Probabilistic assignment of formulas to mass peaks in metabolomics experiments.
BACKGROUND
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.


BACKGROUND
A supplementary document, Matlab code, data and further information are available from http://www.dcs.gla.ac.uk/inference/metsamp.
Rogers
Simon
S
Department of Computing Science, University of Glasgow, Glasgow G12 8QQ, UK. srogers@dcs.gla.ac.uk
Scheltema
Richard A
RA
Girolami
Mark
M
Breitling
Rainer
R
D003198:Computer Simulation; D013058:Mass Spectrometry / Q000379:methods*; D055432:Metabolomics / Q000379:methods*; D020543:Proteome / Q000378:metabolism
D020543:Proteome