Post on 31-Mar-2015
Reporting Protein Identifications from MS/MS Results
Brian C. SearleProteome Software Inc.
Portland, Oregon USA
Brian.Searle@ProteomeSoftware.com
Creative Commons Attribution
Outline
• Assigning Proteins from Peptide IDs
• Correcting for One-Hit-Wonders
• Protein False Discovery Rates?
• Correcting for Shared Peptides
• Publication Standards
Outline
• Assigning Proteins from Peptide IDs
• Correcting for One-Hit-Wonders
• Protein False Discovery Rates?
• Correcting for Shared Peptides
• Publication Standards
Just to Review:
clearlywrong
possiblycorrect
F
R
Elias JE, Gygi SP.Nat Methods. 2007 Mar;4(3):207-14.
Just to Review:# Spectrum Accession Peptide Score
1 scan 3632 P35908 GFSSGSAVVSGGSR 4.6
2 scan 3609 P0AFY8 FSAASQPAAPVTK 3.7
3 scan 3629 P0A940 GFQSNTIGPK 3.0
4 scan 3635 P0A6F9 STRGEVLAVGNGR 2.2
5 scan 3636 P0A870 ELAESEGAIER 2.1
6 scan 3607 P0A799 ADLNVPVKDGK 1.9
7 scan 3626 P0ABC7 EAEAYTNEVQPR 1.6
8 scan 3602 P0A853 IRVIEPVKR 1.4
9 scan 3623 P38489 KLTPEQAEQIK 0.9
10 scan 3616 P00448 GTTLQGDLK 0.8
11 scan 3621 P09546 LLPGPTGER 0.4
12 scan 3615 P0AFG8 AFLEGR 0.2
13 scan 3624 P14565 SAADVAIMK 0.0
14 scan 3613 rev_P06864 EGSLAVNVQGDAAIR -0.4
15 scan 3604 P36562 DPEEVVGIGANLPTDK -0.7
16 scan 3606 P0A9C5 IPVVSSPK -0.7
17 scan 3611 P0ABB0 ASTISNVVR -0.7
18 scan 3614 rev_Q2EEU2 KFVALTCDTLLLGER -0.8
19 scan 3620 rev_P0ACL5 NNESAALMKEYCR -0.9
20 scan 3633 rev_P37309 SDGSCNQRALNR -0.9
21 scan 3627 P32132 VEETEDADAFRVSGR -1.0
22 scan 3618 P37342 ILTQDEIDVR -1.0
23 scan 3610 rev_P0ADK0 IANVSDVVPR -1.2
24 scan 3601 P0AG93 LGMKREHMLQQK -1.3
Just to Review:# Spectrum Accession Peptide Score
1 scan 3632 P35908 GFSSGSAVVSGGSR 4.6
2 scan 3609 P0AFY8 FSAASQPAAPVTK 3.7
3 scan 3629 P0A940 GFQSNTIGPK 3.0
4 scan 3635 P0A6F9 STRGEVLAVGNGR 2.2
5 scan 3636 P0A870 ELAESEGAIER 2.1
6 scan 3607 P0A799 ADLNVPVKDGK 1.9
7 scan 3626 P0ABC7 EAEAYTNEVQPR 1.6
8 scan 3602 P0A853 IRVIEPVKR 1.4
9 scan 3623 P38489 KLTPEQAEQIK 0.9
10 scan 3616 P00448 GTTLQGDLK 0.8
11 scan 3621 P09546 LLPGPTGER 0.4
12 scan 3615 P0AFG8 AFLEGR 0.2
13 scan 3624 P14565 SAADVAIMK 0.0
14 scan 3613 rev_P06864 EGSLAVNVQGDAAIR -0.4
15 scan 3604 P36562 DPEEVVGIGANLPTDK -0.7
16 scan 3606 P0A9C5 IPVVSSPK -0.7
17 scan 3611 P0ABB0 ASTISNVVR -0.7
18 scan 3614 rev_Q2EEU2 KFVALTCDTLLLGER -0.8
19 scan 3620 rev_P0ACL5 NNESAALMKEYCR -0.9
20 scan 3633 rev_P37309 SDGSCNQRALNR -0.9
21 scan 3627 P32132 VEETEDADAFRVSGR -1.0
22 scan 3618 P37342 ILTQDEIDVR -1.0
23 scan 3610 rev_P0ADK0 IANVSDVVPR -1.2
24 scan 3601 P0AG93 LGMKREHMLQQK -1.3
Just to Review:# Spectrum Accession Peptide Score
