jump to searchjump to navigationjump to content

Publications - Stress and Develop Biology

Displaying results 1 to 5 of 5.


Neumann, S.; Thum, A.; Böttcher, C. Nearline acquisition and processing of liquid chromatography-tandem mass spectrometry data Metabolomics 9, 84-91, (2013) DOI: 10.1007/s11306-012-0401-0

Liquid chromatography–mass spectrometry (LC–MS) is a commonly used analytical platform for non-targeted metabolite profiling experiments. Although data acquisition, processing and statistical analyses are almost routine in such experiments, further annotation and subsequent identification of chemical compounds are not. For identification, tandem mass spectra provide valuable information towards the structure of chemical compounds. These are typically acquired online, in data-dependent mode, or offline, using handcrafted acquisition methods and manually extracted from raw data. Here, we present several methods to fast-track and improve both the acquisition and processing of LC–MS/MS data. Our nearly online (nearline) data-dependent tandem MS strategy creates a minimal set of LC–MS/MS acquisition methods for relevant features revealed by a preceding non-targeted profiling experiment. Using different filtering criteria, such as intensity or ion type, the acquisition of irrelevant spectra is minimized. Afterwards, LC–MS/MS raw data are processed with feature detection and grouping algorithms. The extracted tandem mass spectra can be used for both library search and de-novo identification methods. The algorithms are implemented in the R package MetShot and support the export to Bruker, Agilent or Waters QTOF instruments and the vendor-independent TraML standard. We evaluate the performance of our workflow on a Bruker micrOTOF-Q by comparison of automatically acquired and extracted tandem mass spectra obtained from a mixture of natural product standards against manually extracted reference spectra. Using Arabidopsis thaliana wild-type and biosynthetic gene knockout plants, we characterize the metabolic products of a biosynthetic pathway and demonstrate the integration of our approach into a typical non-targeted metabolite profiling workflow.

Kuhl, C.; Tautenhahn, R.; Böttcher, C.; Larson, R.; Neumann, S. CAMERA: An integrated strategy for compound spectra extraction and annotation of LC/MS data sets Anal Chem. 84 (1), 283-289, (2012) DOI: 10.1021/ac202450g

Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis.

Wolf, S.; Schmidt, S.; Müller-Hannemann, M.; Neumann, S. In silico fragmentation for computer assisted identification of metabolite mass spectra BMC Bioinformatics 2010 148, 11, (2010) DOI: 10.1186/1471-2105-11-148

BackgroundMass spectrometry has become the analytical method of choice in metabolomics research. The identification of unknown compounds is the main bottleneck. In addition to the precursor mass, tandem MS spectra carry informative fragment peaks, but the coverage of spectral libraries of measured reference compounds are far from covering the complete chemical space. Compound libraries such as PubChem or KEGG describe a larger number of compounds, which can be used to compare their in silico fragmentation with spectra of unknown metabolites.ResultsWe created the MetFrag suite to obtain a candidate list from compound libraries based on the precursor mass, subsequently ranked by the agreement between measured and in silico fragments. In the evaluation MetFrag was able to rank most of the correct compounds within the top 3 candidates returned by an exact mass query in KEGG. Compared to a previously published study, MetFrag obtained better results than the commercial MassFrontier software. Especially for large compound libraries, the candidates with a good score show a high structural similarity or just different stereochemistry, a subsequent clustering based on chemical distances reduces this redundancy. The in silico fragmentation requires less than a second to process a molecule, and MetFrag performs a search in KEGG or PubChem on average within 30 to 300 seconds, respectively, on an average desktop PC.ConclusionsWe presented a method that is able to identify small molecules from tandem MS measurements, even without spectral reference data or a large set of fragmentation rules. With today's massive general purpose compound libraries we obtain dozens of very similar candidates, which still allows a confident estimate of the correct compound class. Our tool MetFrag improves the identification of unknown substances from tandem MS spectra and delivers better results than comparable commercial software. MetFrag is available through a web application, web services and as java library. The web frontend allows the end-user to analyse single spectra and browse the results, whereas the web service and console application are aimed to perform batch searches and evaluation.

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

BackgroundLiquid 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.ResultsWe 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/

Tautenhahn, R.; Böttcher, C.; Neumann, S. Annotation of LC/ESI-MS Mass Signals 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.
IPB Mainnav Search