Dynamic Programing
Dynamic Programing Dynamic Programing
Scoring matrix represents a specific model of similarity to be applied in aligning two residues • Matrix of numbers that quantify the similarity between residues • To produce good alignment, the choice of a right scoring matrix is important • Common scoring matrices: • Identity Matrix • Genetic Code Matrix • PAM Matrices • BLOSUM Matrices BLOSUM62 The score for aligning a single pair of amino acids • Protein sequences are frequently aligned using PAM or BLOSUM matrices that reflect the frequency with which a amino acid replaces another amino acid in evolutionarily related sequences. - Some amino acid substitutions are commonly found throughout the process of molecular evolution while others are rare. e.g. the probability that Ser mutates into Phe is ~ three times greater than the probability that Trp mutates into Phe
Gap Penalty a score for gap between the residues of sequences in sequence alignment Gaps inserted in a sequence to maximize similarity with another, require a scoring penalty. Gap opening penalty: penalty for starting a new gap in a sequence. Gap extension penalty: penalty for adding gaps to an existing gap. Common Gap Models: • Constant gap: g = - (gap opening penalty) • Linear gap: g(ngap) = - ngap . (gap extension penalty) • Affine gap: w(ngap) = - (gap opening penalty) - [ ngap . (gap extension penalty)] = g + g(ngap) Affine gap model is used extensively in biology domain.
- Page 1 and 2: Dynamic Programming and Pairwise Se
- Page 3 and 4: Importance of Sequence Alignment
- Page 5 and 6: Alignment Operation Transforming on
- Page 7 and 8: Difficulties in measuring sequence
- Page 9 and 10: Efficient way to find a best alignm
- Page 11 and 12: Problems Solvable by Dynamic Progra
- Page 13: Property of DP problems • Have ov
- Page 16 and 17: I. Global Alignment Classes of Pair
- Page 18 and 19: Classes of Pairwise Alignment: I. G
- Page 20 and 21: Classes of Pairwise Alignment: I. G
- Page 22 and 23: Global Alignment spans all the resi
- Page 26 and 27: Needleman-Wunsch Algorithm (Cont.)
- Page 28 and 29: Needleman-Wunsch Algorithm (Cont.)
- Page 30 and 31: Needleman-Wunsch Algorithm (Cont.)
- Page 32 and 33: Needleman-Wunsch Algorithm Efficien
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- Page 36 and 37: Needleman-Wunsch Algorithm for any
- Page 38 and 39: Local Alignment finds the most simi
- Page 40 and 41: Smith-Waterman Algorithm (Cont.)
- Page 42 and 43: Smith-Waterman Algorithm (Cont.) v1
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- Page 46 and 47: Which alignment to use Example 1. O
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- Page 58 and 59: References - Gusfield D. Algorithms
Gap Penalty<br />
a score for gap between the residues of sequences in sequence alignment<br />
Gaps inserted in a sequence to maximize similarity with another, require a scoring penalty.<br />
Gap opening penalty: penalty for starting a new gap in a sequence.<br />
Gap extension penalty: penalty for adding gaps to an existing gap.<br />
Common Gap Models:<br />
• Constant gap: g = - (gap opening penalty)<br />
• Linear gap: g(ngap) = - ngap . (gap extension penalty)<br />
• Affine gap: w(ngap) = - (gap opening penalty) - [ ngap . (gap extension penalty)] = g + g(ngap)<br />
Affine gap model is used extensively in biology domain.
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