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PeptideShaker Online: A User-Friendly Web-Based Framework for the Identification of Mass Spectrometry-Based Proteomics Data.
Farag, Yehia Mokhtar; Horro, Carlos; Vaudel, Marc; Barsnes, Harald.
Afiliação
  • Farag YM; Proteomics Unit, Department of Biomedicine, University of Bergen, 5020 Bergen, Norway.
  • Horro C; Computational Biology Unit, Department of Informatics, University of Bergen, 5008 Bergen, Norway.
  • Vaudel M; Proteomics Unit, Department of Biomedicine, University of Bergen, 5020 Bergen, Norway.
  • Barsnes H; Computational Biology Unit, Department of Informatics, University of Bergen, 5008 Bergen, Norway.
J Proteome Res ; 20(12): 5419-5423, 2021 12 03.
Article em En | MEDLINE | ID: mdl-34709836
ABSTRACT
Mass spectrometry-based proteomics is a high-throughput technology generating ever-larger amounts of data per project. However, storing, processing, and interpreting these data can be a challenge. A key element in simplifying this process is the development of interactive frameworks focusing on visualization that can greatly simplify both the interpretation of data and the generation of new knowledge. Here we present PeptideShaker Online, a user-friendly web-based framework for the identification of mass spectrometry-based proteomics data, from raw file conversion to interactive visualization of the resulting data. Storage and processing of the data are performed via the versatile Galaxy platform (through SearchGUI, PeptideShaker, and moFF), while the interaction with the results happens via a locally installed web server, thus enabling researchers to process and interpret their own data without requiring advanced bioinformatics skills or direct access to compute-intensive infrastructures. The source code, additional documentation, and a fully functional demo is available at https//github.com/barsnes-group/peptide-shaker-online.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteômica Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Software / Proteômica Tipo de estudo: Diagnostic_studies Idioma: En Ano de publicação: 2021 Tipo de documento: Article