Literature DB >> 19358599

MetaboliteDetector: comprehensive analysis tool for targeted and nontargeted GC/MS based metabolome analysis.

Karsten Hiller1, Jasper Hangebrauk, Christian Jäger, Jana Spura, Kerstin Schreiber, Dietmar Schomburg.   

Abstract

We have developed a new software, MetaboliteDetector, for the efficient and automatic analysis of GC/MS-based metabolomics data. Starting with raw MS data, the program detects and subsequently identifies potential metabolites. Moreover, a comparative analysis of a large number of chromatograms can be performed in either a targeted or nontargeted approach. MetaboliteDetector automatically determines appropriate quantification ions and performs an integration of single ion peaks. The analysis results can directly be visualized with a principal component analysis. Since the manual input is limited to absolutely necessary parameters, the program is also usable for the analysis of high-throughput data. However, the intuitive graphical user interface of MetaboliteDetector additionally allows for a detailed examination of a single GC/MS chromatogram including single ion chromatograms, recorded mass spectra, and identified metabolite spectra in combination with the corresponding reference spectra obtained from a reference library. MetaboliteDetector offers the ability to operate with highly resolved profile mass data. Finally, all analysis results can be exported to tab delimited tables. The features of MetaboliteDetector are demonstrated by the analysis of two experimental metabolomics data sets. MetaboliteDetector is freely available under the GNU public license (GPL) at http://metabolitedetector.tu-bs.de.

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Year:  2009        PMID: 19358599     DOI: 10.1021/ac802689c

Source DB:  PubMed          Journal:  Anal Chem        ISSN: 0003-2700            Impact factor:   6.986


  147 in total

1.  A large scale test dataset to determine optimal retention index threshold based on three mass spectral similarity measures.

Authors:  Jun Zhang; Imhoi Koo; Bing Wang; Qing-Wei Gao; Chun-Hou Zheng; Xiang Zhang
Journal:  J Chromatogr A       Date:  2012-06-19       Impact factor: 4.759

2.  PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools.

Authors:  Sean O'Callaghan; David P De Souza; Andrew Isaac; Qiao Wang; Luke Hodkinson; Moshe Olshansky; Tim Erwin; Bill Appelbe; Dedreia L Tull; Ute Roessner; Antony Bacic; Malcolm J McConville; Vladimir A Likić
Journal:  BMC Bioinformatics       Date:  2012-05-30       Impact factor: 3.169

3.  Listeria monocytogenes virulence factors, including listeriolysin O, are secreted in biologically active extracellular vesicles.

Authors:  Carolina Coelho; Lisa Brown; Maria Maryam; Raghav Vij; Daniel F Q Smith; Meagan C Burnet; Jennifer E Kyle; Heino M Heyman; Jasmine Ramirez; Rafael Prados-Rosales; Gregoire Lauvau; Ernesto S Nakayasu; Nathan R Brady; Anne Hamacher-Brady; Isabelle Coppens; Arturo Casadevall
Journal:  J Biol Chem       Date:  2018-11-30       Impact factor: 5.157

4.  Maternal Early Pregnancy Serum Metabolites and Risk of Gestational Diabetes Mellitus.

Authors:  Daniel A Enquobahrie; Marie Denis; Mahlet G Tadesse; Bizu Gelaye; Habtom W Ressom; Michelle A Williams
Journal:  J Clin Endocrinol Metab       Date:  2015-09-25       Impact factor: 5.958

5.  Improved workflow for mass spectrometry-based metabolomics analysis of the heart.

Authors:  Douglas A Andres; Lyndsay E A Young; Sudhakar Veeranki; Tara R Hawkinson; Bryana M Levitan; Daheng He; Chi Wang; Jonathan Satin; Ramon C Sun
Journal:  J Biol Chem       Date:  2020-01-24       Impact factor: 5.157

6.  Formation of dehydroalanine from mimosine and cysteine: artifacts in gas chromatography/mass spectrometry based metabolomics.

Authors:  Young-Mo Kim; Thomas O Metz; Zeping Hu; Susan D Wiedner; Jong-Seo Kim; Richard D Smith; William F Morgan; Qibin Zhang
Journal:  Rapid Commun Mass Spectrom       Date:  2011-09-15       Impact factor: 2.419

7.  Decreased abundance of type III secretion system-inducing signals in Arabidopsis mkp1 enhances resistance against Pseudomonas syringae.

Authors:  Jeffrey C Anderson; Ying Wan; Young-Mo Kim; Ljiljana Pasa-Tolic; Thomas O Metz; Scott C Peck
Journal:  Proc Natl Acad Sci U S A       Date:  2014-04-21       Impact factor: 11.205

8.  Partner switching and metabolic flux in a model cnidarian-dinoflagellate symbiosis.

Authors:  Jennifer L Matthews; Clinton A Oakley; Adrian Lutz; Katie E Hillyer; Ute Roessner; Arthur R Grossman; Virginia M Weis; Simon K Davy
Journal:  Proc Biol Sci       Date:  2018-11-28       Impact factor: 5.349

Review 9.  After the feature presentation: technologies bridging untargeted metabolomics and biology.

Authors:  Kevin Cho; Nathaniel G Mahieu; Stephen L Johnson; Gary J Patti
Journal:  Curr Opin Biotechnol       Date:  2014-05-06       Impact factor: 9.740

10.  The MetabolomeExpress Project: enabling web-based processing, analysis and transparent dissemination of GC/MS metabolomics datasets.

Authors:  Adam J Carroll; Murray R Badger; A Harvey Millar
Journal:  BMC Bioinformatics       Date:  2010-07-14       Impact factor: 3.169

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