2013/12/31 by F. Allen, Felicity Allen, R. Greiner +3 · 2 citations
Biochemistry, Genetics and Molecular Biology · Chemistry · Computer Science · #Advanced Proteomics Techniques and Applications #Chemical space #Fragmentation (computing) #Identification (biology) #Mass Spectrometry Techniques and Applications #Metabolite #Metabolomics and Mass Spectrometry Studies #Pattern recognition (psychology) #Probabilistic logic #PubChem #Tandem mass spectrometry #cs.CE
paper · pdf · doi:10.1007/s11306-014-0676-4
Preprint. The final publication is available at Springer via http://dx.doi.org/10.1007/s11306-014-0676-4. Metabolomics 2014
openalex publication_date 2014/06/04 · arxiv created 2014/06/09 · arxiv updated 2014/06/10 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
Electrospray tandem mass spectrometry (ESI-MS/MS) is commonly used in high throughput metabolomics. One of the key obstacles to the effective use of this technology is the difficulty in interpreting measured spectra to accurately and efficiently identify metabolites. Traditional methods for automated metabolite identification compare the target MS or MS/MS spectrum to the spectra in a reference database, ranking candidates based on the closeness of the match. However the limited coverage of available databases has led to an interest in computational methods for predicting reference MS/MS spectra from chemical structures. This work proposes a probabilistic generative model for the MS/MS fragmentation process, which we call Competitive Fragmentation Modeling (CFM), and a machine learning approach for learning parameters for this model from MS/MS data. We show that CFM can be used in both a MS/MS spectrum prediction task (ie, predicting the mass spectrum from a chemical structure), and in a putative metabolite identification task (ranking possible structures for a target MS/MS spectrum). In the MS/MS spectrum prediction task, CFM shows significantly improved performance when compared to a full enumeration of all peaks corresponding to substructures of the molecule. In the metabolite identification task, CFM obtains substantially better rankings for the correct candidate than existing methods (MetFrag and FingerID) on tripeptide and metabolite data, when querying PubChem or KEGG for candidate structures of similar mass.