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

• Published in: Nature methods Vol. 13; no. 9; pp. 731 - 740
• Main Authors: Tyanova, Stefka, Temu, Tikira, Sinitcyn, Pavel, Carlson, Arthur, Hein, Marco Y, Geiger, Tamar, Mann, Matthias, Cox, Jürgen
• Format: Journal Article
• Language: English
• Published: New York Nature Publishing Group US 01.09.2016; Nature Publishing Group
• Subjects: 631/114/2398; 631/114/2784; 631/1647/2067; 631/45/475; 82/58; Analysis; Bioinformatics; Biological analysis; Biological Microscopy; Biological Techniques; Biomedical Engineering/Biotechnology; Computational Biology - methods; Computer Graphics; Databases, Protein; Documentation; Information management; Life Sciences; Machine Learning; Mass spectrometry; Mass Spectrometry - methods; Pattern recognition; perspective; Protein Processing, Post-Translational; Proteins; Proteins - chemistry; Proteomics; Proteomics - methods; Software; Spectrometry; Time series; Workflow; Workflow software; United States; Germany
• ISSN: 1548-7091; 1548-7105
• DOI: 10.1038/nmeth.3901

Perseus is a comprehensive, user-friendly software platform for the biological analysis of quantitative proteomics data. It is intended to help biologists with little bioinformatics training to interpret protein expression, post-translational modification and interaction data. Also in this issue, see the Perspective by Röst et al . 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.