RIANA - Relative Isotope Abundance Analyzer
RIANA (Relative Isotope Abundance Analyzer) takes in standard mass spectrometry spectra and spectral ID files, and returns mass isotopomer distributions, e.g., for protein turnover analysis.
Install Python 3.7+ and pip. See instructions on Python website for specific instructions for your operating system.
Riana can be installed from PyPI via pip. We recommend using a virtual environment.
$ pip install riana
Launch riana as a module (Usage/Help):
$ python -m riana
Alternatively as a console entry point:
To test that the installation can load test data files in tests/data:
$ pip install tox $ tox
To run the riana test dataset (a single fraction bovine serum albumin file from a Q-Exactive) and print the result to the home directory:
$ python -m riana tests/data/ -u -i 0,1,2,3,4,5 -q 0.1 -r 0.5 -t 10 -o ~/
Notes on the expected input files:
* RIANA.py was tested on the percolator output file from Crux Tide/Percolator or standalone Comet/Percolator. * The following workflow has been tested for both amino acid and heavy water labeling data gathered on a QE: * Convert raw files to mzML, using pwiz 3.0 msconvert in command line, with the following option: ** --filter "peakPicking vendor" * Download Crux 3.1 * Run Tide index with the following options: ** --digestion partial-digest ** --missed-cleavages * Run Tide search with the following options: ** --isotope-error 1,2 (for HW) or 6,12 (for AA) ** --compute-sp T ** --mz-bin-width 0.02 ** --mz-bin-offset 0.0 ** --precursor-window 20 ** --precursor-window-type ppm * Run Percolator with the following options: ** --protein T ** --fido-empirical-protein-q T * Input to RIANA.py: ** Note that the riana argument path should point to the project directory, where each individual sample mzML and search result files are placed under a sub-directory (e.g., sample1/mzml, sample1/percolator, sample2/mzml, sample2/percolator, etc.)
RIANA.py is tested in Python 3.7 and 3.8 and uses the following packages:
matplotlib==3.4.1 pandas==1.2.4 pymzml==2.4.7 tqdm==4.60.0 scikit-learn==0.24.2
Please contact us if you wish to contribute, and submit pull requests to us.
- Edward Lau, PhD - Code/design - ed-lau
See also the list of contributors who participated in this project.
This project is licensed under the MIT License - see the LICENSE.md file for details