msa_ppt - Sequence Comparison Introduction Comparison...

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Sequence Comparison Introduction Comparison Homogy -- Analogy Identity -- Similarity Pairwise -- Multiple Scoring Matrixes Gap -- indel Global -- Local Manual alignment, dot plot visual inspection Dynamic programming Needleman-Wunsch exhaustive global alignment Smith-Waterman exhaustive local alignment Multiple alignment Database search BLAST FASTA
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Sequence Comparison Multiple alignment (Multiple sequence alignment: MSA) Application Procedure Extrapolation Allocation of an uncharacterized sequence to a protein family. Phylogenetic analysis Reconstruction of the history of closely related proteins and protein families. Pattern identification Identification of regions characteristic of a function by conserved positions. Domain identification Turning MSA into a domain or protein family specific profile may be useful in identifying new or remote family members. DNA regulatory elements Turning DNA-MSAs of a binding site into a weight matrix may be used in scanning other DNA sequences for potential similar binding sites. Structure prediction Good MSAs yield high quality prediction of secondary structure and help building 3D models. PCR analysis Identification of less degenerated regions of a protein family are useful in fishing out new members by PCR (primer design).
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Multiple sequence alignment - Computational complexity V S N S A N S V S N S _ S N A Sequence Comparison Multiple alignment
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Alignment of protein sequences with 200 amino acids using dynamic programming # of sequences CPU time (approx.) 2 1 sec 4 10 4 sec – 2,8 hours 5 10 6 sec – 11,6 days 6 10 8 sec – 3,2 years 7 10 10 sec – 371 years Multiple sequence alignment - Computational complexity Sequence Comparison Multiple alignment
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Approximate methods for MSA Sequence Comparison Multiple alignment Multidimensional dynamic programming (MSA, Lipman 1988) Progressive alignments (Clustalw, Higgins 1996; PileUp, Genetics Computer Group (GCG)) Local alignments (e.g. DiAlign, Morgenstern 1996; lots of others) Iterative methods  (e.g. PRRP, Gotoh 1996) Statistical methods  (e.g. Bayesian Hidden Markov Models)
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Multiple sequence alignment - Programs Sequence Comparison Multiple alignment OMA Combalign DCA T-Coffee Clustal Dalign MSA Interalign Prrp Sam HMMER GA SAGA Multidimentional Dynamic programming Progressive Iterative HMMS GAs Non tree based Tree based
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Multiple sequence alignment - Computational complexity Sequence Comparison Multiple alignment Program Seq type Alignment Methode Comment ClustalW Prot/DNA Global Progressive No format limitation Run on Windows too!
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