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791_my_lecture4 - 7.91 Lecture#4 Michael Yaffe Database...

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7.91 – Lecture # 4 Database Searching & Molecular Phylogenetics A A B B C D D ((( A , B ) C ) D ) C Michael Yaffe
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Outline FASTA, Blast searching, Smith-Waterman • Psi-Blast Review of Genomic DNA structure Substitution patterns and mutation rates Synonymous and non-Synonymous substitutions Jukes-Cantor Model Kimura’s Two-Parameter Model Molecular Clocks Phylogenetic Trees – rooted and unrooted Distance Matrix Methods Neighbor-Joining Method and Related Neighbor Methods Maximum Likelihood
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Outline (cont) • Parsimony Branch and Bound Heuristic Seaching • Consensus Trees Software (PHYLIP, PAUP) The Tree of Life Reading: Mount, p. 237-280, 283-286, 291-308
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Database Searching Problem is simple: I want to find homologues to my protein in the database How do I do it? Do the obvious – compare my protein against every other protein in the database and look for local alignments by dynamic programming Uh Oh! 1 n For k sequences in the 1 12345678…. Database 12345678…. this becomes an O(mnk) 12345678…. problem! 12345678…. 12345678…. m 12345678…. ….essentially an O(mn) problem
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Database Searching Still, this can be done - ~ 50x slower than Blast/FASTA, Smith-Waterman algorithm… SSEARCH ( ftp.virginia.edu/pub/fasta ) – do it locally! But in the old days, needed a faster method… 2 approaches – Blast, FASTA – both heuristic (i.e. tried and true) – almost always finds related Proteins but cannot guarantee optimal solution FASTA: Basic Idea 1- Search for matching sequence patterns or words Called k-tuples, which are exact matches of “k” characters between the two sequences i.e. RW = 2-tuple Seq 1: AHFY RW NKLCV Seq 2: D RW NLFCVATYWE
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Database Searching FASTA: Basic Idea 2- Repeat for all possible k-tuples i.e. CV = 2-tuple Seq 1: AHFY RW NKL CV Seq 2: D RW NLF CV ATYWE 3- Make a Hash Table (Hashing) that has the position of each k-tuple in each sequence 2-tuple pos. in Seq1 RW 5 CV 10 AH 1 i.e. pos in Seq 2 Offset (pos1-pos2) 2 3 7 3 ---- ----
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Database Searching Seq 1: AHFY RW NKL CV Seq 2: D RW NLF CV ATYWE 3- Make a Hash Table i.e. (Hashing) that has the position of each k-tuple in each sequence 2-tuple pos. in Seq1 pos in Seq 2 Offset (pos1-pos2) 3 3 RW 5 2 CV 10 7 AH 1 ---- ---- 4- Look for words (k-tuples) with same offset These are in-phase and reveal a region of alignment between the two sequences. 5- Build a local alignment based on these, extend it outwards Seq 1: AHFY RW NKL CV Seq 2: D RW NLF CV ATYWE
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Database Searching With hashing, number of comparisons is proportional To the average sequence length (i.e. an O(n) problem), Not an O(mn) problem as in dynamic programming. Proteins – ktup = 1-2, Nucleotides, ktup=4-6 One big problem – low complexity regions. Seq 1: AHFY PPPPPPPP FSER Seq 2: DVAT PPPPPPPPPPP NLFK
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Database Searching BLAST Same basic idea as FASTA, but faster and more sensitive!
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