Genomics & Informatics

Korea Genome Organization (한국유전체학회)
권호 표지
DOI QR코드

DOI QR Code

Insights of window-based mechanism approach to visualize composite biodata point in feature spaces

  • Daoud, Mosaab (Department of Mathematics and Statistics, York University)
  • Received : 2018.12.21
  • Accepted : 2019.01.18
  • Published : 2019.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this paper, we propose a window-based mechanism visualization approach as an alternative way to measure the seriousness of the difference among data-insights extracted from a composite biodata point. The approach is based on two components: undirected graph and Mosaab-metric space. The significant application of this approach is to visualize the segmented genome of a virus. We use Influenza and Ebola viruses as examples to demonstrate the robustness of this approach and to conduct comparisons. This approach can provide researchers with deep insights about information structures extracted from a segmented genome as a composite biodata point, and consequently, to capture the segmented genetic variations and diversity (variants) in composite data points.

Keywords

References

  1. Vinga S. Editorial: alignment-free methods in computational biology. Brief Bioinform 2014;15:341-342. https://doi.org/10.1093/bib/bbu005
  2. Zielezinski A, Vinga S, Almeida J, Karlowski WM. Alignment-free sequence comparison: benefits, applications, and tools. Genome Biol 2017;18:186. https://doi.org/10.1186/s13059-017-1319-7
  3. Schwende I, Pham TD. Pattern recognition and probabilistic measures in alignment-free sequence analysis. Brief Bioinform 2014;15:354-368. https://doi.org/10.1093/bib/bbt070
  4. Daoud M. Quantum sequence analysis: a new alignment-free technique for analyzing sequences in feature space. In: ACM-BCB 2013: ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics, 2013 Sep 22-25, Washington, DC, USA. New York: Association for Computing Machinery, 2013. p. 702.
  5. Pham TD, Zuegg J. A probabilistic measure for alignment-free sequence comparison. Bioinformatics 2004;20:3455-3461. https://doi.org/10.1093/bioinformatics/bth426
  6. Borozan I, Watt S, Ferretti V. Integrating alignment-based and alignment-free sequence similarity measures for biological sequence classification. Bioinformatics 2015;31:1396-1404. https://doi.org/10.1093/bioinformatics/btv006
  7. Vinga S, Almeida J. Alignment-free sequence comparison: a review. Bioinformatics 2003;19:513-523. https://doi.org/10.1093/bioinformatics/btg005
  8. Daoud M. A new variance-covariance structure-based statistical pattern recognition system for solving the sequence-set proximity problem under the homology-free assumption. Ph.D. Dissertation. Guelph: University of Guelph, 2010.
  9. Hogg RV, McKean JW, Craig AT. Introduction to Mathematical Statistics. 7th ed. Boston: Pearson, 2012.
  10. Schweiger B, Zadow I, Heckler R. Antigenic drift and variability of influenza viruses. Med Microbiol Immunol 2002;191:133-138. https://doi.org/10.1007/s00430-002-0132-3
  11. Cann AJ. Principles of Molecular Virology. 4th ed. Amsterdam: Academic Press, 2005.
  12. Hilleman MR. Realities and enigmas of human viral influenza: pathogenesis, epidemiology and control. Vaccine 2002;20:3068-3087. https://doi.org/10.1016/S0264-410X(02)00254-2
  13. Kurstak E, Marusyk RG, Murphy FA, Van Regenmortel MH. Applied Virology Research. Vol. 2. Virus Variability, Epidemiology and Control. New York: Springer, 1990. pp. 1-7.
  14. Lamb RA, Krug RM. Orthomyxoviridae: the viruses and their replication. In: Fields of Virology. Vol. 2 (Knipe DM, Howley PM, eds.). Philadelphia: Lippincott Williams and Wilkins, 2001. pp. 1487-1579.
  15. Ebolavirus resource. Geneva: Viralzone, 2015. Accessed 2019 Jan 2. Available from: http://viralzone.expasy.org/.