1 scan 3632 P35908 GFSSGSAVVSGGSR 4.6
2 scan 3609 P0AFY8 FSAASQPAAPVTK 3.7
3 scan 3629 P0A940 GFQSNTIGPK 3.0
4 scan 3635 P0A6F9 STRGEVLAVGNGR 2.2
5 scan 3636 P0A870 ELAESEGAIER 2.1
6 scan 3607 P0A799 ADLNVPVKDGK 1.9
7 scan 3626 P0ABC7 EAEAYTNEVQPR 1.6
8 scan 3602 P0A853 IRVIEPVKR 1.4
9 scan 3623 P38489 KLTPEQAEQIK 0.9
10 scan 3616 P00448 GTTLQGDLK 0.8
11 scan 3621 P09546 LLPGPTGER 0.4
12 scan 3615 P0AFG8 AFLEGR 0.2
13 scan 3624 P14565 SAADVAIMK 0.0
14 scan 3613 rev_P06864 EGSLAVNVQGDAAIR -0.4
15 scan 3604 P36562 DPEEVVGIGANLPTDK -0.7
16 scan 3606 P0A9C5 IPVVSSPK -0.7
17 scan 3611 P0ABB0 ASTISNVVR -0.7
18 scan 3614 rev_Q2EEU2 KFVALTCDTLLLGER -0.8
19 scan 3620 rev_P0ACL5 NNESAALMKEYCR -0.9
20 scan 3633 rev_P37309 SDGSCNQRALNR -0.9
21 scan 3627 P32132 VEETEDADAFRVSGR -1.0
22 scan 3618 P37342 ILTQDEIDVR -1.0
23 scan 3610 rev_P0ADK0 IANVSDVVPR -1.2
24 scan 3601 P0AG93 LGMKREHMLQQK -1.3
?
…Well, Maybe
AEPTIR
IDVCIVLLQHK
NTGDR
Protein
AEPTIR
IDVCIVLLQHK
NTGDR
Protein
85%
65%
25%
??%
FDRs for Whole Datasetsvs Individual Peptides
• Cumulative FDRs only estimate the validity of a data set
• Probabilities (or instantaneous FDRs) estimate the validity of a peptide of interest
One Possible Approach• Instantaneous False Discovery Rate
• PeptideProphet (TPP, Scaffold)• Percolator• Spectral Energies• RAId De Novo
Many Others:
Just to Review:# Spectrum Accession Peptide Score
1 scan 3632 P35908 GFSSGSAVVSGGSR 4.6
2 scan 3609 P0AFY8 FSAASQPAAPVTK 3.7
3 scan 3629 P0A940 GFQSNTIGPK 3.0
4 scan 3635 P0A6F9 STRGEVLAVGNGR 2.2
5 scan 3636 P0A870 ELAESEGAIER 2.1
6 scan 3607 P0A799 ADLNVPVKDGK 1.9
7 scan 3626 P0ABC7 EAEAYTNEVQPR 1.6
8 scan 3602 P0A853 IRVIEPVKR 1.4
9 scan 3623 P38489 KLTPEQAEQIK 0.9
10 scan 3616 P00448 GTTLQGDLK 0.8
11 scan 3621 P09546 LLPGPTGER 0.4
12 scan 3615 P0AFG8 AFLEGR 0.2
13 scan 3624 P14565 SAADVAIMK 0.0
14 scan 3613 rev_P06864 EGSLAVNVQGDAAIR -0.4
15 scan 3604 P36562 DPEEVVGIGANLPTDK -0.7
16 scan 3606 P0A9C5 IPVVSSPK -0.7
17 scan 3611 P0ABB0 ASTISNVVR -0.7
18 scan 3614 rev_Q2EEU2 KFVALTCDTLLLGER -0.8
19 scan 3620 rev_P0ACL5 NNESAALMKEYCR -0.9
20 scan 3633 rev_P37309 SDGSCNQRALNR -0.9
21 scan 3627 P32132 VEETEDADAFRVSGR -1.0
22 scan 3618 P37342 ILTQDEIDVR -1.0
23 scan 3610 rev_P0ADK0 IANVSDVVPR -1.2
24 scan 3601 P0AG93 LGMKREHMLQQK -1.3
Just to Review:# Spectrum Accession Peptide Score
1 scan 3632 P35908 GFSSGSAVVSGGSR 4.6
2 scan 3609 P0AFY8 FSAASQPAAPVTK 3.7
3 scan 3629 P0A940 GFQSNTIGPK 3.0
4 scan 3635 P0A6F9 STRGEVLAVGNGR 2.2
5 scan 3636 P0A870 ELAESEGAIER 2.1
6 scan 3607 P0A799 ADLNVPVKDGK 1.9
7 scan 3626 P0ABC7 EAEAYTNEVQPR 1.6
8 scan 3602 P0A853 IRVIEPVKR 1.4
