Literature DB >> 23581547

MRMPROBS: a data assessment and metabolite identification tool for large-scale multiple reaction monitoring based widely targeted metabolomics.

Hiroshi Tsugawa1, Masanori Arita, Mitsuhiro Kanazawa, Atsushi Ogiwara, Takeshi Bamba, Eiichiro Fukusaki.   

Abstract

We developed a new software program, MRMPROBS, for widely targeted metabolomics by using the large-scale multiple reaction monitoring (MRM) mode. The strategy became increasingly popular for the simultaneous analysis of up to several hundred metabolites at high sensitivity, selectivity, and quantitative capability. However, the traditional method of assessing measured metabolomics data without probabilistic criteria is not only time-consuming but is often subjective and makeshift work. Our program overcomes these problems by detecting and identifying metabolites automatically, by separating isomeric metabolites, and by removing background noise using a probabilistic score defined as the odds ratio from an optimized multivariate logistic regression model. Our software program also provides a user-friendly graphical interface to curate and organize data matrices and to apply principal component analyses and statistical tests. For a demonstration, we conducted a widely targeted metabolome analysis (152 metabolites) of propagating Saccharomyces cerevisiae measured at 15 time points by gas and liquid chromatography coupled to triple quadrupole mass spectrometry. MRMPROBS is a useful and practical tool for the assessment of large-scale MRM data available to any instrument or any experimental condition.

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Year:  2013        PMID: 23581547     DOI: 10.1021/ac400515s

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


  21 in total

1.  Application of Metabolomics for High Resolution Phenotype Analysis.

Authors:  Eiichiro Fukusaki
Journal:  Mass Spectrom (Tokyo)       Date:  2015-01-07

2.  Technical Challenges in Mass Spectrometry-Based Metabolomics.

Authors:  Fumio Matsuda
Journal:  Mass Spectrom (Tokyo)       Date:  2016-11-25

3.  compMS2Miner: An Automatable Metabolite Identification, Visualization, and Data-Sharing R Package for High-Resolution LC-MS Data Sets.

Authors:  William M B Edmands; Lauren Petrick; Dinesh K Barupal; Augustin Scalbert; Mark J Wilson; Jeffrey K Wickliffe; Stephen M Rappaport
Journal:  Anal Chem       Date:  2017-03-27       Impact factor: 6.986

Review 4.  Machine Learning and Hybrid Methods for Metabolic Pathway Modeling.

Authors:  Miroslava Cuperlovic-Culf; Thao Nguyen-Tran; Steffany A L Bennett
Journal:  Methods Mol Biol       Date:  2023

5.  Widely-targeted quantitative lipidomics method by supercritical fluid chromatography triple quadrupole mass spectrometry.

Authors:  Hiroaki Takeda; Yoshihiro Izumi; Masatomo Takahashi; Thanai Paxton; Shohei Tamura; Tomonari Koike; Ying Yu; Noriko Kato; Katsutoshi Nagase; Masashi Shiomi; Takeshi Bamba
Journal:  J Lipid Res       Date:  2018-05-03       Impact factor: 5.922

6.  Identifying metabolic elements that contribute to productivity of 1-propanol bioproduction using metabolomic analysis.

Authors:  Sastia Prama Putri; Yasumune Nakayama; Claire Shen; Shingo Noguchi; Katsuaki Nitta; Takeshi Bamba; Sammy Pontrelli; James Liao; Eiichiro Fukusaki
Journal:  Metabolomics       Date:  2018-07-04       Impact factor: 4.290

7.  MRM-DIFF: data processing strategy for differential analysis in large scale MRM-based lipidomics studies.

Authors:  Hiroshi Tsugawa; Erika Ohta; Yoshihiro Izumi; Atsushi Ogiwara; Daichi Yukihira; Takeshi Bamba; Eiichiro Fukusaki; Masanori Arita
Journal:  Front Genet       Date:  2015-01-30       Impact factor: 4.599

8.  MS-DIAL: data-independent MS/MS deconvolution for comprehensive metabolome analysis.

Authors:  Hiroshi Tsugawa; Tomas Cajka; Tobias Kind; Yan Ma; Brendan Higgins; Kazutaka Ikeda; Mitsuhiro Kanazawa; Jean VanderGheynst; Oliver Fiehn; Masanori Arita
Journal:  Nat Methods       Date:  2015-05-04       Impact factor: 28.547

Review 9.  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

10.  Metabolomics for biomarker discovery in gastroenterological cancer.

Authors:  Shin Nishiumi; Makoto Suzuki; Takashi Kobayashi; Atsuki Matsubara; Takeshi Azuma; Masaru Yoshida
Journal:  Metabolites       Date:  2014-07-07
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