jamesetsmith/msanalysis

Python Tools for Mass Spec. Analysis

MSAnalysis

msanalysis is a lightweight python package to read and process mass spectra, specifically those in the mzXML format.

• General
  • Build Info
  • Python Versions Tested
• Dependencies
• Installation
  • Fresh install
  • Patches
• Testing
• Copyright
• Acknowledgements

General

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Build Info

OS	Build Status
Linux
OSX

Python Versions Tested

OS	3.5	3.6	3.7	3.8
Linux	✔️	✔️	✔️	✔️
OSX	✔️	✔️	✔️	❌

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Dependencies

• pytest-cov
• matplotlib
• seaborn
• numpy
• scipy
• pandas
• pyopenms
• black (only required for contributors)

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Installation

Set up is meant to be easy! First we suggest installing all of prerequisites in a clean Conda env (run this inside the main directory of the package):

conda env create -f devtools/conda-envs/msanalysis_env.yaml

Then install using pip and we're done!

python -m pip install -e .

If you aren't doing this in a conda env and don't have root user privileges use:

python -m pip install -e . --user

Fresh install

git clone https://github.com/jamesETsmith/msanalysis.git
cd msanalysis
conda env create -f devtools/conda-envs/msanalysis_env.yaml
python -m pip install -e .
cd patches
python add_elements.py

Patches

Currently there is one patch while we wait to hear back on an issue from PyOpenMS. If you want to work with species like Indium, run the python script to patch up msanalysis.

cd patches
python add_elements.py

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Testing

To check that everything is working, run the following from the main project directory:

pytest -v msanalysis --cov=msanalysis

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Copyright

Copyright (c) 2019, James E. T. Smith/ CU Boulder

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Acknowledgements

Project based on the Computational Molecular Science Python Cookiecutter version 1.1.

msanalysis's Issues

ex6: missing isotope for Ge

ex6_iso_abundancy is missing an isotopic abundance for Germanium (Ge). According to this website (https://www.sisweb.com/mstools/isotope.htm), Ge has an abundance at 76 amu. However, ex6 does not display 76 amu (the plot stops at 74 amu; please see attached). Not sure if the data base needs to updated.

Convert non-uniform spectral data storage to uniform (np.ndarray) storage

Since the actual data from the MS is uniform, unlike what we were seeing in the CDF outputs, we should stop storing the spectral data as lists and use numpy arrays to simplify code and speed things up.

Generate MS fingerprint for molecule

Given a molecular formular we want to generate a mass spec. fingerprint.

Travis CI Security Breach Notice

MolSSI is reaching out to every repository created from the MolSSI Cookiecutter-CMS with a .travis.yml file present to alert them to a potential security breach in using the Travis-CI service.

Between September 3 and September 10 2021, the Secure Environment Variables Travis-CI uses were leaked for ALL projects and injected into the publicly available runtime logs. See more details here. All Travis-CI users should cycle any secure variables/files, and associated objects as soon as possible. We are reaching out to our users in the name of good stewards of the third-party products we recommended and might still be in use and provide a duty-to-warn to our end-users given the potential severity of the breach.

We at MolSSI recommend moving away from Travis-CI to another CI provider as soon as possible. The nature of this breach and the way the response was mis-handled by Travis-CI, MolSSI cannot recommend the Travis-CI platform for any reason at this time. We suggest either GitHub Actions (as is used from v1.5 of the Cookiecutter-CMS) or some other service offered on GitHub.

If you have already addressed this security concern or it does not apply to you, feel free to close this issue.

This issue was created programmatically to reach as many potential end-users as possible. We do apologize if this was sent in error.

Peak loss in log-scale contour plots

Low intensity peaks don't show up in the current log scale contour plots (example 4). One resolution would be to remove the intense bands from precursor that are present throughout the temperature ramp. This requires input of m/z ranges of the intense bands that the users wish to exclude in the codes. Hopefully this will allow the low intensity peaks to show.

could not broadcast input array (aka empty MS scan in mzXML file)

Problem Description

Traceback (most recent call last):
  File "/Users/ann.lii.rosales/Desktop/Python/msanalysis/examples/05_relative_abundance.py", line 41, in <module>
    data = read_mzXML(mzXML_file)
  File "/Users/ann.lii.rosales/Desktop/Python/msanalysis/msanalysis/data_extraction/utils.py", line 125, in read_mzXML
    intensities[i] = s.get_peaks()[1]
ValueError: could not broadcast input array from shape (0) into shape (26947)

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Steps to Reproduce

I modified example 5 to plot data from the real mzXML file (which we now know is corrupted/problematic) and ran the example:

bash python 05_relative_abundance.py

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Expected/Desired Behaviour

The corrupted mzXML files is beyond our control, but we need a more descriptive error message when this happens in the future.

Peak detection/fitting and deconvolution

• Peak detection/fitting would help speed up QMS data analysis immensely. This would include generating isotope patterns based on possible etch products. Then, fitting would entail fitting the calculated patterns to the experimental data. Fitting output would include quality of fitting (eg, percent match to the isotope fingerprints).

• Peak deconvolution would help identify species with overlapping m/z values. Deconvolution would involve linear combination of relative peak intensities from different species.

Add ability to read from txt file