PyMass

Package for analyzing MS with Python

It can provide the following functionalities now:

• mzXMLParser for fast and efficient mzXML parse
• FPIC method for extracting PICs from raw LC-MS dataset effectively and quickly

In future, more file formats will be supported and more methods will be implemented into PyMass package, so researchers can create complex analysis workflows for LC-MS datasets in Python with ease.

Install

Required Dependencies

• Hardware

  • Modern CPUs with Advanced Vector Extensions 2(AVX2) instructions

• Windows:

  • Visual Studio Community 2015 with Update 3
  • Anaconda Python 3.6.0 64bit
  • SWIG 3.0.10
  • CMake 3.7.1
  • Eigen 3.3.3
  • Threading Building Blocks 2017 Update 6
  • libexpat 2.2.4
  • base64SIMD

• Linux:

  • Ubuntu 16.04
  • GCC
  • Python
  • CMake
  • SWIG

Download

• Download pymass
• Unzip it into pymass directory

Compile

• Windows:

  • Open "VS2015 x64 Native Tools Command Prompt"

  • Run following commands in the prompt

     cd pymass
     mkdir build
     cd build
     cmake .. -G "NMake Makefiles" -DCMAKE_BUILD_TYPE=Release
     nmake
     nmake install

• Linux:
  • PyMass can be built and run smoothly in Ubuntu Linux 16.04. We provide a bash script to download thirdparty libraries, apply the patches, build pymass automatically

     wget https://github.com/zmzhang/pymass/raw/master/build.sh
     chmod +x build.sh
     ./build.sh

Usage

• Go to pymass/python directory

• Download MM14 dataset from this url and unzip it

• Run following Python code fragment to parse mzXML file and extract PICs from it

   from _pymass import mzXMLParser, FPICs
   import sys
   mzfile="MM14_20um.mzxml"
   mzfile=mzfile.encode(sys.getfilesystemencoding())
   parser=mzXMLParser()
   lcms = parser.parseFile(mzfile)
   pics = FPICs(lcms, 300.0, 100.0, 0.5)

Contact

For any questions, please contact:

zmzhang@csu.edu.cn