https://github.com/white-lab/pyproteome

Python library for analyzing mass spectrometry proteomics data.
https://github.com/white-lab/pyproteome

Last synced: 2 months ago
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Python library for analyzing mass spectrometry proteomics data.

• Host: GitHub
• URL: https://github.com/white-lab/pyproteome
• Owner: white-lab
• License: bsd-2-clause
• Created: 2016-02-26T20:16:58.000Z (almost 9 years ago)
• Default Branch: master
• Last Pushed: 2024-03-20T19:06:57.000Z (10 months ago)
• Last Synced: 2024-07-02T08:29:26.408Z (7 months ago)
• Language: Python
• Homepage:
• Size: 1.63 MB
• Stars: 10
• Watchers: 2
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog.md
  • License: LICENSE.md

Awesome Lists containing this project

• awesome-proteomics - pyproteome - python - analyzes proteomics data, can filter, normalize, perform motif and pathway enrichment. Currently only supports ProteomeDiscoverer .msf search files - [paper](https://pyproteome.readthedocs.io/en/latest/) (5. Raw Data Analysis / Table of Contents)

README

        
# pyproteome

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Python library for analyzing mass spectrometry proteomics data.

## Installation

To install the core pyproteome python library, [install Python >= 3.6](https://www.python.org/) and [the latest version of pip](https://pip.pypa.io/en/stable/installing/). Then run the following command:

```

pip install pyproteome

```

To install dependencies for [PHOTON](https://github.com/jdrudolph/photon), run the following command:

```

pip install pyproteome[photon]

```

### Windows

If you are using Windows, it is easiest to use the latest version of

[Anaconda](https://www.continuum.io/downloads) for your Python installation, as

pyproteome requires several hard-to-install packages, such as NumPy and SciPy.

Then, you can simply run the above `pip install pyproteome` command to install

this package and the rest of its dependencies.

### CAMV

pyproteome can use CAMV for data validation. If you have the executable

installed on your system, simply add "CAMV.exe" to your system path and

pyproteome will locate it automatically.

## Examples

There are several example analyses located in the [pyproteome-data

repository](https://github.com/white-lab/pyproteome-data/tree/master/examples).

For a full list of package functionality, refer to the

[online documentation](https://pyproteome.readthedocs.io/en/latest/).

## Directory Hierarchy

pyproteome expects a certain directory hierarchy in order to import data files

and interface with CAMV. This pattern is as follows:

```

base_directory/

    CAMV Output/

    Figures/

    MS RAW/

    Searched/

```

See `pyproteome.paths` if you are using a custom directory hierarchy. i.e.:

```

>>> from pyproteome import paths

>>> paths.MS_RAW_DIR = "path/to/raw_files/"

>>> paths.MS_SEARCHED_DIR = "path/to/msf_files/"

```