Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection | Zendy

Christopher J. Conley | Zendy; Rob Smith | Zendy; Ralf J. O. Torgrip | Zendy; Ryan M. Taylor | Zendy; Ralf Tautenhahn | Zendy; John T. Prince | Zendy

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Massifquant: open-source Kalman filter-based XC-MS isotope trace feature detection

Author(s) -

Christopher J. Conley,

Rob Smith,

Ralf J. O. Torgrip,

Ryan M. Taylor,

Ralf Tautenhahn,

John T. Prince

Publication year - 2014

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/btu359

Subject(s) - bioconductor , computer science , source code , trace (psycholinguistics) , data mining , filter (signal processing) , open source , kalman filter , software , chemistry , artificial intelligence , programming language , biochemistry , linguistics , philosophy , computer vision , gene

Isotope trace (IT) detection is a fundamental step for liquid or gas chromatography mass spectrometry (XC-MS) data analysis that faces a multitude of technical challenges on complex samples. The Kalman filter (KF) application to IT detection addresses some of these challenges; it discriminates closely eluting ITs in the m/z dimension, flexibly handles heteroscedastic m/z variances and does not bin the m/z axis. Yet, the behavior of this KF application has not been fully characterized, as no cost-free open-source implementation exists and incomplete evaluation standards for IT detection persist.

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