ProRata: A quantitative proteomics program for accurate protein abundance ratio estimation with confidence interval...

Area of Science:

• Proteomics
• Quantitative Mass Spectrometry
• Bioinformatics

Background:

• Accurate protein abundance ratio estimation is crucial for quantitative proteomics.
• Existing methods for inferring protein ratios from peptide data have limitations in accuracy and precision.
• Peptide abundance ratio variability and bias can be predicted using signal-to-noise ratios.

Purpose of the Study:

• To develop and validate a profile likelihood algorithm for improved protein abundance ratio estimation in quantitative shotgun proteomics.
• To provide accurate point estimates and statistically sound confidence intervals for protein ratios.
• To enhance the reliability of quantitative proteomics data by accounting for peptide-specific variability and bias.

Main Methods:

• Developed a profile likelihood algorithm to infer protein abundance ratios from isotopically labeled peptide ratios.
• The algorithm probabilistically weights peptide ratios based on their estimated variability and bias.
• Outlier suppression and maximum likelihood estimation were incorporated for robust analysis.

Main Results:

• The profile likelihood algorithm provides more accurate point estimates of protein abundance ratios compared to simple averaging.
• It generates profile likelihood confidence intervals, offering a precise measure of estimation uncertainty.
• Benchmarking with standard mixtures confirmed the accuracy of point estimation and the precision/confidence of interval estimation.

Conclusions:

• The profile likelihood algorithm offers a statistically rigorous approach for quantitative proteomics.
• It enhances the accuracy and provides reliable uncertainty quantification for protein abundance ratios.
• The algorithm has been implemented in the ProRata software, available at www.MSProRata.org.