Alignments II
Dynamic programming table
Maximum sum subarray problem
Given array of numbers [a_1, a_2, ... a_n] find
continuous sub array with maximum sum.
Local alignment
Find most common regions of two sequences
Formal problem
Find two substrings with maximal score
Score[0, i] = -gap_penalty * i
Score[j, 0] = -gap_penalty * j
Score[...
Smith-Waterman
Score[0, i] = 0
Score[j, 0] = 0
Score[i, j] =
max(
0,
Score[i-1, j] - gap_penalty,
Score[i, j-1] - gap_pena...
FItting alignment
Align the whole pattern(TAGATA) against
substring of the text(GTAGGCTTAAGGTTA)
Affine gap penalty
total_penalty =
gap_open + gap_extend * (length-1)
- -- --- ----
10 11 12 13
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Bioalgo 2013-07-alignment-2

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Bioalgo 2013-07-alignment-2

  1. 1. Alignments II
  2. 2. Dynamic programming table
  3. 3. Maximum sum subarray problem Given array of numbers [a_1, a_2, ... a_n] find continuous sub array with maximum sum.
  4. 4. Local alignment Find most common regions of two sequences
  5. 5. Formal problem Find two substrings with maximal score Score[0, i] = -gap_penalty * i Score[j, 0] = -gap_penalty * j Score[i, j] = max( Score[i-1, j] - gap_penalty, Score[i, j-1] - gap_penalty, Score[i, j] + match_score)
  6. 6. Smith-Waterman Score[0, i] = 0 Score[j, 0] = 0 Score[i, j] = max( 0, Score[i-1, j] - gap_penalty, Score[i, j-1] - gap_penalty, Score[i, j] + match_score)
  7. 7. FItting alignment Align the whole pattern(TAGATA) against substring of the text(GTAGGCTTAAGGTTA)
  8. 8. Affine gap penalty total_penalty = gap_open + gap_extend * (length-1) - -- --- ---- 10 11 12 13

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