Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014, Article ID 406178, 12 pages
http://dx.doi.org/10.1155/2014/406178
Research Article

An Improved Distance Matrix Computation Algorithm for Multicore Clusters

1Department of Mathematics, Faculty of Science, Al-Azhar University, Cairo, Egypt
2Education College for Girls, Mosul University, Mosul, Iraq
3Department of Mathematics, Faculty of Science, Ain Shams University, Cairo, Egypt

Received 19 January 2014; Revised 11 May 2014; Accepted 18 May 2014; Published 12 June 2014

Academic Editor: Horacio Pérez-Sánchez

Copyright © 2014 Mohammed W. Al-Neama et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. C.-E. Chin, A. C.-C. Shth, and K.-C. Fan, “A novel spectral clustering method based on pairwise distance matrix,” Journal of Information Science and Engineering, vol. 26, no. 2, pp. 649–658, 2010. View at Google Scholar · View at Scopus
  2. R. Hu, W. Jia, H. Ling, and D. Huang, “Multiscale distance matrix for fast plant leaf recognition,” IEEE Transactions on Image Processing, vol. 21, no. 11, pp. 4667–4672, 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. A. Roman-Gonzalez, “Compression techniques for image processing tasks,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 7, pp. 379–388, 2013. View at Google Scholar
  4. J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski, “Information retrieval perspective to nonlinear dimensionality reduction for data visualization,” Journal of Machine Learning Research, vol. 11, pp. 451–490, 2010. View at Google Scholar · View at Scopus
  5. A. Y. Zomaya, Parallel Computing for Bioinformatics and Computational Biology: Models, Enabling Technologies, and Case Studies, John Wiley & Sons, 2006.
  6. J. R. Cole, Q. Wang, E. Cardenas et al., “The Ribosomal Database Project: improved alignments and new tools for rRNA analysis,” Nucleic Acids Research, vol. 37, no. 1, pp. D141–D145, 2009. View at Publisher · View at Google Scholar · View at Scopus
  7. B. Schmidt, Bioinformatics: High Performance Parallel Computer Architectures, CRC Press, 2011.
  8. M. W. Al-Neama, N. M. Reda, and F. F. M. Ghaleb, “Fast vectorized distance matrix computation for multiple sequence alignment on multi-cores,” International Journal of Biomathematics. In press.
  9. K. Katoh, K.-I. Kuma, H. Toh, and T. Miyata, “MAFFT version 5: improvement in accuracy of multiple sequence alignment,” Nucleic Acids Research, vol. 33, no. 2, pp. 511–518, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. A. R. Subramanian, M. Kaufmann, and B. Morgenstern, “DIALIGN-TX: greedy and progressive approaches for segment-based multiple sequence alignment,” Algorithms for Molecular Biology, vol. 3, no. 1, article 6, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. T. Kim and H. Joo, “ClustalXeed: a GUI-based grid computation version for high performance and terabyte size multiple sequence alignment,” BMC Bioinformatics, vol. 11, article 467, 2010. View at Publisher · View at Google Scholar · View at Scopus
  12. R. C. Edgar, “MUSCLE: multiple sequence alignment with high accuracy and high throughput,” Nucleic Acids Research, vol. 32, no. 5, pp. 1792–1797, 2004. View at Publisher · View at Google Scholar · View at Scopus
  13. J. D. Thompson, “The clustal series of programs for multiple sequence alignment,” in The Proteomics Protocols Handbook, pp. 493–502, Humana Press, 2005. View at Google Scholar
  14. J. Zola, X. Yang, S. Rospondek, and S. Aluru, “Parallel T-Coffee: a parallel multiple sequence aligner,” in Proceedings of the 20th ISCA International Conference on Parallel and Distributed Computing Systems (ISCA PDCS '07), pp. 248–253, 2007.
  15. A. Wirawan, C. K. Kwoh, and B. Schmidt, “Multi-threaded vectorized distance matrix computation on the CELL/BE and x86/SSE2 architectures,” Bioinformatics, vol. 26, no. 10, pp. 1368–1369, 2010. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Chaichoompu and S. Kittitornkun, “Multithreaded ClustalW with improved optimization for intel multi-core processor,” in Proceedings of the International Symposium on Communications and Information Technologies (ISCIT '06), pp. 590–594, October 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. K.-B. Li, “ClustalW-MPI: ClustalW analysis using distributed and parallel computing,” Bioinformatics, vol. 19, no. 12, pp. 1585–1586, 2003. View at Publisher · View at Google Scholar · View at Scopus
  18. A. Wirawan, B. Schmidt, and C. K. Kwoh, “Pairwise distance matrix computation for multiple sequence alignment on the cell broadband engine,” in Computational Science—ICCS 2009, vol. 5544 of Lecture Notes in Computer Science, pp. 954–963, 2009. View at Publisher · View at Google Scholar
  19. Y. Liu, B. Schmidt, and D. L. Maskell, “MSAProbs: multiple sequence alignment based on pair hidden Markov models and partition function posterior probabilities,” Bioinformatics, vol. 26, no. 16, pp. 1958–1964, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. D. Hains, Z. Cashero, M. Ottenberg, W. Bohm, and S. Rajopadhye, “Improving CUDASW, a parallelization of smith-waterman for CUDA enabled devices,” in Proceedings of the 25th IEEE International Parallel and Distributed Processing Symposium, Workshops and Phd Forum (IPDPSW '11), pp. 490–501, May 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. Y. Liu, A. Wirawan, and B. Schmidt, “CUDASW++ 3.0: accelerating Smith-Waterman protein database search by coupling CPU and GPU SIMD instructions,” BMC Bioinformatics, vol. 14, article 117, 2013. View at Publisher · View at Google Scholar · View at Scopus
  22. W. Liu, B. Schmidt, G. Voss, and W. Müller-Wittig, “Streaming algorithms for biological sequence alignment on GPUs,” IEEE Transactions on Parallel and Distributed Systems, vol. 18, no. 9, pp. 1270–1281, 2007. View at Publisher · View at Google Scholar · View at Scopus
  23. D. Díaz, F. J. Esteban, P. Hernández et al., “Mc64-clustalwp2: a highly-parallel hybrid strategy to align multiple sequences in many-core architectures,” PLOS ONE, vol. 9, no. 4, Article ID e94044, 2014. View at Google Scholar
  24. J. Cheetham, F. Dehne, S. Pitre, A. Rau-Chaplin, and P. J. Taillon, “Parallel CLUSTAL W for PC clusters,” in Computational Science and Its Applications—ICCSA 2003, vol. 2668 of Lecture Notes in Computer Science, pp. 300–309, 2003. View at Publisher · View at Google Scholar
  25. National Center for Biotechnology Information (NCBI), http://www.ncbi.nlm.nih.gov/.