MUSCLE : automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis: Automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis

James Bradbury, Grégory Genta-Jouve, James Allwood, Warwick B. Dunn, Royston Goodacre, Joshua D. Knowles, Shan He, Mark R. Viant*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
93 Downloads (Pure)

Abstract

Summary: Developing liquid chromatography tandem mass spectrometry (LC-MS/MS) analyses of (bio)chemicals is both time consuming and challenging, largely because of the large number of LC and MS instrument parameters that need to be optimized. This bottleneck significantly impedes our ability to establish new (bio)analytical methods in fields such as pharmacology, metabolomics and pesticide research. We report the development of a multi-platform, user-friendly software tool MUSCLE (multi-platform unbiased optimization of spectrometry via closed-loop experimentation) for the robust and fully automated multi-objective optimization of targeted LC-MS/MS analysis. MUSCLE shortened the analysis times and increased the analytical sensitivities of targeted metabolite analysis, which was demonstrated on two different manufacturer’s LC-MS/MS instruments.
Original languageEnglish
Pages (from-to)975-977
Number of pages3
JournalBioinformatics
Volume31
Issue number6
Early online date11 Nov 2014
DOIs
Publication statusPublished - 2015

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology
  • Computational Theory and Mathematics
  • Computer Science Applications
  • Computational Mathematics
  • Statistics and Probability

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