Journal Article

Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approach

Jan Urban, Jan Vaněk, Jiří Soukup and Dalibor Štys

in Bioinformatics

Volume 25, issue 20, pages 2764-2767
Published in print October 2009 | ISSN: 1367-4803
Published online July 2009 | e-ISSN: 1460-2059 | DOI: https://dx.doi.org/10.1093/bioinformatics/btp427
Expertomica metabolite profiling: getting more information from LC-MS using the stochastic systems approach

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Mass spectrometers are sophisticated, fine instruments which are essential in a variety applications. However, the data they produce are usually interpreted in a rather primitive way, without considering the accuracy of this data and the potential errors in identifying peaks. Our new approach corrects this situation by dividing the LC-MS output into three components: (i) signature of the analyte, (ii) random noise and (iii) systemic noise. The systemic noise is related to the instrument and to the particular experiment; its characteristics change in time and depend on the analyzed substance. Working with these components allows us to quantify the probability of peak errors and, at the same time, to retrieve some peaks which get lost in the noise when using the existing methods. Our software tool, Expertomica metabolite profiling, automatically evaluates the given instrument, detects compounds and calculates the probability of individual peaks. It does not need any artificial user-defined parameters or thresholds.

Availability: MATLAB scripts with a simple graphical user interface are free to download from http://sourceforge.net/projects/expertomica-eda/. The software reads data exported by most Thermo and Agilent spectrometers, and it can also read the more general JCAMP-DX ASCII format. Other formats will be supported on request, assuming that the user can provide representative data samples.

Contact: urban@greentech.cz

Journal Article.  2264 words.  Illustrated.

Subjects: Bioinformatics and Computational Biology

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