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Metabolomics

Nowadays, gene discovery has been made very efficient with the combination of deep sequencing and the exploitation of natural variation. Just in Arabidopsis, hundreds of genetic loci have been identified as influencing a wide variety of processes, and we aim to go from gene-of-interest to characterized protein product using approaches to “take a picture” of the comprehensive metabolome of the plant.

The IPB is currently operating a wide range of NMR and mass spectrometry instruments for metabolomics across all four departments, which are integrated into our Metabolomics Platform.

The experimental work is complemented by extensive Cheminformatics and Bioinformatics research to process and interpret the huge amounts of data. The IPB is operating the first European MassBank server, and hosts several online tools for metabolite identification.

Contact partner for all interests concerning the metabolomics platform is Dr. Steffen Neumann.

Publications by Tag: Metabolomics

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Displaying results 21 to 25 of 25.

Publications

Lange, E., Tautenhahn, R., Neumann, S. & Gröpl, C. Critical assessment of alignment procedures for LC-MS proteomics and metabolomics measurements BMC Bioinformatics 9, 375, (2008) DOI: 10.1186/1471-2105-9-375

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 algorithmshave 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 .

Publications

Kuhn, S., Egert, B., Neumann, S. & Steinbeck, C. Building blocks for automated elucidation of metabolites: Machine learning methods for NMR prediction BMC Bioinformatics 9, 400, (2008) DOI: 10.1186/1471-2105-9-400

Background

Current efforts in Metabolomics, such as the Human Metabolome Project, collect structures of biological metabolites as well as data for their characterisation, such as spectra for identification of substances and measurements of their concentration. Still, only a fraction of existing metabolites and their spectral fingerprints are known. Computer-Assisted Structure Elucidation (CASE) of biological metabolites will be an important tool to leverage this lack of knowledge. Indispensable for CASE are modules to predict spectra for hypothetical structures. This paper evaluates different statistical and machine learning methods to perform predictions of proton NMR spectra based on data from our open database NMRShiftDB.

Results

A mean absolute error of 0.18 ppm was achieved for the prediction of proton NMR shifts ranging from 0 to 11 ppm. Random forest, J48 decision tree and support vector machines achieved similar overall errors. HOSE codes being a notably simple method achieved a comparatively good result of 0.17 ppm mean absolute error.

Conclusion

NMR prediction methods applied in the course of this work delivered precise predictions which can serve as a building block for Computer-Assisted Structure Elucidation for biological metabolites.

Publications

Tautenhahn, R., Böttcher, C. & Neumann, S. Highly sensitive feature detection for high resolution LC/MS BMC Bioinformatics 9, 504, (2008) DOI: 10.1186/1471-2105-9-504

Background

Liquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features – a reliable feature detection is mandatory.

Results

We developed a new feature detection algorithm centWavefor high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.

Conclusion

The new feature detection algorithm meets the requirements of current metabolomics experiments. centWavecan detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter(the original algorithm of XCMS) and the centroidPicker from MZmine. The centWavealgorithm was integrated into the Bioconductor R-package XCMSand is available from http://www.bioconductor.org/

Publications

Gaida, A. & Neumann, S. MetHouse: Raw and Preprocessed Mass Spectrometry Data J. Integr. Bioinf. 4, 56, (2007) DOI: 10.2390/biecoll-jib-2007-56

We are developing a vendor-independent archive and on top of that a data warehouse for mass spectrometry metabolomics data. The archive schema resembles the communitydeveloped object model, the Java implementation of the model classes, and an editor (for both mzData XML files and the database) have been generated using the Eclipse Modeling Framework. Persistence is handled by the JDO2 -compliant framework JPOX. The main content of the Data Warehouse are the results of the signal processing and peak-picking tasks, carried out using the XCMS package from Bioconductor, putative identification and mass decomposition are added to the warehouse afterwards. We present the system architecture, current content, performance observations and describe the analysis tools on top of the warehouse. Availability: http://msbi.ipb-halle.de/

Publications

Tautenhahn, R., Böttcher, C. & Neumann, S. Annotation of LC/ESI-MS Mass Signals. In: Bioinformatics Research and Development BIRD 2007 Proc. of BIRD 2007 - 1st International Conference on Bioinformatics Research and Development 2007 371-380, (2007) DOI: 10.1007/978-3-540-71233-6_29

Mass spectrometry is the work-horse technology of the emerging field of metabolomics. The identification of mass signals remains the largest bottleneck for a non-targeted approach: due to the analytical method, each metabolite in a complex mixture will give rise to a number of mass signals. In contrast to GC/MS measurements, for soft ionisation methods such as ESI-MS there are no extensive libraries of reference spectra or established deconvolution methods. We present a set of annotation methods which aim to group together mass signals measured from a single metabolite, based on rules for mass differences and peak shape comparison.

This page was last modified on 10.03.2014.

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