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

2. Peptide Identifications 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 2

3. Mascot Search Results 3

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) 4

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 5

6. Translation start-site correction 6

7. Search engine scores are inconsistent! 7 Mascot Tandem

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 Apply empirical or theoretical model 8

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

10. Simple approaches (Union) Different search engines confidently identify different spectra: Due to search space, spectral processing, scoring, significance estimation Filter each search engine's results and union Union of results must be more complete But how to estimate significance for the union? What if the results for same spectra disagree? Need to compensate for reduced specificity How much? 10

11. Union of filtered peptide ids 11 Mascot Tandem

12. Union of filtered peptide ids 12 Mascot Tandem

13. Union of filtered peptide ids 13 Mascot Tandem

14. Simple approaches (Intersection) Different search engines agree on many spectra Agreement is unexpected given differences Filter each search engine's results and take the intersection Intersection of results must be more significant But how to estimate significance for the intersection? What about the borderline spectra? Need to compensate for reduced sensitivity How and how much? 14

15. Intersection of filtered peptide ids 15 Mascot Tandem

16. Intersection of filtered peptide ids 16 Mascot Tandem

17. Intersection of filtered peptide ids 17 Mascot Tandem

18. 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? 18

19. Consensus and Multi-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 Boston! Scores and E-values are not comparable How to choose the best answer? Example: Best E-value favors Tandem! 19

20. Search for Consensus Running many search engines is hard! Identifications must have every opportunity to agree: No failed searches, matched search parameters, sequence databases, spectra But the search engines all use: Varying spectral file formats, different parameter specifications for mass tolerance, modifications, pre- processing for sequence databases, different charge- state handling, termini rules Decoy searches must also use identical parameters 20

21. Searching for Consensus Initial methionine loss as tryptic peptide? Missing charge state handling? X!Tandem's refinement mode Pyro-Gln, Pyro-Glu modifications? Precursor mass tolerance (Da vs ppm) Semi-tryptic only (no fully-tryptic mode). 21

22. Configuring for Consensus Search engine configuration can be difficult: Correct spectral format Search parameter files and command-line Pre-processed sequence databases. Must strive to ensure that each search engine is presented with the same search criteria, despite different formats, syntax, and quirks. Search engine configuration must be automated. 22

23. Results Extraction for Consensus Must be able to unambiguously extract peptide identifications from results Spectrum identifiers / scan numbers Modification identifiers Protein accessions How should we handle E-values vs. probabilities vs. FDR (partitioned)? Cannot rely on these to be comparable Must use consistent, external significance calibration 23

24. Search Engine Independent FDR Estimation Comparing search engines is difficult due to different FDR estimation techniques Implicit assumption: Spectra scores can be thresholded Competitive vs Global Competitive controls some spectral variation Reversed vs Shuffled Decoy Sequence Reversed models target redundancy accurately Charge-state partition or Unified Mitigates effect of peptide length dependent scores What about peptide property partitions? 24

25. Search Execution for Consensus Running many search engines take time 7 x 3 searches of the same spectra! Some search engines require licenses or specific operating systems How to use grid/cloud computing effectively? Cannot assume a shared file-system Search engines may crash or be preempted Machine may "disappear" Machine may consistently fail searches 25

26. Combining Multi-Search Results Treat search engines as black-boxes Generate PSMs + scores, features Apply machine learning / statistical modeling to results Use multiple match metrics Combine/refine using multiple search engines Agreement suggests correctness 26

27. Machine Learning / Statistical Modeling Use of multiple metrics of PSM quality: Precursor delta, trypsin digest features, etc Often 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 e.g. PeptideProphet/iProphet Weighted linear combination of features Number of sibling searches 27

28. Available Tools PeptideProphet/iProphet Part of trans-proteomic-pipeline suite Scaffold Commercial reimplementation of PP/iP PepArML Publicly available from the Edwards lab Lots of in-house stuff… Result combining mentioned in talks, lots of papers, etc. but no public tools 28

29. Peptide 8 Peptide 7 For Each Spectrum Get Mascot Identification Get SEQUEST Identification Get X!Tandem Identification Peptide 1 Peptide 3 Peptide 4 Peptide 5 Peptide 6 Peptide 2 p=76% p=81% p=56% Agreement score Using the probabilities given by each search engine and the probability of them agreeing, a better peptide ID is made Brian Searle

30. PepArML Strategy Meta-Search for Multi-Search: Automatic configuration of searches Automatic preprocessing of sequence databases Automatic spectral reformatting Automatic execution of search on local or remote computing resources (AWS/grid/NFS). Result Combining: Decoy-based FDR significance estimation Unsupervised, model-free, machine-learning 30

31. Peptide Identification Meta-Search Simple unified search interface for: Mascot, X!Tandem, K- Score, S-Score, OMSSA, MyriMatch, InsPecT+MSSGF Automatic decoy searches Automatic spectrum file "chunking" Automatic scheduling Serial, Multi-Processor, Cluster, Grid, Cloud 31

32. Grid-Enabled Peptide Identification Meta-Search 32 Amazon Web Services University Cluster Edwards Lab Scheduler & 80+ CPUs Secure communication Heterogeneous compute resources Single, simple search request Scales easily to 250+ simultaneous searches

33. PepArML Combiner Peptide identification arbiter by machine learning Unifies these ideas within a model-free, combining machine learning framework Unsupervised training procedure 33

34. PepArML Overview 34 X!Tandem Mascot OMSSA Other PepArML Feature extraction

35. Dataset Construction 35 T F T X!TandemMascotOMSSA T ……

36. 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 36

37. Supervised Learning 37

38. Search Engine Info. Gain 38

39. Precursor & Digest Info. Gain 39

40. Retention Time & Proteotypic Peptide Properties Info. Gain 40

41. 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 41

42. Model Generalization 42

43. Unsupervised Learning 43

44. Unsupervised Learning Performance 44

45. Unsupervised Learning Convergence 45

46. PepArML Performance 46 LCQQSTAR LTQ-FT Standard Protein Mix Database 18 Standard Proteins – Mix1

47. 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 47