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Knowledge discovery in metabolomics: An overview of MS data handling

被引：127
作者：
Boccard, Julien
Veuthey, Jean-Luc
Rudaz, Serge [1 ]
机构：
[1] Univ Geneva, EPGL, Sch Pharmaceut Sci, CH-1211 Geneva 4, Switzerland
来源：
JOURNAL OF SEPARATION SCIENCE | 2010年 / 33卷 / 03期
关键词：
Data mining; Data processing; Metabolomics; MS; PERFORMANCE LIQUID-CHROMATOGRAPHY; 2-DIMENSIONAL GAS-CHROMATOGRAPHY; SPECTROMETRY-BASED METABOLOMICS; MASS-SPECTROMETRY; LC-MS; QUANTITATIVE-ANALYSIS; TOF-MS; DIFFERENTIAL ANALYSIS; RAT URINE; CAPILLARY-ELECTROPHORESIS;
D O I：
10.1002/jssc.200900609
中图分类号：
O65 [分析化学];
学科分类号：
070302 ; 081704 ;
摘要：
While metabolomics attempts to comprehensively analyse the small molecules characterising a biological system, MS has been promoted as the gold standard to study the wide chemical diversity and range of concentrations of the metabolome On the other hand, extracting the relevant information from the overwhelming amount of data generated by modern analytical platforms has become an important issue for knowledge discovery in this research field The appropriate treatment of such data is therefore of crucial importance in order, for the data, to provide valuable information The aim of this review is to provide a broad overview of the methodologies developed to handle and process MS metabolomic data, compare the samples and highlight the relevant metabolites, starting from the raw data to the biomarker discovery As data handling can be further separated into data processing, data pre-treatment and data analysis, recent advances in each of these steps are detailed separately
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页码：290 / 304
页数：15
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