1. Search Engine Result Combining Nathan Edwards Department of Biochemistry and Molecular & Cellular Biology Georgetown University Medical Center

2. 2 Peptide Identification Results Search engines provide an answer for every spectrum... Can we figure out which ones to believe? Why is this hard? Hard to determine “good” scores Significance estimates are unreliable Need more ids from weak spectra Each search engine has its strengths...... and weaknesses Search engines give different answers

3. 3 Mascot Search Results

4. 4 Translation start-site correction Halobacterium sp. NRC-1 Extreme halophilic Archaeon, insoluble membrane and soluble cytoplasmic proteins Goo, et al. MCP 2003. GdhA1 gene: Glutamate dehydrogenase A1 Multiple significant peptide identifications Observed start is consistent with Glimmer 3.0 prediction(s)

5. 5 Halobacterium sp. NRC-1 ORF: GdhA1 K-score E-value vs PepArML @ 10% FDR Many peptides inconsistent with annotated translation start site of NP_279651

6. 6 Translation start-site correction

7. 7 Search engine scores are inconsistent! Mascot Tandem

8. 8 Common Algorithmic Framework – Different Results Pre-process experimental spectra Charge state, cleaning, binning Filter peptide candidates Decide which PSMs to evaluate Score peptide-spectrum match Fragmentation modeling, dot product Rank peptides per spectrum Retain statistics per spectrum Estimate E-values Appy empirical or theoretical model

9. 9 Comparison of search engines No single score is comprehensive Search engines disagree Many spectra lack confident peptide assignment 4% OMSSA 10% 2% 5%9% 69% 2% X!Tandem Mascot

10. 10 Lots of techniques out there Treat search engines as black-boxes Generate PSMs + scores, features Apply supervised machine learning to results Use multiple match metrics Combine/refine using multiple search engines Agreement suggests correctness Use empirical significance estimates “Decoy” databases (FDR)

11. 11 Machine Learning Use of multiple metrics of PSM quality: Precursor delta, trypsin digest features, etc Requires "training" with examples Different examples will change the result Generalization is always the question Scores can be hard to "understand" Difficult to establish statistical significance Peptide Prophet's discriminant function Weighted linear combination of features

12. 12 Combine / Merge Results Threshold peptide-spectrum matches from each of two search engines PSMs agree → boost specificity PSMs from one → boost sensitivity PSMs disagree → ????? Sometimes agreement is "lost" due to threshold... How much should agreement increase our confidence? Scores easy to "understand" Difficult to establish statistical significance How to generalize to more engines?

13. 13 Consensus and Meta-Search Multiple witnesses increase confidence As long as they are independent Example: Getting the story straight Independent "random" hits unlikely to agree Agreement is indication of biased sampling Example: loaded dice Meta-search is relatively easy Merging and re-ranking is hard Example: Booking a flight to Denver! Scores and E-values are not comparable How to choose the best answer? Example: Best E-value favors Tandem!

14. 14 Searching for Consensus Search engine quirks can destroy consensus Initial methionine loss as tryptic peptide Charge state enumeration or guessing X!Tandem's refinement mode Pyro-Gln, Pyro-Glu modifications Difficulty tracking spectrum identifiers Precursor mass tolerance (Da vs ppm) Decoy searches must be identical!

15. 15 Configuring for Consensus Search engine configuration can be difficult: Correct spectral format Search parameter files and command-line Pre-processed sequence databases. Tracking spectrum identifiers Extracting peptide identifications, especially modifications and protein identifiers

16. 16 Peptide Identification Meta-Search Simple unified search interface for: Mascot, X!Tandem, K- Score, S-Score, OMSSA, MyriMatch, InsPecT Automatic decoy searches Automatic spectrum file "chunking" Automatic scheduling Serial, Multi-Processor, Cluster, Grid

17. 17 Peptide Identification Grid-Enabled Meta-Search NSF TeraGrid 1000+ CPUs UMIACS 250+ CPUs Edwards Lab Scheduler & 80+ CPUs Secure communication Heterogeneous compute resources Single, simple search request Scales easily to 250+ simultaneous searches X!Tandem, KScore, OMSSA, MyriMatch, Mascot (1 core). X!Tandem, KScore, OMSSA. X!Tandem, KScore, OMSSA.

18. 18 PepArML Peptide identification arbiter by machine learning Unifies these ideas within a model- free, combining machine learning framework Unsupervised training procedure

19. 19 PepArML Overview X!Tandem Mascot OMSSA Other PepArML Feature extraction

20. 20 Dataset Construction T F T X!TandemMascotOMSSA T ……

21. 21 Voting Heuristic Combiner Choose PSM with most votes Break ties using FDR Select PSM with min. FDR of tied votes How to apply this to a decoy database? Lots of possibilities – all imperfect Now using: 100*#votes – min. decoy hits

22. 22 Supervised Learning

23. 23 Feature Evaluation

24. 24 Application to Real Data How well do these models generalize? Different instruments Spectral characteristics change scores Search parameters Different parameters change score values Supervised learning requires (Synthetic) experimental data from every instrument Search results from available search engines Training/models for all parameters x search engine sets x instruments

25. 25 Model Generalization

26. 26 Unsupervised Learning

27. 27 Unsupervised Learning Performance

28. 28 Unsupervised Learning Convergence

29. 29 Peptide Atlas A8_IP – LTQ

30. 30 OMICS 17 Protein Mix – LCQ

31. 31 Feature Selection (InfoGain)

32. 32 Conclusions Combining search results from multiple engines can be very powerful Boost both sensitivity and specificity Running multiple search engines is hard Statistical significance is hard Use empirical FDR estimates...but be careful...lots of subtleties Consensus is powerful, but fragile Search engine quirks can destroy it "Witnesses" are not independent