ALL-P

ALL-P estimates protein abundances from peptide-level quantitative proteomics data by using hierarchical modeling that incorporates shared peptides and accounts for biological and technical variability.

Key Features:

• Hierarchical Modeling: Employs a hierarchical statistical model that integrates all quantified peptides and models peptide effects, biological variability, and technical error.
• Inclusion of Shared Peptides: Incorporates peptides that are shared by multiple proteins by attributing their contributions to all relevant proteins simultaneously.
• Simultaneous Analysis: Analyzes all quantified peptides across proteins in a single model to produce coherent protein abundance estimates and enable testing for changes in abundance.
• Scalability: Implemented to handle large quantitative proteomics datasets for high-throughput analyses.

Scientific Applications:

• Quantitative Proteomics: Infers protein abundances from peptide intensities while accounting for peptide-specific effects and measurement errors.
• Protein Abundance Changes: Tests for changes in protein abundance across conditions or treatments using the integrated peptide-level model.

Methodology:

Constructs a hierarchical model that integrates peptide data from all proteins, models shared peptides by attributing their contributions to multiple proteins simultaneously, and accounts for both biological variability and technical measurement error.