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Basic Microbiome Analysis with QIIME Patricio Jeraldo and Bryan White Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White.

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Presentation on theme: "Basic Microbiome Analysis with QIIME Patricio Jeraldo and Bryan White Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White."— Presentation transcript:

1 Basic Microbiome Analysis with QIIME Patricio Jeraldo and Bryan White Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

2 Exercise Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

3 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 class45. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White Now you are all set!

4 Step 0B: Lab Setup The lab is located in the following directory: ~/mayo/white This directory contains 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 4 cpus with an interactive session. You only need to do this once. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

5 Step 0C: Lab Setup Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ qsub –I –l ncpus=4 # Login to a computer on cluster. $ mkdir –p ~/white/results # Make results directory in our working directory. # –p indicates to create ~/white if it doesn’t exist. $ cp ~/mayo/white/data/* ~/white # Copy data to your working directory. $ cd ~/white # Change directory to our working directory. $ module load qiime # We will need QIIME for this lab.

6 Step 0D: Shared Desktop Directory For viewing and manipulating files on the classroom computers, we provide a shared directory in the following folder on the desktop: classes/mayo In today’s lab, we will be using the following folder in the shared directory: classes/mayo/white Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

7 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, 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 v1 | Patrico Jeraldo and Bryan White

8 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 v1 | Patrico Jeraldo and Bryan White

9 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. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ cat ICF.mapping.txt # print file contents to screen #SampleID Barcode Dx SubjectIDDescription ICF-1 GGATCGCAGATCControl 1 IC_fecal1 ICF-2 GCTGATGAGCTGControl 2 IC_fecal2 ICF-3 AGCTGTTGTTTGControl 3 IC_fecal3 ICF-4 GGATGGTGTTGCIC 4 IC_fecal4 The most important column to us. Output:

10 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 v1 | Patrico Jeraldo and Bryan White

11 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. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ cat params.txt # print file contents to screen beta_diversity:metrics bray_curtis,unweighted_unifrac,weighted_unifrac alpha_diversity:metrics chao1,goods_coverage,observed_species,shannon,simpson,PD_whole_tree Output:

12 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 per-library_stats.py script in QIIME to do this. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ per_library_stats.py –i ICF.biom # Get stats. Num samples: 15 Num otus: 260 Num observations (sequences): Table density (fraction of non-zero values): Seqs/sample summary: Min: Max: Output:

13 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 ~/white/results/alpha_diversity. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ alpha_rarefaction.py –i ICF.biom –t ICF.tree –m ICF.mapping.txt –o results/alpha_diversity –p params.txt –e This calculation will take from min to complete.

14 For this next step, let us compare samples using their composition. We will specify we are interested in the Dx column. We will use the beta_diversity_through_plots.py script in QIIME to do this. Results are located in ~/white/results/beta_diversity. We will use these results later in the tutorial. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ 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 from min to complete.

15 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 ~/white/results/taxonomy (1 st command) and ~/white/results/taxonomy_Dx (2 nd command). Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ 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

16 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 ot_category_significance.py script in QIIME to do this. The resulting file, ~/white/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. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ otu_category_significance.py –i ICF.biom –m ICF.mapping.txt –o results/ANOVA.txt –s ANOVA –c Dx

17 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 v1 | Patrico Jeraldo and Bryan White

18 compare_alpha_diversity.py –i results/alpha_diversity/alpha_div_collated/observed_species.txt –c Dx –o results/species_significance.txt –d m ICF.mapping.txt

19 Let us take a look at the results file ~/white/results/species_significance.txt using the cat command. It seems that the categories are very different. We will confirm this later when looking at diversity plots Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ cat results/species_significance.txt Comparison tval pval Control,IC Output:

20 Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ compare_categories.py –-method anosim –i results/beta_diversity/unweighted_unifrac_dm.txt –m ICF.mapping.txt –c Dx –o results/anosim –n 9999

21 Let us take a look at the results file ~/white/results/anosim/anosim_results.txt using the cat command. Although the p-value is significant, the R statistic says that the clustering is only moderately strong. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White $ cat results/anosim/anosim_results.txt Method nameR statisticp-valueNumber of permutations ANOSIM Output:

22 Analysis Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

23 Step 9A:  Diversity Results On the desktop, access our shared directory: classes/mayo/white/results/ Inside the results directory, open the following file: alpha_diversity/alpha_rarefaction_plots/rarefaction_plots.ht ml Select observed_species as metric, and Dx as category. A graph will be displayed. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

24 Step 9A:  Diversity Results Control is significantly different than IC! Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

25 On the desktop, access our shared directory: classes/mayo/white/results/ Inside the results directory, open the HTML file in the following dir: beta_diversity/unweighted_unifrac_2d_discrete This will open a 2d PCA plot, based on unweighted UniFrac distances, colored by sample type (Dx, Control) Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

26 Hover on the data points to obtain information about that sample… Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

27 Control and IC samples segregate, but only moderately. This is in agreement with the ANOSIM results (R=0.4694, p = ). Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

28 Step 11: Taxonomy Results On the desktop, access our shared directory: classes/mayo/white/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 v1 | Patrico Jeraldo and Bryan White

29 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! Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

30 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. Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

31 Let’s see if there is something hidden in the taxonomy. In the results directory, open the ANOVA.txt file. OTUprobBonferroni_correctedFDR_correctedControl_mean IC_meanConsensus 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 OTUprobBonferroni_correctedFDR_correctedControl_mean IC_meanConsensus 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… Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

32 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). Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White

33 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 v1 | Patrico Jeraldo and Bryan White

34 Exercise Conclusions Basic microbiome analysis: 1.Calculate various diversity metrics for samples 2.Calculate statistical support for differences found between samples types 3.Plot taxonomy composition of samples 4.Basic tests for potential microbial markers Basic Microbiome Analysis with QIIME v1 | Patrico Jeraldo and Bryan White


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