Biological Sequenc e Anal ysis - CBS€¦ · CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS BLOSUM...
Transcript of Biological Sequenc e Anal ysis - CBS€¦ · CENTER FOR BIOLOGICAL SEQUENCE ANALYSIS BLOSUM...
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Biological Sequence Analysis
• Chapter 3
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Protein Families
Organism 1
Organism 2
Enzyme 1
Enzyme 2
Closely related Same Function
MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS::::::.::::::::::::::::::::.::::::::::::::::::::::::::::::::::::::::::MSEKKQTVDLGLLEEDDEFEEFPAEDWTGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
Related Sequences
Protein Family
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Alignments
ACDEFGHIKLMNACEDFGHIPLMN
ACDEFGHIKLMNACACFGKIKLMN
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Alignments
ACDEFGHIKLMNACEDFGHIPLMN
75%ID
ACDEFGHIKLMNACACFGKIKLMN
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Alignments
ACDEFGHIKLMNACEDFGHIPLMN
75%ID
ACDEFGHIKLMNACACFGKIKLMN
75%ID
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Substitutions
AlanineA
W Tryptophane
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Substitutions
Glutamic acid
Aspartic acidD
E
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Substitutions
T Threonine
S Serine
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Deriving Substitution ScoresBLOSUM, Henikoff & Henikoff, 1992
Protein Family
Block A Block B
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BLOSUM MatricesHenikoff & Henikoff, 1992
...A...
...A...
...A......S......A...
...A...
...A...
...A...
...A...
...A...A 8 AA 1 AS
7 AA6 AA5 AA4 AA3 AA2 AA0 AA1 AA
1 AS1 AS1 AS1 AS1 AS2 SA0 AS
1 AS
36 9
45
s
w
ws(s-1)/2 = 1x10x9/2 =
f
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BLOSUM MatricesHenikoff & Henikoff, 1992
AA AC AD AE
0.8 0 0 0 .........AR AS AT AV AW AY CC CD
0 0.2 0 0 0 0 0 0
VY WW WY YY
0 0 0 0.........
€
qij =f ij
i=1
20
∑ fijj=1
i
∑
fAA = 36,qAA = 36 /45 = 0.8fAS = 9,qAS = 9 /45 = 0.2
q
Raw Frequencies
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BLOSUM MatricesHenikoff & Henikoff, 1992
pi = qii +!
j !=i
qij/2
The probability of occurrence of the i’th amino acid in an i, j pair is:
45 pairs = 90 participants in pairs
A’s in pairs: 36x2 + 9x1 = 81AA AS
S’s in pairs: 0x2 + 9x1 = 9
Probability pA for encountering an A: 81/90 = 0.9Probability pS for encountering an S: 9/90 = 0.1
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BLOSUM MatricesHenikoff & Henikoff, 1992
Expected probability, e, of occurrence of pairs:
eAA = pApA = 0.9x0.9 = 0.81
eAS = pApS + pSpA = 0.9x0.1 + 0.1x0.9 = 2x(0.9x0.1) = 0.18
eSS = pSpS = 0.1x0.1 = 0.01
eij = pipj(i = j), eij = 2pipj(i != j)
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BLOSUM MatricesHenikoff & Henikoff, 1992
Odds and logodds:Odd ratio: qij/eij
logodd, s: sij = log2(qij/eij)
sij = 0 means that the observed frequencies are as expected
sij < 0 means that the observed frequencies are lower than expectedsij > 0 means that the observed frequencies are higher than expected
In the final BLOSUM matrices values are presented in half-bits, i.e., logodds are multiplied with 2 and rounded to nearest integer.
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BLOSUM MatricesHenikoff & Henikoff, 1992
Segment clusteringSequences with more than X% ID are represented as one average sequence (cluster)
Sequences are added to the cluster if it has more than X% ID to any of the sequences already in the cluster
If the clustering level is more than 50% ID, the final Matrix is a BLOSUM50, more than 62% leads to the BLOSUM62 matrix, etc.
