testing statistical significance scores of sequence comparison methods with structure similarity
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Testing statistical significance scores of sequence comparison methods with structure similarity. Tim Hulsen NCMLS PhD Two-Day Conference 2006-04-27. Introduction. Sequence comparison: Important for finding similar proteins (homologs) for a protein with unknown function - PowerPoint PPT PresentationTRANSCRIPT
Testing statistical significance scores of sequence comparison methods with structure similarity
Tim HulsenNCMLS PhD Two-Day Conference
2006-04-27
Introduction
• Sequence comparison: Important for finding similar proteins
(homologs) for a protein with unknown function
• Algorithms: BLAST, FASTA, Smith-Waterman
• Statistical scores: E-value (standard), Z-value
E-value or Z-value?
• Smith-Waterman sequence comparison with Z-value statistics:100 randomized shuffles to test significance of SW score
O. MFTGQEYHSV
shuffle
1. GQHMSVFTEY2. YMSHQFTVGEetc.
# seqs
SW score
rnd ori: 5*SD Z = 5
E-value or Z-value?
• Z-value calculation takes much time (2x100 randomizations)
• Comet et al. (1999) and Bastien et al. (2004): Z-value is theoretically more sensitive and more selective than E-value
• BUT Advantage of Z-value has never been proven by experimental results
How to compare?
• Structural comparison is better than sequence comparison
• ASTRAL SCOP: Structural Classification Of Proteins
• e.g. a.2.1.3, c.1.2.4; same number ~ same structure
• Use structural classification as benchmark for sequence comparison methods
ASTRAL SCOP statisticsmax. % identity members families avg. fam. size max. fam. size families =1 families >1
10% 3631 2250 1.614 25 1655 595
20% 3968 2297 1.727 29 1605 692
25% 4357 2313 1.884 32 1530 783
30% 4821 2320 2.078 39 1435 885
35% 5301 2322 2.283 46 1333 989
40% 5674 2322 2.444 47 1269 1053
50% 6442 2324 2.772 50 1178 1146
70% 7551 2325 3.248 127 1087 1238
90% 8759 2326 3.766 405 1023 1303
95% 9498 2326 4.083 479 977 1349
Methods (1)• Smith-Waterman algorithms: dynamic programming; computationally
intensive– Paracel with e-value (PA E):
• SW implementation of Paracel– Biofacet with z-value (BF Z):
• SW implementation of Gene-IT– ParAlign with e-value (PA E):
• SW implementation of Sencel– SSEARCH with e-value (SS E):
• SW implementation of FASTA (see next page)
Methods (2)
• Heuristic algorithms:– FASTA (FA E)
• Pearson & Lipman, 1988• Heuristic approximation; performs better than
BLAST with strongly diverged proteins– BLAST (BL E):
• Altschul et al., 1990• Heuristic approximation; stretches local alignments
(HSPs) to global alignment• Should be faster than FASTA
Method parameters- all:
- matrix: BLOSUM62- gap open penalty: 12- gap extension penalty: 1
- Biofacet with z-value: 100 randomizations
Receiver Operating Characteristic
• R.O.C.: statistical value, mostly used in clinical medicine
• Proposed by Gribskov & Robinson (1996) to be used for sequence comparison analysis
ROC50 Examplequery d1c75a_ a.3.1.1
hit # pc e
1 d1gcya1 b.71.1.1 0.31
2 d1h32b_ a.3.1.1 0.4
3 d1gks__ a.3.1.1 0.52
4 d1a56__ a.3.1.1 0.52
5 d1kx2a_ a.3.1.1 0.67
6 d1etpa1 a.3.1.4 0.67
7 d1zpda3 c.36.1.9 0.87
8 d1eu1a2 c.81.1.1 0.87
9 d451c__ a.3.1.1 1.1
10 d1flca2 c.23.10.2 1.1
11 d1mdwa_ d.3.1.3 1.1
12 d2dvh__ a.3.1.1 1.5
13 d1shsa_ b.15.1.1 1.5
14 d1mg2d_ a.3.1.1 1.5
15 d1c53__ a.3.1.1 2.4
16 d3c2c__ a.3.1.1 2.4
17 d1bvsa1 a.5.1.1 6.8
18 d1dvva_ a.3.1.1 6.8
19 d1cyi__ a.3.1.1 6.8
20 d1dw0a_ a.3.1.1 6.8
21 d1h0ba_ b.29.1.11 6.8
22 d3pfk__ c.89.1.1 6.8
23 d1kful3 d.3.1.3 6.8
24 d1ixrc1 a.4.5.11 14
25 d1ixsb1 a.4.5.11 14
- Take 100 best hits- True positives: in same SCOP family, or false positives: not in same family- For each of first 50 false positives: calculate number of true positives higher in list (0,4,4,4,5,5,6,9,12,12,12,12,12)- Divide sum of these numbers by number of false positives (50) and by total number of possible true positives (size of family -1) = ROC50
(0,167)- Take average of ROC50 scores for all entries
ROC50 results
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
0.45
0.50
pdb010 pdb020 pdb025 pdb030 pdb035 pdb040 pdb050 pdb070 pdb090 pdb095
ASTRAL SCOP set
mea
n R
OC
50 pc ebf zbl efa ess epa e
Coverage vs. Error
• C.V.E. = Coverage vs. Error (Brenner et al., 1998)
• E.P.Q. = selectivity indicator (how much false positives?)
