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Highly sensitive feature detection for high resolution LC/MS

BMC Bioinformatics · 2008 · Vol. 9(1) · pp. 504–504
Ralf TautenhahnChristoph BöttcherSteffen Neumann

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
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