lecture sequence alignments_spring11

lecture sequence alignments_spring11 - Sequence Analysis...

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Unformatted text preview: Sequence Analysis Determining how similar 2 (or more) gene/protein sequences are (too each other) is a staple function in bioinformatics. This information is utilized for: 1) Gene/Protein Identification 2) Infer Gene/Protein Function 3) Measure Genetic Distance This ENTIRE exercise relies on the comparison between 2 (or more) sequences, and is independent of any functional content within the sequence(s). In Pair Wise analysis and Multiple Sequence Alignments , two (or more) sequences are compared to each other and a similarity measurement is derived. This process is completely computational and there is no need for a database query. From this process we can: 1) Identify common regions of sequence identity (infer function). 2) Rank order multiple sequences to identify the sequences that are most similar (measure genetic distance). In Sequence Identification , we compare our sequence(s) of interest to an entire database of (known) sequences, and identify those sequences that are most similar to our sequence of interest. Theoretical Basis of Pairwise Sequence Analysis Needleman-Wunsch Algorithm : Global Alignment (entire sequence contributes to alignment) Fundamental Principle: calculate the alignment score across two sequences. All possible pairs are represented by a two-dimensional array, and all possible comparisons are represented by pathways through the array. Represents Dynamic Programming : Solving a series of subsets of a computational problem to solve the entire problem. Divide and Conquer. DYNAMIC PROGRAMMING and SEQUENCE ALIGNMENTS 'Dynamic programming' is an efficient programming technique for solving certain combinatorial problems. It is particularly important in bioinformatics as it certain combinatorial problems....
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lecture sequence alignments_spring11 - Sequence Analysis...

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