Abstract
MSeasy performs unsupervised data mining on gas chromatography-mass spectrometry data. It detects putative compounds within complex metabolic mixtures through the clustering of mass spectra. Retention times or retention indices are used after clustering, together with other validation criteria, for quality control of putative compounds. The package generates a fingerprinting or profiling matrix compatible with NIST mass spectral search program and ARISTO webtool (Automatic Reduction of Ion Spectra To Ontology) for molecule identification. Most commonly used file formats, NetCDF, mzXML and ASCII, are acceptable. A graphical and user-friendly interface, MSeasyTkGUI, is available for R novices.