PICRUSt Lab
Introduction
This lab will walk you through the basic steps of using PICRUSt to make functional predictions (e.g. predicted metagenome) from your 16S data.
It uses an OTU table that has already been generated for use with PICRUSt. See detailed instructions on how to do this using closed reference picking here or using open-reference picking here.
The data we will be using in this lab comes from the stool of three groups of mice that are of different ages (e.g. young, middle, and old).
Preliminaries
Amazon node
Read these directions for information on how to log in to your assigned Amazon node.
Work directory
Create a new directory that will store all of the files created in this lab.
rm -rf ~/workspace/module_picrust
mkdir -p ~/workspace/module_picrust
cd ~/workspace/module_picrust
Now we need to download the data using ‘wget’:
wget https://www.dropbox.com/sh/a35f90j8eh3r23j/AADzQ9zLrEud5xHAHG8kKxlua?dl#1 -O picrust_data.zip
Now decompress the data using “unzip” command:
unzip picrust_data.zip
rm picrust_data.zip
Main Lab Steps
Correct the OTU table based on the predicted 16S copy number for each organism in the OTU table:
normalize_by_copy_number.py -i otus.biom -o otus_corrected.biom
You can use STAMP with the corrected OTU table by first converting it using the Microbiome Helper script:
biom_to_stamp.py -m taxonomy otus_corrected.biom > otus_corrected.spf
Make KEGG Ortholog (KOs) predictions using the corrected OTU table as input:
predict_metagenomes.py -i otus_corrected.biom -o ko_predictions.biom
Default predictions from PICRUSt are KOs (KEGG Orthologs) but PICRUSt can also predict COGs and Rfams.
PICRUSt can also collapse KOs to KEGG Pathways.We will do that with the PICRUSt script “categorize_by_function.py”
categorize_by_function.py -i ko_predictions.biom -c KEGG_Pathways -l 3 -o pathway_predictions.biom
PICRUSt can directly connect the OTUs that are contributing to each KO by using the metagenome_contributions.py script:
metagenome_contributions.py -i otus_corrected.biom -l K01727,K01194,K01216,K11049,K00389,K00449 -o metagenome_contributions.tab
We can view these KEGG Pathways within STAMP. First we need to change the BIOM version we are using:
export PYTHONPATH#~/local/lib/python2.7/site-packages
Then we use a script from Microbiome Helper to convert the BIOM file into a STAMP profile file:
biom_to_stamp.py -m KEGG_Pathways pathway_predictions.biom > pathway_predictions.spf
Now download the pathway_predictions.spf file and the map.tsv file to your local computer and load these files within STAMP (File->Load).
Change Profile Level to “Level 3” which actually represents the KEGG Pathways. Then change the “Group Field” (top right) to “Age_Group”.
Apply a multiple test correction and then view the individual KEGG Pathways using a “Box Plot” (instead of PCA plot). What is the most significant KEGG Pathway?
If you like you can explore other visualizations with STAMP or attempt to load KEGG Modules or KOs instead within STAMP.
Now lets look at getting more detail for the individual KOs that we focused on with the metagenome_contributions.py command from a few steps ago. You can browse the file using ‘less’:
less metagenome_contributions.tab
The output should look like this:
Gene Sample OTU GeneCountPerGenome OTUAbundanceInSample CountContributedByOTU ContributionPercentOfSample ContributionPercentOfAllSamples
K01727 9Y-June1 190026 1.0 1.66666666667 1.66666666667 0.251889168766 0.000792700810933
K01727 9Y-June1 4331760 3.0 1.0 3.0 0.453400503778 0.00142686145968
K01727 9Y-June1 2594570 1.0 0.333333333333 0.333333333333 0.0503778337531 0.000158540162187
K01727 9Y-June1 1106050 1.0 0.333333333333 0.333333333333 0.0503778337531 0.000158540162187
K01727 9Y-June1 3090117 1.0 0.2 0.2 0.0302267002519 9.5124097312e-05
K01727 9Y-June1 1051299 1.0 0.75 0.75 0.113350125945 0.00035671536492
K01727 9Y-June1 2617854 1.0 0.333333333333 0.333333333333 0.0503778337531 0.000158540162187
Each line in this file relates how much a single OTU (third column) contributes to a single KO (first column) within a single sample (second column). The fifth column contains the actual relative abundance contributed by this OTU, and the other columns contain other information about the abundance of the OTU the percentage contribution of this OTU.
You could use your favourite plotting program (e.g. excel, sigmaplot, etc) to plot the information from columns 1-3 and column 5. As an example of what the output might look, I have created the following image:
This plot shows that the large increase in K00449 within sample 25 is contributed by the genus Citrobacter.