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Optimal Sum of Pairs Multiple Sequence Alignment. David Kelley. Dynamic Programming Extension. Standard pairwise sequence alignment methods can be extended to handle k strings. But…. Runtime is O(2 k N k ) k = # of sequences N = average length of sequences Space is O(N k )

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Optimal Sum of Pairs Multiple Sequence Alignment


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dynamic programming extension
Dynamic Programming Extension
  • Standard pairwise sequence alignment methods can be extended to handle k strings
slide3
But…
  • Runtime is O(2kNk)
    • k = # of sequences
    • N = average length of sequences
  • Space is O(Nk)
  • Quickly becomes unfeasible
enter carillo lipman
Enter Carillo-Lipman
  • Lower bound the score
  • Estimate distance from cell to end
    • Calculate sum of all pairwise distances from cell to end
  • If current score + estimate < lower bound
    • Ignore that path
slide5
MSA
  • Implemented in 1989 program MSA.
  • Used a simple progressive alignment procedure to obtain a lower bound
  • “generally can align 6 to 8 sequences of length 200-300 residues”
gupta 1995 update
Gupta 1995 update
  • Re-implemented MSA more efficiently
  • Uses a star-tree heuristic for lower bound
  • Ran on Sun SparcStation 10 with 128MB of RAM
  • Runtimes varied (based on similarity of sequences too)
    • 10 Globin B proteins of ~150 a.a. took 10 min
can we do better
Can we do better?
  • Better hardware
    • more RAM
    • multi-core processors
  • Better heuristics
    • MUSCLE, MAFFT very fast, accurate
    • Higher lower bound means more of the matrix can be ignored
my project
My Project
  • Implement concepts from Carillo-Lipman
    • Use MUSCLE for lower bound
    • Look for opportunities to parallelize
      • Using openMP
  • Run on modern hardware
can optimal alignment be made practical
Can optimal alignment be made practical?
  • How much better can we do than the previous attempts?
  • How will maximizing sum of pairs compare to more popular alignment programs?
    • Compare on multiple sequence alignment database, BAliBase