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

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Presentation transcript:

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

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.

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

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

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

Graph Representation

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

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

Computing G(n)

Heuristics Methods Examined 2-fold (h pair ) divide-conquer 3-fold (h 3,all ) 4-fold (h 4,all )

Heuristic Comparison

Resource Usage

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

The End Any Questions?