Mixed-data acquisition: next-generation quantitative proteomics data acquisition
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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.
Santos MD.M., Camillo-Andrade AC, Kurt LU., Clasen MA., Lyra E, Gozzo FC., et al. Mixed-data acquisition: next-generation quantitative proteomics data acquisition. J. Proteomics. 2020 May;222:103803. doi:10.1016/j.jprot.2020.103803.
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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) ; Inova Fiocruz ; Programa de Expansão da Educação Profissional (PROEP) ; Pasteur Network Talent Award ; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) ; Fundação de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ) ; Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
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