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

doi:10.1093/bioinformatics/btu740

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 journal › Article › peer-review

8 Citations (Scopus)

135 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 language	English
Pages (from-to)	975-977
Number of pages	3
Journal	Bioinformatics
Volume	31
Issue number	6
Early online date	11 Nov 2014
DOIs	https://doi.org/10.1093/bioinformatics/btu740
Publication status	Published - 2015

ASJC Scopus subject areas

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

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Bradbury_et_al_MUSCLE_automated_multi_objective_Bioinformatics_2015
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MUSCLE: Multi-platform Unbiased-optimisation of Spectrometry via Closed Loop Experimentation
Viant, M., He, S. & Cooper, H.
Biotechnology & Biological Sciences Research Council
23/04/12 → 22/06/13
Project: Research Councils

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Bradbury, J., Genta-Jouve, G., Allwood, J., Dunn, W. B., Goodacre, R., Knowles, J. D., He, S., & Viant, M. R. (2015). MUSCLE : automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis: Automated multi-objective evolutionary optimization of targeted LC-MS/MS analysis. Bioinformatics, 31(6), 975-977. https://doi.org/10.1093/bioinformatics/btu740

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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{\textquoteright}s LC-MS/MS instruments.",

author = "James Bradbury and Gr{\'e}gory Genta-Jouve and James Allwood and Dunn, {Warwick B.} and Royston Goodacre and Knowles, {Joshua D.} and Shan He and Viant, {Mark R.}",

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doi = "10.1093/bioinformatics/btu740",

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AU - Allwood, James

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AU - Knowles, Joshua D.

AU - He, Shan

AU - Viant, Mark R.

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

Abstract

ASJC Scopus subject areas

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Projects

MUSCLE: Multi-platform Unbiased-optimisation of Spectrometry via Closed Loop Experimentation

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