Peptide Identification Statistics Pin the tail on the donkey

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Peptide Identification StatisticsPin the tail on
the donkey?

• US HUPO Bioinformatics for Proteomics
• Nathan Edwards March 12, 2005

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Peptide Identification

• Peptide fragmentation by CID is poorly understood
• MS/MS spectra represent incomplete information
  about amino-acid sequence
• I/L, K/Q, GG/N,
• Correct identifications dont come with a
  certificate!

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Peptide Identification

• High-throughput workflows demand we analyze all
  spectra, all the time.
• Spectra may not contain enough information to be
  interpreted correctly
• bad static on a cell phone
• Peptides may not match our assumptions
• its all Greek to me
• Dont know is an acceptable answer!

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Peptide Identification

• We cant prove we are right
• so can we prove we arent wrong?

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Peptide Identification

• We cant prove we are right
• so can we prove we arent wrong?

NO!
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Peptide Identification

• We cant prove we are right
• so can we prove we arent wrong?
• The best we can do is to show our answer is
  better than guessing!

NO!
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Better than guessing

• Better implies comparison
• Score or measure of degree of success
• Guessing implies randomness
• Probability and statistics

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Pin the tail on the donkey
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Probability Concepts

• Throwing darts
• One at a time
• Blindfolded
• Identically distributed?
• Uniform distribution?
• Mutually exclusive?
• Independent?
• Pr Dart hits x 0.05

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Probability Concepts

• Throwing darts
• One at a time
• Blindfolded
• Three darts
• Pr Hitting 20 3 times
• 0.05 0.05 0.05
• Pr Hit 20 at least twice
• 0.007125 0.000125

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Probability Concepts
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Probability Concepts

• Throwing darts
• One at a time
• Blindfolded
• Three darts
• Pr Hitting evens 3 times
• Pr Hitting 1-10 3 times
• 0.5 0.5 0.5
• Pr Evens at least twice
• 0.5

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Probability Concepts
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Probability Concepts

• Throwing darts
• One at a time
• Blindfolded
• 100 darts
• Pr Hitting 20 3 times
• 0.139575
• Pr Hit 20 at least twice
• 0.9629188

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Probability Concepts
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Match Score

• Dartboard is peaks in a spectrum
• Each dart is a peptide fragment
• Pr Match s peaks
• Binomial( p , n )
• Poisson( p n ), for small p and large n
• p is prob. of fragment / peak match,
• n is number of fragments

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Match Score

• Theoretical distribution
• Used by OMSSA
• Proposed, in various forms, by many.
• Probability of fragment / peak match
• IID (independent, identically distributed)
• Based on match tolerance
• Can use fragments or peaks as darts!

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Match Score

• Theoretical distribution assumptions
• Each dart is independent
• Peaks are not related
• Each dart is identically distributed
• Chance of fragment / peak match is the same for
  all peaks and fragments

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Tournament Size
100 people
1000 people
100 Darts, 20s
100000 people
10000 people
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Tournament Size
100 people
1000 people
100 Darts, 20s
100000 people
10000 people
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Number of Trials

• Tournament size number of trials
• Number of peptides tried
• Related to sequence database size
• Probability that a random match score is s
• 1 Pr all match scores lt s
• 1 Pr match score lt s Trials ()
• Assumes IID!
• Expect value
• E Trials Pr match s
• Corresponds to Bonferroni bound on ()

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Better Dart Throwers
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Better Random Models

• Comparison with completely random model isnt
  really fair
• Match scores for real spectra with real peptides
  obey rules
• Even incorrect peptides match with non-random
  structure!

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Better Random Models

• Want to generate random fragment masses (darts)
  that behave more like the real thing
• Some fragments are more likely than others
• Some fragments depend on others
• Theoretical models can only incorporate this
  structure to a limited extent.
• Cannot model the properties of a particular
  peptide!
• Must capture behavior of fragments in general

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Better Random Models

• Generate random peptides
• Real looking fragment masses
• No theoretical model!
• Must use empirical distribution
• Usually require they have the correct precursor
  mass
• Score function can model anything we like!

