About LipidHunter 2

LipidHunter 2 has significant improvement from the original LipidHunter. The major improvements are listed below:

• Special Feature for TG & DG

  • ID based both on FA neutral losses & fragments
  • Accurate ID of isomeric species
  • Correction for all identified FA
  • Correction for the fragment intensity of multiply identified FAs
  • Check for cross-contamination signals e.g. PL fragments

• Other Major Feature Updates

  • Multiprocessing
  • Batch mode
  • 10 times faster processing speed
  • Command line mode
  • KNIME workflow integration
  • Multiple vendor support
  • Improved output style
  • Simplified configuration
  • View run parameters in report

• Currently supported Lipid classes in LipidHunter 2:

LipidHunter 2 new graphical user interface

This repository contains the source code of LipidHunter.

LipidHunter Windows .exe executable version can be found in release page:

https://github.com/SysMedOs/lipidhunter/releases

Please read the following instructions before you start to run LipidHunter.

Instructions

Windows version

• The binary excutable version of LipidHunter 2 is provided for Windows users. (Windows 7, 8, and 10, 64bit system required)

  • .exe installation file

  • .zip portable zip file

How to install LipidHunter 2 from source code

• Download the LipidHunter 2 as zip file for your system

  • Please use the source code page in the release page https://github.com/SysMedOs/lipidhunter/releases/
  • Download LipidHunter source Code as .zip. Please notice the date and version of LipidHunter source code.
  • Professional users can use git to clone the whole repository, please make sure that you switched to the correct branch.
  • Only the released version is recommended for real data processing. Other development branches may lead to unknown issues and miss interpretation of the data.

• Rename the downloaded file to LipidHunter.zip

• Unzip LipidHunter.zip file to any folder.

• Downloaded LipidHunter test spectra files: LipidHunter_Test_mzML_File

• Python environment

  • LipidHunter 2 is developed under python 3.6, the current version can still run on python 2.7 (not recommended).
  • The best way is to use virtual environment such as conda
  • The requirements is listed in conda_env.yml for conda users and requirements.txt for pip users.
    • conda: run conda env create -f conda_env.yml you will get an environment named envlipidhunter
    • pip: activate your virtual environment, then pip install -r requirements.txt
  • Main dependencies are:
    • Data processing: numpy, pandas, scipy, numba, natsort
    • mzML reader: pymzml == 0.7.8
    • Image plot: matplotlib, plotly(required by pymzml)
    • Excel output: openpyxl, xlrd, xlwt
    • Graphic interface: pyside
  • Test source code installation:
    • LipidHunter 2 is configured to use travis-ci with py.test to test on Windows, Linux, and macOS.
    • Use command pytest in LipidHunter folder to run all tests.
    • Optionally, you can test individual unit test in the test folder

• LipidHunter user guide

• Errors/bugs

  In case you experienced any problems with running LipidHunter

  please report an issue in the issue tracker or contact us.

License

• LipidHunter is Dual-licensed

  • For academic and non-commercial use: GPLv2 License:

    The GNU General Public License version 2

  • For commercial use: please contact the develop team by email.

• Please cite our publication in an appropriate form.

  • Ni, Zhixu, Georgia Angelidou, Mike Lange, Ralf Hoffmann, and Maria Fedorova. "LipidHunter identifies phospholipids by high-throughput processing of LC-MS and shotgun lipidomics datasets." Analytical Chemistry (2017).
    • DOI: 10.1021/acs.analchem.7b01126

Further questions?

• Report any issues here: https://github.com/SysMedOs/lipidhunter/issues

Fundings

We acknowledge all projects that supports the development of LipidHunter:

• BMBF - Federal Ministry of Education and Research Germany:

  https://www.bmbf.de/en/

• e:Med Systems Medicine Network:

  http://www.sys-med.de/en/

• SysMedOS Project :

  https://home.uni-leipzig.de/fedorova/sysmedos/