Dynamic Programing

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Which alignment to use Example 1. Overlap detection: Aligning exon to a gene sequence V = ATCCGAACATCCAATCGAAGC W = AGCATGCAAT Aligning scores: match = 2, gap = -1 mismatch = -2
Which alignment to use Example 1. Overlap detection: Aligning exon to a gene sequence V = ATCCGAACATCCAATCGAAGC W = AGCATGCAAT Aligning scores: match = 2, gap = -1 mismatch = -2 Global alignment
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 24 and 25: Scoring matrix represents a specifi
Page 26 and 27: Needleman-Wunsch Algorithm (Cont.)
Page 32 and 33: Needleman-Wunsch Algorithm Efficien
Page 34 and 35: Needleman-Wunsch Algorithm for any
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
Page 44 and 45: Smith-Waterman Algorithm (Cont.) Ex
Page 48 and 49: Which alignment to use Example 1. O
Page 50 and 51: Which alignment to use Example 1. O
Page 52 and 53: Which alignment to use example 1 co
Page 54 and 55: Which alignment to use (Cont.) Exam
Page 56 and 57: Versatility of DP Algorithm • Mem
Page 58 and 59: References - Gusfield D. Algorithms

Dynamic Programing

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