Table of Contents Author Guidelines Submit a Manuscript
Journal of Biomedicine and Biotechnology
Volume 2008, Article ID 513701, 10 pages
http://dx.doi.org/10.1155/2008/513701
Methodology Report

Using Growing Self-Organising Maps to Improve the Binning Process in Environmental Whole-Genome Shotgun Sequencing

1Dynamic Systems & Control Group, Department of Mechanical Engineering, University of Melbourne, VIC 3010, Australia
2Research Center for Biodiversity, Academia Sinica, Taipei 115, Taiwan

Received 31 August 2007; Accepted 18 November 2007

Academic Editor: Daniel Howard

Copyright © 2008 Chon-Kit Kenneth Chan 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. G. W. Tyson, J. Chapman, P. Hugenholtz et al., “Community structure and metabolism through reconstruction of microbial genomes from the environment,” Nature, vol. 428, no. 6978, pp. 37–43, 2004. View at Publisher · View at Google Scholar
  2. J. C. Venter, K. Remington, J. F. Heidelberg et al., “Environmental genome shotgun sequencing of the sargasso sea,” Science, vol. 304, no. 5667, pp. 66–74, 2004. View at Publisher · View at Google Scholar
  3. S. G. Tringe, C. Von Mering, A. Kobayashi et al., “Comparative metagenomics of microbial communities,” Science, vol. 308, no. 5721, pp. 554–557, 2005. View at Publisher · View at Google Scholar
  4. T. Woyke, H. Teeling, N. N. Ivanova et al., “Symbiosis insights through metagenomic analysis of a microbial consortium,” Nature, vol. 443, no. 7114, pp. 950–955, 2006. View at Publisher · View at Google Scholar
  5. D. B. Rusch, A. L. Halpern, G. Sutton et al., “The sorcerer II global ocean sampling expedition: northwest atlantic through eastern tropical pacific,” PLoS Biology, vol. 5, no. 3, p. e77, 2007. View at Publisher · View at Google Scholar
  6. S. Yooseph, G. Sutton, D. B. Rusch et al., “The sorcerer II global ocean sampling expedition: expanding the universe of protein families,” PLoS Biology, vol. 5, no. 3, p. e16, 2007. View at Publisher · View at Google Scholar
  7. K. Chen and L. B. Pachter, “Bioinformatics for whole-genome shotgun sequencing of microbial communities,” PLoS Computational Biology, vol. 1, no. 2, p. e24, 2005. View at Publisher · View at Google Scholar
  8. J. A. Eisen, “Environmental shotgun sequencing: its potential and challenges for studying the hidden world of microbes,” PLoS Biology, vol. 5, no. 3, p. e82, 2007. View at Publisher · View at Google Scholar
  9. A. Rodriguez, Y. Zhang, N. Maltsev, and E. Marland, “Chisel—a framework for identification and characterization of taxonomic and phenotypic versions of enzymes,” in Proceedings of the Institute of Structural Molecular Biology (ISMB '06), Fortaleza, Brazil. View at Google Scholar
  10. N. Maltsev, M. Syed, A. Rodriguez, B. Gopalan, and F. Brockman, “A novel binning approach and its application to a metagenome from a multiple extreme environment,” in Proceedings of the Joint Genomics: GTL Awardee Workshop V and Metabolic Engineering and USDA-DOE Plant Feedstock Genomics for Bioenergy Awardee Workshop, North Bethesda, Md, USA. View at Google Scholar
  11. M. Huntemann, MetaClust—entwicklung eines modularen Programms zum Clustern von Metagenomfragmenten anhand verschiedener intrinsischer DNA-Signaturen, Diploma thesis.
  12. H. Teeling, A. Meyerdierks, M. Bauer, R. Amann, and F. O. Glöckner, “Application of tetranucleotide frequencies for the assignment of genomic fragments,” Environmental Microbiology, vol. 6, no. 9, pp. 938–947, 2004. View at Publisher · View at Google Scholar
  13. H. Teeling, J. Waldmann, T. Lombardot, M. Bauer, and F. O. Glöckner, “TETRA: a web-service and a stand-alone program for the analysis and comparison of tetranucleotide usage patterns in DNA sequences,” BMC Bioinformatics, vol. 5, no. 163, 2004. View at Publisher · View at Google Scholar
  14. A. C. McHardy, H. G. Martín, A. Tsirigos, P. Hugenholtz, and I. Rigoutsos, “Accurate phylogenetic classification of variable-length DNA fragments,” Nature Methods, vol. 4, no. 1, pp. 63–72, 2007. View at Publisher · View at Google Scholar
  15. T. Abe, S. Kanaya, M. Kinouchi, Y. Ichiba, T. Kozuki, and T. Ikemura, “Informatics for unveiling hidden genome signatures,” Genome Research, vol. 13, no. 4, pp. 693–702, 2003. View at Publisher · View at Google Scholar
  16. S. Karlin, J. Mrázek, and A. M. Campbell, “Compositional biases of bacterial genomes and evolutionary implications,” Journal of Bacteriology, vol. 179, no. 12, pp. 3899–3913, 1997. View at Google Scholar
