The power of detecting enriched patterns: An HMM approach

Journal of Computational Biology

Volumn 17, Issue 4, 2010, Pages 581-592

  Zhai, Zhiyuan ^a   Ku, Shih Yen ^b   Luan, Yihui ^a   Reinert, Gesine ^c   Waterman, Michael S ^b,d   Sun, Fengzhu ^b,d  

^a SHANDONG UNIVERSITY   (China)

^b UNIVERSITY OF SOUTHERN CALIFORNIA   (United States)

^c UNIVERSITY OF OXFORD   (United Kingdom)

^d TSINGHUA UNIVERSITY   (China)

Author keywords

Hidden Markov model; Motif; Pattern recognition; Statistical power

Indexed keywords

ARTICLE; AUTOMATED PATTERN RECOGNITION; CPG ISLAND; DNA BASE COMPOSITION; DNA SEQUENCE; GENETICS; INTERNET; MATHEMATICAL COMPUTING; METHODOLOGY; NUCLEOTIDE SEQUENCE; POISSON DISTRIBUTION; PROBABILITY; PROCEDURES;

BASE COMPOSITION; BASE SEQUENCE; CPG ISLANDS; INTERNET; MARKOV CHAINS; NUMERICAL ANALYSIS, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; POISSON DISTRIBUTION; SEQUENCE ANALYSIS, DNA;

EID: 77951997235     PISSN: 10665277     EISSN: None     Source Type: Journal    
DOI: 10.1089/cmb.2009.0218     Document Type: Article

References (48)

