A Parallel Solution to Global Sequence Comparisons

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A Parallel Solution to Global Sequence Comparisons

• CSC 583 Parallel Programming
• By Nnamdi Ihuegbu
• 12/19/03

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Brief Introduction

• Human Genome Project (and others) -gt Vast amount
  of biological data
• Venture Computer Science and Biology (BCB) -gt
  Genetic Databases (map,genomic,proteomic)
• Expected date of Completed map of human genome
  end of 2003
• Next stage Sequence comp. and Seq-Protein
  function.
• Useful to Pharm. Companies (CADD e.g. SKBs
  Relenza).

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Results - Sequence

• Current Sequence Generation Technologies
• Maxam-Gilbert (use chemicals to cleave DNA at a
  specific base/length)
• Sanger (use enzymatic procedures to produce DNA
  based on specific basei.e. length)

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Derivation of nucleotide sequence from human
chromosome
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Sequence Comparison Methods

• Types of Sequence Comparisons/alignmts.
• Global (How similar are these two sequences?)
• To find best overall alignment b/w two sequences
• 1970 Needleman and Wunch (global, dynamic)
• Shortcomings in small similarities w/in 2
  subseq.
• Local (What sequences in a database are most
  similar to this sequence?)
• To find the best subseq. match b/w two sequences
• 1981 Smith and Waterman (local, dynamic)
• Shortcomings not computationally efficient, slow

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Results - Sequence
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Results - Sequence

• Heuristic Search (Quick, Approximate)
• Quickly search for words that match sequence.
  Then recursively perform local search on each
  matched word until no other matches
• FASTA (1998), BLAST(1990)
• Shortcomings approximate not exact, E-Value (sig
  if lt0.05)

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Results Sequence (CSC Implementation)

• Sequence alignment can be represented as matrices
  and graphs (using rules and costs)
• When converted into a directed acyclic graph,
  solution of the sequence alignment is the
  shortest-path with maximum value (max. path
  problem).

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Sequencing (CSC Implementation)

• Can be solved dynamically as a running max
  score (RMS).
• For each D(i,j), best RMS max(westgap1,
  northgap2, NWcurrent_score)
• Replace D(i,j) with max
• Needleman-Wunch Dynamic Program

Diag. edge character matches down edge gap
in string 2 across edge gap in string 1
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Parallel Solution
Work (Slaves) allocated in stripes
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Parallel Solution (Contd)
 
Allocating Strips in SubMatrix
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Parallel Results
Path
T A -1
G T -3
_ T -6
-10
 
Each cell in each strip computes maximum of
NEIGHBORS (running max)
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Improvements

• Parallel Smith-Waterman (localized start and
  continue while gt0 then end) (BLAZE-Stanford).
• Pipeline implementation on an actual Mesh
  Topology
• Other possible data infrastructures to traverse
  data in search of shortest path (e.g. Trees --
  specialized)

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Improvements (Contd)

• Faster means of comparing and aligning multiple
  sequences simultaneously (e.g. comparing novel
  protein sequence to family).

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Any Questions?
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