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Design of multiple sequence alignment algorithms on parallel, distributed memory supercomputers.

Philip C Church1, Andrzej Goscinski, Kathryn Holt

  • 1Deakin University, Science and Technology. pcc@deakin.edu.au

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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PubMed
Summary
This summary is machine-generated.

Comparing hundreds of genomes is now feasible with a new massively parallel algorithm. This approach significantly reduces computation time and memory usage for large-scale comparative genomics.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Comparing large numbers of genomes is crucial for evolutionary and functional studies.
  • Existing multiple sequence alignment methods struggle with hundreds of genomes due to computational demands.

Purpose of the Study:

  • To design a scalable multiple sequence alignment algorithm for massively parallel supercomputers.
  • To enable comparative genomics on datasets of hundreds of genomes.

Main Methods:

  • Developed a parallel algorithm based on the progressiveMauve methodology.
  • Designed specialized data structures for sequences and k-mer lists on distributed memory supercomputers (IBM Blue Gene/P).

Main Results:

  • The algorithm demonstrates potential for aligning over 250 bacterial genomes on a single compute node.
  • Achieved a 2x speedup and 4x reduction in memory footprint for scaffold building compared to the sequential algorithm.
  • Verified results on E.coli, Shigella, and S.pneumoniae genome datasets.

Conclusions:

  • This work lays the foundation for large-scale multiple sequence alignment on supercomputers.
  • Enables efficient comparison of hundreds of genome sequences within practical timeframes.