9 scan 3623 P38489 KLTPEQAEQIK 0.9
10 scan 3616 P00448 GTTLQGDLK 0.8
11 scan 3621 P09546 LLPGPTGER 0.4
12 scan 3615 P0AFG8 AFLEGR 0.2
13 scan 3624 P14565 SAADVAIMK 0.0
14 scan 3613 rev_P06864 EGSLAVNVQGDAAIR -0.4
15 scan 3604 P36562 DPEEVVGIGANLPTDK -0.7
16 scan 3606 P0A9C5 IPVVSSPK -0.7
17 scan 3611 P0ABB0 ASTISNVVR -0.7
18 scan 3614 rev_Q2EEU2 KFVALTCDTLLLGER -0.8
19 scan 3620 rev_P0ACL5 NNESAALMKEYCR -0.9
20 scan 3633 rev_P37309 SDGSCNQRALNR -0.9
21 scan 3627 P32132 VEETEDADAFRVSGR -1.0
22 scan 3618 P37342 ILTQDEIDVR -1.0
23 scan 3610 rev_P0ADK0 IANVSDVVPR -1.2
24 scan 3601 P0AG93 LGMKREHMLQQK -1.3
4 to 53 to 4
2 to 3
1 to 2
0 to 1
-1 to 0
-2 to -1
# of
Mat
ches
0
100
200
300
400
500
600
700
800
-40 -30 -20 -10 0 10 20 30 40 50 60
“Correct”
Ion Score – Identity Score
“2x Decoy”
Histogram of Decoy Matches
# of
Mat
ches
0
100
200
300
400
500
600
700
800
-40 -30 -20 -10 0 10 20 30 40 50 60
“Correct”
Ion Score – Identity Score
Histogram of Decoy Matches“2x Decoy”
# of
Mat
ches
Ion Score – Identity Score
Curve Fit Distributions
0
100
200
300
400
500
600
700
800
-40 -30 -20 -10 0 10 20 30 40 50 60
“2x Decoy”
“Correct”
Choi H, Ghosh D, Nesvizhskii AI.J Proteome Res. 2008 Jan;7(1):286-92.
0
100
200
300
400
500
600
700
800
-40 -30 -20 -10 0 10 20 30 40 50 60
Instantaneous FDR Method#
of M
atch
es
“Correct”
“2x Decoy”
Ion Score – Identity Score
p( | D)
p(D | ) p()
p(D | ) p() p(D | ) p( )
Choi H, Ghosh D, Nesvizhskii AI.J Proteome Res. 2008 Jan;7(1):286-92.
AEPTIR
IDVCIVLLQHK
NTGDR
Protein
85%
65%
25%
??%
AEPTIR
IDVCIVLLQHK
NTGDR
Protein
(15%)
(35%)
(75%)
(??%)
Feng J, Naiman DQ, Cooper B.Anal Chem. 2007 May 15;79(10):3901-11.
AEPTIR
IDVCIVLLQHK
NTGDR
Protein
(15%)
(35%)
(75%)
(4%)
0.15 * 0.35 * 0.75 = 0.04Feng J, Naiman DQ, Cooper B.Anal Chem. 2007 May 15;79(10):3901-11.
AEPTIR
IDVCIVLLQHK
NTGDR
Protein
85%
65%
25%
96%
0.15 * 0.35 * 0.75 = 0.04Feng J, Naiman DQ, Cooper B.Anal Chem. 2007 May 15;79(10):3901-11.
If only it were so easy!
Peptide 1
Peptide 2
Peptide 3
Peptide 4
Peptide 5
Peptide 6
Peptide 7
Peptide 8
Peptide 9
Peptide 10
80% Peptides
Peptide 1
Peptide 2
Peptide 3
Peptide 4
Peptide 5
Peptide 6
Peptide 7
Peptide 8
Peptide 9
Peptide 10
CorrectProtein A
CorrectProtein B
80% Peptides
Peptide 1
Peptide 2
Peptide 3
Peptide 4
Peptide 5
Peptide 6
Peptide 7
Peptide 8
Peptide 9
Peptide 10
CorrectProtein A
CorrectProtein B
IncorrectProtein C
IncorrectProtein D
80% Peptides 50% Proteins
One hit wonders aredubious at best
Outline
• Assigning Proteins from Peptide IDs
• Correcting for One-Hit-Wonders
• Protein False Discovery Rates?
