Dx, Control)

30. Results: b-diversity
Hover on the data points to obtain information about that sample…

31. Control and IC samples segregate, but only moderately
Control and IC samples segregate, but only moderately. This is in agreement with the ANOSIM results (R= , p = ).

32. Results: taxonomy
Let’s examine the taxonomy results. In the results directory, go to taxonomy -> taxa_summary_plots and open area_charts.html

33. This is the taxonomy at phylum level, for all samples
This is the taxonomy at phylum level, for all samples. Hover over each color to find out about each color (colors may differ from this plot).
These look like otherwise normal stool samples, with Firmicutes and Bacteroides dominating. Note the Fusobacteria in sample 2, a control!

34. Things get more complex as we go down the taxonomy hierarchy
Things get more complex as we go down the taxonomy hierarchy. This is the plot at genus level, typical of stool samples. There seems to be no obvious pattern, the usual case unless there’s something very wrong, or a known pathogen.
Hover over each color to see its taxonomy information.

35. Odoribacter has 0.3% abundance in controls, 0.02% in IC…
Let’s see if there is something hidden in the taxonomy. In the results directory, open the ANOVA.txt file.
OTU prob Bonferroni_corrected FDR_corrected Control_mean IC_mean Consensus Lineage
k__Bacteria; p__Bacteroidetes; c__Bacteroidia; o__Bacteroidales; f__Porphyromonadaceae; g__Odoribacter; s__unclassified
k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__unclassified; s__unclassified
k__Bacteria; p__Firmicutes; c__Clostridia; o__Clostridiales; f__Lachnospiraceae; g__Clostridium; s__unclassified
e k__Bacteria; p__Tenericutes; c__Erysipelotrichi; o__Erysipelotrichales; f__Erysipelotrichaceae; g__Clostridium; s__Clostridium_ramosum
Odoribacter has 0.3% abundance in controls, 0.02% in IC…

36. Indeed, it seems to be a good marker despite its low relative abundance. Its absence seems correlated with IC (samples 4,7,8,9,10,12,14,15).

37. Analysis conclusions
Microbial composition and structure significantly different in stool between IC patients and controls:
IC stool microbiota significantly less diverse
Overall IC microbiota different (it clusters away from controls)
Potential marker found
Lack of Odoribacter associated with IC

38. Exercise conclusions Basic microbiome analysis:
Calculate various diversity metrics for samples
Calculate statistical support for differences found between samples types
Plot taxonomy composition of samples
Basic tests for potential microbial markers