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      Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements

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      1 , , 2 , , 2 , 3
      BMC Bioinformatics
      BioMed Central

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          Abstract

          Background

          Liquid chromatography coupled to mass spectrometry (LC-MS) has become a prominent tool for the analysis of complex proteomics and metabolomics samples. In many applications multiple LC-MS measurements need to be compared, e. g. to improve reliability or to combine results from different samples in a statistical comparative analysis. As in all physical experiments, LC-MS data are affected by uncertainties, and variability of retention time is encountered in all data sets. It is therefore necessary to estimate and correct the underlying distortions of the retention time axis to search for corresponding compounds in different samples. To this end, a variety of so-called LC-MS map alignment algorithms have been developed during the last four years. Most of these approaches are well documented, but they are usually evaluated on very specific samples only. So far, no publication has been assessing different alignment algorithms using a standard LC-MS sample along with commonly used quality criteria.

          Results

          We propose two LC-MS proteomics as well as two LC-MS metabolomics data sets that represent typical alignment scenarios. Furthermore, we introduce a new quality measure for the evaluation of LC-MS alignment algorithms. Using the four data sets to compare six freely available alignment algorithms proposed for the alignment of metabolomics and proteomics LC-MS measurements, we found significant differences with respect to alignment quality, running time, and usability in general.

          Conclusion

          The multitude of available alignment methods necessitates the generation of standard data sets and quality measures that allow users as well as developers to benchmark and compare their map alignment tools on a fair basis. Our study represents a first step in this direction. Currently, the installation and evaluation of the "correct" parameter settings can be quite a time-consuming task, and the success of a particular method is still highly dependent on the experience of the user. Therefore, we propose to continue and extend this type of study to a community-wide competition. All data as well as our evaluation scripts are available at http://msbi.ipb-halle.de/msbi/caap.

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          Most cited references34

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          PARAFAC. Tutorial and applications

          Rasmus Bro (1997)
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            MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data.

            New additional methods are presented for processing and visualizing mass spectrometry based molecular profile data, implemented as part of the recently introduced MZmine software. They include new features and extensions such as support for mzXML data format, capability to perform batch processing for large number of files, support for parallel processing, new methods for calculating peak areas using post-alignment peak picking algorithm and implementation of Sammon's mapping and curvilinear distance analysis for data visualization and exploratory analysis. MZmine is available under GNU Public license from http://mzmine.sourceforge.net/.
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              Temporal analysis of sucrose-induced phosphorylation changes in plasma membrane proteins of Arabidopsis.

              Sucrose is the main product of photosynthesis and the most common transport form of carbon in plants. In addition, sucrose is a compound that serves as a signal affecting metabolic flux and development. Here we provide first results of externally induced phosphorylation changes of plasma membrane proteins in Arabidopsis. In an unbiased approach, seedlings were grown in liquid medium with sucrose and then depleted of carbon before sucrose was resupplied. Plasma membranes were purified, and phosphopeptides were enriched and subsequently analyzed quantitatively by mass spectrometry. In total, 67 phosphopeptides were identified, most of which were quantified over five time points of sucrose resupply. Among the identified phosphorylation sites, the well described phosphorylation site at the C terminus of plasma membrane H(+)-ATPases showed a relative increase in phosphorylation level in response to sucrose. This corresponded to a significant increase of proton pumping activity of plasma membrane vesicles from sucrose-supplied seedlings. A new phosphorylation site was identified in the plasma membrane H(+)-ATPase AHA1 and/or AHA2. This phosphorylation site was shown to be crucial for ATPase activity and overrode regulation via the well known C-terminal phosphorylation site. Novel phosphorylation sites were identified for both receptor kinases and cytosolic kinases that showed rapid increases in relative intensities after short times of sucrose treatment. Seven response classes were identified including non-responsive, rapid increase (within 3 min), slow increase, and rapid decrease. Relative quantification of phosphorylation changes by phosphoproteomics provides a means for identification of fast responses to external stimuli in plants as a basis for further functional characterization.
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                Author and article information

                Journal
                BMC Bioinformatics
                BMC Bioinformatics
                BioMed Central
                1471-2105
                2008
                15 September 2008
                : 9
                : 375
                Affiliations
                [1 ]Beatson Institute for Cancer Research, Proteomics and Mass Spectrometry Group, Scotland, UK
                [2 ]Leibniz Institute of Plant Biochemistry, Bioinformatics and Mass Spectrometry, Halle, Germany
                [3 ]Free University Berlin, Department of Mathematics and Computer Science, Berlin, Germany
                Article
                1471-2105-9-375
                10.1186/1471-2105-9-375
                2570366
                18793413
                24350703-4997-4a3c-9563-b2073da515c7
                Copyright © 2008 Lange et al; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 31 March 2008
                : 15 September 2008
                Categories
                Research Article

                Bioinformatics & Computational biology
                Bioinformatics & Computational biology

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