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Global alignment

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Pinky Vincent
Pinky Vincent

The document discusses global sequence alignment and the Needleman-Wunsch algorithm. Global alignment finds the optimal alignment of two sequences over their entire lengths by allowing gaps. It uses a dynamic programming approach to fill a matrix to find the highest scoring alignment based on matches, mismatches and gap penalties. The algorithm allows obtaining an alignment score that accounts for the total number of matches and mismatches as well as gap penalties.

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GLOBAL ALIGNMENT Pinky Sheetal V M.tech Bioinformatics
CONTENTS  Sequence Alignment  Dynamic Programming Algorithm  Global Alignment
The result of inserting gaps into the strings such that afterwards as many positions as possible coincides. X: AGGCTATCA Y: TAGCTATCA
Scoring weights: For a match : +m For a mismatch : -s For a gap : -d Alignment Score: F = (# matches) x m - (# mismatches) x s – (#gaps) x d
Complex Problem Sub prob1 Soln 1 Sub prob2 Sub prob3 Soln 2 Soln 3
GLOBAL ALIGNMENT
•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.
Termination Condition: Optimal score between the two sequences obtained at the last cell of the last row and last column.
Sequences: S: ATTATCT T: TTTCTA T S 0 _ T T T C T A _ 0 -1 -2 -3 -4 -5 -6 A -1 0 -1 -2 -3 -4 -5 T -2 1 2 1 0 -1 -2 T -3 0 3 4 3 2 1 A -4 -1 2 3 4 3 4 Match Score = +2 Mismatch Score = 0 T -5 -2 1 4 3 6 5 Gap Penalty = -1 C -6 -3 0 3 6 5 6 T -7 -4 -1 2 5 8 7
T 0 _ T T T C T A S _ 0 -1 -2 -3 -4 -5 -6 A -1 0 -1 -2 -3 -4 -5 T -2 1 2 1 0 -1 -2 T -3 0 3 4 3 2 1 A -4 -1 2 3 4 3 4 T -5 -2 1 4 3 6 5 C -6 -3 0 3 6 5 6 T -7 -4 -1 2 5 8 7
 Optimal Alignment: S ATTATC T– T - TT – TC TA No: of matches = 5 No: of mismatches = 3 (5 x 2) – (3 x -1) = 7
Tools that utilize Global Alignment Algorithm  EMBOSS Needle  EMBOSS Stretcher Applications:  Identify Conserved Interaction Pathways and Complexes [Brian P. Kelley,et al.2003]  Functional Orthology Detection [ Rohit Singh.et al.2008] Advantages: The similar sequence region is of the same order and orientation. Disadvantage: Slow, Memory Intensive Cannot be applied on genome-sized sequences
Global alignment

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Global alignment

  • 1. GLOBAL ALIGNMENT Pinky Sheetal V M.tech Bioinformatics
  • 2. CONTENTS  Sequence Alignment  Dynamic Programming Algorithm  Global Alignment
  • 3. The result of inserting gaps into the strings such that afterwards as many positions as possible coincides. X: AGGCTATCA Y: TAGCTATCA
  • 4. Scoring weights: For a match : +m For a mismatch : -s For a gap : -d Alignment Score: F = (# matches) x m - (# mismatches) x s – (#gaps) x d
  • 5. Complex Problem Sub prob1 Soln 1 Sub prob2 Sub prob3 Soln 2 Soln 3
  • 7. •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.
  • 8. Termination Condition: Optimal score between the two sequences obtained at the last cell of the last row and last column.
  • 9. Sequences: S: ATTATCT T: TTTCTA T S 0 _ T T T C T A _ 0 -1 -2 -3 -4 -5 -6 A -1 0 -1 -2 -3 -4 -5 T -2 1 2 1 0 -1 -2 T -3 0 3 4 3 2 1 A -4 -1 2 3 4 3 4 Match Score = +2 Mismatch Score = 0 T -5 -2 1 4 3 6 5 Gap Penalty = -1 C -6 -3 0 3 6 5 6 T -7 -4 -1 2 5 8 7
  • 10. T 0 _ T T T C T A S _ 0 -1 -2 -3 -4 -5 -6 A -1 0 -1 -2 -3 -4 -5 T -2 1 2 1 0 -1 -2 T -3 0 3 4 3 2 1 A -4 -1 2 3 4 3 4 T -5 -2 1 4 3 6 5 C -6 -3 0 3 6 5 6 T -7 -4 -1 2 5 8 7
  • 11. Optimal Alignment: S ATTATC T– T - TT – TC TA No: of matches = 5 No: of mismatches = 3 (5 x 2) – (3 x -1) = 7
  • 12. Tools that utilize Global Alignment Algorithm  EMBOSS Needle  EMBOSS Stretcher Applications:  Identify Conserved Interaction Pathways and Complexes [Brian P. Kelley,et al.2003]  Functional Orthology Detection [ Rohit Singh.et al.2008] Advantages: The similar sequence region is of the same order and orientation. Disadvantage: Slow, Memory Intensive Cannot be applied on genome-sized sequences