• Correcting for Shared Peptides
• Publication Standards
Computed Probability
Actu
al P
roba
bilit
y
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Computed Probability
Actu
al P
roba
bilit
y
UNDERestimation
OVERestimation
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
UNDERestimation
OVERestimation
Computed Probability
Actu
al P
roba
bilit
y
What if we could scoreone-hit-wonderness?
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Combining different peptides
• Quantify as a score:If different peptides agree: Good!If peptides are one-hit-wonders: Bad!
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Combining different peptides
• Quantify as a score:If different peptides agree: Good!If peptides are one-hit-wonders: Bad!
• Peptide agreement score:
'
'
( | )k k
k k
NSP p D
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Combining different peptides
• Quantify as a score:If different peptides agree: Good!If peptides are one-hit-wonders: Bad!
• Peptide agreement score:
'
'
( | )k k
k k
NSP p D
NSP score for peptide (k) is the sum of other
agreeing peptides (not k)Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Protein Prophet Distributions
Multi-hitProteins
One-hitWonders
Protein Prophet Distributions
Protein Prophet Distributions
Protein Prophet Distributions
in between(keep same)
one hit wonders(decrease prob)
multi-hit proteins(increase prob)
UNDERestimation
OVERestimation
Computed Probability
Actu
al P
roba
bilit
y
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Computed Probability
Actu
al P
roba
bilit
y
with NSP
without NSP
Nesvizhskii, A. I.; Keller, A. et al Anal. Chem. 75, 4646-4658
Brian, I hate math.What do I do?
Option 1:Throw Out One-Hit-Wonders
Advantages: Easy, works!
Disadvantages: Loss of sensitivity!
Option 2: Use Multiple FiltersFilter 1 - Protein Mode
• ≥2 peptides/protein• moderate spectrum threshold
Filter 2 - Peptide Mode• 1 peptide/protein• high spectrum threshold
Option 2: Use Multiple Filters
Advantages: More sensitive!
Disadvantages: Pretty arbitrary!
Option 3:
• Assigning Proteins from Peptide IDs
• Correcting for One-Hit-Wonders
• Protein False Discovery Rates?
• Correcting for Shared Peptides
• Publication Standards
# Accession Protein Score
1 P0ABH7 4258.08
2 P0ABJ9 2423.84
3 P0A7S3 1670.86
4 P0ACF0 1230.35
5 P0AES0 896.12
6 P21165 702.89
7 P0AG59 524.04
8 P17952 409.74
9 P08997 327.85
10 rev_P76577 276.03
11 P41407 246.88
12 P39177 219.44
13 P37689 195.37
14 P0A951 177.02
15 P0AGG4 164.52
16 P29131 153.92
17 rev_P0AEQ1 146.86
18 rev_P09155 140.07
19 P0A9S5 132.29
20 P0AE45 125.41
21 P77718 120.12
22 P76115 116.15
23 rev_P76463 111.37
24 rev_P0A6E4 107.58
# Accession Protein Score
1 P0ABH7 4258.08
2 P0ABJ9 2423.84
3 P0A7S3 1670.86
4 P0ACF0 1230.35
5 P0AES0 896.12
6 P21165 702.89
7 P0AG59 524.04
8 P17952 409.74
9 P08997 327.85
10 rev_P76577 276.03
11 P41407 246.88
12 P39177 219.44
13 P37689 195.37
14 P0A951 177.02
15 P0AGG4 164.52
16 P29131 153.92
17 rev_P0AEQ1 146.86
18 rev_P09155 140.07
19 P0A9S5 132.29
20 P0AE45 125.41
21 P77718 120.12
22 P76115 116.15
23 rev_P76463 111.37
24 rev_P0A6E4 107.58
Protein FDRs only accurate with >100 Proteins
Number of Confidently IDed Proteins
Unc
erta
inty
in P
rote
in F
DR
1% Error In FDR Estimation
Histogram of Decoy PROTEIN Matches
Protein Score
# Pr
otei
n Id
entifi
catio
ns
“Correct”
“2x Decoy”
Instantaneous Protein FDRs…
• Estimate the likelihood that a single protein of interest is present
• Are trouble at best due to stochastic sampling
• Shouldn’t be used with <500 likely proteins– Better off calculating protein probabilities using a
model like ProteinProphet
Proteins don’t existin isolation
Outline
• Assigning Proteins from Peptide IDs
• Correcting for One-Hit-Wonders
• Protein False Discovery Rates?
