Global and local alignment (bioinformatics)
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Transcript of Global and local alignment (bioinformatics)
05/02/2023Introduction to Bioinform
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05/02/2023Introduction to Bioinform
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151-15-255
151-15-453
151-15-240
151-15-245
05/02/2023Introduction to Bioinform
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05/02/2023Introduction to Bioinform
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Why align sequences?
• Useful for discovering• Functional• Structural and• Evolutionary relationship
– For example• To find whether two (or more) genes or proteins are
evolutionarily related to each other• Two proteins with similar sequences will probably be
structurally or functionally similar
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Global Vs Local Alignment
• Global Alignment– A general global alignment technique is the Needleman–
Wunsch algorithm, which is based on dynamic programming.– Attempts to align the maximum of the entire sequence– Suitable for similar and equal length sequences
• Local Alignment– Local alignments are more useful fordissimilar sequences that are suspected to contain regions of similarity or similar sequence motifs within their larger sequence context.– Stretches of sequences with highest density of matches are
aligned– Suitable for partially similar, different length and conserved
region containing sequences
05/02/2023Introduction to Bioinform
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05/02/2023Introduction to Bioinform
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Allows obtaining the optimal alignment with linear gap cost has been proposed by Needleman and Wunsch by providing a score, for each position of the aligned sequences.
Based on the dynamic programming technique.
For two sequences of length m and n we define a matrix of dimensions m+1 and n+1.
Global Alignment
8 Global Alignment
Three steps in dynamic programming Initialization Matrix fill (scoring) Traceback (alignment)
Smith–Waterman algorithm
05/02/2023Introduction to Bioinform
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Sequences:S: ATTATCTT: TTTCTA
T
S 0
_
A
T
T
A
T
C
T
_ T T T C T A
0
-1
-2
-3
-4
-5
-6
-7
-1 -2 -3 -4 -5 -6
0 -1 -2 -3 -4 -5
1 2 1 0 -1 -2
0 3 4 3 2 1
-1 2 3 4 3 4
-2 1 4 3 6 5
-3 0 3 6 5 6
-4 -1 2 5 8 7
Match Score = +2Mismatch Score = 0Gap Penalty = -1
i-1, j-1 i-1, j
I, j-1 I, j
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0
_
A
T
T
A
T
C
T
_ T T T C T A
0
-1
-2
-3
-4
-5
-6
-7
-1 -2 -3 -4 -5 -6
0 -1 -2 -3 -4 -5
1 2 1 0 -1 -2
0 3 4 3 2 1
-1 2 3 4 3 4
-2 1 4 3 6 5
-3 0 3 6 5 6
-4 -1 2 5 8 7
T
S
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Optimal Alignment:S
T
No: of matches = 5No: of mismatches = 3
(5 x 2) – (3 x -1) = 7
A T T A T C T –
- T T – T C T A
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S/T 0 A T G A T G T A G
0 0 0 0 0 0 0 0 0 0 0
G 0 0 0
A 0
G 0
A 0
T 0
G 0
T 0
G 0
C 0
0 + 2 0 +-2
0 + -2 2
0 + 2 = 2 0 + -2 = 0 0 + -2 = 0
Match : 2, Mismatch : -1, Gap : -2
0 + -1 0 + -2
0 + -2 0
0 + 2 = 0 0 + -2 = 0 0 + -2 = 0
Matrix fill (scoring)
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S/T 0 A T G A T G T A G
0 0 0 0 0 0 0 0 0 0 0
G 0 0 0 2 0 0 2 0 0 0
A 0 2 0 0 4 2 0 0 2 0
G 0 0 1 2 2 3 4 2 0 4
A 0 2 0 0 4 2 2 2 4 2
T 0 0 4 2 2 6 4 4 2 3
G 0 0 2 6 4 4 8 6 4 4
T 0 0 2 4 4 6 6 10 8 6
G 0 0 0 4 3 4 8 8 9 10
C 0 0 0 2 3 1 6 7 7 8
Match : 2, Mismatch : -1, Gap : -2 Matrix fill (scoring)
Trace back15
S/T 0 A T G A T G T A G
0 0 0 0 0 0 0 0 0 0 0
G 0 0 0 2 0 0 2 0 0 0
A 0 2 0 0 4 2 0 0 2 0
G 0 0 1 2 2 3 4 2 0 4
A 0 2 0 0 4 2 2 2 4 2
T 0 0 4 2 2 6 4 4 2 3
G 0 0 2 6 4 4 8 6 4 4
T 0 0 2 4 4 6 6 10 8 6
G 0 0 0 4 3 4 8 8 9 10
C 0 0 0 2 3 1 6 7 7 8
Match : 2, Mismatch : -1, Gap : -2
Alignment16
G A T G T A G| | | | | | |G A T G T - G2 2 2 2 2 -2 2
6 X 2 = 121 X -2 = -2 10
G A T G T| | | | |G A T G T2 2 2 2 2
5 X 2 = 10 10
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