QuPE

QuPE analyzes LC-MS/MS tandem mass spectrometry isotopic-labeling proteomics data to quantify protein synthesis, degradation, and turnover and to perform statistical and multivariate analyses for protein-level regulation.

Key Features:

• Data Management and Analysis: Imports, manages, and analyzes mass spectra from LC-MS/MS experiments, performs database searches for protein identification, and calculates protein abundance ratios.
• Statistical Evaluation: Applies statistical inference and multivariate analysis, including variance analysis and hierarchical cluster analysis, to identify significantly up- or down-regulated genes and detect clusters of proteins with similar expression profiles.
• Integration and Interoperability: Exposes a programming interface and web services to integrate novel analytical approaches and exchange data with other -omics software.
• High-Throughput Analysis: Processes high-throughput experiments and measures protein synthesis and degradation rates using a multilabeling approach with stable isotopes of nitrogen and carbon to trace label incorporation and loss.
• Computational Efficiency: Distributes computationally intensive calculations across compute clusters for efficient processing of large proteomics datasets.
• Application Study: Demonstrated on Corynebacterium glutamicum to analyze protein turnover and regulatory mechanisms such as the heat shock response.

Scientific Applications:

• Quantitative proteomics: Quantification of protein abundance ratios and statistical identification of regulated genes and proteins from LC-MS/MS datasets.
• Protein turnover analysis: Measurement of synthesis and degradation rates via stable isotope multilabeling to study protein turnover dynamics.
• Systems biology and multi-omics integration: Integration of proteomics results with other -omics data via web services and a programming interface to support systems-level analyses.

Methodology:

Implemented in Java using Hibernate, Echo2, R, and Spring; performs database searches for protein identification, calculates protein abundance ratios, applies multivariate statistical methods including variance analysis and hierarchical cluster analysis, exposes a programming interface and web services, and distributes computations on compute clusters.