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Fast Deisotoping Algorithm and Its Implementation in the MSFragger Search Engine.
Teo, Guo Ci; Polasky, Daniel A; Yu, Fengchao; Nesvizhskii, Alexey I.
Afiliação
  • Teo GC; Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.
  • Polasky DA; Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.
  • Yu F; Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.
  • Nesvizhskii AI; Department of Pathology, University of Michigan, Ann Arbor, Michigan 48109, United States.
J Proteome Res ; 20(1): 498-505, 2021 01 01.
Article em En | MEDLINE | ID: mdl-33332123
Deisotoping, or the process of removing peaks in a mass spectrum resulting from the incorporation of naturally occurring heavy isotopes, has long been used to reduce complexity and improve the effectiveness of spectral annotation methods in proteomics. We have previously described MSFragger, an ultrafast search engine for proteomics, that did not utilize deisotoping in processing input spectra. Here, we present a new, high-speed parallelized deisotoping algorithm, based on elements of several existing methods, that we have incorporated into the MSFragger search engine. Applying deisotoping with MSFragger reveals substantial improvements to database search speed and performance, particularly for complex methods like open or nonspecific searches. Finally, we evaluate our deisotoping method on data from several instrument types and vendors, revealing a wide range in performance and offering an updated perspective on deisotoping in the modern proteomics environment.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Bases de Dados de Proteínas / Ferramenta de Busca Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Algoritmos / Bases de Dados de Proteínas / Ferramenta de Busca Tipo de estudo: Prognostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article