PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Probabilistic assignment of formulas to mass peaks in metabolomics experiments.
Simon Rogers, R Scheltema, Mark Girolami and R Breitling
Bioinformatics Volume 25, Number 4, pp. 512-518, 2008. ISSN 1460-2059

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:4857
Deposited By:Simon Rogers
Deposited On:24 March 2009