X-Rank: A Robust Algorithm for Small Molecule Identification Using Tandem Mass Spectometry

The diversity of experimental workflows involving LC-MS/MS and the extended range of mass spectrometers tend to produce extremely variable spectra. Variability reduces the accuracy of compound identification produced by commonly available software for a spectral library search. We introduce here a n...

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Bibliographic Details
Published in:Analytical chemistry (Washington) 2009-09, Vol.81 (18), p.7604
Main Authors: Mylonas, Roman, Mauron, Yann, Masselot, Alexandre, Binz, Pierre-Alain, Budin, Nicolas, Fathi, Marc, Viette, Véronique, Hochstrasser, Denis F, Lisacek, Frederique
Format: Article
Language:English
Subjects:
Algorithms
Analytical chemistry
Chromatography
Mass spectrometry
Molecular chemistry
Molecules
Online Access:Get full text
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Summary:The diversity of experimental workflows involving LC-MS/MS and the extended range of mass spectrometers tend to produce extremely variable spectra. Variability reduces the accuracy of compound identification produced by commonly available software for a spectral library search. We introduce here a new algorithm that successfully matches MS/MS spectra generated by a range of instruments, acquired under different conditions. Our algorithm called X-Rank first sorts peak intensities of a spectrum and second establishes a correlation between two sorted spectra. X-Rank then computes the probability that a rank from an experimental spectrum matches a rank from a reference library spectrum. In a training step, characteristic parameter values are generated for a given data set. We compared the efficiency of the X-Rank algorithm with the dot-product algorithm implemented by MS Search from the National Institute of Standards and Technology (NIST) on two test sets produced with different instruments. Overall the X-Rank algorithm accurately discriminates correct from wrong matches and detects more correct substances than the MS Search. Furthermore, X-Rank could correctly identify and top rank eight chemical compounds in a commercially available test mix. This confirms the ability of the algorithm to perform both a straight single-platform identification and a cross-platform library search in comparison to other tools. It also opens the possibility for efficient general unknown screening (GUS) against large compound libraries. [PUBLICATION ABSTRACT]
ISSN:0003-2700
1520-6882