Skip to main content
NCBI home page
EURASIP Journal on Bioinformatics and Systems Biology logoLink to EURASIP Journal on Bioinformatics and Systems Biology
. 2006 Aug 13;2006(1):35809. doi: 10.1155/BSB/2006/35809

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

David KY Chiu 1,✉, Yan Wang 1
PMCID: PMC3171317  PMID: 18427583

Abstract

Decomposing a biological sequence into its functional regions is an important prerequisite to understand the molecule. Using the multiple alignments of the sequences, we evaluate a segmentation based on the type of statistical variation pattern from each of the aligned sites. To describe such a more general pattern, we introduce multipattern consensus regions as segmented regions based on conserved as well as interdependent patterns. Thus the proposed consensus region considers patterns that are statistically significant and extends a local neighborhood. To show its relevance in protein sequence analysis, a cancer suppressor gene called p53 is examined. The results show significant associations between the detected regions and tendency of mutations, location on the 3D structure, and cancer hereditable factors that can be inferred from human twin studies.

[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27]

References

  1. Chiu DKY, Kolodziejczak T. Inferring consensus structure from nucleic acid sequences. Computer Applications in the Biosciences. 1991;7(3):347–352. doi: 10.1093/bioinformatics/7.3.347. [DOI] [PubMed] [Google Scholar]
  2. Chiu DKY, Harauz G. A method for inferring probabilistic consensus structure with applications to molecular sequence data. Pattern Recognition. 1993;26(4):643–654. doi: 10.1016/0031-3203(93)90117-F. [DOI] [Google Scholar]
  3. Chiu DKY, Lui TWH. Integrated use of multiple interdependent patterns for biomolecular sequence analysis. International Journal of Fuzzy Systems. 2002;4(3):766–775. [Google Scholar]
  4. Chiu DKY, Wong AKC. Multiple pattern associations for interpreting structural and functional characteristics of biomolecules. Information Sciences. 2004;167(1–4):23–39. [Google Scholar]
  5. Chiu DKY, Lui TWH. A multiple-pattern biosequence analysis method for diverse source association mining. Applied Bioinformatics. 2005;4(2):85–92. doi: 10.2165/00822942-200504020-00002. [DOI] [PubMed] [Google Scholar]
  6. Greenblatt MS, Bennett WP, Hollstein M, Harris CC. Mutations in the p53 tumor suppressor gene: clues to cancer etiology and molecular pathogenesis. Cancer Research. 1994;54(18):4855–4878. [PubMed] [Google Scholar]
  7. Boys RJ, Henderson DA. A Bayesian approach to DNA sequence segmentation. Biometrics. 2004;60:573–588. doi: 10.1111/j.0006-341X.2004.00206.x. [DOI] [PubMed] [Google Scholar]
  8. Li W, Bernaola-Galván P, Haghighi F, Grosse I. Applications of recursive segmentation to the analysis of DNA sequences. Computers and Chemistry. 2002;26(5):491–510. doi: 10.1016/S0097-8485(02)00010-4. [DOI] [PubMed] [Google Scholar]
  9. Chiu DKY, Rao G. The 2-level pattern analysis of genome comparisons. WSEAS Transactions on Biology and Biomedicine. 2006;3(3):167–174. [Google Scholar]
  10. Yan W. A segmentation algorithm for consensus regions in biosequences, M.S. thesis. Department of Computing and Information Science, University of Guelph, Guelph, Ontario, Canada; 2003. [Google Scholar]
  11. Zhang J. Analysis of information content for biological sequences. Journal of Computational Biology. 2002;9(3):487–503. doi: 10.1089/106652702760138583. [DOI] [PubMed] [Google Scholar]
  12. Lichtenstein P, Holm NV, Verkasalo PK. et al. Environmental and heritable factors in the causation of cancer: analyses of cohorts of twins from Sweden, Denmark, and Finland. New England Journal of Medicine. 2000;343(2):78–85. doi: 10.1056/NEJM200007133430201. [DOI] [PubMed] [Google Scholar]
  13. Magnusson PKE, Sparen P, Gyllensten UB. Genetic link to cervical tumours. Nature. 1999;400(6739):29–30. doi: 10.1038/21801. [DOI] [PubMed] [Google Scholar]
  14. Wong AKC, Liu TS, Wang CC. Statistical analysis of residue variability in cytochrome c. Journal of Molecular Biology. 1976;102(2):287–295. doi: 10.1016/S0022-2836(76)80054-X. [DOI] [PubMed] [Google Scholar]
  15. Shannon CE. A mathematical theory of communication. Bell System Technical Journal. 1948;27:379–423, 623–656. reprinted in C. E. Shannon and W. Weaver, The Mathematical Theory of Communication, University of Illinois Press, Urbana, Ill, USA, 1949. [Google Scholar]
  16. Gatlin LL. The information content of DNA. Journal of Theoretical Biology. 1966;10(2):281–300. doi: 10.1016/0022-5193(66)90127-5. [DOI] [PubMed] [Google Scholar]
  17. Wong AKC, Wang Y. High-order pattern discovery from discrete-valued data. IEEE Transactions on Knowledge and Data Engineering. 1997;9(6):877–893. doi: 10.1109/69.649314. [DOI] [Google Scholar]
  18. Haberman SJ. The analysis of residuals in cross-classified tables. Biometrics. 1973;29:205–220. doi: 10.2307/2529686. [DOI] [Google Scholar]
  19. Kalbfleisch JG. Probability and Statistical Inference, Vol. 2: Statistical Inference. 2. Springer, New York, NY, USA; 1985. [Google Scholar]
  20. Berman HM, Westbrook J, Feng Z. et al. The protein data bank. Nucleic Acids Research. 2000;28(1):235–242. doi: 10.1093/nar/28.1.235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Hollstein M, Sidransky D, Vogelstein B, Harris CC. p53 mutations in human cancers. Science. 1991;253(5015):49–53. doi: 10.1126/science.1905840. [DOI] [PubMed] [Google Scholar]
  22. Levine AJ, Momand J, Finlay CA. The p53 tumour suppressor gene. Nature. 1991;351(6326):453–456. doi: 10.1038/351453a0. [DOI] [PubMed] [Google Scholar]
  23. Levine AJ. p53, the cellular gatekeeper for growth and division. Cell. 1997;88(3):323–331. doi: 10.1016/S0092-8674(00)81871-1. [DOI] [PubMed] [Google Scholar]
  24. Boeckmann B, Bairoch A, Apweiler R. et al. The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Research. 2003;31(1):365–370. doi: 10.1093/nar/gkg095. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Cho Y, Gorina S, Jeffrey PD, Pavletich NP. Crystal structure of a p53 tumor suppressor-DNA complex: understanding tumorigenic mutations. Science. 1994;265(5170):346–355. doi: 10.1126/science.8023157. [DOI] [PubMed] [Google Scholar]
  26. Hamroun D, Kato S, Ishioka C, Claustres M, Beroud C, Soussi T. The UMD TP53 database and website: update and revisions. Human Mutation. 2005;27(1):14–20. doi: 10.1002/humu.20269. [DOI] [PubMed] [Google Scholar]
  27. Chiu DKY, Chen X, Wong AKC. Association between statistical and functional patterns in biomolecules. Proceedings of the Atlantic Symposium on Computational Biology and Genome Information Systems and Technolgoy (CBGIST '01), Durham, NC, USA March 2001. pp. 64–69.

Articles from EURASIP Journal on Bioinformatics and Systems Biology are provided here courtesy of Springer

RESOURCES