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An Improved Search Algorithm for Optimal Multiple-Sequence Alignment

An Improved Search Algorithm for Optimal Multiple-Sequence Alignment. Paper by: Stefan Schroedl Presentation by: Bryan Franklin. Outline. Multiple-Sequence Alignment (MSA) Graph Representation Computing Path Costs Heuristics Experimental Results Other Optimizations.

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An Improved Search Algorithm for Optimal Multiple-Sequence Alignment

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  1. An Improved Search Algorithm for Optimal Multiple-Sequence Alignment • Paper by: Stefan Schroedl • Presentation by: Bryan Franklin

  2. Outline • Multiple-Sequence Alignment (MSA) • Graph Representation • Computing Path Costs • Heuristics • Experimental Results • Other Optimizations

  3. Multiple-Sequence-Alignment • Sequence: • DNA: String over alphabet {A,C,G,T} • Protein: String with |Σ|=20 (one symbol for each amino acid) • Alignment: • Insert gaps (_) into sequences to line up matching characters.

  4. Multiple-Sequence-Alignment Sequences: ABCB, BCD, DB

  5. Multiple-Sequence-Alignment • Indel: Insertion, Deletion, Point mutation (single symbol replacement) • Find a minimum set of indels between two or more sequences. • NP-Hard for an arbitrary number of sequences

  6. Multiple-Sequence-Alignment • Applications • Common ancestry between species • Locating useful portions of DNA • Predicting structure of folded proteins

  7. Graph Representation

  8. Computing G(n) • Considerations • Biological Meaning • Cost of computation • Sum-of-pairs • Substitution matrix ((|Σ|+1)2) • Sum alignment costs for all pairs • Costs can depend on neighbors

  9. Computing G(n) Sequences: ABCB, BCD, DB

  10. Computing G(n) 7 7 8 7 6

  11. Computing G(n)

  12. Heuristics • Methods Examined • 2-fold (hpair) • divide-conquer • 3-fold (h3,all) • 4-fold (h4,all)

  13. Heuristic Comparison

  14. Resource Usage

  15. Optimizations • Sparse Path Representation • Curve fitting for predicting threshold values • Sub-optimal paths periodically deleted

  16. The End • Any Questions?

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