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BNFO 236 Smith Waterman alignment

BNFO 236 Smith Waterman alignment. Usman Roshan. Local alignment. Global alignment may not find local similarities Modification of Needleman-Wunsch yields the Smith-Watermn algorithm for local alignment Useful in motif detection, database search, short read mapping. Local alignment.

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BNFO 236 Smith Waterman alignment

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  1. BNFO 236Smith Waterman alignment Usman Roshan

  2. Local alignment • Global alignment may not find local similarities • Modification of Needleman-Wunsch yields the Smith-Watermn algorithm for local alignment • Useful in motif detection, database search, short read mapping

  3. Local alignment • Global alignment initialization: • Local alignment recurrence

  4. Local alignment • Global alignment recurrence: • Local alignment recurrence

  5. Local alignment traceback • Let T(i,j) be the traceback matrices and m and n be length of input sequences. • Global alignment traceback: • Begin from T(m,n) and stop at T(0,0). • Local alignment traceback: • Find i*,j* such that T(i*,j*) is the maximum over all T(i,j). • Begin traceback from T(i*,j*) and stop when T(i,j) <= 0.

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