pastaq 0.11.4

Pipelines And Systems for Threshold Avoiding Quantification (PASTAQ): Pre-processing tools for LC-MS/MS data

Test PASTAQ on binder

Installation

Python 3 virtual environment

pip install pastaq

Installing from source

You need to install a suitable C++ compiler and corresponding build tools for your platform as well as CMake. The instructions listed here refer to the installation of PASTAQ's Python bindings. Currently the only external dependencies, including zlib, are included as git submodules.

To get started, clone this repository and initialize git submodules:

git clone https://github.com/PASTAQ-MS/PASTAQ.git
cd PASTAQ
git submodule init
git submodule update --remote

As usual, it is strongly recommended to create a Python 3 environment in which to build Pastaq, and the core development has been with Python 3.9, but 3.10, 3.11 and 3.12 should also work.

python -m pip install --upgrade pip
python -m pip install build
python -m pip install wheel

# create the .whl file in the ./dist folder
python -m build --installer pip --wheel

Windows

When building Pastaq in Windows, it may be helpful to first open a Visual Studio command prompt using Tools->Visual Studio Command Prompt in the Visual Studio IDE so that you have access to the compiler and linker. Then, in that command window, activate your PASTAQ Python environment and proceed with the instructions.

Powershell

Get-ChildItem ./dist/*.whl | ForEach-Object { pip install $_.FullName }

CMD command prompt

for %f in (./dist\*.whl) do pip install %f

Linux

find ./dist/*.whl | xargs pip install 

Now it can be imported and used in python as follows:

import pastaq
raw_data = pastaq.read_mzxml(...)

Usage

Examples of the usage of the PASTAQ can be found in the examples folder. To run them, install pastaq as previously described, update the input path of the mzXML and mzID files, change any necessary parameters and run it with:

python examples/small_range.py

You can use any mzXML files and identifications in mzIdentML v1.1+. If no identifications are available, remove the ident_path from the input files array or set it to 'none'. You can find the files we used for testing and development via ProteomeXchange, with identifier PXD024584.

Processing of mzML files is in an early stage and may lead to some issues.

For more information about PASTAQ and the configuration of the parameters, please visit the official website.

Tutorial Jupyter notebook

Tutorial demonstrating PASTAQ functionality to read and process LC-MS/MS data is available in examples/pastaqExamples.ipynb. Click the Binder icon above to try out the tutorial in Binder virtual environment.

How to cite this work

The main manuscript has been published in as Open Access Analytical Chemistry with the following details: Alejandro Sánchez Brotons, Jonatan O. Eriksson, Marcel Kwiatkowski, Justina C. Wolters, Ido P. Kema, Andrei Barcaru, Folkert Kuipers, Stephan J. L. Bakker, Rainer Bischoff, Frank Suits, and Péter Horvatovich, Pipelines and Systems for Threshold-Avoiding Quantification of LC–MS/MS Data, Analytical Chemistry, 2021, 93, 32, 11215–11224.