biocogito/msproteomicstools

Some hopefully useful tools for mass spectrometry applied to proteomics

This repository contains some hopefully useful tools for mass spectrometry applied to proteomics. Speficically, it contains

• the TAPIR visualization software (available in ./gui)
• the MS Proteomics Tools Library (available in ./msproteomicstoolslib)
• a set of executables and scripts (available under ./analysis) including the TRIC alignment tool

The code is under the 3-clause BSD licence (see the LICENSE and the AUTHORS files). For full documentation, see the online documentation.

TRIC

The ./analysis folder contains the TRIC alignment tool, see the TRIC manual for further information.

TAPIR

TAPIR is a software that allows visualization of targeted proteomics data. See the TAPIR install instructions for further information on installation.

Please also see the project homepage for further information. Binaries are available for Windows and Mac OS X. For a source installation of the package, see below.

Executables

The ./analysis folder contains multiple potentially useful executables, including a tools for high throughput targeted proteomics data analysis (such as SWATH-MS data analysis). See the TRIC manual for further information on the TRIC alignment tool.

MS Proteomics Tools Library

The mass spectrometric (MS) Proteomics Tools Library contains multiple Python functions useful for MS-based proteomics.

Install

After installing the dependencies, you can proceed to install msproteomicstools itself:

pip install numpy
pip install msproteomicstools

If you are on windows, there is a chance the above will not work as Biopython and numpy need to be compiled from source. Currently, the way to get these is by installing Anaconda and then running in the Anaconda prompt:

conda install biopython
pip install msproteomicstools

Alternatively, you can also install from source:

git checkout https://github.com/msproteomicstools/msproteomicstools.git
pip install numpy
python setup.py install --prefix=/your/install/path 

Optional packages

There are some extra packages that can increase the features and improve the speed of the toolset

Fast lowess

To get fast lowess performance (several orders of magnitude faster), do the following

git clone https://github.com/carljv/Will_it_Python.git
cd Will_it_Python/MLFH/CH2/lowess\ work/
python setup.py build
sudo python setup.py install

Rpy2

If you would like to use the rpy2 bridge, you should install the rpy2 package.

Documentation

Documentation for the library can be found at http://proteomics.ethz.ch/msproteomicstools/ which contains source code documentation of the available functions and objects.