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Search Engine Result
Combining
Search Engine Result
Combining
Nathan EdwardsDepartment of Biochemistry and Molecular & Cellular BiologyGeorgetown University Medical Center
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
Mascot Search Results
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
Halobacterium sp. NRC-1ORF: GdhA1
• K-score E-value vs PepArML @ 10% FDR• Many peptides inconsistent with annotated
translation start site of NP_279651
0 40 80 120 160 200 240 280 320 360 400 440
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Translation start-site correction
7
Search engine scores are inconsistent!
Mascot
Tan
dem
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
Comparison of search engines
• No single score is comprehensive
• Search engines disagree
• Many spectra lack confident peptide assignment
4%
OMSSA10%
2%
5%9%
69%
2%
X!Tandem
Mascot
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
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
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
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
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
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
Peptide Identification Meta-Search
• Simple unified search interface for:• Mascot, X!Tandem, K-
Score, S-Score, OMSSA, MyriMatch, InsPecT
• Automatic decoy searches
• Automatic spectrumfile "chunking"
• Automatic scheduling• Serial, Multi-Processor,
Cluster, Grid
17
Peptide Identification Grid-Enabled Meta-Search
NSF TeraGrid1000+ CPUs
UMIACS250+ CPUs
Edwards LabScheduler &80+ CPUs
Securecommunication
Heterogeneouscompute resources
Single, simplesearch request
Scales easily to 250+ simultaneous
searches
X!Tandem,KScore,OMSSA,
MyriMatch,Mascot(1 core).
X!Tandem,KScore,OMSSA.
X!Tandem,KScore,OMSSA.
18
PepArML
• Peptide identification arbiter by machine learning
• Unifies these ideas within a model-free, combining machine learning framework
• Unsupervised training procedure
19
PepArML Overview
X!Tandem
Mascot
OMSSA
Other
PepArML
Feature extraction
20
Dataset Construction
T),( 11 PS
F),( 21 PS
T),( 12 PS
X!Tandem Mascot OMSSA
T),( mn PS
……
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
Supervised Learning
23
Feature Evaluation
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
Model Generalization
26
Unsupervised Learning
27
Unsupervised Learning Performance
28
Unsupervised Learning Convergence
29
Peptide Atlas A8_IP – LTQ
30
OMICS 17 Protein Mix – LCQ
31
Feature Selection (InfoGain)
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