Algorithms and tools for the preprocessing of LC-MS metabolomics data

Sandra Castillo (Corresponding Author), Peddinti Gopalacharyulu, Laxman Yetukuri, Matej Oresic

    Research output: Contribution to journalArticleScientificpeer-review

    111 Citations (Scopus)

    Abstract

    Metabolomics encompasses the study of small molecules in a biological sample. Liquid Chromatography coupled with Mass Spectrometry (LC–MS) profiling is an important approach for the identification and quantification of metabolites from complex biological samples. The amount and complexity of data produced in an LC–MS profiling experiment demand automatic tools for the preprocessing, analysis, and extraction of useful biological information. Data preprocessing—a topic that covers noise filtering, peak detection, deisotoping, alignment, identification, and normalization—is thus an active area of metabolomics research. Recent years have witnessed development of many software for data preprocessing, and still there is a need for further improvement of the data preprocessing pipeline. This review presents an overview of selected software tools for preprocessing LC–MS based metabolomics data and tries to provide future directions.
    Original languageEnglish
    Pages (from-to)23-32
    Number of pages10
    JournalChemometrics and Intelligent Laboratory Systems
    Volume108
    Issue number1
    DOIs
    Publication statusPublished - 2011
    MoE publication typeA1 Journal article-refereed

    Keywords

    • Metabolomics
    • Liquid chromatography coupled with mass spectrometry (LC-MS)
    • Biological data
    • Data preprocessing software

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