• Correcting for Shared Peptides
• Publication Standards
Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, 1419-1440, 2005
Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, 1419-1440, 2005
Nesvizhskii, A. I.; Aebersold, R. Mol. Cell. Proteom. 4.10, 1419-1440, 2005
Tubulinalpha 6
Tubulinalpha 3
YMACCLLYR
Tubulinalpha 4
85%
??%
??%
??%
Tubulinalpha 6
Tubulinalpha 3
YMACCLLYR
Tubulinalpha 4
85%
85%3
85%3
85%3Nesvizhskii, A. I.; Keller, A. et al
Anal. Chem. 75, 4646-4658
Tubulinalpha 6
Tubulinalpha 3
YMACCLLYR
SIQFVDWCPTGFK
Tubulinalpha 4
??%
??%
??%
Tubulinalpha 6
Tubulinalpha 3
YMACCLLYR
SIQFVDWCPTGFK
Tubulinalpha 4
Peptide 1 Peptide 2
Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
Distinct Proteins
100% 100%
100% 100%
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
Indistinguishable Proteins
50% 50% 50% 50%
50% 50% 50% 50%
Peptide 1 Peptide 2 Peptide 3
Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
Differentiable Proteins
100% 50% 50%
50% 50% 100%
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
Subset Proteins
100% 100% 100% 100%
0% 0% 0%
Indistinguishable
Differentiable
Subset
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
The QuantitativeSubset Complication
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
The QuantitativeSubset Complication
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
The QuantitativeSubset Complication
?
Peptide 1 Peptide 2 Peptide 3 Peptide 4
Peptide 2 Peptide 3 Peptide 4
Prot
ein
BPr
otei
nA
The QuantitativeSubset Complication
?
EAFIDHGEEFSGR GSFPMAEK
NLGMGK
Specific to 2c29Specific to 2c40 Common to both
Ratio ≈ 1.1
P450 2c40 P450 2c29
Ratio ≈ 1.6 Ratio ≈ 2.2
The Hidden Subset Complication
Peptide 1
Prot
ein
BPr
otei
nA Peptide 2
Peptide 3Peptide 2
Peptide 3 Peptide 4
Prot
ein
C
The Hidden Subset Complication
Peptide 1
Prot
ein
BPr
otei
nA Peptide 2
Peptide 3Peptide 2
Peptide 3 Peptide 4
Prot
ein
C
100%
100%
The Hidden Subset Complication
Peptide 1
Prot
ein
BPr
otei
nA Peptide 2
Peptide 3Peptide 2
Peptide 3 Peptide 4
Prot
ein
C
100% 100%
0% 0%
100%
100%
The Bold Red Complication
Peptide 1
Prot
ein
BPr
otei
nA Peptide 2 Peptide 3 Peptide 4
Peptide 3 Peptide 4 Peptide 5
The Bold Red Complication
Peptide 1
Prot
ein
BPr
otei
nA
100%
Peptide 2 Peptide 3 Peptide 4
Peptide 3 Peptide 4 Peptide 5
100% 100%
100%
0% 0% 100%
The Bold Red Complication
Peptide 1
Prot
ein
BPr
otei
nA
100%
Peptide 2 Peptide 3 Peptide 4
Peptide 3 Peptide 4 Peptide 5
100% 100%
100%
0% 0% 100%
?
The Bold Red Complication
Peptide 1
Prot
ein
BPr
otei
nA Peptide 2 Peptide 3 Peptide 4
Peptide 3 Peptide 4 Peptide 5
Protein Identification Unique Peptides TrustFamily of A and B 5 Unique, 5
TotalHigh
•Definitive ID of Protein A 2 Unique, 4 Total
Med
•Definitive ID of Protein B 1 Unique, 3 Total
Low
The Similar Peptide Complication
AVGNLR
Scan Number: 2435
GLGNLR
The Similar Peptide Complication
AVGNLR
Scan Number: 2435 TLR9_HUMAN
GLGNLR
TRFE_HUMAN
LRFN1_HUMAN
The Similar Peptide Complication
AVGNLR
Scan Number: 2435 TLR9_HUMAN
TRFE_HUMAN
LRFN1_HUMAN
No software deals withall of these issues
Outline
• Assigning Proteins from Peptide IDs
• Correcting for One-Hit-Wonders
• Protein False Discovery Rates?
