www.bioalgorithms.infoan introduction to bioinformatics algorithms sequence alignment
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www.bioalgorithms.infoAn Introduction to Bioinformatics Algorithms
Sequence Alignment
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Outline
• Global Alignment • Scoring Matrices• Local Alignment• Alignment with Affine Gap Penalties
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
From LCS to Alignment: Change Scoring
• The Longest Common Subsequence (LCS) problem—the simplest form of sequence alignment – allows only insertions and deletions (no mismatches).
• In the LCS Problem, we scored 1 for matches and 0 for indels
• Consider penalizing indels and mismatches with negative scores
• Simplest scoring schema: +1 : match premium -μ : mismatch penalty -σ : indel penalty
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Simple Scoring
• When mismatches are penalized by –μ, indels are penalized by –σ,
and matches are rewarded with +1,
the resulting score is:
#matches – μ(#mismatches) – σ (#indels)
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The Global Alignment ProblemFind the best alignment between two strings under a given scoring
schema
Input : Strings v and w and a scoring schemaOutput : Alignment of maximum score
→ = -б = 1 if match = -µ if mismatch
si-1,j-1 +1 if vi = wj
si,j = max s i-1,j-1 -µ if vi ≠ wj
s i-1,j - σ s i,j-1 - σ
: mismatch penaltyσ : indel penalty
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Scoring Matrices
To generalize scoring, consider a (4+1) x(4+1) scoring matrix δ.
In the case of an amino acid sequence alignment, the scoring matrix would be a (20+1)x(20+1) size. The addition of 1 is to include the score for comparison of a gap character “-”.
This will simplify the algorithm as follows:
si-1,j-1 + δ (vi, wj)
si,j = max s i-1,j + δ (vi, -)
s i,j-1 + δ (-, wj)
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Measuring Similarity
• Measuring the extent of similarity between two sequences• Based on percent sequence identity• Based on conservation
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Percent Sequence Identity
• The extent to which two nucleotide or amino acid sequences are invariant
A C C T G A G – A G A C G T G – G C A G
70% identical
mismatchindel
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Making a Scoring Matrix
• Scoring matrices are created based on biological evidence.
• Alignments can be thought of as two sequences that differ due to mutations.
• Some of these mutations have little effect on the protein’s function, therefore some penalties, δ(vi , wj), will be less harsh than others.
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Scoring Matrix: ExampleA R N K
A 5 -2 -1 -1
R - 7 -1 3
N - - 7 0
K - - - 6
• Notice that although R and K are different amino acids, they have a positive score.
• Why? They are both positively charged amino acids will not greatly change function of protein.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Scoring matrices
• Amino acid substitution matrices• PAM• BLOSUM
• DNA substitution matrices• DNA is less conserved than protein
sequences• Less effective to compare coding regions at
nucleotide level
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PAM• Point Accepted Mutation (Dayhoff et al.)• 1 PAM = PAM1 = 1% average change of all amino
acid positions• After 100 PAMs of evolution, not every residue will
have changed• some residues may have mutated several
times• some residues may have returned to their
original state• some residues may not changed at all
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PAMX
• PAMx = PAM1x
• PAM250 = PAM1250
• PAM250 is a widely used scoring matrix:
Ala Arg Asn Asp Cys Gln Glu Gly His Ile Leu Lys ... A R N D C Q E G H I L K ...Ala A 13 6 9 9 5 8 9 12 6 8 6 7 ...Arg R 3 17 4 3 2 5 3 2 6 3 2 9Asn N 4 4 6 7 2 5 6 4 6 3 2 5Asp D 5 4 8 11 1 7 10 5 6 3 2 5Cys C 2 1 1 1 52 1 1 2 2 2 1 1Gln Q 3 5 5 6 1 10 7 3 7 2 3 5...Trp W 0 2 0 0 0 0 0 0 1 0 1 0Tyr Y 1 1 2 1 3 1 1 1 3 2 2 1Val V 7 4 4 4 4 4 4 4 5 4 15 10
A 2 R -2 6 N 0 0 2 D 0 -1 2 4 C -2 -4 -4 -5 12 Q 0 1 1 2 -5 4 E 0 -1 1 3 -5 2 4 G 1 -3 0 1 -3 -1 0 5 H -1 2 2 1 -3 3 1 -2 6 I -1 -2 -2 -2 -2 -2 -2 -3 -2 5 L -2 -3 -3 -4 -6 -2 -3 -4 -2 -2 6 K -1 3 1 0 -5 1 0 -2 0 -2 -3 5 M -1 0 -2 -3 -5 -1 -2 -3 -2 2 4 0 6 F -3 -4 -3 -6 -4 -5 -5 -5 -2 1 2 -5 0 9 P 1 0 0 -1 -3 0 -1 0 0 -2 -3 -1 -2 -5 6 S 1 0 1 0 0 -1 0 1 -1 -1 -3 0 -2 -3 1 2 T 1 -1 0 0 -2 -1 0 0 -1 0 -2 0 -1 -3 0 1 3 W -6 2 -4 -7 -8 -5 -7 -7 -3 -5 -2 -3 -4 0 -6 -2 -5 17 Y -3 -4 -2 -4 0 -4 -4 -5 0 -1 -1 -4 -2 7 -5 -3 -3 0 10 V 0 -2 -2 -2 -2 -2 -2 -1 -2 4 2 -2 2 -1 -1 -1 0 -6 -2 4 A R N D C Q E G H I L K M F P S T W Y V
PAM250 log oddsscoring matrix
Why do we go from a mutation probabilitymatrix to a log odds matrix?
