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ANALYZING PROTEOMICS
RESULTS
SEPARATING THE WHEAT
FROM THE CHAFF
Amit Kumar Yadav
5th Dec 2012
Course B
INTERPRETING THE RESULTS
1. Significance
assessment of
a Peptide-
Spectrum
match (PSM)
2.Controlling
error rates in a
high...
SIGNIFICANCE ASSESSMENT OF
PEPTIDE ASSIGNMENTS
I found some matches. Are they correct?
MS/MS DATABASE SEARCH
Understanding and Exploiting Peptide Fragment Ion Intensities Using Experimental and Informatic Appr...
WHAT’S THE PROBABILITY?
Computational Methods for Mass Spectrometry Proteomics I. Eidhammer, K. Flikka, L. Martens and S.-...
FALSE DISCOVERY RATE
DECOY DATABASE
• e.g. ABCDEFGHIJ
Database Sequence (target)
• e.g. JIHGFEDCBA
Reversed Sequence (decoy)
• e.g. JDIBGEAFCH
...
DECOY DATABASE TYPES
Comparison of Novel Decoy Database Designs for Optimizing Protein Identification Searches Using ABRF ...
IS DECOY A GOOD NULL MODEL?
50
150
250
350
450
550
650
750
850
950
1050
1150
1250
1350
1450
1550
1650
1750
1850
1950
2050
...
FALSE DISCOVERY RATE
Target
Decoy
SortedScores
Threshold score
FALSE DISCOVERY RATE
0
500
1000
1500
2000
2500
3000
3500
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Score
Peptide mass ...
FDR FORMULA
Concatenated
target-decoy
search
• FDR= 2 x #decoy / # ( target
+ decoy )
Separate target
and decoy search
• F...
FLEXIFDR
Yadav AK, Kumar D, Dash D (2012) Learning from Decoys to Improve the Sensitivity and Specificity of Proteomics Da...
PROTEIN INFERENCE
…but I wanted to know the proteins in my sample?
…I have the peptides, but which proteins did
they come from?
Interpretation of Shotgun Proteomic Data:The Protein Inference Problem
Alexey I. Nesvizhskii and Ruedi Aebersold, MCP , Ju...
PROTEIN INFERENCE PROBLEM
Interpretation of Shotgun Proteomic Data:The Protein Inference Problem
Alexey I. Nesvizhskii and...
PROTEIN GROUPING
Interpretation of Shotgun
Proteomic Data:The Protein
Inference Problem
Alexey I. Nesvizhskii and
Ruedi Ae...
QUANTITATION
How much of a protein is present?
QUANTITATION: SILAC
1. Achieving In-Depth Proteomics Profiling by Mass -ACS CHEMICAL BIOLOGY VOL.2 NO.1 • 39–52 • 2007
2. ...
APPLICATIONS OF PROTEOMICS
Proteomics: a pragmatic perspective, Nature biotechnology volume 28 number 7 july 2010
Different -omics sciences describe many levels of biomolecular organization –
but if used in isolation may give misleading...
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2.proteomics coursework 5-dec2012_aky

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2.proteomics coursework 5-dec2012_aky

  1. 1. ANALYZING PROTEOMICS RESULTS SEPARATING THE WHEAT FROM THE CHAFF Amit Kumar Yadav 5th Dec 2012 Course B
  2. 2. INTERPRETING THE RESULTS 1. Significance assessment of a Peptide- Spectrum match (PSM) 2.Controlling error rates in a high through- put experiment 3. Inferring the proteins
  3. 3. SIGNIFICANCE ASSESSMENT OF PEPTIDE ASSIGNMENTS I found some matches. Are they correct?
  4. 4. MS/MS DATABASE SEARCH Understanding and Exploiting Peptide Fragment Ion Intensities Using Experimental and Informatic Approaches Ashley C. Gucinski, Eric D. Dodds, Wenzhou Li, and Vicki H. Wysocki. Methods in Molecular Biology , Vol.604
  5. 5. WHAT’S THE PROBABILITY? Computational Methods for Mass Spectrometry Proteomics I. Eidhammer, K. Flikka, L. Martens and S.-O. Mikalsen Threshold score
  6. 6. FALSE DISCOVERY RATE
  7. 7. DECOY DATABASE • e.g. ABCDEFGHIJ Database Sequence (target) • e.g. JIHGFEDCBA Reversed Sequence (decoy) • e.g. JDIBGEAFCH Randomized Sequence (decoy)
  8. 8. DECOY DATABASE TYPES Comparison of Novel Decoy Database Designs for Optimizing Protein Identification Searches Using ABRF sPRG2006 Standard MS/MS Data Sets. Luca Bianco, Jennifer A. Mead, and Conrad Bessant, J. Proteome Res., 2009, 8 (4), 1782-1791
  9. 9. IS DECOY A GOOD NULL MODEL? 50 150 250 350 450 550 650 750 850 950 1050 1150 1250 1350 1450 1550 1650 1750 1850 1950 2050 2150 2250 2350 2450 2550 2650 2750 2850 2950 3050 3150 3250 3300 3400 3500 3600 3700 3800 3900 4000 4100 4200 Numberofhits Score Target Decoy p- value? e- value?
  10. 10. FALSE DISCOVERY RATE Target Decoy SortedScores Threshold score
  11. 11. FALSE DISCOVERY RATE 0 500 1000 1500 2000 2500 3000 3500 0 500 1000 1500 2000 2500 3000 3500 4000 4500 Score Peptide mass (Mr) 1 % FDR Target Decoy 5 % FDR q- value? Threshold score
  12. 12. FDR FORMULA Concatenated target-decoy search • FDR= 2 x #decoy / # ( target + decoy ) Separate target and decoy search • FDR= #decoy / #target target decoy
  13. 13. FLEXIFDR Yadav AK, Kumar D, Dash D (2012) Learning from Decoys to Improve the Sensitivity and Specificity of Proteomics Database Search Results. PLoS ONE 7(11): e50651
  14. 14. PROTEIN INFERENCE …but I wanted to know the proteins in my sample?
  15. 15. …I have the peptides, but which proteins did they come from?
  16. 16. Interpretation of Shotgun Proteomic Data:The Protein Inference Problem Alexey I. Nesvizhskii and Ruedi Aebersold, MCP , July 11, 2005
  17. 17. PROTEIN INFERENCE PROBLEM Interpretation of Shotgun Proteomic Data:The Protein Inference Problem Alexey I. Nesvizhskii and Ruedi Aebersold, MCP , July 11, 2005
  18. 18. PROTEIN GROUPING Interpretation of Shotgun Proteomic Data:The Protein Inference Problem Alexey I. Nesvizhskii and Ruedi Aebersold, MCP , July 11, 2005
  19. 19. QUANTITATION How much of a protein is present?
  20. 20. QUANTITATION: SILAC 1. Achieving In-Depth Proteomics Profiling by Mass -ACS CHEMICAL BIOLOGY VOL.2 NO.1 • 39–52 • 2007 2. Proteomics: a pragmatic perspective, Nature biotechnology volume 28 number 7 july 2010
  21. 21. APPLICATIONS OF PROTEOMICS Proteomics: a pragmatic perspective, Nature biotechnology volume 28 number 7 july 2010
  22. 22. Different -omics sciences describe many levels of biomolecular organization – but if used in isolation may give misleading inferences about the system !

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