title: A data parallel strategy for aligning multiple biological sequences on multi-core computers.
abstract: In this paper, we address the large-scale biological sequence alignment problem, which has an increasing demand in computational biology. We employ data parallelism paradigm that is suitable for handling large-scale processing on multi-core computers to achieve a high degree of parallelism. Using the data parallelism paradigm, we propose a general strategy which can be used to speed up any multiple sequence alignment method. We applied five different clustering algorithms in our strategy and implemented rigorous tests on an 8-core computer using four traditional benchmarks and artificially generated sequences. The results show that our multi-core-based implementations can achieve up to 151-fold improvements in execution time while losing 2.19% accuracy on average. The source code of the proposed strategy, together with the test sets used in our analysis, is available on request.
authors: College of Information Science and Engineering, Hunan University, Changsha, Hunan 410082, China. hnzxy@hnu.edu.cn
authors: Xiangyuan
authors: X
authors: Zhu
authors: Kenli
authors: K
authors: Li
authors: Ahmad
authors: A
authors: Salah
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D006801:Humans; D015203:Reproducibility of Results; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods; D012984:Software