Local alignment and BLAST

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Presentation transcript:

Local alignment and BLAST Usman Roshan BNFO 601

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 Global alignment initialization: Local alignment recurrence

Local alignment Global alignment recurrence: Local alignment recurrence

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.

BLAST Local pairwise alignment heuristic Faster than standard pairwise alignment programs such as SSEARCH, but less sensitive. Online server: http://www.ncbi.nlm.nih.gov/blast

BLAST Given a query q and a target sequence, find substrings of length k (k-mers) of score at least t --- also called hits. k is normally 3 to 5 for amino acids and 12 for nucleotides. Extend each hit to a locally maximal segment. Terminate the extension when the reduction in score exceeds a pre-defined threshold Report maximal segments above score S.

Finding k-mers quickly Preprocess the database of sequences: For each sequence in the database store all k-mers in hash-table. This takes linear time Query sequence: For each k-mer in the query sequence look up the hash table of the target to see if it exists Also takes linear time