ProRata

ProRata infers protein abundance ratios from isotopically labeled peptide data in shotgun proteomics using a profile likelihood framework for quantitative differential proteomics with stable isotope labeling.

Key Features:

• Profile Likelihood Algorithm: Probabilistically weighs peptide-level abundance ratios based on estimated variability and bias and accounts for the signal-to-noise ratio of peptide profiles.
• Handling Multiple Peptides: Integrates multiple quantified peptides per protein by weighting them according to inferred estimation variability, adjusts for expected biases, and suppresses outlier contributions.
• Maximum Likelihood Estimation: Produces maximum likelihood point estimates of protein abundance ratios rather than simple averages of peptide ratios.
• Confidence Interval Estimation: Computes profile likelihood confidence intervals for each protein abundance ratio to quantify precision and statistical uncertainty.
• Benchmarking with Standard Mixtures: Validation and benchmarking performed using standard mixtures of isotopically labeled proteomes to assess point-estimate accuracy and confidence-interval precision.

Scientific Applications:

• Differential Proteomics: Quantitative comparison of protein abundance changes across conditions using stable isotope labeling and shotgun proteomics.
• Complex Biological Samples: Quantification in experiments with proteins represented by multiple peptides and variable signal-to-noise characteristics.
• Biomarker Discovery: Accurate protein abundance estimation to support identification of candidate biomarkers.
• Disease Pathology and Therapeutic Target Identification: Precise quantification of protein dynamics to inform studies of disease mechanisms and potential targets.

Methodology:

Applies a profile likelihood algorithm to weight peptide abundance ratios by estimated variability and bias (considering signal-to-noise), integrates multiple peptides by variance-based weighting and outlier suppression, performs maximum likelihood point estimation and profile likelihood confidence-interval estimation, and benchmarks performance using standard mixtures of isotopically labeled proteomes.