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https://github.com/arcadia-science/nucleoside-finder

Code used to identify nucleosides from Orbitrap LC-MS/MS data

https://github.com/arcadia-science/nucleoside-finder

Science Score: 23.0%

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    CITATION.cff file
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    .zenodo.json file
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    Found 1 DOI reference(s) in README
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    Links to: zenodo.org
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    Low similarity (8.4%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Code used to identify nucleosides from Orbitrap LC-MS/MS data

Basic Info
  • Host: GitHub
  • Owner: Arcadia-Science
  • License: mit
  • Language: Jupyter Notebook
  • Default Branch: main
  • Homepage:
  • Size: 408 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created over 3 years ago · Last pushed over 3 years ago

https://github.com/Arcadia-Science/nucleoside-finder/blob/main/ 

# nucleoside-finder

This repository contains a Jupyter Notebook used to search Orbitrap LC-MS/MS data for nucleosides. This notebook was written by [Peter Thuy-Boun](https://github.com/petertb), with additions by [Adair Borges](https://github.com/borgesadair1). 

## Inputs: 
This repo contains mass lists for nucleosides, charged adducts, and neutral adducts as inputs to the script: `mass_list_charged_adducts.csv`, `mass_list_neutral_adducts.csv`, `mass_list_nucleosides.csv`. Orbitrap data analyzed here are available through Zenodo: [10.5281/zenodo.7319990](https://zenodo.org/record/7319990#.Y4UDYuxuewk)

## Outputs: 
We have included the outputs from the script: `dRibose_neutral_loss_combined_raw_results.csv` contains raw results from this analysis, and `dRibose_neutral_loss_scatter.jpg` is a scatterplot of precursor ion m/z to fragment m/z. `dRibose_neutral_loss_summary_results.csv` is the most useful file with the nicely summarized results. 

## Development 
This software was developed and tested on Apple M1 macOS Monterey v12.1. To run the notebook, install the packages listed in the `env.yml` file using `conda`.
You can find operating system-specific instructions for installing miniconda [here](https://docs.conda.io/en/latest/miniconda.html).
After installing conda and [mamba](https://mamba.readthedocs.io/en/latest/), run the following command to create the environment.

```
mamba env create -n jupyter_nbs --file env.yml
conda activate jupyter_nbs
```

Once you have the environment activated, you can start a jupyter instance using:
```
jupyter notebook
```

Owner

  • Name: Arcadia Science
  • Login: Arcadia-Science
  • Kind: organization
  • Location: United States of America

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