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The Perseus computational platform for comprehensive analysis of (prote)omics data

Nature Methods volume 13, pages 731740 (2016) | Download Citation

Abstract

A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.

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Acknowledgements

This project has received funding from the European Union′s Horizon 2020 research and innovation programme under grant agreement no. 686547 (J.C.) and from the FP7 grant agreement GA ERC-2012-SyG_318987ToPAG (J.C.).

Author information

Affiliations

  1. Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany.

    • Stefka Tyanova
    • , Tikira Temu
    • , Pavel Sinitcyn
    • , Arthur Carlson
    •  & Jürgen Cox
  2. Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA.

    • Marco Y Hein
  3. Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.

    • Tamar Geiger
  4. Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.

    • Matthias Mann

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Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Jürgen Cox.

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https://doi.org/10.1038/nmeth.3901

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