alexa Algorithms for automatic processing of data from mass spectrometric analyses of lipids.
Bioinformatics & Systems Biology

Bioinformatics & Systems Biology

Journal of Glycomics & Lipidomics

Author(s): Song H, Ladenson J, Turk J

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Abstract Lipidomics comprises large-scale studies of the structures, quantities, and functions of lipid molecular species. Recently developed mass spectrometric methods for lipid analyses, especially electrospray ionization (ESI) tandem mass spectrometry, permit identification and quantitation of an enormous variety of distinct lipid molecular species from small amounts of biological samples but generate a huge amount of experimental data within a brief interval. Processing such data sets so that comprehensible information is derived from them requires bioinformatics tools, and algorithms developed for proteomics and genomics have provided some strategies that can be directly adapted to lipidomics. The structural diversity and complexity of lipids, however, also requires the development and application of new algorithms and software tools that are specifically directed at processing data from lipid analyses. Several such tools are reviewed here, including LipidQA. This program employs searches of a fragment ion database constructed from acquired and theoretical spectra of a wide variety of lipid molecular species, and raw mass spectrometric data can be processed by the program to achieve identification and quantification of many distinct lipids in mixtures. Other approaches that are reviewed here include LIMSA (Lipid Mass Spectrum Analysis), SECD (Spectrum Extraction from Chromatographic Data), MPIS (Multiple Precursor Ion Scanning), FIDS (Fragment Ion Database Searching), LipidInspector, Lipid Profiler, FAAT (Fatty Acid Analysis Tool), and LIPID Arrays. Internet resources for lipid analyses are also summarized.
This article was published in J Chromatogr B Analyt Technol Biomed Life Sci and referenced in Journal of Glycomics & Lipidomics

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