# Lecture6 - Lecture 6: BLAST • Local Sequence Alignment-...

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Unformatted text preview: Lecture 6: BLAST • Local Sequence Alignment- Smith-Waterman Algorithm • BLAST Adapted from slides by Dr. Keith Dunker Local Sequence Alignment: Smith-Waterman algorithm § The Needleman-Wunsch algorithm is an example of global sequence alignment – Aligns entire sequences § Local sequence alignment is a method for aligning segments of two sequences with the highest density of matches § For example, two protein sequences may share a common subdomain (i.e., a SET domain) -- this shared domain can be found more easily using local sequence alignment methods § The most commonly used local sequence alignment method is the Smith-Waterman algorithm ( J. Mol. Biol. (1981) 147, 195-197) Smith-Waterman Algorithm (simplified) § Use dynamic programming to calculate alignment matrix (similar to Needleman-Wunsch method) § The following scoring method is used to calculate alignment matrix (H): • where H i,j = score of matrix element i, j; g = gap penalty; and s(a i , b j ) = score of matching a i with b j § Traceback begins at the element in the matrix with the maximum score § Traceback continues until a cell with a score of zero is reached § Construct local alignment based on traceback path H i , j = max H i " 1 , j " 1 + s ( a i , b j ) H i " 1 , j " g H i , j " 1 " g # \$ % % & % % Scoring the Matrix: Example ? A A T G T A T G A C Scoring Metric: Match: s(a i , b j ) = 1 Mismatch: s(a i , b j ) = -1 Gap: -2 penalty Maximum of possible scores: (a) 0 + s(A,A) = 0 + 1 = 1 (b) 0 - g = 0 - 2 = -2 (c) 0 - g = 0 - 2 = -2 (d) 0 (no pointer) a c b Scoring the Matrix: Example 1 A A T G T A T G A C Scoring Metric: Match: s(a i , b j ) = 1 Mismatch: s(a i , b j ) = -1 Gap: -2 penalty Maximum of possible scores: (a) 0 + s(A,A) = 0 + 1 = 1 (b) 0 - g = 0 - 2 = -2 (c) 0 - g = 0 - 2 = -2 (d) 0 (no pointer) Option (a) gives the maximum score so this value is placed in the matrix, and option (a) pointer is retained a Scoring the Matrix: Example (continued) ? 1 A A T G T A T G A C Scoring Metric: Match: s(a i , b j ) = 1 Mismatch: s(a i , b j ) = -1 Gap: -2 penalty Maximum of possible scores: (a) 0 + s(A,A) = 0 + 1 = 1 (b) 1 - g = 1 - 2 = -1 (c) 0 - g = 0 - 2 = -2 (d) 0 (no pointer) a c b 1 1 A A T G T A T G A C Scoring Metric: Match: s(a i , b j ) = 1 Mismatch: s(a i , b j ) = -1 Gap: -2 penalty Maximum of possible scores: (a) 0 + s(A,A) = 0 + 1 = 1 (b) 1 - g = 1 - 2 = -1 (c) 0 - g = 0 - 2 = -2 (d) 0 (no pointer) Option (a) gives the maximum score so this value is placed in the matrix, and option (a) pointer is retained Scoring the Matrix: Example (continued) a ?...
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## This note was uploaded on 01/20/2012 for the course MBIOS 478 taught by Professor Staff during the Fall '11 term at Washington State University .

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Lecture6 - Lecture 6: BLAST • Local Sequence Alignment-...

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