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.
| Originalsprog | Engelsk |
|---|---|
| Tidsskrift | Nature Methods |
| Vol/bind | 13 |
| Udgave nummer | 9 |
| Sider (fra-til) | 731-40 |
| Antal sider | 10 |
| ISSN | 1548-7091 |
| DOI | |
| Status | Udgivet - 30 aug. 2016 |
| Udgivet eksternt | Ja |