Data processing for mass spectrometry-based metabolomics.
By Mikko Katajamaa, Matej Oresic. Journal of Chromatography A, 2007
596 citations recorded by the source corpus.
Subfields: Biomedical Engineering, Molecular Biology, Spectroscopy
View Paper: https://www.semanticscholar.org/paper/dec24fb6b497adac85d31ced38d44ec9f0013fff
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In Biomedical Engineering
- Ralf Tautenhahn
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- Christoph Böttcher
- Sandra Castillo
- Alejandro Villar-Briones
- Tomás Pluskal
- Youngja Park
- Tianwei Yu
In Molecular Biology
- David S. Wishart
- Jeremy K. Nicholson
- Ian A. Wilson
- Steffen Neumann
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In Spectroscopy
- Steffen Neumann
- Ralf Tautenhahn
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- Tomás Pluskal
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- Ian A. Wilson
- Sebastian Böcker
Citations in the corpus
- Organization of GC/MS and LC/MS metabolomics data into chemical libraries (2010, background): “One of the standard approaches of data analysis in the biochemical profiling field is to perform statistical analyses using every individual ion signal whether or not they are redundant ions produced by a single chemical [10-12].”
- Computational mass spectrometry for small molecules (2013, background): “Review of reviews Existing reviews on computational MS for small molecules, usually focus on a much more narrow area of the field such as raw data processing [29], metabolomics databases and laboratory information management systems [30], or metabolite identification through reference libraries [31]”
- apLCMS - adaptive processing of high-resolution LC/MS data (2009, methodology): “For such profiles, binning in the m/z dimension is widely used (Katajamaa and Oresic, 2007; Smith et al., 2006).”
- MZmine 2: Modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data (2010, methodology): “Several software packages have been developed for this purpose, and have been extensively reviewed by Katajamaa and Ore š i č [1].”
- Global metabolic profiling procedures for urine using UPLC–MS (2010, background): “An in-depth review of these data analysis challenges is beyond the scope of this protocol, but good reviews can be found in reference”
- Highly sensitive feature detection for high resolution LC/MS (2008, methodology): “3 Parameter optimisation Beside centWave, there are currently only two other feature detection algorithms available [9], which meet the following criteria: freely available, open source, and suited for feature detection in metabolomic LC/MS samples measured in centroid mode: matchedFilter- the origi”