Open Access

Multipattern Consensus Regions in Multiple Aligned Protein Sequences and Their Segmentation

  • David KY Chiu1 and
  • Yan Wang1
EURASIP Journal on Bioinformatics and Systems Biology20062006:35809

https://doi.org/10.1155/BSB/2006/35809

Received: 22 May 2005

Accepted: 7 June 2006

Published: 13 August 2006

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.

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Authors’ Affiliations

(1)
Department of Computing and Information Science, University of Guelph

References

  1. Chiu DKY, Kolodziejczak T: Inferring consensus structure from nucleic acid sequences. Computer Applications in the Biosciences 1991,7(3):347-352.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. 10.1016/0031-3203(93)90117-FView ArticleGoogle 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.MATHMathSciNetView ArticleGoogle Scholar
  5. Chiu DKY, Lui TWH: A multiple-pattern biosequence analysis method for diverse source association mining. Applied Bioinformatics 2005,4(2):85-92. 10.2165/00822942-200504020-00002View ArticleGoogle 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.Google Scholar
  7. Boys RJ, Henderson DA: A Bayesian approach to DNA sequence segmentation. Biometrics 2004, 60: 573-588. 10.1111/j.0006-341X.2004.00206.xMATHMathSciNetView ArticleGoogle 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. 10.1016/S0097-8485(02)00010-4View ArticleGoogle 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. 10.1089/106652702760138583View ArticleGoogle 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. 10.1056/NEJM200007133430201View ArticleGoogle Scholar
  13. Magnusson PKE, Sparen P, Gyllensten UB: Genetic link to cervical tumours. Nature 1999,400(6739):29-30. 10.1038/21801View ArticleGoogle 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. 10.1016/S0022-2836(76)80054-XView ArticleGoogle 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, 1949MATHMathSciNetView ArticleGoogle Scholar
  16. Gatlin LL: The information content of DNA. Journal of Theoretical Biology 1966,10(2):281-300. 10.1016/0022-5193(66)90127-5View ArticleGoogle 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. 10.1109/69.649314View ArticleGoogle Scholar
  18. Haberman SJ: The analysis of residuals in cross-classified tables. Biometrics 1973, 29: 205-220. 10.2307/2529686View ArticleGoogle Scholar
  19. Kalbfleisch JG: Probability and Statistical Inference, Vol. 2: Statistical Inference. 2nd edition. 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. 10.1093/nar/28.1.235View ArticleGoogle Scholar
  21. Hollstein M, Sidransky D, Vogelstein B, Harris CC: p53 mutations in human cancers. Science 1991,253(5015):49-53. 10.1126/science.1905840View ArticleGoogle Scholar
  22. Levine AJ, Momand J, Finlay CA: The p53 tumour suppressor gene. Nature 1991,351(6326):453-456. 10.1038/351453a0View ArticleGoogle Scholar
  23. Levine AJ: p53, the cellular gatekeeper for growth and division. Cell 1997,88(3):323-331. 10.1016/S0092-8674(00)81871-1View ArticleGoogle 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. 10.1093/nar/gkg095View ArticleGoogle 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. 10.1126/science.8023157View ArticleGoogle 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.View ArticleGoogle 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 64-69.Google Scholar

Copyright

© D. K. Y. Chiu and Y. Wang. 2006

This article is published under license to BioMed Central Ltd. 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.