1. Parallel Characteristics of Sequence Alignments Kyle R. Junik

2. Overview Introduction to papers Five sequencing algorithms –Needleman, Wunsch, and Sellers (NWS) –Fickett’s algorithm –Parallel NWS –Parallel Fickett’s –Wilbur and Lipman’s algorithm Case Study – Pittsburgh Supercomputing Center

3. Needleman, Wunsch, & Sellers (NWS) Uses alignment matrix with sequence S 1 across the top and sequence S 2 down the left Linear algorithm which calculates each array position starting in upper left corner Movement down or right in matrix consists of a gap penalty, gp Movement along a diagonal consists of a match or substitution, subs Value at any index, (i,j) is the min( (i-1,j) + gp, (i,j-1) +gp, subs) Running time = O(|S 1 | * |S 2 |)

4. Fickett’s Algorithm Uses same techniques as NWS algorithm Extra parameter, d max which represents a maximum value for any alignment in the matrix Idea being that if at some index you have a value greater than or equal to d max you can avoid evaluating certain indexes that depend solely on that index Running time = O( |S1| * |S2| ) but will generally run faster than NWS due to ability to pick and choose indices Drawback – Fickett’s algorithm doesn’t guarantee an alignment

5. Parallel NWS Inherently simple parallel implementation Each element in an anti-diagonal solely depends on previous anti-diagonals Therefore an anti-diagonal can be calculated in parallel Running Time is reduced to O( |S 1 | + |S 2 | )

6. Parallel Fickett’s Uses anti-diagonal concept from parallel NWS Redefines process for eliminating elements from evaluation Similar speed up over parallel NWS as regular Fickett’s had against NWS Similar drawbacks as Fickett’s algorithm

7. Wilbur and Lipman’s Algorithm Heuristic algorithm – does not guarantee optimal alignment Searches for k-tuple matches Uses hash tables for parallel search for k-tuples Finds best path amongst k-tuple matches using restricting parameter, w 2 which is a limiting leap of diagonals

8. Pittsburgh Supercomputing Center Hardware – Thinking Machines CM-2 and Cray Y-MP Uses CM-2 to filter between pairs of sequences to determine if further processing is ideal Uses Cray to analyze alignments using parallel NWS configured to utilized the Cray’s vector processing capabilities Coordination managed by Distributed Code Manager (DCM) which manages operation amongst heterogeneous computing environments