Extension of molecular similarity analysis approach to classification of dna sequences using dna descriptors

SAR and QSAR in Environmental Research

Volumn 22, Issue 1-2, 2011, Pages 21-34

  Jayalakshmi, R ^a   Natarajan, R ^b   Vivekanandan, M ^a  

^a BHARATHIDASAN UNIVERSITY   (India)

^b Centre for Mathematical Sciences   (India)

Author keywords

Cluster analysis; Descriptors; Dna; Molecular similarity analysis; N dimensional similarity space; Sequence comparison

Indexed keywords

CLASSIFICATION (OF INFORMATION); COMPUTATION THEORY; DNA; DNA SEQUENCES; INFORMATION THEORY; NUMERICAL METHODS; PRINCIPAL COMPONENT ANALYSIS;

ANALYSIS APPROACH; DESCRIPTORS; DNA; MOLECULAR SIMILARITY; MOLECULAR SIMILARITY ANALYSE; N-DIMENSIONAL SIMILARITY SPACE; PRINCIPAL COMPONENTS; SEQUENCE COMPARISONS; SIMILARITY ANALYSIS; SIMILARITY SPACES;

CLUSTER ANALYSIS;

EID: 79952514751     PISSN: 1062936X     EISSN: 1029046X     Source Type: Journal    
DOI: 10.1080/1062936X.2010.528255     Document Type: Article

References (41)

