Basic Microbiome Analysis with QIIME Bryan White PowerPoint by Casey Hanson Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Exercise In this exercise we will do the following: Calculate sample diversity (𝛼-diversity), and test if different sample types have different numbers of OTUs (species). Calculate differences in microbial community structure (𝛽-diversity); in particular, we will 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. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 0A: Accessing the IGB Biocluster Open Putty.exe In the hostname textbox type: biocluster.igb.illinois.edu Click Open If popup appears, Click Yes Enter login credentials assigned to you; example, user class00. Now you are all set! Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 0B: Lab Setup The lab is located in the following directory: /home/classroom/mayo/2015/06_Metagenomics/ This directory contains the data and the finished version of the lab (i.e. the version of the lab after the tutorial). Consult it if you unsure about your runs. You don’t have write permissions to the lab directory. Create a working directory of this lab in your home directory for your output to be stored. Note ~ is a symbol in unix paths referring to your home directory. Copy the files Make sure you login to a machine on the cluster using the qsub command. The exact syntax for this command is given below. This particular command will login you into a computer with 2 cpus with an interactive session. You only need to do this once. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 0C: Local Files For viewing and manipulating the files needed for this laboratory exercise, insert your flash drive. Denote the path to the flash drive as the following: [course_directory] We will use the files found in: [course_directory]/06_Metagenomics/results/ Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 0D: Lab Setup $ qsub -I -q classroom -l ncpus=2 # Login to a computer on cluster. $ mkdir -p ~/06_Metagenomics/results # Make results directory in our working directory. # -p indicates to create ~/06_Metagenomics if it doesn’t exist. $ cp /home/classroom/mayo/2015/06_Metagenomics/data/* ~/06_Metagenomics/ # Copy data to your working directory. $ cd ~/06_Metagenomics # Change directory to our working directory. $ module load qiime # We will need QIIME for this lab. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Interstitial Cystitis Interstitial cystitis (IC) is a chronic inflammation of the bowels. In this exercise, we will examine differences between the microbiota of women with and without IC to understand the effect IC has on the community. Our data consists of 16S sequencing of stools samples from 8 women with IC and 7 without it. Using QIMME 1.8.0, we will examine Using this data, we will test the hypothesis that IC induces significant change in gut microbiota. Additionally, we will examine whether or not there is a change in the community and what bacteria are implicated in causing such change. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 1A: Dataset Characteristics ICF.biom The ICF.biom file is an OTU observation file. It is a matrix of observed OTUs, or species, for each sample, annotated with their taxonomy. The ICF.biom file was created using our own TORNADO pipeline for 16S reads: quality check, chimera check, align, assign taxonomy and cluster to 97% similarity to find OTUs The TORNADO pipeline can take from HOURS to DAYS depending on the complexity of the project. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 1B: Dataset Characteristics ICF.mapping.txt The mapping file contains metadata associated with samples. Let us examine the file using the Unix cat command. $ cat ICF.mapping.txt # print file contents to screen Output: #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 The most important column to us. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 1C: Dataset Characteristics ICF.tree The ICF.tree file is a Newick-formatted phylogenetic tree file. It contains phylogenetic relationships between the OTUs found in our samples. It is another output of the 16S pipeline required for various comparison metrics. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 1D: Dataset Characteristics params.txt The params.txt file contains alternative parameters to run QIIME. Let us examine the file using the Unix cat command. $ cat params.txt# print file contents to screen Output: beta_diversity:metrics bray_curtis,unweighted_unifrac,weighted_unifrac alpha_diversity:metrics chao1,goods_coverage,observed_species,shannon,simpson,PD_whole_tree Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 2: Get Basic Statistics The first step we will do is to get some basic statistics on our ICF.biom file. We will use the biom summarize-table command in QIIME to do this. $ biom summarize-table -i ICF.biom -o results/summary.txt $ cat results/summary.txt # Show stats. Output: Num samples: 15 Num observations: 260 Total count: 399985 Table density (fraction of non-zero values): 0.608 Table md5 (unzipped): be4b6e26ff80ca9ff173d6bbfeda162b Counts/sample summary: Min: 10267.0 Max: 48123.0 Change output Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 3: Calculating 𝛼 Diversity For this next step, let us 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. We will use the alpha_rarefaction.py script in QIIME to do this. Results are located in ~/06_Metagenomics/results/alpha_diversity $ 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 - 7 min to complete. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 4: Calculating 𝛽 Diversity For this next step, let us compare samples using their composition. We will use the beta_diversity_through_plots.py script in QIIME to do this. Results are located in : ~/06_Metagenomics/results/beta_diversity We will use these results later in the tutorial. $ beta_diversity_through_plots.py -i ICF.biom -t ICF.tree -m ICF.mapping.txt -o results/beta_diversity -p params.txt -e 10267 This calculation will take from 1 - 5 min to complete. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 5: Taxonomy Computations For this next step, we will create a graphical summary of the taxonomical composition of the samples. Let us do the same thing as above, only this time merging the control and IC samples using the Dx column. Results are located in : ~/06_Metagenomics/results/taxonomy (1st command) ~/06_Metagenomics/results/taxonomy_Dx (2nd command). $ 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 Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 6: ANOVA Tests ANOVA stands for Analysis of Variance. It is a standard suite of statistical tests aimed at explaining differences between groups of data. We will use ANOVA in this step to see if there are any OTUs that explain the differences between sample categories. We will use the group_significance.py script in QIIME to do this. The resulting file, ~/06_Metagenomics/results/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. $ group_significance.py -i ICF.biom -m ICF.mapping.txt -o results/ANOVA.txt -s ANOVA -c Dx Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Statistical Tests In this exercise, we will test our hypotheses. In particular, 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. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 7A: Statistical Tests - 𝛼 Diversity In this step, we will see whether or not the IC and control samples differ significantly using the 𝛼 diversity results computed earlier. We will use the compare_alpha_diversity.py script in QIIME to do this. The result file is located in: ~/06_Metagenomics/results/signif compare_alpha_diversity.py -i results/alpha_diversity/alpha_div_collated/observed_species.txt -c Dx -o results/signif -d 10260 -m ICF.mapping.txt Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 7B: Statistical Tests - 𝛼 Diversity Let us take a look at the results file using the cat command: ~/06_Metagenomics/results/signif/Dx_stats.txt It seems that the categories are very different. Note: your output may be slightly different We will confirm this later when looking at diversity plots $ cat results/signif/Dx_stats.txt Output: Group1 Group2 … t stat p-value Control IC … 3.57527959 0.003 Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 8A: Statistical Tests - 𝛽 Diversity In this step, we will see whether or not the IC and control samples differ significantly using the 𝛽 diversity results computed earlier. We will use the UniFrac matrix and the ANOSIM test. We will use the compare_categories.py script in QIIME to do this. The result file is located in: ~/06_Metagenomics/results/anosim/anosim_results.txt $ compare_categories.py –-method anosim –i results/beta_diversity/unweighted_unifrac_dm.txt –m ICF.mapping.txt –c Dx –o results/anosim –n 9999 Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 8B: Statistical Tests - 𝛽 Diversity Let us take a look at the results file using the cat command : ~/06_Metagenomics/results/anosim/anosim_results.txt Although the p-value is significant, the R statistic says that the clustering is only moderately strong. Note: your output may be slightly different $ cat results/anosim/anosim_results.txt Output: Method name R statistic p-value Number of permutations ANOSIM 0.4069 0.0009 9999 Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 We will now analyze the files we generated during the 𝛼 and 𝛽 diversity runs and tests. Note: the output you generated in lab may be slightly different. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 9A: a Diversity Results Access the downloaded results directory: [course_directory]/06_Metagenomics/results Inside the results directory, open the following file: alpha_diversity/alpha_rarefaction_plots/rarefaction_plots.html Select observed_species as metric, and Dx as category. A graph will be displayed. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 9A: a Diversity Results Control is significantly different than IC! Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 10A: 𝛽 Diversity Results Access the downloaded results directory: [course_directory]/06_Metagenomics/results Inside the results directory, open the HTML file in the following dir: beta_diversity/unweighted_unifrac_emperor_pcoa_plot/index.html This will open a 3D PCA plot, based on unweighted UniFrac distances, colored by sample type (Dx, Control) Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 10B: 𝛽 Diversity Results Rotate the plot to see if the points separate in when viewed from other directions. Identify individual samples from using the ‘Key’ tab Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 10C: 𝛽 Diversity Results Control and IC samples segregate, but only moderately. This is in agreement with the ANOSIM results (R = 0.4069 , p = 0.0009 from Slide 21 ). Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 11A: Taxonomy Results Access the downloaded results directory: [course_directory]/06_Metagenomics/results Inside the results directory, open the HTML file in the following dir: taxonomy/taxa_summary_plots/area_charts.html Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 11B: Taxonomy Results 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). Basic Microbiome Analysis with QIIME | Bryan White | 2015

