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.
| Original language | English |
|---|---|
| Journal | Nature Methods |
| Volume | 13 |
| Issue number | 9 |
| Pages (from-to) | 731-40 |
| Number of pages | 10 |
| ISSN | 1548-7091 |
| DOIs | |
| Publication status | Published - 30 Aug 2016 |
| Externally published | Yes |
Keywords
- Computational Biology
- Computer Graphics
- Databases, Protein
- Machine Learning
- Mass Spectrometry
- Protein Processing, Post-Translational
- Proteins
- Proteomics
- Software
- Workflow
- Journal Article