Step by step TMT analysis
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This tutorial will show you how to use Philosopher for a complete proteomics data analysis, starting from a raw LC-MS file and ending with TMT-quantified protein reports. Philosopher can also be used on Windows, though the commands in this tutorial are formatted for GNU/Linux
The commands in this tutorial should be executed in a particular order. Consider this the "default" order in which to perform an analysis:
- Create a workspace
- Download a database
- Search with MSFragger
For this tutorial, we will use a publicly-available LC-MS data file from a TMT 10-plex phosphorylation-enriched human cell line sample described in this publication. Download the file 06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.mzML from the dataset FTP location (the full listing is here).
Philosopher requires this mzML spectral format for quantification. A tutorial on raw file conversion can be found here.
For additional help on any of the Philosopher commands, you can use the
--help flag (e.g.
philosopher workspace --help), which will provide a description of all available flags for each command.
(Note: use the full path to the Philosopher binary file in place of philosopher in the following steps.) Place the 06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.mzML in a new folder, which we will call the 'workspace'. We will create the workspace with the Philosopher workspace command, which will enable the program to store processed data in a special binary format for quick access.
Inside your workspace folder, open a new terminal window and run this command:
philosopher workspace --init
From now on, all steps should be executed inside this same directory.
For the first step we will download and format a database file using the database command, but first we need to find the Proteome ID (PID) for our organism. Searching the UniProt proteome list, we can see that the Homo sapiens proteome ID is UP000005640, so let's prepare the database file with the following command:
philosopher database --id UP000005640 --reviewed --contam
Philosopher will retrieve all reviewed protein sequences from this proteome, add common contaminants, and generate decoy sequences labeled with the tag rev_.
(Note: use the full path to the MSFragger.jar file in place of MSFragger.jar in the following steps.)
java -jar MSFragger.jar --config to print three MSFragger parameter files (closed, nonspecific, and open).
In the closed_fragger.params file, update the database_name parameter to the name of the database file we downloaded in the previous step (e.g.
Update the variable modifications (the TMT label must be specified as a variable modification):
variable_mod_03 = 79.966331 STY 3to account for phosphorylation
variable_mod_04 = 229.162932 n^ 1to account for the TMT isobaric label on the peptide N-terminus
Towards the bottom of the parameter file, change the
add_K_lysine_ value from
You can also change the calibrate_mass parameter near the top of the file from
0 to speed up the search even more.
Launch the search by running the following command, adjust the
-Xmx flag to set the appropriate amount of RAM for your computer:
java -Xmx32g -jar MSFragger.jar closed_fragger.params 06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.mzML
The search should be done in a few minutes or less. The search hits are now stored in a file called 06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.pepXML.
The next step is to validate the peptide hits with PeptideProphet:
philosopher peptideprophet --database 2019-11-04-td-rev-UP000005640.fas --ppm --accmass --expectscore --decoyprobs --decoy rev_ --nonparam 06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.pepXML
This will generate a new file called interact-06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.pep.xml.
Next, perform protein inference and generate a protXML file:
philosopher proteinprophet interact-06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.pep.xml
Now we have all necessary files to filter our data using the FDR approach:
philosopher filter --razor --pepxml interact-06_CPTAC_TMTS1-NCI7_P_JHUZ_20170509_LUMOS.pep.xml --protxml interact.prot.xml
The filter algorithm can be applied in many different ways, use the
--help flag and choose the best method to analyze your data. Scoring results will be shown in the console, and all processed data will be stored in your workspace for further analysis.
Filtered search results can now be quantified. Before quantifying each TMT channel, make a new text file called
annotation.txt and fill it with the following to tell Philosopher what sample is in each TMT channel:
annotation.txt file, then run the quantification.
philosopher labelquant --brand tmt --plex 10 --dir . , where the
. indicates the current workspace.
For other types of TMT labeling (6, 11, or 16-plex), use the appropriate
--plex value. For iTRAQ experiments, use
Now we can inspect the results by printing the PSM, peptide, and protein reports:
As an optional last step, backup your data in case you wish to print the reports again later.
philosopher workspace --backup
We've demonstrated how to run a complete proteomics analysis with TMT quantification using Philosopher. By providing easy access to advanced analysis software and custom processing algorithms, quantitative protein reports can be obtained from LC-MS files in just a few minutes.
Note: To use these results with TMT-Integrator, you'll also need to do MS1 quantification by runing
philosopher freequant --dir . before the