Highly sensitive feature detection for high resolution LC/MS

By Ralf Tautenhahn, Christoph Böttcher, Steffen Neumann. BMC Bioinformatics, 2008

1,102 citations recorded by the source corpus.

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 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.ConclusionThe 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 http://www.bioconductor.org/

Subfields: Analytical Chemistry, Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/9034a666296c380ce6543c8c6f82861f2622b86d

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Citations in the corpus

Analytical Methods in Untargeted Metabolomics: State of the Art in 2015 (2015, positive): “From these, however, the Wavelet transform-based filters have demonstrated a superior performance, although at the expense of a higher computation time (Du et al., 2006; Tautenhahn et al., 2008).”
IPO: a tool for automated optimization of XCMS parameters (2015, neutral): “The first ‘ xcmsSet ’ -method ‘ centWave ’ [16] deals with peak picking .”
Using MetaboAnalyst 5.0 for LC–HRMS spectra processing, multi-omics integration and covariate adjustment of global metabolomics data (2022, methodology): “The left panel shows box plots summarizing the intensity distributions of the selected feature across sample groups de ﬁ ned by the metadata ﬁ le uploaded in Step 2 ● Double-clicking a data point on the box plot will generate its corresponding extracted ion chromatogram (EIC) plot.”
MetaboAnalyst 5.0: narrowing the gap between raw spectra and functional insights (2021, methodology): “Both now use the CentWave algorithm for chromatographic peak detection (32).”
Comprehending crystalline β-carotene accumulation by comparing engineered cell models and the natural carotenoid-rich system of citrus (2011, positive): “Peak picking, grouping of associated peaks, and retention time alignment was performed using the xcms software package (version 2.14.1, Smith et al., 2006; Tautenhahn et al., 2008) in the statistical software R (version 2.”
DIA-Umpire: comprehensive computational framework for data independent acquisition proteomics (2015, methodology): “As a peak curve might have multiple maxima, we apply a continuous wavelet transform (CWT)-based approach 41 for splitting the curve into several separate peak curves using a Mexican-hat wavelet.”
MetaboLights: open data repository for metabolomics (2023, background): “XCMS is a framework that allows users to process chromatographic MS data ( 32 ,33 ).”