Abstract
Molecular networking (MN) allows one to organize tandem mass spectrometry (MS/MS) data by spectral similarities. Cosine-score used as a metric to calculate the distance between two spectra is based on peak lists containing fragments and neutral losses from MS/MS spectra. Until now, the workflow excluded the generation of the molecular network from electron ionization (EI) MS data as no selection of the putative parent ion is achieved when performing classical gas chromatography (GC)-EI-MS analysis. In order to fill this gap, new functionalities on MetGem 1.2.2 software ( https://github.com/metgem/metgem/releases ) have been implemented, and results from a large EI-MS database and GC-EI-MS analysis will be exemplified.
Highlights
The molecular network (MN) workflow was first described by the group of Dorrestein[1] based on an original approach introduced by Gross and co-workers in 2002.2 Molecular networking offers the unique possibility to classify large collections of tandem mass spectrometry (MS/MS) data by spectral similarity and significantly accelerated their annotation by searching in experimental and in silico MS/ MS databases
The workflow to generate a MN from gas chromatography (GC)-IE-MS first consists of converting the original file in a vendor format into a .mzXML by MSconvert freely distributed by ProteoWizard.[8]
Mass detection of centroid data is performed followed by ADAP Chromatogram builder to construct extracted ion chromatograms (EICs) and detect chromatographic peaks from EICs
Summary
The molecular network (MN) workflow was first described by the group of Dorrestein[1] based on an original approach introduced by Gross and co-workers in 2002.2 Molecular networking offers the unique possibility to classify large collections of tandem mass spectrometry (MS/MS) data by spectral similarity and significantly accelerated their annotation by searching in experimental and in silico MS/ MS databases. The peak list was generated by aligning the data set using RANSAC aligner[7] with the RT tolerance before correction at 0.5 and after correction at 0.1, RANSAC iterations at 0, minimum number of points at 80%, and threshold value at 0.2. Data were exported as .mgf files for spectral information and .csv files for metadata information (peak area, peak intensity, retention times, etc.).
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