• We want a scoring matrix so that when we do a pairwise alignment (or a BLAST search) we know what score to assign to two aligned amino acid residues.
• Logarithms are easier to use for a scoring system. They allow us to sum the scores of aligned residues (rather than having to multiply them).
How do we go from a mutation probabilitymatrix to a log odds matrix?
• The cells in a log odds matrix consist of an “odds ratio”:
the probability that an alignment is authenticthe probability that the alignment was random
The score S for an alignment of residues a,b is given by:
S(a,b) = 10 log10 ( Mab / pb )
As an example, for tryptophan,
S( W, W ) = 10 log10 ( 0.55 / 0.01 ) = 17.4
What do the numbers meanin a log odds matrix?
S( W, W ) = 10 log10 ( 0.55 / 0.010 ) = 17.4
A score of +17 for tryptophan means that this alignmentis 50 times more likely than a chance alignment of twotryptophan residues.
S(W, W) = 17Probability of replacement ( Mab / pb ) = xThen
17 = 10 log10 x1.7 = log10 x101.7 = x = 50
What do the numbers meanin a log odds matrix?
A score of +2 indicates that the amino acid replacementoccurs 1.6 times as frequently as expected by chance.
A score of 0 is neutral.
A score of –10 indicates that the correspondence of two amino acids in an alignment that accurately representshomology (evolutionary descent) is one tenth as frequentas the chance alignment of these amino acids.
PAM10 log oddsscoring matrix
A 7
R -10 9
N -7 -9 9
D -6 -17 -1 8
C -10 -11 -17 -21 10
Q -7 -4 -7 -6 -20 9
E -5 -15 -5 0 -20 -1 8
G -4 -13 -6 -6 -13 -10 -7 7
H -11 -4 -2 -7 -10 -2 -9 -13 10
I -8 -8 -8 -11 -9 -11 -8 -17 -13 9
L -9 -12 -10 -19 -21 -8 -13 -14 -9 -4 7
K -10 -2 -4 -8 -20 -6 -7 -10 -10 -9 -11 7
M -8 -7 -15 -17 -20 -7 -10 -12 -17 -3 -2 -4 12
F -12 -12 -12 -21 -19 -19 -20 -12 -9 -5 -5 -20 -7 9
P -4 -7 -9 -12 -11 -6 -9 -10 -7 -12 -10 -10 -11 -13 8
S -3 -6 -2 -7 -6 -8 -7 -4 -9 -10 -12 -7 -8 -9 -4 7
T -3 -10 -5 -8 -11 -9 -9 -10 -11 -5 -10 -6 -7 -12 -7 -2 8
W -20 -5 -11 -21 -22 -19 -23 -21 -10 -20 -9 -18 -19 -7 -20 -8 -19 13
Y -11 -14 -7 -17 -7 -18 -11 -20 -6 -9 -10 -12 -17 -1 -20 -10 -9 -8 10
V -5 -11 -12 -11 -9 -10 -10 -9 -9 -1 -5 -13 -4 -12 -9 -10 -6 -22 -10 8
A R N D C Q E G H I L K M F P S T W Y V
BLOSUM Outline• Idea: use aligned ungapped regions of protein
families.These are assumed to have a common ancestor. Similar ideas but better statistics and modeling.
• Procedure:– Cluster together sequences in a family whenever more than
L% identical residues are shared, for BLOSUM-L.– Count number of substitutions across different clusters (in
the same family).– Estimate frequencies using the counts.
