Probabilistic assignment of formulas to mass peaks in metabolomics experiments

Rogers, S. , Scheltema, R. A., Girolami, M. and Breitling, R. (2009) Probabilistic assignment of formulas to mass peaks in metabolomics experiments. Bioinformatics, 25(4), pp. 512-518. (doi:10.1093/bioinformatics/btn642)

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Publisher's URL: http://dx.doi.org/10.1093/bioinformatics/btn642

Abstract

Motivation: High-accuracy mass spectrometry is a popular technology for high-throughput measurements of cellular metabolites (metabolomics). One of the major challenges is the correct identification of the observed mass peaks, including the assignment of their empirical formula, based on the measured mass.

Results: We propose a novel probabilistic method for the assignment of empirical formulas to mass peaks in high-throughput metabolomics mass spectrometry measurements. The method incorporates information about possible biochemical transformations between the empirical formulas to assign higher probability to formulas that could be created from other metabolites in the sample. In a series of experiments, we show that the method performs well and provides greater insight than assignments based on mass alone. In addition, we extend the model to incorporate isotope information to achieve even more reliable formula identification.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon and Breitling, Professor Rainer and Girolami, Prof Mark
Authors: Rogers, S., Scheltema, R. A., Girolami, M., and Breitling, R.
Subjects:Q Science > QH Natural history > QH345 Biochemistry
College/School:College of Medical Veterinary and Life Sciences
College of Medical Veterinary and Life Sciences > Institute of Molecular Cell and Systems Biology
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN:1367-4803
ISSN (Online):1460-2059
Published Online:18 December 2008
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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
396841Probabilistic Reconstruction of Signalling Pathways & Identification of Novel Transcription Factors Employing Heterogeneous Genome-Wide dataMark GirolamiMedical Research Council (MRC)G0401466Computing Science