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‎CandyCrunch.egg-info/PKG-INFO

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Metadata-Version: 2.1
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Name: CandyCrunch
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Version: 0.1.1
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Version: 0.1.2
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Summary: Package for predicting glycan structure from LC-MS/MS data
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Home-page: https://github.com/BojarLab/CandyCrunch
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Author: Daniel Bojar
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License-File: LICENSE.txt
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<p align="center">
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<img src="/images/candycrunch_logo.jpg" style="height:50%;width:50%;">
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<img src="/images/candycrunch_logo_light_banner_pt.svg" style="height:100%;width:100%;">
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</p>
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-----------------
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# CandyCrunch
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[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7940047.svg)](https://doi.org/10.5281/zenodo.7940047)
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[![License](https://img.shields.io/badge/license-MIT-red.svg)](https://github.com/bojarlab/candycrunch/blob/main/LICENSE)
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## What is CandyCrunch?
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**CandyCrunch** is a package for predicting glycan structure from LC-MS/MS data. It contains the CandyCrunch model, along with the rest of the inference pipeline and and downstream spectrum processing tools. These are further described in our upcoming manuscript (Urban et al., bioRxiv 2023).
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**CandyCrunch** is a package for predicting glycan structure from LC-MS/MS data. It contains the CandyCrunch model, along with the rest of the inference pipeline and and downstream spectrum processing tools. These are further described in our manuscript [Urban et al. (2023)](https://www.biorxiv.org/content/10.1101/2023.06.13.544793v1.full) &ndash; ***Predicting glycan structure from tandem mass spectrometry via deep learning*** on bioRxiv.
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## Install CandyCrunch
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#### Development version:
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```
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## `CandyCrunch.ipynb` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/BojarLab/CandyCrunch/blob/main/CandyCrunch.ipynb)
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If you are looking for a **convenient** and **easy-to-run** version of the code that does not require any local installations, we have also created a Google Colaboratory notebook.
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The notebook contains an example pipeline ready to run, which can be copied, executed, and customised in any way.
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The notebook contains an example pipeline ready to run, which can be copied, executed, and customised in any way.
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The example file included in the notebook is the same as in `examples/` and is ready for use in the notebook workflow.
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## Using CandyCrunch &ndash; LC-MS/MS glycan annotation
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#### `wrap_inference` (in `CandyCrunch.prediction`) <br>
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```python
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annotated_spectra_df = wrap_inference(C:/myfiles/my_spectra.mzML, glycan_class)
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```
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<details>
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<summary>
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#### This is what a truncated example of `annotated_spectra_df` would look like
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</summary>
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| | predictions | composition | num_spectra | charge | RT | top_fragments | adduct | evidence |
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|--------:|:----------------------------------------------------------------------------------------------------------------------------------------|:------------------------|--------------:|---------:|------:|:-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------:|:-----------|
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| 384.157 | [('Gal(b1-3)GalNAc', 0.9625)] | {'Hex': 1, 'HexNAc': 1} | 8 | -1 | 6.75 | [204.0202, 222.1731, 156.0888, 179.031, 160.7594, ...] | nan | strong |
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| 425.036 | [('GalNAc(a1-3)GalNAc', 0.7947394540942927), ('GlcNAc(b1-3)GalNAc', 0.17965260545905706), ('HexNAc(?1-3)GalNAc', 0.025607940446650122)] | {'HexNAc': 2} | 2 | -1 | 38.88 | [381.005, 389.9802, 406.871, 326.8488, 212.01, ...] | nan | strong |
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| ... | ... | ... | ... | ... | ... | ... | ... | ... | | ... | ... | ... | ... | ... | ... | ... |
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</details>
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## Using CandyCrumbs &ndash; MS2 fragment annotation
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#### `CandyCrumbs` (in `CandyCrunch.analysis`) <br>
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Wrapper function to annotate MS2 fragments using `CandyCrumbs`
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annotated_fragments_dict = CandyCrumbs(condensed_iupac_glycan,fragment_masses=ms2_fragment_masses,mass_threshold=1)
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```
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<details>
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<summary>This is what `annotated_fragments_dict` would look like</summary>
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<summary>
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#### This is what `annotated_fragments_dict` would look like
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</summary>
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<pre>{425.07: {'Theoretical fragment masses': [425.12955],
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'Domon-Costello nomenclatures': [['02A_3_Alpha', 'M_H2O']],
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'Fragment charges': [-1]},
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GlycoDraw(fragment_iupac)
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```
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<p align="center">
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<img width="460" height="300" src="/images/frag_iupac_demo.svg">
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<img width="460" height="300" src="/images/frag_iupac_demo_white.svg">
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</p>
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## Modules
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- Contains other functions to manipulate glycan string representations, e.g., `domon_costello_to_fragIUPAC` <br>
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#### `model` <br>
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- Includes code for model definition, dataset handling, and data augmentation; only used in the back-end <br>
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#### `examples` <br>
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- Includes the extracted spectra of an example mzML file from Kouka et al. 2022
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## Citation
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If you use `CandyCrunch` or any of our datasets in your work, please cite Urban et al., bioRxiv 2023.

‎CandyCrunch.egg-info/requires.txt

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glycowork==0.7
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glycowork~=0.8.1
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regex
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networkx
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scipy
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pyteomics
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[draw]
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glycowork[draw]==0.7
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glycowork[draw]~=0.8.1

‎dist/CandyCrunch-0.1.1.tar.gz renamed to ‎dist/CandyCrunch-0.1.2-py3-none-any.whl

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‎dist/CandyCrunch-0.1.1-py3-none-any.whl renamed to ‎dist/CandyCrunch-0.1.2.tar.gz

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‎setup.py

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setuptools.setup(
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name="CandyCrunch",
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version="0.1.1",
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version="0.1.2",
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author="Daniel Bojar",
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author_email="daniel.bojar@gu.se",
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description="Package for predicting glycan structure from LC-MS/MS data",

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