MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data

By Mikko Katajamaa, Jarkko Miettinen, Matej Oresic. Bioinform., 2006

788 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Molecular Biology, Spectroscopy

View Paper: https://www.semanticscholar.org/paper/b92da2c87e3e5d1bd0802d69a5314a0571f903a2

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Sebastian Böcker
2. Kerstin Scheubert
3. Franziska Hufsky
4. Oliver Fiehn
5. Tobias Kind
6. Ole Schulz-Trieglaff
7. Oliver Kohlbacher
8. Jianguo Xia

In Molecular Biology

1. David S. Wishart
2. Jianguo Xia
3. Oliver Fiehn
4. Sebastian Böcker
5. Tobias Kind
6. Kerstin Scheubert
7. Franziska Hufsky
8. Mark R. Viant

In Spectroscopy

1. David S. Wishart
2. Jianguo Xia
3. Oliver Fiehn
4. Mark R. Viant
5. Tomás Pluskal
6. Sandra Castillo
7. Alejandro Villar-Briones
8. Tobias Kind

Citations in the corpus

• xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data (2013, background): “[11,12], OpenMS [13,14], SpecArray [15], and XAlign [16] for the analysis of metabolomics data.”
• Quality control for plant metabolomics: reporting MSI-compliant studies. (2008, methodology): “Freely available software programs such as AMDIS (Stein, 1999), mzmine (Katajamaa et al., 2006) or SpectConnect (Styczynski et al.”
• TOPP - the OpenMS proteomics pipeline (2007, background): “Several groups, such as Katajamaa et al. (2006), Leoptos et al. (2006), Li et al. (2005), Palagi et al. (2005), Radulovic et al. (2004), Samuelsson et al. (2004), have developed other working systems for proteomic data analyses.”
• Computational mass spectrometry for small molecules (2013, methodology): “For both, GC-MS and LC-MS data, MZmine [252] and MZmine2 [253] allow for data visualization, peak identifi-cation and peak list alignment.”
• Metabolic fingerprinting as an indicator of biodiversity: towards understanding inter-specific relationships among Homoscleromorpha sponges (2011, methodology): “BPC) analyses were exported as line spectra and converted to netCDF file format to process unit mass resolution data in centroid mode including fragmentation (MSn) scans, with MZmine toolbox ( Katajamaa et al. 2006 ).”
• Metabolomics in pesticide research and development: review and future perspectives (2011, background): “…Kerber et al. (2001) MSFACTs http://www.noble.org/PlantBio/Sumner/msfacts/index.html Duran et al. (2003) Mzmine http://mzmine.sourceforge.net/ Katajamaa et al. (2006) Omicspace http://omicspace.riken.jp/gps/ Toyoda and Wada (2004) PathVisio http://www.pathvisio.org/ van Iersel et al.…”
• Non-targeted UHPLC-MS metabolomic data processing methods: a comparative investigation of normalisation, missing value imputation, transformation and scaling (2016, methodology): “Software such as XCMS (Smith et al. 2006) and mzMine (Katajamaa et al. 2006) are freely available and commonly applied.”
• Using MetaboAnalyst 3.0 for Comprehensive Metabolomics Data Analysis (2016, background): “…step can be done by any number of locally installable, freely available MS spectral processing tools such as MetAlign (Lommen, 2009), OpenMS (Sturm et al., 2008), MZmine (Katajamaa et al., 2006), XCMS (Smith et al., 2006), XCMS-Online (Tautenhahn et al., 2012), and MS-DIAL (Tsugawa et al., 2015).”