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      MS-DIAL: Data Independent MS/MS Deconvolution for Comprehensive Metabolome Analysis

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          Abstract

          Data-independent acquisition (DIA) in liquid chromatography tandem mass spectrometry (LC-MS/MS) provides more comprehensive untargeted acquisition of molecular data. Here we provide an open-source software pipeline, MS-DIAL, to demonstrate how DIA improves simultaneous identification and quantification of small molecules by mass spectral deconvolution. For reversed phase LC-MS/MS, our program with an enriched LipidBlast library identified total 1,023 lipid compounds from nine algal strains to highlight their chemotaxonomic relationships.

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          Most cited references30

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          Lipid extraction by methyl-tert-butyl ether for high-throughput lipidomics.

          Accurate profiling of lipidomes relies upon the quantitative and unbiased recovery of lipid species from analyzed cells, fluids, or tissues and is usually achieved by two-phase extraction with chloroform. We demonstrated that methyl-tert-butyl ether (MTBE) extraction allows faster and cleaner lipid recovery and is well suited for automated shotgun profiling. Because of MTBE's low density, lipid-containing organic phase forms the upper layer during phase separation, which simplifies its collection and minimizes dripping losses. Nonextractable matrix forms a dense pellet at the bottom of the extraction tube and is easily removed by centrifugation. Rigorous testing demonstrated that the MTBE protocol delivers similar or better recoveries of species of most all major lipid classes compared with the "gold-standard" Folch or Bligh and Dyer recipes.
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            MassBank: a public repository for sharing mass spectral data for life sciences.

            MassBank is the first public repository of mass spectra of small chemical compounds for life sciences (<3000 Da). The database contains 605 electron-ionization mass spectrometry (EI-MS), 137 fast atom bombardment MS and 9276 electrospray ionization (ESI)-MS(n) data of 2337 authentic compounds of metabolites, 11 545 EI-MS and 834 other-MS data of 10,286 volatile natural and synthetic compounds, and 3045 ESI-MS(2) data of 679 synthetic drugs contributed by 16 research groups (January 2010). ESI-MS(2) data were analyzed under nonstandardized, independent experimental conditions. MassBank is a distributed database. Each research group provides data from its own MassBank data servers distributed on the Internet. MassBank users can access either all of the MassBank data or a subset of the data by specifying one or more experimental conditions. In a spectral search to retrieve mass spectra similar to a query mass spectrum, the similarity score is calculated by a weighted cosine correlation in which weighting exponents on peak intensity and the mass-to-charge ratio are optimized to the ESI-MS(2) data. MassBank also provides a merged spectrum for each compound prepared by merging the analyzed ESI-MS(2) data on an identical compound under different collision-induced dissociation conditions. Data merging has significantly improved the precision of the identification of a chemical compound by 21-23% at a similarity score of 0.6. Thus, MassBank is useful for the identification of chemical compounds and the publication of experimental data. 2010 John Wiley & Sons, Ltd.
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              OpenSWATH enables automated, targeted analysis of data-independent acquisition MS data.

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                Author and article information

                Journal
                101215604
                32338
                Nat Methods
                Nat. Methods
                Nature methods
                1548-7091
                1548-7105
                23 April 2015
                04 May 2015
                June 2015
                01 December 2015
                : 12
                : 6
                : 523-526
                Affiliations
                [1 ]RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
                [2 ]Department of Biotechnology, Graduate School of Engineering, Osaka University, Suita, Osaka, Japan
                [3 ]Genome Center, University of California Davis, Davis, California, USA
                [4 ]Department of Biological and Agricultural Engineering, University of California Davis, Davis, California, USA
                [5 ]RIKEN Center for Integrative Medical Sciences, Yokohama, Kanagawa, Japan
                [6 ]Japan Science and Technology Agency, Kawaguchi, Saitama, Japan
                [7 ]Reifycs Inc., Minato-ku, Tokyo, Japan
                [8 ]Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah, Saudi-Arabia
                [9 ]National Institute of Genetics, Mishima, Shizuoka, Japan
                Author notes
                CORRESPONDING AUTHORS. Oliver Fiehn ( ofiehn@ 123456ucdavis.edu ), Masanori Arita ( arita@ 123456nig.ac.jp )
                Article
                NIHMS680470
                10.1038/nmeth.3393
                4449330
                25938372
                9b79f001-c2fe-4920-8483-e1ac6237cc26
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                Life sciences
                Life sciences

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