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

Christopher J Conley1, Rob Smith1, Ralf J O Torgrip1

  • 1Department 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.

Bioinformatics (Oxford, England)
|May 30, 2014
PubMed
Summary
This summary is machine-generated.

Massifquant, an open-source tool for isotope trace (IT) detection in mass spectrometry, improves accuracy in complex samples. This Kalman filter-based approach enhances the detection of low-intensity signals compared to existing methods.

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Area of Science:

  • Analytical Chemistry
  • Computational Biology
  • Bioinformatics

Background:

  • Isotope trace (IT) detection is crucial for chromatography mass spectrometry (XC-MS) but faces challenges with complex samples.
  • The Kalman filter (KF) offers advantages for IT detection, including discrimination of closely eluting traces and flexible variance handling.
  • Existing KF implementations lack open-source availability and standardized evaluation methods, hindering characterization.

Purpose of the Study:

  • To introduce Massifquant, an open-source implementation of the Kalman filter for isotope trace detection in XC-MS data analysis.
  • To establish rigorous performance evaluation standards for IT detection methods.
  • To compare Massifquant's performance against existing IT detection engines.

Main Methods:

  • Massifquant is implemented within the XCMS package, available under GPL license ≥ 2.0 on Bioconductor.
  • Performance evaluation employed novel and rigorous methods, including accompanying annotations and an optimization guide.
  • Comparative analysis was conducted against centWave, matchedFilter, and MZMine2 IT detection engines.

Main Results:

  • Massifquant demonstrated superior detection of true isotope traces in complex LC-MS samples compared to other engines, particularly for low-intensity signals.
  • The software offers competitive specificity and quantitation accuracy.
  • The evaluation methodology sets a new benchmark for comparative IT detection analysis.

Conclusions:

  • Massifquant provides a robust, open-source solution for Kalman filter-based isotope trace detection.
  • The developed evaluation framework advances the field of IT detection performance assessment.
  • Massifquant enhances the analysis of complex mass spectrometry data, especially for low-abundance analytes.