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

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.

Biallelic Mutations in MRPS34 Lead to Instability of the Small Mitoribosomal Subunit and Leigh Syndrome

“…Unique and razor peptides were used for quantification, using a minimum ratio count of 2. Using the Perseus platform v.1.5.5.3, 45 proteins identified using <2 unique peptides were excluded, as were identifications marked ''Only identified by site,'' ''Reverse,'' and ''Potential contaminant.'' Mitochondrial proteins were defined through matching of gene names and Ensembl gene IDs to the Mitocarta2.0 dataset.…”

Quantitative analysis of the tomato nuclear proteome during Phytophthora capsici infection unveils regulators of immunity

“…Protein identification and quantification data were analysed in the P erseus software package (Tyanova et al ., 2016) using two‐sample t ‐tests with a false discovery rate (FDR) of 0.05 to identify proteins showing significant differences in expression. Proteins identified with at least two peptides (shared, unique or razor) were selected for downstream analysis.…”

SUMO1‐conjugation is altered during normal aging but not by increased amyloid burden

“…For statistical analysis and visualization of the quantification data, protein abundance values in amol were log2‐transformed and imported into the Perseus computational platform (Tyanova et al., 2016). Missing values were imputed using a downshifted normal distribution (width of 0.3 and downshift of 1.8 standard deviations).…”