1. Antequera, F., and Bird, A. 1999. CpG islands as genomic footprints of promoters that are associated with replication origins. Curr. Biol. 9, 661667.
2. Bailey, T. L., and Elkan, C. 1994. Fitting a mixture model by expectation maximization to discover motifs in biopolymers. Proc. 2nd Int. Conf. Intell. Syst. Mol. Biol. 28-36.
3. Bailey, T. L., and Elkan, C. 1995. Unsupervised learning of multiple motifs in biopolymers using expectation maxi-mization. Mach. Learn. 21, 51-80
4. Boeva, V., Clément, J., Régnier M., et al. 2007. Exact p-value calculation for heterotypic clusters of regulatory motifs and its application in computational annotation of cis-regulatory modules. Algorithms Mol. Biol. 2, 13.
5. Carpentier, M., Brouillet, S., and Pothier, J. 2005. YAKUSA: a fast structural database scanning method. Proteins 61, 137-151.
6. Ewens, W. J., and Grant, G. 2005. Statistical Methods in Bioinformatics: An Introduction, 2nd ed. Springer-Verlag, New York.
7. Fu, J. C., and Lou, W. Y. W. 2007. On the normal approximation for the distribution of the number of simple or compound patterns in a random sequence of multi-state trials. Methodol. Comput. Appl. Probabil. 9, 195-205.
8. Gentles, A. J., and Karlin, S. 2001. Genome-scale compositional comparisons in eukaryotes. Genome Res. 11, 540546.
9. Godbole, A. P. 1991. Poisson approximations for runs and patterns of rare events. Adv. Appl. Probabil. 23, 851-865.
10. Huang, H. 2002. Error bounds on multivariate normal approximations for word count statistics. Adv. Appl. Probabil. 34, 559-586.
11. Karlin, S., Burge, C., and Campbell, A. M. 1992. Statistical analyses of counts and distributions of restriction sites in DNA sequences. Nucleic Acids Res. 20, 1363-1370.
12. Kleffe, J., and Borodovsky, M. 1992. First and second moment of count of words in random texts generated by Markov chains. Comput. Appl. Biosci. 8, 433-441.
13. Kleffe, J., and Langbecker, U. 1990. Exact computation of pattern probabilities in random sequences generated by Markov chains. Comput. Appl. Biosci. 6, 347-353.
14. Krishna, S., Majumdar, I., and Grishin, N. V. 2003. Structural classification of zinc fingers: survey and summary. Nucleic Acids Res. 31, 532-550.
15. Ku, S. Y., and Hu, Y. J. 2008. Protein structure search and local structure characterization. BMC Bioinform. 9, 349.
16. Lawrence, C. E., Altschul, S. F., Boguski, M. S., et al. 1993. Detecting subtle sequence signals-a Gibbs sampling strategy for multiple alignment. Science 262, 208-214.
17. Liu, J. S. 1994. The collapsed Gibbs sampler in Bayesian computations with applications to a gene-regulation problem. J. Am. Statist. Assoc. 89, 958-966.
18. Liu, X. L., Brutlag, D. L., and Liu, J. S. 2002. An algorithm for finding protein-DNA binding sites with applications to chromatin-immunoprecipitation microarray experiments. Nat. Biotechnol. 20, 835-839.
19. Lo, W. C., Chang, C. H., Huang, P. J., et al. 2007. Protein structural similarity search by Ramachandran codes. BMC Bioinform. 8, 307.
20. Nguyen, C., Liang, G. M., Nguyen, T. T., et al. 2001. Susceptibility of nonpromoter CpG islands to de novo methylation in normal and neoplastic cells. J. Natl. Cancer Inst. 93, 1465-1472.
21. Nuel, G. 2004. LD-SPatt: large deviations statistics for patterns on Markov chains. J. Comp. Biol. 11, 1023-1033.
22. Nuel, G. 2006a. Effective P-value computations using finite Markov chain imbedding (FMCI) : application to local score and to pattern statistics. Algorithms Mol. Biol. 1, 5.
23. Nuel, G. 2006b. Numerical solutions for pattern statistics on Markov chains. Statist. Appl. Genet. Mol. Biol. 5, 26.
24. Nuel, G. 2007. Pattern Markov chains: optimal Markov chain embedding through deterministic finite automata. J. Appl. Probabil. 45, 226-243.
25. Panjer, H. H. 1981. Recursive evaluation of a family of compound distributions. Astin Bull. 12, 22-26.
26. Pavesi, G., Mereghetti, P., Mauri, G., et al. 2004. Weeder Web: discovery of transcription factor binding sites in a set of sequences from co-regulated genes. Nucleic Acids Res. 32, W199-W203.
27. Rabiner, L. R. 1989. A tutorial on hidden Markov models and selected applications inspeech recognition. Proc. IEEE 77, 257-286.
28. Regnier, M. 2000. A unified approach to word occurrence probabilities. Discr. Appl. Math. 104, 259-280.
29. Reinert, G., and Schbath, S. 1998. Compound Poisson and Poisson process approximations for occurrences of multiple words in Markov chains. J. Comput. Biol. 15, 223-253.
30. Reinert, G., Schbath, S., and Waterman, M. S. 2000. Probabilistic and statistical properties of words: an overview. J. Comput. Biol. 7, 1-46.
31. Reinert, G., Schbath, S., and Waterman, M. S. 2005. Statistics on words with applications to biological sequences, 251-328. In Berstel, J., and Perrin, D., eds., Lothaire: Applied Combinatorics on Words. Cambridge University Press, New York.
32. Reinert, G., Chew, D., Sun F. Z., et al. 2009. Alignment free sequence comparison (I) : statistics and power. J. Comput. Biol. 16, 1-20.
33. Ribeca, P., and Raineri, E. 2008. Faster exact Markovian probability functions for motif occurrences: a DFA-only approach. Bioinformatics. 24, 2839-2848.
34. Robin, S., and Daudin, J. J. 1999. Exact distribution of word occurrences in a random sequence of letters. J. Appl. Probabil. 36, 179-193.
35. Robin, S., and Schbath, S. 2001. Numerical comparison of several approximations of the word count distribution in random sequences. J. Comput. Biol. 8, 349-359.
36. Royden, H. L. 1988. Real Analysis. Prentice Hall, Englewood Cliffs, NJ.
37. Schbath, S. 1995. Compound Poisson approximation of word counts in DNA sequences. ESAIM Probabil. Statist. 1, 1-16.
38. Schbath, S. 2000. An overview on the distribution of word counts in Markov chains. J. Comput. Biol. 7, 193-201.
39. Shan, G., and Zheng, W. M. 2009. Counting of oligomers in sequences generated by Markov chains for DNA motif discovery. Bioinform. Comput. Biol. 7, 39-54.
40. Takai, D., and Jones, P. A. 2002. Comprehensive analysis of CpG islands in human chromosomes 21 and 22. Proc. Natl. Acad. Sci. USA 99, 3740-3745.
41. Thiesen, H., and Bach, C. 1990. Target detection assay (TDA) : a versatile procedure to determine DNA binding sites as demonstrated on SP1 protein. Nucleic Acids Res. 18, 3203-3209.
42. Tompa, M., Li, N., Bailey, T. L., et al. 2005. Assessing computational tools for the discovery of transcription factor binding sites. Nat. Biotechnol. 23, 137-144.
43. Tyagi, M., Sharma, P., Swamy, C. S., et al. 2006. Protein Block Expert (PBE) : a web-based protein structure analysis server using a structural alphabet. Nucleic Acids Res. 34, W119-W123.
44. Vergne, N., and Abadi, M. 2008. Poisson approximation for search of rare words in DNA sequences. Latin Am. J. Probabil. Math. Statist. 4, 223-244.
45. Waterman, M. S. 1995. Introduction to Computational Biology. Maps, Sequences and Genomes. Chapman & Hall, New York.
46. Willmot, G. E., and Panjer, H. H. 1987. Difference equation approaches in evaluation of compound distributions. Insurance Math. Econ. 6, 43-56.
47. Yang, J. M., and Tung, C. H. 2006. Protein structure database search and evolutionary classification. Nucleic Acids Res. 34, 3646-3659.
48. Zhang, J., Jiang, B., Li, M., et al. 2007. Computing exact P-values for DNA motifs. Bioinformatics 23, 531-537