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Lecture7 - Lecture 7 Multiple Sequence Alignment(MSA What...

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Lecture 7: Multiple Sequence Alignment (MSA) • Motivation for and challenge of MSA • Sum of Pairs (SP) method • Progressive MSA: ClustalW algorithm Some of the notes are derived from slides by Dr. Donald R. Williamson at the University of Delaware Some slides adapted from slides created by Dr. Keith Dunker What is Multiple Sequence Alignment? § Multiple sequence alignment is the alignment of N sequences (amino acids/nucleotides), where N > 2 § Goal: to write each sequence along the others to express any similarity between the sequences • Each element of sequence is either placed alongside a corresponding element in the other sequences and/or a gap character § Example: TGCG , AGCTG , and AGCG can be aligned as follows: T-GC-G -AGCTG -AGC-G § Problems: • How do we efficiently find this alignment? • Can we find a better alignment? Species 3: -- TGGACGTTATCACAGTTTGTCCG ----------Gene CGATATGC GGACSWTAT § The addition of related sequences to a pair-wise alignment facilitates the identification of subsequences of high functional importance. Why? § Example: Aligning genomic sequence of related organisms to identify conserved non-coding regulatory sequence elements Species 1: - GGGACGATATGCAATATGAAATT -----------Gene Species 2: - GACGCGATATGCTCCGATTAAGT -----------Gene Not always so easy to pick out conserved motifs. How about this example? Species 1: -- GGGACGATATGCAATATGAAATT ----------Gene Species 2: -- AGGACCTTATATATTAGCAATGT ----------Gene Motivation for Multiple Sequence Alignment Motivation for Multiple Sequence Alignment (continued) § Multiple sequence alignment can find biologically important sequence similarities that may be widely dispersed or hidden in the sequences § Multiple sequence alignment can provide information about the evolutionary history of the respective sequences § Multiple sequence alignment can give insight into the basis for sequence similarities between homologous sequence
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Example of a Multiple Sequence Alignment (MSA) Baeyer-Villiger monooxygenases (BVMOs) - taken from Fraaije, et al (2002) FEBS Letters 518 :43-47 Efficiently Computing a MSA: The Complexity Problem § Adding additional sequences results in an exponential increase in the number of computations required to find the optimal alignment § For m -wise comparisons, even the dynamic programming methods quickly break down § Example : number of comparisons made to align m protein sequences, each 300 amino acids in length • m = 2: 90,000 comparisons • m = 3: 2.7 x 10 7 comparisons • m = 4: ~ 8 x 10 9 comparisons • m = 5: ~ 2.4 x 10 12 comparisons A Solution: Dynamic Programming (DP) and the Carrillo-Lipman Bound
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