article Open AccessTop 10% cited
Highly sensitive feature detection for high resolution LC/MS
BMC Bioinformatics · 2008 · Vol. 9(1) · pp. 504–504
Ralf Tautenhahn✉(Leibniz Institute of Plant Biochemistry)Christoph Böttcher(Leibniz Institute of Plant Biochemistry)Steffen Neumann(Leibniz Institute of Plant Biochemistry)
Abstract
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 (http://www.bioconductor.org/).
Metabolomics and Mass Spectrometry StudiesSpectroscopy and Chemometric AnalysesAdvanced Chemical Sensor TechnologiesBioconductorFeature (linguistics)Transformation (genetics)Pattern recognition (psychology)Mass spectrometryComputer sciencePrecision and recallMetabolomicsArtificial intelligenceData mining
MeSH terms
AlgorithmsChromatography, LiquidSensitivity and SpecificityMass SpectrometrySequence Analysis, ProteinProteome
Citations
1,169
FWCI
6.48
field-weighted impact
References
21
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97%
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References
Metabolite profiling for plant functional genomics
Nature Biotechnology · 2000 · 2,121 citations
XCMS: Processing Mass Spectrometry Data for Metabolite Profiling Using Nonlinear Peak Alignment, Matching, and Identification
Analytical Chemistry · 2006 · 5,142 citations
Data processing for mass spectrometry-based metabolomics
Journal of Chromatography A · 2007 · 594 citations
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