• Practice: BlOSUM-50 and BLOSOM62 are wildly used.
Considered the state of the art nowadays.
BLOSUM matrices are based on local alignments.
BLOSUM stands for blocks substitution matrix.
BLOSUM62 is a matrix calculated from comparisons of sequences with less than 62% identical sites.
BLOSUM Matrices
BLOSUM Matrices
100
62
30
Per
cent
am
ino
acid
iden
tity
BLOSUM62
colla
pse
BLOSUM Matrices
100
62
30
Per
cent
am
ino
acid
iden
tity
BLOSUM62
100
62
30
BLOSUM30
100
62
30
BLOSUM80
colla
pse
colla
pse
colla
pse
All BLOSUM matrices are based on observed alignments; they are not extrapolated from comparisons of closely related proteins.
The BLOCKS database contains thousands of groups ofmultiple sequence alignments.
BLOSUM62 is the default matrix in BLAST 2.0. Though it is tailored for comparisons of moderately distant proteins, it performs well in detecting closer relationships. A search for distant relatives may be more sensitive with a different matrix.
BLOSUM Matrices
A 4R -1 5N -2 0 6D -2 -2 1 6C 0 -3 -3 -3 9Q -1 1 0 0 -3 5E -1 0 0 2 -4 2 5G 0 -2 0 -1 -3 -2 -2 6H -2 0 1 -1 -3 0 0 -2 8I -1 -3 -3 -3 -1 -3 -3 -4 -3 4L -1 -2 -3 -4 -1 -2 -3 -4 -3 2 4K -1 2 0 -1 -1 1 1 -2 -1 -3 -2 5M -1 -2 -2 -3 -1 0 -2 -3 -2 1 2 -1 5F -2 -3 -3 -3 -2 -3 -3 -3 -1 0 0 -3 0 6P -1 -2 -2 -1 -3 -1 -1 -2 -2 -3 -3 -1 -2 -4 7S 1 -1 1 0 -1 0 0 0 -1 -2 -2 0 -1 -2 -1 4T 0 -1 0 -1 -1 -1 -1 -2 -2 -1 -1 -1 -1 -2 -1 1 5W -3 -3 -4 -4 -2 -2 -3 -2 -2 -3 -2 -3 -1 1 -4 -3 -2 11Y -2 -2 -2 -3 -2 -1 -2 -3 2 -1 -1 -2 -1 3 -3 -2 -2 2 7V 0 -3 -3 -3 -1 -2 -2 -3 -3 3 1 -2 1 -1 -2 -2 0 -3 -1 4
A R N D C Q E G H I L K M F P S T W Y V
Blosum62 scoring matrix
Rat versus mouse RBP
Rat versus bacteriallipocalin
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local vs. Global Alignment
• The Global Alignment Problem tries to find the longest path between vertices (0,0) and (n,m) in the edit graph.
• The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph.
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local vs. Global Alignment• The Global Alignment Problem tries to find the
longest path between vertices (0,0) and (n,m) in the edit graph.
• The Local Alignment Problem tries to find the longest path among paths between arbitrary vertices (i,j) and (i’, j’) in the edit graph.
• In the edit graph with negatively-scored edges, Local Alignmet may score higher than Global Alignment
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local vs. Global Alignment (cont’d)• Global Alignment
• Local Alignment—better alignment to find conserved segment
--T—-CC-C-AGT—-TATGT-CAGGGGACACG—A-GCATGCAGA-GAC | || | || | | | ||| || | | | | |||| | AATTGCCGCC-GTCGT-T-TTCAG----CA-GTTATG—T-CAGAT--C
tccCAGTTATGTCAGgggacacgagcatgcagagac ||||||||||||
aattgccgccgtcgttttcagCAGTTATGTCAGatc
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Local Alignment: Example
Global alignment
Local alignment
Compute a “mini” Global Alignment to get Local
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignments: Why?
• Two genes in different species may be similar over short conserved regions and dissimilar over remaining regions.
• Example:• Homeobox genes have a short region
called the homeodomain that is highly conserved between species.
• A global alignment would not find the homeodomain because it would try to align the ENTIRE sequence
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The Local Alignment Problem
• Goal: Find the best local alignment between two strings
• Input : Strings v, w and scoring matrix δ• Output : Alignment of substrings of v and w
whose alignment score is maximum among all possible alignment of all possible substrings
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The Problem with this Problem• Long run time O(n4):
- In the grid of size n x n there are ~n2 vertices (i,j) that may serve as a source.