  17. S. Karlin, “Global dinucleotide signatures and analysis of genomic heterogeneity,” Current Opinion in Microbiology, vol. 1, no. 5, pp. 598–610, 1998. View at Publisher · View at Google Scholar
  18. C. Weinel, K. E. Nelson, and B. Tümmler, “Global features of the Pseudomonas putida KT2440 genome sequence,” Environmental Microbiology, vol. 4, no. 12, pp. 809–818, 2002. View at Publisher · View at Google Scholar
  19. R. Sandberg, G. Winberg, C.-I. Bränden, A. Kaske, I. Ernberg, and J. Cöster, “Capturing whole-genome characteristics in short sequences using a naïve Bayesian classifier,” Genome Research, vol. 11, no. 8, pp. 1404–1409, 2001. View at Publisher · View at Google Scholar
  20. Y. Z. Zhai, A. Hsu, and S. K. Halgamuge, “Scalable dynamic self-organising maps for mining massive textual data,” in Proceedings of the Lecture Notes in Computer Science (including subseries: Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4234, LNCS III, pp. 260–267, Springer, Berlin, Germany. View at Google Scholar
  21. R. Amarasiri and D. Alahakoon, “Applying dynamic self organizing maps for identifying changes in data sequences,” in Design and Application of Hybrid Intelligent Systems, pp. 682–691, IOS Press, Amsterdam, The Netherlands, 2003. View at Google Scholar
  22. S. Chen, D. Alahakoon, and M. Indrawan, “Background knowledge driven ontology discovery,” in Proceedings of the IEEE International Conference on e-Technology, e-Commerce and e-Service, (EEE '05), pp. 202–207. View at Google Scholar
  23. H. Wang, F. Azuaje, and N. Black, “Improving biomolecular pattern discovery and visualization with hybrid self-adaptive networks,” IEEE Transactions on Nanobioscience, vol. 1, no. 4, pp. 146–166, 2002. View at Publisher · View at Google Scholar
  24. H. Wang, F. Azuaje, and N. Black, “Interactive GSOM-Based approaches for improving biomedical pattern discovery and visualization,” in Computational and Information Science, vol. 3314 of Lecture Notes in Computer Science, pp. 556–561, Springer, Berlin, Germany, 2004. View at Google Scholar
  25. M. A. Karim, S. Halgamuge, A. J. R. Smith, and A. L. Hsu, “Manufacturing yield improvement by clustering,” in Neural Information Processing, vol. 4234 of Lecture Notes in Computer Science, pp. 526–534, Springer, Berlin, Germany, 2006. View at Google Scholar
  26. A. L. Hsu, S.-L. Tang, and S. K. Halgamuge, “An unsupervised hierarchical dynamic self-organizing approach to cancer class discovery and marker gene identification in microarray data,” Bioinformatics, vol. 19, no. 16, pp. 2131–2140, 2003. View at Publisher · View at Google Scholar
  27. A. L. Hsu and S. K. Halgamuge, “Enhancement of topology preservation and hierarchical dynamic self-organising maps for data visualisation,” International Journal of Approximate Reasoning, vol. 32, no. 2-3, pp. 259–279, 2003. View at Publisher · View at Google Scholar
  28. D. Alahakoon, S. K. Halgamuge, and B. Srinivasan, “Dynamic self-organizing maps with controlled growth for knowledge discovery,” IEEE Transactions on Neural Networks, vol. 11, no. 3, pp. 601–614, 2000. View at Publisher · View at Google Scholar
  29. T. Kohonen, Self-Organizing Maps, Springer, Berlin, Germany, 2nd edition, 1997.
  30. C. J. van Rijsbergen, Information Retrieval, Butterworths, London, UK, 2nd edition, 1979.
  31. K. Mavromatis, N. Ivanova, K. Barry et al., “Use of simulated data sets to evaluate the fidelity of metagenomic processing methods,” Nature Methods, vol. 4, no. 6, pp. 495–500, 2007. View at Publisher · View at Google Scholar
  32. T. Abe, H. Sugawara, S. Kanaya, M. Kinouchi, and T. Ikemura, “Self-Organizing Map (SOM) unveils and visualizes hidden sequence characteristics of a wide range of eukaryote genomes,” Gene, vol. 365, no. 1-2, pp. 27–34, 2006. View at Publisher · View at Google Scholar
  33. T. Jarvie, L. Du, and J. Knight, “Shotgun sequencing and assembly of microbial genomes: comparing 454 and Sanger methods,” Biochemica, pp. 11–14, 2005. View at Google Scholar
  34. L. Bonetta, “Genome sequencing in the fast lane,” Nature Methods, vol. 3, no. 2, pp. 141–146, 2006. View at Publisher · View at Google Scholar
  35. S. K. Halgamuge and M. Glesner, “Fuzzy neural networks: between functional equivalence and applicability,” International Journal of Neural Systems, vol. 6, no. 2, pp. 185–196, 1995. View at Publisher · View at Google Scholar
  36. S. K. Halgamuge, “Self-evolving neural networks for rule-based data processing,” IEEE Transactions on Signal Processing, vol. 45, no. 11, pp. 2766–2773, 1997. View at Publisher · View at Google Scholar