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BLOSUM MatricesHenikoff & Henikoff, 1992
A R N D C Q E G H I L K M F P S T W Y V B Z X *A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -4 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 -4 * -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
ACDEFGHIKLMNACEDFGHIPLMN
ACDEFGHIKLMNACACFGKIKLMN
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BLOSUM MatricesHenikoff & Henikoff, 1992
A R N D C Q E G H I L K M F P S T W Y V B Z X *A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -4 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 -4 * -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
ACDEFGHIKLMNACEDFGHIPLMN
ACDEFGHIKLMNACACFGKIKLMN
4+9-2-4+6+6-1+4+5+4+5+6=42
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BLOSUM MatricesHenikoff & Henikoff, 1992
A R N D C Q E G H I L K M F P S T W Y V B Z X *A 4 -1 -2 -2 0 -1 -1 0 -2 -1 -1 -1 -1 -2 -1 1 0 -3 -2 0 -2 -1 0 -4 R -1 5 0 -2 -3 1 0 -2 0 -3 -2 2 -1 -3 -2 -1 -1 -3 -2 -3 -1 0 -1 -4 N -2 0 6 1 -3 0 0 0 1 -3 -3 0 -2 -3 -2 1 0 -4 -2 -3 3 0 -1 -4 D -2 -2 1 6 -3 0 2 -1 -1 -3 -4 -1 -3 -3 -1 0 -1 -4 -3 -3 4 1 -1 -4 C 0 -3 -3 -3 9 -3 -4 -3 -3 -1 -1 -3 -1 -2 -3 -1 -1 -2 -2 -1 -3 -3 -2 -4 Q -1 1 0 0 -3 5 2 -2 0 -3 -2 1 0 -3 -1 0 -1 -2 -1 -2 0 3 -1 -4 E -1 0 0 2 -4 2 5 -2 0 -3 -3 1 -2 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 G 0 -2 0 -1 -3 -2 -2 6 -2 -4 -4 -2 -3 -3 -2 0 -2 -2 -3 -3 -1 -2 -1 -4 H -2 0 1 -1 -3 0 0 -2 8 -3 -3 -1 -2 -1 -2 -1 -2 -2 2 -3 0 0 -1 -4 I -1 -3 -3 -3 -1 -3 -3 -4 -3 4 2 -3 1 0 -3 -2 -1 -3 -1 3 -3 -3 -1 -4 L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4 -2 2 0 -3 -2 -1 -2 -1 1 -4 -3 -1 -4 K -1 2 0 -1 -3 1 1 -2 -1 -3 -2 5 -1 -3 -1 0 -1 -3 -2 -2 0 1 -1 -4 M -1 -1 -2 -3 -1 0 -2 -3 -2 1 2 -1 5 0 -2 -1 -1 -1 -1 1 -3 -1 -1 -4 F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6 -4 -2 -2 1 3 -1 -3 -3 -1 -4 P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7 -1 -1 -4 -3 -2 -2 -1 -2 -4 S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4 1 -3 -2 -2 0 0 0 -4 T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5 -2 -2 0 -1 -1 0 -4 W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11 2 -3 -4 -3 -2 -4 Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7 -1 -3 -2 -1 -4 V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4 -3 -2 -1 -4 B -2 -1 3 4 -3 0 1 -1 0 -3 -4 0 -3 -3 -2 0 -1 -4 -3 -3 4 1 -1 -4 Z -1 0 0 1 -3 3 4 -2 0 -3 -3 1 -1 -3 -1 0 -1 -3 -2 -2 1 4 -1 -4 X 0 -1 -1 -1 -2 -1 -1 -1 -1 -1 -1 -1 -1 -1 -2 0 0 -2 -1 -1 -1 -1 -1 -4 * -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 -4 1
ACDEFGHIKLMNACEDFGHIPLMN
ACDEFGHIKLMNACACFGKIKLMN
4+9-2-4+6+6-1+4+5+4+5+6=42 4+9+2+2+6+6+8+4-1+4+5+6=55
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A
10 20 30 40 50 60 70humanD -----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
..: . : :.:. : . .. ...:. :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------
10 20 30 40 50 60
B
10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
....:...:::::::::::::::::::::..:::..........::....:..::..........Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK-----
10 20 30 40 50 60
Figure 3.3: (A) The human proteasomal subunit aligned to the mosquito homolog using theBLOSUM50 matrix. (B) The human proteasomal subunit aligned to the mosquito homolog using
identity scores.
