David A. Bader
IEEE Fellow
AAAS Fellow
Professor
College of Computing
Georgia Tech
Atlanta, GA 30332


 
 

 

PASQUAL: Parallel Techniques for Next Generation Genome Sequence Assembly

The study of genomes has been revolutionized by sequencing machines that output many short overlapping substrings (called reads). The task of sequence assembly in practice is to reconstruct long contiguous genome subsequences from the reads. With Next Generation Sequencing (NGS) technologies, assembly software needs to be more accurate, faster, and more memoryefficient due to the problem complexity and the size of the data sets. In this paper, we develop parallel algorithms and compressed data structures to address several computational challenges of NGS assembly. We demonstrate how commonly available multicore architectures can be efficiently utilized for sequence assembly. In all stages (indexing input strings, string graph construction and simplification, extraction of contiguous subsequences) of our software PASQUAL, we use shared-memory parallelism to speed up the assembly process. In our experiments with data of up to 6.8 billion base pairs, we demonstrate that PASQUAL generally delivers the best tradeoff between speed, memory consumption, and solution quality. On synthetic and real data sets PASQUAL scales well on our test machine with 40 CPU cores with increasing number of threads. Given enough cores, PASQUAL is fastest in our comparison.

PASQUAL web site: http://www.cc.gatech.edu/pasqual/

Publication History

Versions of this paper appeared as:
  1. X. Liu, P. Pande, H. Meyerhenke, and D.A. Bader, ``PASQUAL: Parallel Techniques for Next Generation Genome Sequence Assembly,'' IEEE Transactions on Parallel & Distributed Systems, 24(5):977-986, 2013.

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Last updated: April 26, 2013

 




Computational Biology



Parallel Computing



Combinatorics