• Coverage = sensitivity indicator (how much true positives of total?)
CVE Examplequery d1c75a_ a.3.1.1
hit # pc e
1 d1gcya1 b.71.1.1 0.31
2 d1h32b_ a.3.1.1 0.4
3 d1gks__ a.3.1.1 0.52
4 d1a56__ a.3.1.1 0.52
5 d1kx2a_ a.3.1.1 0.67
6 d1etpa1 a.3.1.4 0.67
7 d1zpda3 c.36.1.9 0.87
8 d1eu1a2 c.81.1.1 0.87
9 d451c__ a.3.1.1 1.1
10 d1flca2 c.23.10.2 1.1
11 d1mdwa_ d.3.1.3 1.1
12 d2dvh__ a.3.1.1 1.5
13 d1shsa_ b.15.1.1 1.5
14 d1mg2d_ a.3.1.1 1.5
15 d1c53__ a.3.1.1 2.4
16 d3c2c__ a.3.1.1 2.4
17 d1bvsa1 a.5.1.1 6.8
18 d1dvva_ a.3.1.1 6.8
19 d1cyi__ a.3.1.1 6.8
20 d1dw0a_ a.3.1.1 6.8
21 d1h0ba_ b.29.1.11 6.8
22 d3pfk__ c.89.1.1 6.8
23 d1kful3 d.3.1.3 6.8
24 d1ixrc1 a.4.5.11 14
25 d1ixsb1 a.4.5.11 14
- Vary threshold above which a hit is seen as a positive: e.g. e=10,e=1,e=0.1,e=0.01- True positives: in same SCOP family, or false positives: not in same family- For each threshold, calculate coverage: number of true positives divided by total number of possible true positives- For each threshold, calculate errors-per-query: number of false positives divided by number of queries- Plot coverage on x-axis and errors-per-query on y-axis; right-bottom is best
CVE results
(for PDB010)
-
+
Mean Average Precision
• A.P.: borrowed from information retrieval search (Salton, 1991)
• Recall: true positives divided by number of homologs
• Precision: true positives divided by number of hits
• A.P. = approximate integral to calculate area under recall-precision curve
Mean AP Examplequery d1c75a_ a.3.1.1
hit # pc e
1 d1gcya1 b.71.1.1 0.31
2 d1h32b_ a.3.1.1 0.4
3 d1gks__ a.3.1.1 0.52
4 d1a56__ a.3.1.1 0.52
5 d1kx2a_ a.3.1.1 0.67
6 d1etpa1 a.3.1.4 0.67
7 d1zpda3 c.36.1.9 0.87
8 d1eu1a2 c.81.1.1 0.87
9 d451c__ a.3.1.1 1.1
10 d1flca2 c.23.10.2 1.1
11 d1mdwa_ d.3.1.3 1.1
12 d2dvh__ a.3.1.1 1.5
13 d1shsa_ b.15.1.1 1.5
14 d1mg2d_ a.3.1.1 1.5
15 d1c53__ a.3.1.1 2.4
16 d3c2c__ a.3.1.1 2.4
17 d1bvsa1 a.5.1.1 6.8
18 d1dvva_ a.3.1.1 6.8
19 d1cyi__ a.3.1.1 6.8
20 d1dw0a_ a.3.1.1 6.8
21 d1h0ba_ b.29.1.11 6.8
22 d3pfk__ c.89.1.1 6.8
23 d1kful3 d.3.1.3 6.8
24 d1ixrc1 a.4.5.11 14
25 d1ixsb1 a.4.5.11 14
- Take 100 best hits- True positives: in same SCOP family, or false positives: not in same family-For each of the true positives: divide the positive rank (1,2,3,4,5,6,7,8,9,10,11,12) by the true positive rank (2,3,4,5,9,12,14,15,16,18,19,20)- Divide the sum of all of these numbers by the total number of hits (100) = AP (0.140)- Take average of AP scores for all entries = mean AP
Mean AP results
0.00
0.03
0.06
0.09
0.12
0.15
0.18
0.21
0.24
0.27
0.30
pdb010 pdb020 pdb025 pdb030 pdb035 pdb040 pdb050 pdb070 pdb090 pdb095
ASTRAL SCOP set
mea
n A
P
pc ebf zbl efa ess epa e
Time consumption
• PDB095 all-against-all comparison:– Biofacet: multiple days (Z-value calc.!)– SSEARCH: 5h49m– ParAlign: 47m– FASTA: 40m– BLAST: 15m
Conclusions
• e-value better than Z-value(!)• SW implementations are (more or less)
the same (SSEARCH, ParAlign and Biofacet), but SSEARCH with e-value scores best of all
• Use FASTA/BLAST only when time is important
• Larger structural comparison database needed for better analysis
Credits
Peter Groenen
Wilco Fleuren
Jack Leunissen