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Mixed-data acquisition: next-generation quantitative proteomics data acquisition
Santos, Marlon D.M; Camillo-Andrade, Amanda Caroline; Kurt, Louise U; Clasen, Milan A; Lyra, Eduardo; Gozzo, Fabio C; Batista, Michel; Valente, Richard H; Brunoro, Giselle Villa Flor; Barbosa, Valmir C; Fischer, Juliana S.G; Carvalho, Paulo C.
Affiliation
  • Camillo-Andrade, Amanda Caroline; Instituto Butantan. Centro de Excelência para Descoberta de Alvos Moleculares (CENTD).
J. Proteomics ; 222: 103803, 2020.
Article in En | SES-SP, SESSP-IBPROD, SES-SP | ID: but-ib17672
Responsible library: BR78.1
Localization: BR78.1
ABSTRACT
We present the Mixed-Data Acquisition (MDA) strategy for mass spectrometry data acquisition. MDA combines Data-Dependent Acquisition (DDA) and Data-Independent Acquisition (DIA) in the same run, thus doing away with the requirements for separate DDA spectral libraries. MDA is a natural result from advances in mass spectrometry, such as high scan rates and multiple analyzers, and is tailored toward exploiting these features. We demonstrate MDA's effectiveness on a yeast proteome analysis by overcoming a common bottleneck for XIC-based label-free quantitation; namely, the coelution of precursors when m/z values cannot be distinguished. We anticipate that MDA will become the next mainstream data generation approach for proteomics. MDA can also serve as an orthogonal validation approach for DDA experiments. Specialized software for MDA data analysis is made available on the project's website.
Full text: 1 Collection: 06-national / BR Database: SES-SP / SESSP-IBPROD Language: En Journal: J. Proteomics Year: 2020 Document type: Article
Full text: 1 Collection: 06-national / BR Database: SES-SP / SESSP-IBPROD Language: En Journal: J. Proteomics Year: 2020 Document type: Article