Gaps
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A
10 20 30 40 50 60 70humanD -----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
..: . : :.:. : . .. ...:. :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------
10 20 30 40 50 60
B
10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
....:...:::::::::::::::::::::..:::..........::....:..::..........Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK-----
10 20 30 40 50 60
Figure 3.3: (A) The human proteasomal subunit aligned to the mosquito homolog using theBLOSUM50 matrix. (B) The human proteasomal subunit aligned to the mosquito homolog using
identity scores.
Gaps
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10 20 30 40 50 60 70humanD -----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
..: . : :.:. : . .. ...:. :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------
10 20 30 40 50 60
B
10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
....:...:::::::::::::::::::::..:::..........::....:..::..........Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK-----
10 20 30 40 50 60
Figure 3.3: (A) The human proteasomal subunit aligned to the mosquito homolog using theBLOSUM50 matrix. (B) The human proteasomal subunit aligned to the mosquito homolog using
identity scores.
Gaps
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A
10 20 30 40 50 60 70humanD -----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
..: . : :.:. : . .. ...:. :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------
10 20 30 40 50 60
B
10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAHVWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
....:...:::::::::::::::::::::..:::..........::....:..::..........Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK-----
10 20 30 40 50 60
Figure 3.3: (A) The human proteasomal subunit aligned to the mosquito homolog using theBLOSUM50 matrix. (B) The human proteasomal subunit aligned to the mosquito homolog using
identity scores.
Gaps
10 20 30 40 50 60 70humanD ----MSEKKQPVDLGLLEEDDEFEEFPAEDWAGLDEDEDAH-VWEDNWDDDNVEDDFSNQLRAELEKHGYKMETS
....:...:::::::::::::::::::::..:::... :::::::::::::::..::::.::::Anophe MSDKENKDKPKLDLGLLEEDDEFEEFPAEDWAGNKEDEEELSVWEDNWDDDNVEDDFNQQLRAQLEKHK------
10 20 30 40 50 60
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Gap PenaltiesA gap is a kind like a mismatch but...
Often the gap score (gap penalty) has an even lower value (higher penalty) than the lowest mismatch score
Having only one type of gap penalties is called a linear gap cost
Biologically gaps are often inserted/deleted as a one or more event
In most alignment algorithms is two gap penalties.
One for making the first gap
Another (higher score=less penalty) for making an additional gap
This is called affine gap penalty
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Dynamic programming: example
A C G TA 1 -1 -1 -1C -1 1 -1 -1G -1 -1 1 -1T -1 -1 -1 1
Gaps: -2
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Dynamic programming: example
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Dynamic programming: example
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Dynamic programming: example
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Dynamic programming: example
T C G C A: : : :T C - C A1+1-2+1+1= 2
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Global versus local alignmentsGlobal alignment: align full length of both sequences. (The
“Needleman-Wunsch” algorithm).
Local alignment: find best partial alignment of two sequences (the “Smith-Waterman” algorithm).
Global alignment
Seq 1
Seq 2
Local alignment
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Local alignment overview
• The recursive formula is changed by adding a fourth possibility: zero. This means local alignment scores are never negative.
• Trace-back is started at the highest value rather than in lower right corner
• Trace-back is stopped as soon as a zero is encountered
score(x,y) = max
score(x,y-1) - gap-penalty
score(x-1,y-1) + substitution-score(x,y)
score(x-1,y) - gap-penalty
0
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Local alignment: example
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Substitution matrices and sequence similarity
• Substitution matrices come as series of matrices calculated for different degrees of sequence similarity (different evolutionary distances).
