MetAssign: probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach

Daly, R., Rogers, S., Wandy, J., Jankevics, A., Burgess, K. E.V. and Breitling, R. (2014) MetAssign: probabilistic annotation of metabolites from LC–MS data using a Bayesian clustering approach. Bioinformatics, 30(19), pp. 2764-2771. (doi:10.1093/bioinformatics/btu370) (PMID:24916385) (PMCID:PMC4173012)

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Abstract

Motivation: The use of liquid chromatography coupled to mass spectrometry (LC–MS) has enabled the high-throughput profiling of the metabolite composition of biological samples. However, the large amount of data obtained can be difficult to analyse and often requires computational processing to understand which metabolites are present in a sample. This paper looks at the dual problem of annotating peaks in a sample with a metabolite, together with putatively annotating whether a metabolite is present in the sample. The starting point of the approach is a Bayesian clustering of peaks into groups, each corresponding to putative adducts and isotopes of a single metabolite.

Results: The Bayesian modelling introduced here combines information from the mass-to-charge ratio, retention time and intensity of each peak, together with a model of the inter-peak dependency structure, to increase the accuracy of peak annotation. The results inherently contain a quantitative estimate of confidence in the peak annotations and allow an accurate trade off between precision and recall. Extensive validation experiments using authentic chemical standards show that this system is able to produce more accurate putative identifications than other state-of-the-art systems, while at the same time giving a probabilistic measure of confidence in the annotations.

Availability: The software has been implemented as part of the mzMatch metabolomics analysis pipeline, which is available for download at http://mzmatch.sourceforge.net/.

Item Type:Articles
Status:Published
Refereed:Yes
Glasgow Author(s) Enlighten ID:Rogers, Dr Simon and Breitling, Professor Rainer and Burgess, Dr Karl and Daly, Dr Ronan
Authors: Daly, R., Rogers, S., Wandy, J., Jankevics, A., Burgess, K. E.V., and Breitling, R.
College/School:College of Medical Veterinary and Life Sciences
College of Medical Veterinary and Life Sciences > Institute of Infection Immunity and Inflammation
College of Medical Veterinary and Life Sciences > Institute of Molecular Cell and Systems Biology
College of Science and Engineering > School of Computing Science
Journal Name:Bioinformatics
Publisher:Oxford University Press
ISSN:1367-4803
ISSN (Online):1460-2059
Copyright Holders:Copyright © 2014 The Authors
First Published:First published in Bioinformatics 30(19):2764-2771
Publisher Policy:Reproduced under a Creative Commons License

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
680241Unifying metabolome and proteome informaticsSimon RogersBiotechnology and Biological Sciences Research Council (BBSRC)BB/L018616/1COM - COMPUTING SCIENCE
620261Metabolomic characterisation of clinical biofilms: monitoring of their formation and dispersal on different nanoscale patterned surfaces (ISSF Catalyst)Karl BurgessWellcome Trust (WELLCOME)097821/Z/11/ZIII - PARASITOLOGY
619351Bayesian Methods for Metabolite Identification and Analysis (ISSF 2012)Simon RogersWellcome Trust (WELLCOME)097821/Z/11/ZCOM - COMPUTING SCIENCE