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

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

doi:10.1093/bioinformatics/btu359

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

Bioinformatics. 2014 Sep 15;30(18):2636-43. doi: 10.1093/bioinformatics/btu359. Epub 2014 May 28.

Authors

Christopher J Conley¹, Rob Smith¹, Ralf J O Torgrip¹, Ryan M Taylor¹, Ralf Tautenhahn², John T Prince¹

Affiliations

¹ Department of Statistics, University of California Davis, Davis, CA 95616, Department of Computer Science, Brigham Young University, Provo, UT 84606, USA, Department of Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden, Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84606, Department of Chemistry, Department of Molecular Biology and Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.
² Department of Statistics, University of California Davis, Davis, CA 95616, Department of Computer Science, Brigham Young University, Provo, UT 84606, USA, Department of Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden, Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84606, Department of Chemistry, Department of Molecular Biology and Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA Department of Statistics, University of California Davis, Davis, CA 95616, Department of Computer Science, Brigham Young University, Provo, UT 84606, USA, Department of Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden, Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84606, Department of Chemistry, Department of Molecular Biology and Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA Department of Statistics, University of California Davis, Davis, CA 95616, Department of Computer Science, Brigham Young University, Provo, UT 84606, USA, Department of Analytical Chemistry, Stockholm University, SE-106 91, Stockholm, Sweden, Department of Chemistry and Biochemistry, Brigham Young University, Provo, UT 84606, Department of Chemistry, Department of Molecular Biology and Center for Metabolomics, The Scripps Research Institute, La Jolla, CA 92037, USA.

PMID: 24872423
DOI: 10.1093/bioinformatics/btu359

Abstract

Motivation: 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.

Results: Massifquant is an open-source solution for KF IT detection that has been subjected to novel and rigorous methods of performance evaluation. The presented evaluation with accompanying annotations and optimization guide sets a new standard for comparative IT detection. Compared with centWave, matchedFilter and MZMine2-alternative IT detection engines-Massifquant detected more true ITs in a real LC-MS complex sample, especially low-intensity ITs. It also offers competitive specificity and equally effective quantitation accuracy.

Availability and implementation: Massifquant is integrated into XCMS with GPL license ≥ 2.0 and hosted by Bioconductor: http://bioconductor.org. Annotation data are archived at http://hdl.lib.byu.edu/1877/3232. Parameter optimization code and documentation is hosted at https://github.com/topherconley/optimize-it.

Publication types

Research Support, Non-U.S. Gov't
Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

Chromatography, Liquid / methods*
Computational Biology / methods*
Data Mining
Gas Chromatography-Mass Spectrometry / methods*
Isotopes
Software*
Statistics as Topic / methods*

Substances

Isotopes