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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Author and article information

Journal

Journal ID (nlm-journal-id): 101215604

Journal ID (pubmed-jr-id): 32338

Journal ID (nlm-ta): Nat Methods

Journal ID (iso-abbrev): Nat. Methods

Title: Nature methods

ISSN (Print): 1548-7091

ISSN (Electronic): 1548-7105

Publication date Nihms-submitted: 23 April 2015

Publication date (Electronic): 04 May 2015

Publication date (Print): June 2015

Publication date PMC-release: 01 December 2015

Volume: 12

Issue: 6

Pages: 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

Manuscript ID: NIHMS680470

DOI: 10.1038/nmeth.3393

PMC ID: 4449330

PubMed ID: 25938372

SO-VID: 9b79f001-c2fe-4920-8483-e1ac6237cc26

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