MSeasy: unsupervised and untargeted GC-MS data processing | Zendy

Florence Nicolè | Zendy; Yann Guitton | Zendy; Élodie A. Courtois | Zendy; Sandrine Moja | Zendy; Laurent Legendre | Zendy; Martine HossaertMcKey | Zendy

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MSeasy: unsupervised and untargeted GC-MS data processing

Author(s) -

Florence Nicolè,

Yann Guitton,

Élodie A. Courtois,

Sandrine Moja,

Laurent Legendre,

Martine HossaertMcKey

Publication year - 2012

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/bts427

Subject(s) - ascii , netcdf , computer science , cluster analysis , data mining , nist , mass spectrum , graphical user interface , kovats retention index , interface (matter) , gas chromatography–mass spectrometry , database , mass spectrometry , information retrieval , chemistry , chromatography , artificial intelligence , gas chromatography , operating system , bubble , maximum bubble pressure method , natural language processing

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.

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