<p>Metadata plays an essential role in the analysis and dissemination of proteomics data. It annotates sample information for output tables from library searches and displays sample information from data files in public repositories. However, integrating metadata into data analysis can be time-consuming and is not well standardized. Inconsistent metadata formats in public repositories hinder other researchers’ ability to reproduce and reuse these public datasets. Here we present the metadata integration in MaxQuant, which provides a user-friendly way to export metadata as SDRF, the standard format that maps sample properties to proteomics data files. We also implemented the annotation of output tables with the SDRF file, enabling users to perform seamless downstream data analysis with annotated output tables. These features provide a simple and standardized approach to creating and leveraging standardized metadata, thereby facilitating data analysis and improving the reusability and reproducibility of public proteomics datasets.</p>

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Facilitating analysis and dissemination of proteomics data through metadata integration in MaxQuant

  • Walter Viegener,
  • Shamil Urazbakhtin,
  • Daniela Ferretti,
  • Jürgen Cox,
  • Jinqiu Xiao

摘要

Metadata plays an essential role in the analysis and dissemination of proteomics data. It annotates sample information for output tables from library searches and displays sample information from data files in public repositories. However, integrating metadata into data analysis can be time-consuming and is not well standardized. Inconsistent metadata formats in public repositories hinder other researchers’ ability to reproduce and reuse these public datasets. Here we present the metadata integration in MaxQuant, which provides a user-friendly way to export metadata as SDRF, the standard format that maps sample properties to proteomics data files. We also implemented the annotation of output tables with the SDRF file, enabling users to perform seamless downstream data analysis with annotated output tables. These features provide a simple and standardized approach to creating and leveraging standardized metadata, thereby facilitating data analysis and improving the reusability and reproducibility of public proteomics datasets.