Metadata-Version: 2.2
Name: MSCI
Version: 0.2.1
Summary: MSCI assesses peptide fragmentation spectra information content.
Home-page: https://github.com/proteomicsunitcrg/MSCI
Author: Zahra ELHAMRAOUI
Author-email: zahra.elhamraoui@crg.eu
License: MIT license
Keywords: MSCI
Classifier: Development Status :: 2 - Pre-Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3.8
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
License-File: AUTHORS.rst
Requires-Dist: Click>=7.0
Requires-Dist: streamlit
Requires-Dist: matchms
Requires-Dist: scipy
Dynamic: author
Dynamic: author-email
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: keywords
Dynamic: license
Dynamic: requires-dist
Dynamic: requires-python
Dynamic: summary


   <p align="center">
      <img src="docs/MSCI_logo.png" alt="logo" width="300" height="300">
   </p>


* Documentation available at: https://msci.readthedocs.io.
* Web application available at: [msci--proteomicsunit.streamlit.app](https://msci--proteomicsunit.streamlit.app/) 


Peptide identification by mass spectrometry relies on the interpretation of fragmentation spectra based on the m/z pattern, relative intensities, and retention time (RT). Given a proteome, we wondered how many peptides generate very similar fragmentation spectra with current MS methods. MSCI is a Python package built to assess the information content of peptide fragmentation spectra, we aimed calculating an information-content index for all peptides in a given proteome would enable us to design data acquisition and data analysis strategies that generate and prioritize the most informative fragment ions to be queried for peptide quantification.

  <p >
      <img src="docs/INTRODUCTION.png" alt="workflow illustration">
   </p>

Installation
==================
You can install MSCI directly using pip, which will also handle the necessary dependencies.

 

    pip install MSCI==0.2.0


Implementation and example 
==================

**Open the Notebook**: Click on the following [Tutorial_google_colab](https://colab.research.google.com/drive/1ny97RNgvnpD7ZrHW8TTRXWCAQvIcavkk) 



Contribution
==================

If you would like to contribute to this project, feel free to fork the repository on GitHub and submit a pull request.
