From mass spectral features to molecules in molecular networks: MolNotator LDB dataset.

  • Damien Olivier-Jimenez (Creator)
  • Zakaria Bouchouireb (Creator)
  • Simon Ollivier (Creator)
  • Julia Mocquard (Creator)
  • Pierre Marie Allard (Creator)
  • Guillaume Bernadat (Creator)
  • Marylène Chollet-Krugler (Creator)
  • David Rondeau (Creator)
  • Joël Boustie (Creator)
  • Justin van der Hooft (Creator)
  • Jean Luc Wolfender (Creator)



Finding actual molecules in LC-MS/MS experiments can prove challenging due to the considerable amount of redundant ions generated during ionization. In this context, MolNotator was created and validation with this dataset.

MolNotator is a Python 3.7 package designed to predict molecules (molecular masses) by combinatorial triangulation in LC-MS/MS experiments after a preprocessing step using MZMine. An MGF and a CSV files output from MZmine are required as input for MolNotator which are placed in the "mzmine_out" folder of the project folder (the uploaded dataset).

Instructions for the use of MolNotator are available on GitHub (, Pypi ( and in the associated publication.

The dataset consists of 193 LC-MS/MS analyses of lichen pure standards (previously used for the Lichen Database, LDB), 156 of which were detected by manual curation and served to benchmark MolNotator. Results indicated more than 90% of the 156 molecular masses were predicted by MolNotator under 2 ppm error on average.

The project folder contains, in addition to the mzmine_out folder, a database and a params folder (see GitHub and Pypi) as well as a styles folder, containing different styles that can be imported on Cytoscape ( to visualise MolNotator's network output.
Date made available16 Dec 2021


  • metabolomics
  • LC-MS/MS
  • Lichen Database
  • molecular networking
  • small molecules

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