Global Sequence Alignment: the Needleman-âWunsch Algorithm

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Dynamic Programming • Is not a lively way of wriLng computer code. • Is a way to exploit substructure within a problem by keeping good records of parLal answers. • Is a very common and powerful algorithmic technique.
Some NotaLon • s 1 [1..i] = the first i characters of s 1 (i in 0 to n) • s 2 [1..j] = the first j characters of s 2 (j in 0 to m) • M(i,j) = score of opLmal alignment of s 1 [1..i] with s 2 [1..j] – in other words, M(i,j) = the maximum # of matches in an opLmal alignment of s 1 [1..i] with s 2 [1..j]
Page 1 and 2: Global Sequence Alignment: the
Page 3 and 4: What is the intuiLon behind s
Page 5 and 6: Why align sequences? • Alignme
Page 7 and 8: Why align sequences? Alignments
Page 9 and 10: There are many possible alignme
Page 11 and 12: Which is the best alignment? s
Page 13 and 14: Idea: How can we find an opLm
Page 15: So what can we do?
Page 19 and 20: The Key Insight • Think about
Page 21 and 22: Dynamic Programming Approach •
Page 23 and 24: Start with base condiLons D(i,j
Page 25 and 26: Now compute M(i,j) row by row.
Page 27 and 28: Now compute M(i,j) row by row.
Page 29 and 30: How do we construct an opLmal
Page 31 and 32: Is this algorithm feasible? •
Page 33 and 34: When would you want a local a

Global Sequence Alignment: the Needleman-âWunsch Algorithm

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Global Sequence Alignment: the Needleman-âWunsch Algorithm