- For each such vertex computing alignments from (i,j) to (i’,j’) takes O(n2) time.
• This can be remedied by giving free rides
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Example
Global alignment
Local alignment
Compute a “mini” Global Alignment to get Local
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Example
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Example
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Example
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Example
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Example
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Local Alignment: Running Time • Long run time O(n4):
- In the grid of size n x n there are ~n2 vertices (i,j) that may serve as a source.
- For each such vertex computing alignments from (i,j) to (i’,j’) takes O(n2) time.
• This can be remedied by giving free rides
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Local Alignment: Free Rides
Vertex (0,0)
The dashed edges represent the free rides from (0,0) to every other node.
Yeah, a free ride!
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
The Local Alignment Recurrence• The largest value of si,j over the whole edit
graph is the score of the best local alignment.
• The recurrence:
0 si,j = max si-1,j-1 + δ (vi, wj)
s i-1,j + δ (vi, -)
s i,j-1 + δ (-, wj)
Notice there is only this change from the original recurrence of a Global Alignment
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
The Local Alignment Recurrence• The largest value of si,j over the whole edit
graph is the score of the best local alignment.
• The recurrence:
0 si,j = max si-1,j-1 + δ (vi, wj)
s i-1,j + δ (vi, -)
s i,j-1 + δ (-, wj)
Power of ZERO: there is only this change from the original recurrence of a Global Alignment - since there is only one “free ride” edge entering into every vertex
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Scoring Indels: Naive Approach
• A fixed penalty σ is given to every indel:• -σ for 1 indel, • -2σ for 2 consecutive indels• -3σ for 3 consecutive indels, etc.
Can be too severe penalty for a series of 100 consecutive indels
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Affine Gap Penalties
• In nature, a series of k indels often come as a single event rather than a series of k single nucleotide events:
Normal scoring would give the same score for both alignments
This is more likely.
This is less likely.
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Accounting for Gaps• Gaps- contiguous sequence of spaces in one of the
rows
• Score for a gap of length x is: -(ρ + σx) where ρ >0 is the penalty for introducing a gap: gap opening penalty ρ will be large relative to σ: gap extension penalty because you do not want to add too much of a
penalty for extending the gap.
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Affine Gap Penalties
• Gap penalties:• -ρ-σ when there is 1 indel• -ρ-2σ when there are 2 indels• -ρ-3σ when there are 3 indels, etc. • -ρ- x·σ (-gap opening - x gap extensions)
• Somehow reduced penalties (as compared to naïve scoring) are given to runs of horizontal and vertical edges
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Affine Gap Penalties and Edit Graph
To reflect affine gap penalties we have to add “long” horizontal and vertical edges to the edit graph. Each such edge of length x should have weight
- - x *
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Adding “Affine Penalty” Edges to the Edit Graph
There are many such edges!
Adding them to the graph increases the running time of the alignment algorithm by a factor of n (where n is the number of vertices)
So the complexity increases from O(n2) to O(n3)
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Manhattan in 3 Layers
ρ
ρ
σ
σδ
δ
δ
δδ
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Affine Gap Penalties and 3 Layer Manhattan Grid
• The three recurrences for the scoring algorithm creates a 3-layered graph.
• The top level creates/extends gaps in the sequence w.
• The bottom level creates/extends gaps in sequence v.
• The middle level extends matches and mismatches.
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Switching between 3 Layers
• Levels:• The main level is for diagonal edges • The lower level is for horizontal edges• The upper level is for vertical edges
• A jumping penalty is assigned to moving from the main level to either the upper level or the lower level (-)
• There is a gap extension penalty for each continuation on a level other than the main level (-)
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The 3 Grids
-σ
+δ(vi,wj)
-σ
+0+0
-(ρ + σ)
-(ρ + σ)
Away from mid-level
Toward mid-level
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The 3-leveled Manhattan Grid
An Introduction to Bioinformatics Algorithms www.bioalgorithms.info
Affine Gap Penalty Recurrencessi,j = s i-1,j - σ
max s i-1,j –(ρ+σ)
si,j = s i,j-1 - σ
max s i,j-1 –(ρ+σ)
si,j = si-1,j-1 + δ (vi, wj)
max s i,j
s i,j
Continue Gap in w (deletion)
Start Gap in w (deletion): from middle
Continue Gap in v (insertion)
Start Gap in v (insertion):from middle
Match or Mismatch
End deletion: from top
End insertion: from bottom