Package: protexam
protexamInspect your quantitative proteomics results using this streamlit-powered dashboard. The app is specifically tailored for comprehensive examination of result files from isobaric labeling-based quantitative experiments.protexamhas been tested on Python 3.8 on Windows 10 and Ubuntu 20.04. The package is dependent on multiple packages, such asstreamlit,bokeh,matplotlib. Because of that, we strongly advise creating a virtual environment. One option isvenv, which is a part of Python's standard library.First, we need to choose a path and create a virtual environment there. On Linux, create the environment using the command line, with your custom path and project name:python3 -m venv /your_project_path/project_nameNow we can activate the newly created environment:source /your_project_path/project_name/bin/activateIf the environment has been activated correctly, the name of the environment should be displayed in the console.(project_name) user:~$It is a good idea to update pip within the virtual environment before installing other packages:pip3 install --upgrade pipWe can now installprotexam. The installation will take a while due to the long list of dependencies.pip3 install protexamThe dashboard is based onstreamlit, so we will need to run the scriptapp.pyin order to deploy the dashboard. The module will help us to find the path to theapp.pyfile. Run the module as follows:python3 -m protexamThe output in the console will contain a few notes, including the path to the source file. Change the working directory using the suggested path:cd /path_to_app_sourceFinally, run the app:streamlit run app.py --server.maxUploadSize 1000 --server.port 8501where:
server.maxUploadSize - max size of a single text file in MB
server.port - port for the appWe would need to modify the above-mentioned commands if we are using a Windows machine:>python -m venv /your_project_path/project_name
>your_project_path/project_name/Scripts/activate
>pip install --upgrade pip
>pip install protexam
>python -m protexam
>cd path_to_app_source
>streamlit run app.py --server.maxUploadSize 1000 --server.port 8501Right after launching the app,streamlitwill give us a hint on how to access the dashboard in a web browser. To view locally, go to:http://localhost:8501Use the IP address to view the dashboard in the local network:http://ip-of-the-server:8501The dashboard has been specifically adapted for theProteome Discoverer (PD)tab-delimited output files. However, a few of the MaxQuant tables are also supported. There are two options for creating tab-delimited text output in PD 2.4:add the Result Exporter node to tconsensus workflow. We usually choose "True" for the "R-Friendly header" option, but the app works with "False" as well. The resulting text files can be found in the study folder.if the workflows have been already completed, open the PD result file and go to the menu File -> Export -> To Text (tab delimited).The files are uploaded and visualized one-by-one. Choose one of the permitted tables and upload. The app will try to determine the type of the uploaded file and show it below the upload widget. If the type has been inferred correctly, press the "Generate Plots" button. If you know the type of the table, but it has not been determined automatically, you can override it manually by selecting from the list, an then pressing the "Generate Plots" button.