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    ABSTRACT: The metabolic investigation of the human population is becoming increasingly important in the study of health and disease. The phenotypic variation can be investigated through the application of metabolomics; to provide a statistically robust investigation, the study of hundreds to thousands of individuals is required. In untargeted and MS-focused metabolomic studies this once provided significant hurdles. However, recent innovations have enabled the application of MS platforms in large-scale, untargeted studies of humans. Herein we describe the importance of experimental design, the separation of the biological study into multiple analytical experiments and the incorporation of QC samples to provide the ability to perform signal correction in order to reduce analytical variation and to quantitatively determine analytical precision. In addition, we describe how to apply this in quality assurance processes. These innovations have opened up the capabilities to perform routine, large-scale, untargeted, MS-focused studies.
    Bioanalysis 09/2012; 4(18):2249-64. DOI:10.4155/bio.12.204 · 3.00 Impact Factor
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    ABSTRACT: The purpose of normalization of data generated by liquid chromatography coupled with mass spectrometry (LC-MS) is to reduce bias due to differences in sample collection, biomolecule extraction, and instrument variability. In this paper several normalization methods are reviewed and evaluated based on LC-MS data acquired from experimental and quality control (QC) samples. Specifically, LC-MS data from a metabolomic study aimed at discovering liver cancer biomarkers are analyzed to evaluate the performance of the normalization methods. ANOVA models are used for identification of ions with statistically significant peak intensities between liver cancer and cirrhotic controls. Also, LC-MS data from QC samples are analyzed to assess the ability of the normalization methods in decreasing the variability of ion intensity measurements in multiple runs. Significant run to run variability is observed despite normalizing the LC-MS data by various methods. Thus, it is important to select a suitable normalization method for each data set, as it is difficult to find a method that is applicable for all types of LC-MS data.
    Bioinformatics and Biomedicine Workshops (BIBMW), 2012 IEEE International Conference on; 10/2012
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    ABSTRACT: Abstract Gas chromatography/mass spectrometry (GC/MS) is one of the major analytical drivers in metabonomics. Metabonomics involves the quantitative measurement of the dynamic multiparametric metabolic response of living systems to pathophysiological stimuli or genetic modification. Through the analyses of metabolites, researchers endeavor to unravel novel biomarkers that facilitate diagnosis or prognosis of diseases, prediction of therapeutic and toxicological outcomes, and assessment of potential drug candidates. The high sensitivity, high peak resolution, and reproducibility of GC/MS combined with the availability of databases and spectral libraries for metabolite identification are unique attributes that facilitate its application in metabonomics. The technical maturity of GC/MS has culminated in its application in large-scale metabonomic studies. In this chapter, we provide an overview of GC/MS-based metabonomics, its applications in biomarker discovery, and specific strategies adopted in large-scale metabonomic investigations.
    06/2013: pages 131-144; Academic Press., ISBN: 9780123944467
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