Processing methods for differential analysis of LC/MS profile data

By Mikko Katajamaa, Matej Oresic. BMC Bioinformatics, 2005

394 citations recorded by the source corpus.

BackgroundLiquid chromatography coupled to mass spectrometry (LC/MS) has been widely used in proteomics and metabolomics research. In this context, the technology has been increasingly used for differential profiling, i.e. broad screening of biomolecular components across multiple samples in order to elucidate the observed phenotypes and discover biomarkers. One of the major challenges in this domain remains development of better solutions for processing of LC/MS data.ResultsWe present a software package MZmine that enables differential LC/MS analysis of metabolomics data. This software is a toolbox containing methods for all data processing stages preceding differential analysis: spectral filtering, peak detection, alignment and normalization. Specifically, we developed and implemented a new recursive peak search algorithm and a secondary peak picking method for improving already aligned results, as well as a normalization tool that uses multiple internal standards. Visualization tools enable comparative viewing of data across multiple samples. Peak lists can be exported into other data analysis programs. The toolbox has already been utilized in a wide range of applications. We demonstrate its utility on an example of metabolic profiling of Catharanthus roseus cell cultures.ConclusionThe software is freely available under the GNU General Public License and it can be obtained from the project web page at: http://mzmine.sourceforge.net/.

Subfields: Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/c93e93be686c1365362e4d47bd1c5661c37fb4ec

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Citations in the corpus

Lipid classification, structures and tools. (2011, background): “Accordingly, considerable effort has been made over the last 10 years to develop software to deconvolute and process MS data from lipidomics experiments [24,39,40].”
Highly sensitive feature detection for high resolution LC/MS (2008, methodology): “Several frameworks for feature detection (and alignment) of metabolomics LC/MS data have been developed in the last years, both commercial products such as MarkerLynx (Waters), the closed-source (but freely-available) MetAlign [6], or XCMS [7] and MZmine [8] which have opensource licenses.”
MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data (2006, methodology): “We have recently introduced a suite of tools for the processing of mass spectrometry based profile data (Katajamaa and Oresic, 2005).”
IPO: a tool for automated optimization of XCMS parameters (2015, methodology): “A number of software packages for processing LC-MS data have already been developed for data sets of samples that do not rely on labeling [3-12].”
Metabolomics technology and bioinformatics (2006, background): “Although a series of commercial and public tools exist [105, 107–111], none of them provides a comprehensive solution to meet the challenges of metabolomics.”
Metabolomics for functional genomics, systems biology, and biotechnology. (2010, background): “which was a technical bottleneck in the early stage of metabolomics study, can now be performed routinely by developing a series of software dealing with GC, CE, LC-MS, and NMR data, in which matrices containing intensity values of all signals in samples are generated (4, 25, 29, 36, 89, 92, 114, 15”
MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data (2010, methodology): “The typical MS data processing workflow comprises raw data file import, filtering/smoothing (optional), peak picking, peak list deisotoping, alignment, gap filling, and normalization [4].”
Proposed minimum reporting standards for data analysis in metabolomics (2007, methodology): “see CODA (Windig et al. 1996), and ( Katajamaa and Oresic 2005 )”