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

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

Research output: Contribution to journalArticle

43 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)5191-5199
Number of pages9
JournalAnalytical chemistry
Volume85
Issue number10
DOIs
Publication statusPublished - May 21 2013

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry

Fingerprint Dive into the research topics of 'MRMPROBS: A data assessment and metabolite identification tool for large-scale multiple reaction monitoring based widely targeted metabolomics'. Together they form a unique fingerprint.

  • Cite this