• Correcting for Shared Peptides
• Publication Standards
Publication Standards
• In 2006 MCP published guidelines for reporting peptide and protein identifications
• Other proteomics journals have adopted similar standards
• Revised “Paris 2” guidelines are forthcoming Expected to be enforced 1/1/2010!
Guidelines remind you:• To present a complete methods/results section
I. Search Parameters and Acceptance CriteriaVI. Raw Data Submission
Guidelines remind you:• To present a complete methods/results section
I. Search Parameters and Acceptance CriteriaVI. Raw Data Submission
• Follow smart criteria for choosing results to publish
II. Protein and Peptide IdentificationIV. Protein Inference from Peptide AssignmentsV. Quantification
Guidelines remind you:• To present a complete methods/results section
I. Search Parameters and Acceptance CriteriaVI. Raw Data Submission
• Follow smart criteria for choosing results to publish
II. Protein and Peptide IdentificationIV. Protein Inference from Peptide AssignmentsV. Quantification
• To not over-report your resultsIII. Post-Translational Modifications
Software Can MakeGuideline Fulfillment Easier
• Peak picking software, version, altered parameters
• Database Selection– Database name and version
– Species restriction
– Number of proteins searched
• Database search parameters– Search engine name and version
– Enzyme specificity
– # missed cleavages
– Fixed/variable modifications
– Mass tolerances
• Peptide selection criteria
XML Standards Can Make Guideline Fulfillment Easier
I. Search Parameters and Acceptance Criteria
II. Protein and Peptide Identification
III. Post-Translational Modifications
IV. Protein Inference from Peptide Assignments
V. Quantification
VI. Raw Data Submission
mzIdentML
mzMLhttp://www.psidev.info/
XML Standards Can Make Guideline Fulfillment Easier
I. Search Parameters and Acceptance Criteria
II. Protein and Peptide Identification
III. Post-Translational Modifications
IV. Protein Inference from Peptide Assignments
V. Quantification
VI. Raw Data Submission
mzIdentML
mzMLhttp://www.psidev.info/
Where are they?
http://www.mcponline.org/misc/ParisReport_Final.dtl
Molecular & Cellular Proteomics: Bradshaw, R. A., Burlingame, A. L., Carr, S., Aebersold, R., Reporting Protein Identification Data: The next Generation of Guidelines. Mol. Cell. Proteomics, 5:787-788, 2006.
Journal of Proteome Research: Beavis, R., Editorial: The Paris Consensus. J. Proteome Res., 2005, 4 (5), p 1475
Proteomics: Wilkins, M. R., Appel, R. D., Van Eyk, J. E., Maxey, C. M., et al., Guidelines for the next 10 years of proteomics. Proteomics. 2006, 6, 1, 4-8.
Conclusions• We identify Proteins (not Peptides)!
– Can’t stop at Peptide FDRs and Probabilities
Conclusions• We identify Proteins (not Peptides)!
– Can’t stop at Peptide FDRs and Probabilities
• One-Hit-Wonders are often wrong and need to be seriously investigated (manually or mathematically)
Conclusions• We identify Proteins (not Peptides)!
– Can’t stop at Peptide FDRs and Probabilities
• One-Hit-Wonders are often wrong and need to be seriously investigated (manually or mathematically)
• You can compute Protein level FDRs– But take them with a grain of salt!
Conclusions• We identify Proteins (not Peptides)!
– Can’t stop at Peptide FDRs and Probabilities
• One-Hit-Wonders are often wrong and need to be seriously investigated (manually or mathematically)
• You can compute Protein level FDRs– But take them with a grain of salt!
• Occam’s Razor can simplify Shared Peptides
Conclusions• We identify Proteins (not Peptides)!
– Can’t stop at Peptide FDRs and Probabilities
• One-Hit-Wonders are often wrong and need to be seriously investigated (manually or mathematically)
• You can compute Protein level FDRs– But take them with a grain of salt!
• Occam’s Razor can simplify Shared Peptides
• Publication Standards exist to help you
Conclusions• We identify Proteins (not Peptides)!
– Can’t stop at Peptide FDRs and Probabilities
• One-Hit-Wonders are often wrong and need to be seriously investigated (manually or mathematically)
• You can compute Protein level FDRs– But take them with a grain of salt!
• Occam’s Razor can simplify Shared Peptides
• Publication Standards exist to help you