bbc 2015

BeNeLux Bioinformatics Conference – Antwerp, December 7-8 2015
Abstract ID: P
Poster
10th Benelux Bioinformatics Conference bbc 2015
P7. XCMS OPTIMISATION IN HIGH-THROUGHPUT LC-MS QC
Charlie Beirnaert 1,2* , Matthias Cuykx 3 , Adrian Covaci 3 & Kris Laukens 1,2 .
Advanced Database Research and Modeling (ADReM), University of Antwerp 1 ; Biomedical Informatics Research Centre
Antwerp (biomina) 2 ; Toxicological Centre, University of Antwerp 3 . * charlie.beirnaert@uantwerpen.be
In high-throughput untargeted metabolomics studies, quality control is still a prominent bottleneck. In analogy to a
recently developed QC tool for proteomics, work in our research group aims to develop a QC environment specific for
metabolomics. One component in this work is the XCMS analysis software for LC-MS data, which is very inputparameter-sensitive.
The presented work deals with the automatic optimisation of the XCMS parameters by building
further upon an existing framework for XCMS optimisation. The additions to this framework will be the inclusion of
quantified resolution data by using the otherwise ignored profile-data and intelligent use of the isotopic profile of
measured compounds.
INTRODUCTION
Metabolomics is the study of small molecules or
metabolites. These metabolites have an enormous
chemical diversity and are only now starting to be
identified in a high-throughput fashion. Reason for this is
the adoption of high performance liquid chromatography
mass spectrometry and nuclear magnetic resonance
spectroscopy. However, the data analysis of these large
datasets is not trivial, specifically for LC-MS there are
almost more ways of analysing data than there are
researchers. Arguably, the most common used software
platform for the initial analysis is XCMS (Smith et al.,
2006). However, the output of XCMS is very dependent
on the input-parameters. Often the default parameters are
chosen or they are adapted to the intuition of the
researcher, with no account of the introduction of false
positives etc. Optimization algorithms have been
constructed by using a dilution series (Eliasson et al.,
2012) and by using the carbon isotope (Libiseller et al.,
2015). In this work, we build further upon the latter by
including quantified information from the profile m/z
domain (the continuous data in the m/z dimension) where
accurate resolutions can be obtained for the mono-isotopic
peaks and other isotopes. The developed optimisation can
be used for both the data analysis and the quality control
framework that is under development.
METHODS
The proposed work uses XCMS to find the peaks of
interest in the data. To optimise this process, the results
from XCMS are analysed for the occurrence of peaks and
their isotopes. In this step, the raw profile data is inspected
around the, by XCMS, identified peaks for the
quantification of the peak resolution and for the
occurrence of missed isotopes.
Centroid vs Profile data: Modern day MS specialists use
centroid data because the file size is considerably lower.
The mass spectrometer converts the continuous data in the
m/z dimension to a collection of spikes where each
approximately Gaussian peak is converted to a single
spike (delta function with the same height as the original
peak). All other data is discarded. The result is a huge
reduction in the file size but a loss of the peak shape and,
as a result, no quantification of the resolution is possible.
Optimization parameter: The peaks and their isotopes
are characterized by a Gaussian in the chromatographic
dimension and spaced apart by 1.0063 Da in the m/z
dimension. When an isotope is missing or the extracted
peak does not appear in enough samples (for example in
50% of the samples in the sample group), the peak is
categorized as “unreliable”. When a peak is present in all
samples or has a clear isotopic distribution it is considered
as “reliable”. With these measures a so called peak picking
score can be calculated, which in turn can be optimised by
a variety of methods. This results in an increase in reliable
peaks, while not increasing false positives.
Analysis & Quality control: The optimisation of the
XCMs parameters is useful both in the analysis of the data
itself, but it is also applicable in quality control for large
scale LC-MS experiments. By being able to quantify the
resolutions of all relevant peaks in a dataset corresponding
to a control sample, it is possible to monitor the quality of
spectra, and when combining this with other QC
frameworks, like iMonDB (Bittremieux et al., 2015) it is
possible to assure the quality of all experiments in a long
lasting study.
RESULTS & DISCUSSION
The aim is to use the profile data to improve the available
optimization algorithms available. It remains to be seen
whether the extra information in this data (compared to
centroid data) justifies the increased need of computer
resources. Nonetheless, profile data provides a valuable
contribution in LC-MS optimization, because it enables
researchers to evaluate (quantitatively) and improve the
m/z resolution.
REFERENCES
Smith CA et al. Anal. Chem. 78(3), 779-789, (2006).
Eliasson M. et al. Anal. Chem. 84(15), 6869-6876, (2012).
Libiseller G. et al. BMC Bioinformatics 16:118, (2015).
Bittremieux W. et al. J. Proteome Res. 14(5), 2360-2366, (2015).
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