Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

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Abstract

Mass spectrometry is a key analytical platform for metabolomics. The precise quantification and identification of small molecules is a prerequisite for elucidating the metabolism and the detection, validation, and evaluation of isotope clusters in LC-MS data is important for this task. Here, we present an approach for the improved detection of isotope clusters using chemical prior knowledge and the validation of detected isotope clusters depending on the substance mass using database statistics. We find remarkable improvements regarding the number of detected isotope clusters and are able to predict the correct molecular formula in the top three ranks in $92 %$ of the cases. We make our methodology freely available as part of the Bioconductor packages xcms version 1.50.0 and CAMERA version 1.30.0.

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Most cited references 36

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CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

Carsten Kuhl, Ralf Tautenhahn, Christoph Böttcher … (2012)

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. © 2011 American Chemical Society

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LMSD: LIPID MAPS structure database

Manish Sud, Eoin Fahy, Dawn Cotter … (2006)

The LIPID MAPS Structure Database (LMSD) is a relational database encompassing structures and annotations of biologically relevant lipids. Structures of lipids in the database come from four sources: (i) LIPID MAPS Consortium's core laboratories and partners; (ii) lipids identified by LIPID MAPS experiments; (iii) computationally generated structures for appropriate lipid classes; (iv) biologically relevant lipids manually curated from LIPID BANK, LIPIDAT and other public sources. All the lipid structures in LMSD are drawn in a consistent fashion. In addition to a classification-based retrieval of lipids, users can search LMSD using either text-based or structure-based search options. The text-based search implementation supports data retrieval by any combination of these data fields: LIPID MAPS ID, systematic or common name, mass, formula, category, main class, and subclass data fields. The structure-based search, in conjunction with optional data fields, provides the capability to perform a substructure search or exact match for the structure drawn by the user. Search results, in addition to structure and annotations, also include relevant links to external databases. The LMSD is publicly available at

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Highly sensitive feature detection for high resolution LC/MS

Ralf Tautenhahn, Christoph Böttcher, Steffen Neumann (2008)

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 centWave for 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. centWave can 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 centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from

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Peter D. Karp: Role: Academic Editor

Journal

Journal ID (nlm-ta): Metabolites

Journal ID (iso-abbrev): Metabolites

Journal ID (publisher-id): metabolites

Title: Metabolites

Publisher: MDPI

ISSN (Electronic): 2218-1989

Publication date (Electronic): 20 October 2016

Publication date Collection: December 2016

Volume: 6

Issue: 4

Electronic Location Identifier: 37

Affiliations

[1 ]Department of Stress and Developmental Biology, Leibniz Institute for Plant Biochemistry, Weinberg 3, Halle 06120, Germany; steffen.neumann@ 123456ipb-halle.de

[2 ]Institute of Computer Science, Martin-Luther-University Halle-Wittenberg, Von-Seckendorff-Platz 1, Halle 06120, Germany

Author notes

[* ]Correspondence: hendrik.treutler@ 123456ipb-halle.de ; Tel.: +49-345-5582-1472

Article

Publisher ID: metabolites-06-00037

DOI: 10.3390/metabo6040037

PMC ID: 5192443

PubMed ID: 27775610

SO-VID: 4de07028-65a7-4048-88d2-dd08aef28097

License:

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license ( http://creativecommons.org/licenses/by/4.0/).

Prediction, Detection, and Validation of Isotope Clusters in Mass Spectrometry Data

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Software for SAXS correction and analysis

Most cited references 36

CAMERA: an integrated strategy for compound spectra extraction and annotation of liquid chromatography/mass spectrometry data sets.

LMSD: LIPID MAPS structure database

Highly sensitive feature detection for high resolution LC/MS

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