Step 11C: Taxonomy Results These look like otherwise normal stool samples, with Firmicutes and Bacteroides dominating. Note the Fusobacteria in sample 2, a control! Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 11D: Taxonomy Things get more complex as we go down the taxonomy hierarchy. This is the plot at the genus level, typical of stool samples. Hover over each color to see its taxonomy information. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 11D: Taxonomy There seems to be no obvious pattern (which is the usual case unless there’s something very wrong or a known pathogen). Hover over each color to see its taxonomy information. Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 Step 11E: Taxonomy Let’s see if there is something hidden in the taxonomy. In the results directory, open the ANOVA.txt file. Below is the readout from one significant genus, Odoribacter. OTU 111 Test-Statistic 11.82051724 P 0.004407693 FDR_P 0.313682109 Bonferroni_P 1 Control_mean 92.71428571 IC_mean 9.125 taxonomy k__Bacteria;p__Bacteroidetes;c__Bacteroidia;o__Bacteroidales;f__Porphyromonadaceae;g__Odoribacter;s__unclassified Basic Microbiome Analysis with QIIME | Bryan White | 2015

(Plot below from the bottom of area_plots.html) Step 11F: Taxonomy Odoribacter has 0.3% mean abundance in controls and 0.02% mean abundance in IC. (Plot below from the bottom of area_plots.html) Indeed, it seems to be a good marker despite its low relative abundance. (Look at abundances in red vs blue columns) Its absence seems correlated with IC (samples 4,7,8,9,10,12,14,15). Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 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 Basic Microbiome Analysis with QIIME | Bryan White | 2015

Basic Microbiome Analysis with QIIME | Bryan White | 2015 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 Basic Microbiome Analysis with QIIME | Bryan White | 2015