• ”Hard” matrices are designed for similar sequences– Hard matrices a designated by high numbers in the BLOSUM series
(e.g., BLOSUM80)– Hard matrices yield short, highly conserved alignments
• ”Soft” matrices are designed for less similar sequences– Soft matrices have low BLOSUM values (45)– Soft matrices yield longer, less well conserved alignments
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Alignments: things to keep in mind
“Optimal alignment” means “having the highest possible score, given substitution matrix and set of gap penalties”.
This is NOT necessarily the biologically most meaningful alignment.
Specifically, the underlying assumptions are often wrong: substitutions are not equally frequent at all positions, affine gap penalties do not model insertion/deletion well, etc.
Pairwise alignment programs always produce an alignment - even when it does not make sense to align sequences.
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Database searching
Using pairwise alignments to search
databases for similar sequences
Database
Query sequence
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Database searchingMost common use of pairwise sequence alignments is to search databases for related sequences. For instance: find probable function of newly isolated protein by identifying similar proteins with known function.
Most often, local alignment ( “Smith-Waterman”) is used for database searching: you are interested in finding out if ANY domain in your protein looks like something that is known.
Often, full Smith-Waterman is too time-consuming for searching large databases, so heuristic methods are used (fasta, BLAST).
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Database searching: heuristic search algorithms
FASTA (Pearson 1995)
Uses heuristics to avoid calculating the full dynamic programming matrix
Speed up searches by an order of magnitude compared to full Smith-Waterman
The statistical side of FASTA is still stronger than BLAST
BLAST (Altschul 1990, 1997)
Uses rapid word lookup methods to completely skip most of the database entries
Extremely fastOne order of magnitude faster
than FASTATwo orders of magnitude faster
than Smith-Waterman
Almost as sensitive as FASTA
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BLAST flavors
BLASTNNucleotide query sequenceNucleotide database
BLASTPProtein query sequenceProtein database
BLASTXNucleotide query sequenceProtein databaseCompares all six reading frames
with the database
TBLASTNProtein query sequenceNucleotide database”On the fly” six frame translation of
database
TBLASTXNucleotide query sequenceNucleotide databaseCompares all reading frames of query
with all reading frames of the database
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Searching on the web: BLAST at NCBI
Very fast computer dedicated to running BLAST searches
Many databases that are always up to date
Nice simple web interface
But you still need knowledge about BLAST to use it properly
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When is a database hit significant?
• Problem:
• Even unrelated sequences can be aligned (yielding a low score)
• How do we know if a database hit is meaningful?
• When is an alignment score sufficiently high?
• Solution:
• Determine the range of alignment scores you would expect to get for random reasons (i.e., when aligning unrelated sequences).
• Compare actual scores to the distribution of random scores.
• Is the real score much higher than you’d expect by chance?
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Random alignment scores follow extreme value distributions
The exact shape and location of the distribution depends on the exact nature of the database and the query sequence
Searching a database of unrelated sequences result in scores following an extreme value distribution
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Significance of a hit: one possible solution
(1) Align query sequence to all sequences in database, note scores
(2) Fit actual scores to a mixture of two sub-distributions: (a) an extreme value distribution and (b) a normal distribution
(3) Use fitted extreme-value distribution to predict how many random hits to expect for any given score (the “E-value”)
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Significance of a hit: exampleSearch against a database of 10,000 sequences.
An extreme-value distribution (blue) is fitted to the distribution of all scores.
It is found that 99.9% of the blue distribution has a score below 112.
This means that when searching a database of 10,000 sequences you’d expect to get 0.1% * 10,000 = 10 hits with a score of 112 or better for random reasons
10 is the E-value of a hit with score 112. You want E-values well below 1!
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Database searching: E-values in BLAST
BLAST uses precomputed extreme value distributions to calculate E-values from alignment scores
For this reason BLAST only allows certain combinations of substitution matrices and gap penalties
This also means that the fit is based on a different data set than the one you are working on
A word of caution: BLAST tends to overestimate the significance of its matches
E-values from BLAST are fine for identifying sure hitsOne should be careful using BLAST’s E-values to judge if a marginal hit can be
trusted (e.g., you may want to use E-values of 10-4 to 10-5).