Alignment of high resolution mass spectra: development of a heuristic approach for metabolomics

Abstract

One of the challenges of using mass spectrometry for metabolomic analyses of samples consisting of thousands of compounds is that of peak identification and alignment. This paper addresses the issue of aligning mass spectral data from different samples in order to determine average component m/z peak values. The alignment scheme developed takes the instrument m/z measurement error into consideration in order to heuristically align two or more samples using a technique comparable to automated visual inspection and alignment. The results obtained using mass spectral profiles of replicate human urine samples suggest that this heuristic alignment approach is more efficient than other approaches using hierarchical clustering algorithms. The output consists of an average m/z and intensity value for the spectral components together with the number of matches from the different samples. One of the major advantages of using this alignment strategy is that it eliminates the boundary problem that occurs when using predetermined fixed bins to identify and combine peaks for averaging and the efficient runtime allows large datasets to be processed quickly.

A unified modeling framework for metabonomic profile development and covariate selection for acute trauma subjects

“…Owing to the inherent variability of the mass spectrometer, intensities may get registered at slightly different positions even within the replicates of a single subject. In this paper we have used the alignment algorithm developed by Kazmi et al [17] to align the replicates first. It has been reported [17] that this algorithm does a near-perfect job in aligning m/z values for such a data set.…”

“…In this paper we have used the alignment algorithm developed by Kazmi et al [17] to align the replicates first. It has been reported [17] that this algorithm does a near-perfect job in aligning m/z values for such a data set. Notably perfect alignment if possible improves the signal-to-noise ratio, which in turn enhances the confidence in estimation itself [16].…”

“…This introduces additional variability and thus needs proper alignment of the mass spectrometry output before any fruitful analysis can be carried out. We have used the alignment algorithm presented in Kazmi et al [17] to align each subject using all of its replicates (namely three). It has been reported [17] that this algorithm has a linear runtime complexity (i.e.…”

“…We have used the alignment algorithm presented in Kazmi et al [17] to align each subject using all of its replicates (namely three). It has been reported [17] that this algorithm has a linear runtime complexity (i.e. much faster) and produces an output that is perfectly aligned.…”

A simpler method of preprocessing MALDI-TOF MS data for differential biomarker analysis: stem cell and melanoma cancer studies

“…Secondly, most of the publicly available preprocessing techniques focuses on either SELDI-TOF MS, often on intact proteins at low resolution compared to modern instrumentation [3,11] or liquid chromatography (LC) MS [12-14]. These existing preprocessing techniques have limited functions which can be applied to high resolution MALDI-TOF MS peptide data.…”