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Nat Methods. 2016 Sep;13(9):731-40. doi: 10.1038/nmeth.3901. Epub 2016 Jun 27.

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

Author information

1
Computational Systems Biochemistry, Max Planck Institute of Biochemistry, Martinsried, Germany.
2
Cellular and Molecular Pharmacology, University of California, San Francisco, San Francisco, California, USA.
3
Human Molecular Genetics and Biochemistry, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
4
Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany.

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.

PMID:
27348712
DOI:
10.1038/nmeth.3901
[Indexed for MEDLINE]
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