The analytical process of assessing confidence in peptide and protein identifications, often performed post-database search, utilizes statistical modeling tools such as PeptideProphet and ProteinProphet. These algorithms estimate the probability that a given peptide or protein identification is correct based on various search engine scores and features. The process involves initially scoring individual peptide-spectrum matches (PSMs) and then aggregating these scores to infer protein-level confidence.
Employing such statistical methods is critical for minimizing false positive identifications and improving the reliability of proteomics datasets. This approach enhances downstream analyses, facilitates more accurate biological interpretations, and strengthens the conclusions drawn from proteomic experiments. Historically, manual validation was the standard, but these automated, statistically driven methods enable higher throughput and more objective assessment of large datasets.