1. M. Johnson and G.M. Maggiora (eds.), Concepts and Applications of Molecular Similarity, John Wiley & Sons, New York, 1990.
2. S.C. Basak, B.D. Gute, and D. Mills, Similarity methods in analog selection, property estimation and clustering of diverse chemicals, ARKIVOC. ix (2006), pp. 157-210.
3. M. Johnson, S.C. Basak, and G. Maggiora, A characterization of molecular similarity methods for property prediction, Math. Comput. Model. 11 (1998), pp. 630-634.
4. S.C. Basak and G.D. Grunwald, Use of topological space and property space in selecting structural analogs, Math. Model. Sci. Comput. 4 (1994), pp. 464-469.
5. S.C. Basak, S. Bertelsen, and G.D. Grunwald, Application of graph theoretical parameters in quantifying molecular similarity and structure-activity relationships, J. Chem. Inf. Comput. Sci. 34 (1994), pp. 270-276.
6. S.B. Needleman and C.D. Wunch, A general method applicable to the search for similarities in the amino acid sequences of two proteins, J. Mol. Biol. 48 (1970), pp. 443-453.
7. T.F. Smith and M.S. Waterman, Identification of common molecular subsequences, J. Mol. Biol. 147 (1981), pp. 195-197.
8. S.F. Altschul, W. Gish, W. Miller, E.W. Myers, and D.J. Lipman, Basic local alignment search tool, J. Mol. Biol. 215 (1990), pp. 403-410.
9. D.J. Lipman, S.F. Altschul, and J.D. Kececioglu, A tool for multiple sequence alignment, Proc. Natl. Acad. Sci. U.S.A., 86 (1989), pp. 4412-4415.
10. ClustalW2. Available at www.ebi.ac.uk/Tools/clustalw2/index.html.
11. J.D. Thompson, D.G. Higgins, and T.J. Gibson, CLUSTALW: Improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice, Nucleic Acids Res. 22 (1994), pp. 4673-4680.
12. B.E. Blaisdell, A measure of the similarity of sets of sequences not requiring sequence alignment, Proc. Natl. Acad. Sci. U.S.A. 3 (1986), pp. 5155-5159.
13. B.E. Blaisdell, Average values of dissimilarity measure not requiring sequence alignment are twice the averages of conventional mismatch counts requiring sequence alignment for a computergenerated model system, J. Mol. Evol. 29 (1989), pp. 538-547.
14. B.E. Blaisdell, Effectiveness of measures requiring and not requiring prior sequence alignment for estimating the dissimilarity of natural sequences I, J. Mol. Evol. 29 (1989), pp. 526-537.
15. J.S. Almeida and S. Vinga, Alignment-free sequence comparison - a review, Bioinformatics. 19 (2003), pp. 513-523.
16. C.E. Shannon, A mathematical theory of communication, Bell. Syst. Tech. J. 27 (1948), pp. 379-423.
17. E. Hamori and J. Ruskin, H Curves: A novel method of representation of nucleotide series especially suited for long DNA sequences, J. Biol. Chem. 258 (1983), pp. 1318-1327.
18. E. Hamori, Novel DNA sequence representations, Nature. 314 (1983), pp. 585-586.
19. A. Nandy, A new graphical representation and analysis of DNA sequence structure: I. Methodology and application to globin genes, Curr. Sci. 66 (1994), pp. 309-313.
20. M.A. Gates, A simple way to look at DNA, J. Theor. Biol. 119 (1985), pp. 319-328.
21. P.M. Leong and S. Morgenthaler, Random walk and gap plots of DNA sequences, Comput. Appl. Biosci. 11 (1995), pp. 503-507.
22. S.S.T. Yau, J. Wang, A. Niknejad, A.C. Lu, N. Jin, and Y.K. Ho, DNA sequence representation without degeneracy, Nucleic Acids Res. 31 (2003), pp. 3078-3080.
23. M. Randic, M. Vracko, A. Nandy, and S.C. Basak, On 3-D graphical representation of DNA primary sequences and their numerical characterization, J. Chem. Inf. Comput. Sci. 40 (2000), pp. 1235-1244.
24. M. Randic, M. Vracko, N. Lers, and D. Plasvic, Novel 2-D representation of DNA sequences and their numerical characterization, Chem. Phys. Lett. 368 (2002), pp. 1-6.
25. M. Randic, D. Novic, D. Vikic-Topic, and D. Plasvic, Novel numerical and graphical representation of DNA sequences and proteins, SAR QSAR Environ. Res. 17 (2006), pp. 583-585.
26. J. Song and H. Tang, A new 2-D graphical representation of DNA sequences and their numerical characterization, J. Biochem. Biophys. Methods. 63 (2005), pp. 228-239.
27. J. Song, Analysis of similarity/dissimilarity DNA sequences by a new three-dimensional graphical representation, J. Biol. Syst. 15 (2007), pp. 287-297.
28. Z.H. Qi and T.R. Fan, PN curve: A 3D graphical representation of DNA sequences and their numerical characterization, Chem. Phys. Lett. 444 (2007), pp. 434-440.
29. B. Liao and T.M. Wang, 3D graphical representation of DNA sequences and their numerical characterization, J. Mol. Struct. THEOCHEM. 681 (2004), pp. 209-212.
30. F.I. Bai, Y.Z. Liu, and T.M. Wang, A representation of DNA sequences by random walk, Math. Biosci. 209 (2007), pp. 282-291.
31. Y. Chunzin, B. Lia, and T.M. Wang, New 3-D graphical representation of DNA sequences and their numerical characterization, Chem. Phys. Lett. 379 (2003), pp. 412-417.
32. A. Nandy, M. Harle, and S.C. Basak, Mathematical descriptors of DNA sequences: Development and applications, ARKIVOC. ix (2006), pp. 211-238.
33. R. Natarajan, R. Jayalakshmi, and M. Vivekanandan, Numerical characterization of DNA sequences: Connectivity type indices derived from DNA line graphs, J. Math. Chem. 48 (2010), pp. 521-529.
34. R. Jayalakshmi, R. Natarajan, M. Vivekanandan, and N. Ganapathy Subramanian, Descriptor based on information theory for numerical characterization of DNA sequences, Curr. Sci. 99 (2010), pp. 370-375.
35. D.A. Benson, I. Karsch-Mizrachi, D.J. Lipman, J. Ostell, and D.L. Wheeler, GenBank: Update, Nucleic Acids Res. 33 (2005), pp. D34-D38.
36. S.C. Basak, Information theoretic indices of neighborhood complexity and their applications, in Topological Indices and Related Descriptors in QSAR and QSPR, J. Devillers and A.T. Balaban, eds., Gordon and Breach Scientific Publishers, Amsterdam, 1999, pp. 563-593.
37. M. Randic, Characterization of molecular branching, J. Am. Chem. Soc. 97(3) (1975), pp. 6609-6615.
38. L.B. Kier and L.H. Hall, Molecular Connectivity in Structure-Activity Analysis, Research Studies Press, Letchworth, UK, 1986, p. 262.
39. A. Nandy, S.C. Basak, and B.D. Gute, Graphical representation and numerical characterisation of H5N1 avian flu neuraminidase gene sequence, J. Chem. Inf. Model. 47(3) (2007), pp. 945-951.
40. A. Nandy, P. Nandy, and A. Ghosh, Computational analysis and determination of a highly surface exposed segment in H5N1 avian flu and H5N1 swine flu neuraminidase, BMC Struct. Biol. 6 (2010), pp. 1-10.
41. H. Wiener, Structural determination of paraffin boiling points, J. Am. Chem. Soc. 69 (1947), pp. 17-20.