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Enhancements in Peptide Probability Modeling in Scaffold 4: Innovations and Applications

This presentation by Brian C. Searle at the 2013 Scaffold Users Meeting discusses significant improvements in peptide probability modeling within Scaffold 4. Key topics include enhanced probability estimation using LFDR, target/decoy classification of multiple scores, and advancements in delta mass error modeling. The presentation showcases the implementation of Naïve Bayes classifiers for robust protein identification, as well as the integration of various new search engines compatible with mzIdentML standard. These innovations aim to improve accuracy in protein-level false discovery rates (FDR) and streamline data analysis for researchers.

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Enhancements in Peptide Probability Modeling in Scaffold 4: Innovations and Applications

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  1. Improving Peptide Probability Modeling in Scaffold 4 Brian C. Searle brian.searle@proteomesoftware.com Scaffold Users Meeting, 2013 Creative Commons Attribution

  2. Scaffold 4 Improvements • Probability Estimation using LFDR • Target/Decoy Classification of multiple scores • Delta Mass Error Modeling Improvements • Requires Target/Decoy analysis (1:1 … 1:10)

  3. “Incorrect” “Correct”

  4. Number of Identified Proteins Protein-Level False Discovery Rate

  5. Number of Identified Proteins Protein-Level False Discovery Rate

  6. XCorr DeltaCN % Ions Identified …

  7. XCorr DeltaCN % Ions Identified …

  8. XCorr DeltaCN % Ions Identified

  9. Naïve Bayes Classifier • Trained to each data set • Simple (can calculate with a formula, no magic!) • Robust to over-fitting

  10. Number of Identified Proteins Protein-Level False Discovery Rate

  11. Number of Identified Proteins Protein-Level False Discovery Rate

  12. Probability the ID is Correct Probability the ID is Wrong

  13. Number of Identified Proteins Protein-Level False Discovery Rate

  14. Number of Identified Proteins Protein-Level False Discovery Rate

  15. 1% Peptide FDR Number of Identified Proteins

  16. 1% Peptide FDR > 10% Protein FDR?!? Number of Identified Proteins Protein-Level FDR

  17. New Search Engines? • Difficult to add new search engines with PeptideProphet (new seeds) • Easy to add with Naïve Bayes / LFDR • mzIdentML interchange (HUPO standard)

  18. New Search Enginesin Scaffold 4 • Peaks • Byonic • Myrimatch (Tabb Lab) • SQID (Wysocki Lab) • MS-GF+ (Pevzner Lab) • MS-Amanda (Mechtler Lab, PD)

  19. New Search Enginesin Scaffold 4 • Peaks • Byonic • Myrimatch (Tabb Lab) • SQID (Wysocki Lab) • MS-GF+ (Pevzner Lab) • MS-Amanda (Mechtler Lab, PD) • ... Any engine with decoys & mzIdentML!

  20. Scaffold 4 Improvements • New Naïve Bayes / LFDR Probabilities • Probability Estimation using LFDR • Target/Decoy Classification • Delta Mass Error Modeling • “Next generation” search engine interpretation • New mzIdentML File Loading • Several newly supported search engines • Any search engine with decoys

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