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Better Random Models
Fenyo Beavis, Anal. Chem., 2003
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Better Random Models
Fenyo Beavis, Anal. Chem., 2003
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Better Random Models

• Truly random peptides dont look much like real
  peptides
• Just use peptides from the sequence database!
• Caveats
• Correct peptide (non-random) may be included
• Peptides are not independent
• Reverse sequence avoids only the first problem

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Extrapolating from the Empirical Distribution
Fenyo Beavis, Anal. Chem., 2003
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Extrapolating from the Empirical Distribution

• Often, the empirical shape is consistent with a
  theoretical model

Fenyo Beavis, Anal. Chem., 2003
Geer et al., J. Proteome Research, 2004
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Peptide Prophet

• From the Institute for Systems Biology
• Keller et al., Anal. Chem. 2002
• Re-analysis of SEQUEST results
• Spectra are trials (NOT peptides!)
• Assumes that many of the spectra are not
  correctly identified

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Peptide Prophet
Keller et al., Anal. Chem. 2002
Distribution of spectral scores in the results
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Peptide Prophet

• Assumes a bimodal distribution of scores, with a
  particular shape
• Ignores database size
• but it is included implicitly
• Like empirical distribution for peptide sampling,
  can be applied to any score function
• Can be applied to any search engines results

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Peptide Prophet

• Caveats
• Are spectra scores sampled from the same
  distribution?
• Is there enough correct identifications for
  second peak?
• Are spectra independent observations?
• Are distributions appropriately shaped?
• Huge improvement over raw SEQUEST results

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Peptides to Proteins
Nesvizhskii et al., Anal. Chem. 2003
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Peptides to Proteins
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Peptides to Proteins

• A peptide sequence may occur in many different
  protein sequences
• Variants, paralogues, protein families
• Separation, digestion and ionization is not well
  understood
• Proteins in sequence database are extremely
  non-random, and very dependent

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Peptides to Proteins
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Peptides to Proteins

• Mascot
• Protein score is sum of peptide scores
• Assumes peptide identifications are independent!
• SEQUEST
• Keeps only one of the proteins for each peptide?

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Peptides to Proteins

• Peptide Prophet
• Nesvizhskii, et al. Anal. Chem 2003
• Models probability that a protein is correct
  based on
• Probability that its peptides are correct
• Models probability that a peptide is correct
  based on
• Probability that its proteins are correct
• Proteins with one high-probability peptide are
  not eliminated
• but are down-weighted
• Assumes identification probabilities from the
  same protein are independent (like Mascot)

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Peptides to Proteins

• Best available method, to date, is Protein
  Prophet.
• The problem will only get worse, as we search
  variants and isoform sequences
• Proteins do not have a single sequence!
• Peptide identification is not protein
  identification!

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Publication Guidelines
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Publication Guidelines

• Computational parameters
• Spectral processing
• Sequence database
• Search program
• Statistical analysis
• Number of peptides per protein
• Each peptide sequence counts once!
• Multiple forms of the same peptide count once!

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Publication Guidelines

• Single-peptide proteins must be explicitly
  justified by
• Peptide sequence
• N and C terminal amino-acids
• Precursor mass and charge
• Peptide Scores
• Multiple forms of the peptide counted once!
• Biological conclusions based on single-peptide
  proteins must show the spectrum

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Publication Guidelines

• More stringent requirements for PMF data
  analysis
• Similar to that for tandem mass spectra
• Management of protein redundancy
• Peptides identified from a different species?
• Spectra submission encouraged

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Summary

• Could guessing be as effective as a search?
• More guesses improves the best guess
• Better guessers help us be more discriminating
• Independent observations only count if they are
  independent!
• Peptide to proteins is not as simple as it seems
• Publication guidelines reflect sound statistical
  principles.