Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Bradbury J

PROVIDER: S-EPMC4380028 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature

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

Bradbury James J Genta-Jouve Grégory G Allwood J William JW Dunn Warwick B WB Goodacre Royston R Knowles Joshua D JD He Shan S Viant Mark R MR

Bioinformatics (Oxford, England) 20141111 6

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 ...[more]

PMID: 25388146

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Dataset Information

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

Publications

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

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