MAP

MAP performs statistical comparison of isotope-labeling based mass spectrometry (MS) proteomic data to identify differentially abundant proteins across biological conditions.

Key Features:

• Isotope-labeling MS support: Analyzes isotope-labeling based mass spectrometry (MS) data to quantify protein abundances.
• Relative abundance profiling: Profiles the relative abundance of thousands of proteins in parallel.
• Model-based framework: Employs a model-based framework to address technical and systematic errors inherent in proteomic studies.
• Step-by-step regression analysis: Directly models technical variation using a step-by-step regression analysis.
• No technical replicates required: Identifies significant changes in protein abundance without necessitating additional technical replicates.
• Statistical significance assessment: Provides direct assessment of the statistical significance of observed protein abundance changes.
• Performance: Demonstrated improved sensitivity and accuracy relative to existing methods in benchmark analyses.

Scientific Applications:

• Comparative proteomics: Comparative analysis of proteomic profiles across different biological samples or experimental conditions to detect differential protein expression.
• mESC differentiation analysis: Identification of proteins differentially expressed between undifferentiated and differentiated mouse embryonic stem cells (mESCs).

Methodology:

Uses a model-based statistical framework that models technical and systematic errors and applies a step-by-step regression analysis to model technical variations and assess the statistical significance of protein abundance changes.