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Smith and waterman algorithm
Vimal Priya Subramanian
WhatisS-W
algorithm??
 The S-W algorithm performs in local sequence
alignment for determining two similar regions between
two strings nucleotide sequences or protein sequence.
 Instead of looking for entire sequence, S-W algorithm
compares sequence of all possible lengths and
optimizes similarity length.
 The algorithm was first proposed by Temple F.
Smith and Michael S. Waterman in 1981. Like
the Needleman–Wunsch algorithm, of which it is a variation,
Smith–Waterman is a dynamic programming algorithm.
 As such, it has the desirable property that it is guaranteed to
find the optimal local alignment with respect to the scoring
system being used (which includes the substitution
matrix and the gap-scoring scheme).
 The main difference to the Needleman–Wunsch algorithm is
that negative scoring matrix cells are set to zero, which
renders the (thus positively scoring) local alignments visible.
 Traceback procedure starts at the highest scoring matrix cell
and proceeds until a cell with score zero is encountered,
yielding the highest scoring local alignment.
Substitution
matrix and gap
penalty schemes
 A substitution matrix assigns each pair of bases or amino
acids a score for match or mismatch.
 Usually matches get positive scores, whereas
mismatches get relatively lower scores.
 A gap penalty function determines the score cost for
opening or extending gaps.
 It is suggested that users choose the appropriate scoring
system based on the goals.
 In addition, it is also a good practice to try different
combinations of substitution matrices and gap penalties.
Scoringmatrix
 The dimensions of the scoring matrix are 1+length of
each sequence respectively.
 All the elements of the first row and the first column are
set to 0.
 The extra first row and first column make it possible to
align one sequence to another at any position, and setting
them to 0 makes the terminal gap free from penalty.
smith - waterman algorithm.pptx
smith - waterman algorithm.pptx
Scoring
 Score each element from left to right, top to bottom in the
matrix, considering the outcomes of substitutions
(diagonal scores) or adding gaps (horizontal and vertical
scores).
 If none of the scores are positive, this element gets a 0.
Otherwise the highest score is used and the source of
that score is recorded.
Scoringmethod
forS-W
algorithm
smith - waterman algorithm.pptx
Traceback
 Starting at the element with the highest score, traceback
based on the source of each score recursively, until 0 is
encountered.
 The segments that have the highest similarity score
based on the given scoring system is generated in this
process.
 To obtain the second best local alignment, apply the
traceback process starting at the second highest score
outside the trace of the best alignment.
smith - waterman algorithm.pptx
smith - waterman algorithm.pptx
smith - waterman algorithm.pptx
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smith - waterman algorithm.pptx

  • 1. Smith and waterman algorithm Vimal Priya Subramanian
  • 2. WhatisS-W algorithm??  The S-W algorithm performs in local sequence alignment for determining two similar regions between two strings nucleotide sequences or protein sequence.  Instead of looking for entire sequence, S-W algorithm compares sequence of all possible lengths and optimizes similarity length.
  • 3.  The algorithm was first proposed by Temple F. Smith and Michael S. Waterman in 1981. Like the Needleman–Wunsch algorithm, of which it is a variation, Smith–Waterman is a dynamic programming algorithm.  As such, it has the desirable property that it is guaranteed to find the optimal local alignment with respect to the scoring system being used (which includes the substitution matrix and the gap-scoring scheme).  The main difference to the Needleman–Wunsch algorithm is that negative scoring matrix cells are set to zero, which renders the (thus positively scoring) local alignments visible.  Traceback procedure starts at the highest scoring matrix cell and proceeds until a cell with score zero is encountered, yielding the highest scoring local alignment.
  • 4. Substitution matrix and gap penalty schemes  A substitution matrix assigns each pair of bases or amino acids a score for match or mismatch.  Usually matches get positive scores, whereas mismatches get relatively lower scores.  A gap penalty function determines the score cost for opening or extending gaps.  It is suggested that users choose the appropriate scoring system based on the goals.  In addition, it is also a good practice to try different combinations of substitution matrices and gap penalties.
  • 5. Scoringmatrix  The dimensions of the scoring matrix are 1+length of each sequence respectively.  All the elements of the first row and the first column are set to 0.  The extra first row and first column make it possible to align one sequence to another at any position, and setting them to 0 makes the terminal gap free from penalty.
  • 8. Scoring  Score each element from left to right, top to bottom in the matrix, considering the outcomes of substitutions (diagonal scores) or adding gaps (horizontal and vertical scores).  If none of the scores are positive, this element gets a 0. Otherwise the highest score is used and the source of that score is recorded.
  • 11. Traceback  Starting at the element with the highest score, traceback based on the source of each score recursively, until 0 is encountered.  The segments that have the highest similarity score based on the given scoring system is generated in this process.  To obtain the second best local alignment, apply the traceback process starting at the second highest score outside the trace of the best alignment.