Literature DB >> 24856452

MET-COFEA: a liquid chromatography/mass spectrometry data processing platform for metabolite compound feature extraction and annotation.

Wenchao Zhang1, Junil Chang, Zhentian Lei, David Huhman, Lloyd W Sumner, Patrick X Zhao.   

Abstract

In this paper, we present a novel liquid chromatography/mass spectrometry (LC/MS) data processing and analysis platform, MET-COFEA (METabolite COmpound Feature Extraction and Annotation). MET-COFEA detects and clusters chromatographic peak features for each metabolite compound by first comprehensively evaluating retention time and peak shape criteria and then annotating the associations between each peak's observed m/z value with the corresponding metabolite compound's molecular mass. MET-COFEA integrates a series of innovative approaches, including novel mass trace based extracted-ion chromatogram (EIC) extraction, continuous wavelet transform (CWT)-based peak detection, and compound-associated peak clustering and peak annotation algorithms. On the basis of the deduced neutral molecular mass and retention time, we have also developed a new alignment algorithm that uses compound-associated peak groups instead of individual peaks to align the same metabolite compound across samples from different electrospray ionization (ESI) modes, different instruments, even different experimental conditions. MET-COFEA has been systematically tested on a series of LC/MS profiles of mixed standards at different concentrations as well as real untargeted LC/MS plant metabolomics data. We compared the performances of MET-COFEA with the existing publicly available tools at LC/MS peak analysis level and demonstrated its excellent performance in this arena. MET-COFEA is freely available at http://bioinfo.noble.org/manuscript-support/met-cofea/.

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Year:  2014        PMID: 24856452     DOI: 10.1021/ac501162k

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


  14 in total

1.  Two complementary reversed-phase separations for comprehensive coverage of the semipolar and nonpolar metabolome.

Authors:  Fuad J Naser; Nathaniel G Mahieu; Lingjue Wang; Jonathan L Spalding; Stephen L Johnson; Gary J Patti
Journal:  Anal Bioanal Chem       Date:  2017-12-18       Impact factor: 4.142

Review 2.  Annotation: A Computational Solution for Streamlining Metabolomics Analysis.

Authors:  Xavier Domingo-Almenara; J Rafael Montenegro-Burke; H Paul Benton; Gary Siuzdak
Journal:  Anal Chem       Date:  2017-11-03       Impact factor: 6.986

3.  Defining and Detecting Complex Peak Relationships in Mass Spectral Data: The Mz.unity Algorithm.

Authors:  Nathaniel G Mahieu; Jonathan L Spalding; Susan J Gelman; Gary J Patti
Journal:  Anal Chem       Date:  2016-08-31       Impact factor: 6.986

4.  Quality evaluation of extracted ion chromatograms and chromatographic peaks in liquid chromatography/mass spectrometry-based metabolomics data.

Authors:  Wenchao Zhang; Patrick X Zhao
Journal:  BMC Bioinformatics       Date:  2014-10-21       Impact factor: 3.169

5.  ALLocator: an interactive web platform for the analysis of metabolomic LC-ESI-MS datasets, enabling semi-automated, user-revised compound annotation and mass isotopomer ratio analysis.

Authors:  Nikolas Kessler; Frederik Walter; Marcus Persicke; Stefan P Albaum; Jörn Kalinowski; Alexander Goesmann; Karsten Niehaus; Tim W Nattkemper
Journal:  PLoS One       Date:  2014-11-26       Impact factor: 3.240

Review 6.  Metabolomics for Plant Improvement: Status and Prospects.

Authors:  Rakesh Kumar; Abhishek Bohra; Arun K Pandey; Manish K Pandey; Anirudh Kumar
Journal:  Front Plant Sci       Date:  2017-08-07       Impact factor: 5.753

Review 7.  From chromatogram to analyte to metabolite. How to pick horses for courses from the massive web resources for mass spectral plant metabolomics.

Authors:  Leonardo Perez de Souza; Thomas Naake; Takayuki Tohge; Alisdair R Fernie
Journal:  Gigascience       Date:  2017-07-01       Impact factor: 6.524

8.  Seed Metabolism and Pathogen Resistance Enhancement in Pisum sativum During Colonization of Arbuscular Mycorrhizal Fungi: An Integrative Metabolomics-Proteomics Approach.

Authors:  Nima Ranjbar Sistani; Getinet Desalegn; Hans-Peter Kaul; Stefanie Wienkoop
Journal:  Front Plant Sci       Date:  2020-06-12       Impact factor: 5.753

9.  Rhizobium Impacts on Seed Productivity, Quality, and Protection of Pisum sativum upon Disease Stress Caused by Didymella pinodes: Phenotypic, Proteomic, and Metabolomic Traits.

Authors:  Nima Ranjbar Sistani; Hans-Peter Kaul; Getinet Desalegn; Stefanie Wienkoop
Journal:  Front Plant Sci       Date:  2017-11-15       Impact factor: 5.753

10.  2D association and integrative omics analysis in rice provides systems biology view in trait analysis.

Authors:  Wenchao Zhang; Xinbin Dai; Shizhong Xu; Patrick X Zhao
Journal:  Commun Biol       Date:  2018-09-27
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