A protocol for LC-MS metabolomic data processing using chemometric tools
Liquid chromatography- mass spectrometry (LC-MS) is a powerful methodology for metabolomics. However, LC-MS data processing comes out as the “bottleneck” of omic sciences due to its complexity. The present protocol, easy to execute in MATLAB environment, covers all data analysis steps (conversion an...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2015 |
| País: | España |
| Institución: | Consejo Superior de Investigaciones Científicas (CSIC) |
| Repositorio: | DIGITAL.CSIC. Repositorio Institucional del CSIC |
| OAI Identifier: | oai:digital.csic.es:10261/168034 |
| Acceso en línea: | http://hdl.handle.net/10261/168034 |
| Access Level: | acceso abierto |
| Palabra clave: | Lipidomics Metabolomics Computational biology LC-MS MCR-ALS |
| Sumario: | Liquid chromatography- mass spectrometry (LC-MS) is a powerful methodology for metabolomics. However, LC-MS data processing comes out as the “bottleneck” of omic sciences due to its complexity. The present protocol, easy to execute in MATLAB environment, covers all data analysis steps (conversion and import, compression and processing) of LC-MS data sets and it is specifically designed for users with limited background in chemometric and data analysis tools. Data conversion and import are described for most important LC-MS manufacturers (i.e., Waters, Thermo Fischer, Agilent, AB Sciex and Bruker), data compression consists on the search of “regions of interest” (ROI) and data processing is based on the use of Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS), a powerful chemometric tool that allows chromatographic resolution. Results are rapidly achieved (usually ˂ 15 min per sample), and they are easy to interpret and evaluate both in terms of chemistry and biology. |
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