n-Gram characterization of genomic islands in bacterial genomes

Computer Methods and Programs in Biomedicine

Volumn 93, Issue 3, 2009, Pages 241-256

  Pavlović Lažetić, Gordana M ^a   Mitić, Nenad S ^a   Beljanski, Miloš V ^b  

^a UNIVERSITY OF BELGRADE   (Serbia)

^b IGPC   (Serbia)

Author keywords

Backbone sequence; Escherichia coli O157:H7 EDL933; Genomic islands; Horizontal gene transfer; n Grams; Statistical analysis; Zipf like analysis

Indexed keywords

BACKBONE SEQUENCE; ESCHERICHIA COLI O157:H7 EDL933; GENOMIC ISLANDS; HORIZONTAL GENE TRANSFER; N-GRAMS; STATISTICAL ANALYSIS; ZIPF-LIKE ANALYSIS;

BACTERIOLOGY; ESCHERICHIA COLI; MARKOV PROCESSES;

GENE TRANSFER;

BASE COMPOSITION; BASE SEQUENCE; CODON; COMPUTATIONAL BIOLOGY; ESCHERICHIA COLI O157; GENE TRANSFER, HORIZONTAL; GENOME, BACTERIAL; GENOMIC ISLANDS; GENOMICS; MARKOV CHAINS; MODELS, STATISTICAL; MOLECULAR SEQUENCE DATA;

EID: 59249103863     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2008.10.014     Document Type: Article

References (70)

1. Hacker J., and Carniel E. Ecological fitness, genomic islands and bacterial pathogenicity. EMBO Rep. 2 5 (2001) 376-381
2. Karlin S. Detecting anomalous gene clusters and pathogenicity islands in diverse bacterial genomes. Trends Microbiol. 9 7 (2001) 335-343
3. Blattner F.R., Ill G.P., Blochet C.A., Perna N.T., Burland V., Riley M., Collado-Vides J., Glasner J.D., Rode C.K., Mayhew G.F., Gregor J., Davis N.V., Kirkpatrick H.A., Goeden M.A., Rose D.J., Mau B., and Shao Y. The complete genome sequence of Escherichia coli K-12. Science 277 (1997) 1453-1462
4. Lloyd A.L., Rasko D.A., and Mobley H.L.T. Defining genomic islands and uropathogen-specific genes in uropathogenic Escherichia coli. J. Bacteriol. 189 (2007) 3532-3546
5. Perna N.T., Plunkett III G., Burland V., Mau B., Glasner J.D., Rose D.J., Mayhew G.F., Evans P.S., Gregor J., Kirkpatrick H.A., Posfai G., Hackett J., Klink S., Boutin A., Shao Y., Miller L., Grotbeck E.J., Davis N.W., Limk A., Dimalantak E.T., Potamousis K.D., Apodaca J., Anantharaman T.S., Lin J., Yen G., Schwartz D.C., Welch R.A., and Blattner F.R. Genome sequence of enterohemorrhagic Escherichia coli O157:H7. Nature 409 (2001) 529-533
6. Konopka A.K., and Martindale C. Noncoding DNA, Zipf's law, and language. Science 268 5212 (1995) 789
7. Shannon C.A. Mathematical theory of communication. Bell Syst. Techn. J. 27 (1948) 379-423 623-656
8. Vinga S., and Almeida J. Alignment-free sequence comparison-a review. Bioinformatics 19 4 (2003) 513-523
9. Schwartz E.S. A dictionary for minimum redundancy encoding. JACM 10 4 (1963) 413-439
10. Wisniewski J.L. Effective text compression with simultaneous digram and trigram encoding. J. Inform. Sci. 13 (1997) 159-164
11. Zamora E.M., Pollock J.J., and Zamora A. The use of trigram analysis for spelling error detection. Inform. Process. Manage. 17 (1981) 305-316
12. Angell R.C., Freund G.E., and Willett P. Automatic spelling correction using trigram similarity measure. Inform. Process. Manage. 19 (1983) 255-261
13. J.C. Schmitt, Trigram-based method of language identification, US Patent 5,062,143 (1991).
14. Cavnar W.B., and Trenkle J.M. n-Gram-based text categorization. Symposium on Document Analysis and Information Retrieval (1994), University of Nevada, Las Vegas
15. Kešelj V., Peng F., Cercone N., and Thomas C. n-Gram-based author profiles for authorship attribution, PACLING'03. Pacific Association for Computational Linguistics (2003), Dalhousie University, Halifax, NS, Canada
16. Damashek M. Gauging similarity with n-grams: language-independent categorization of text. Science 267 5199 (1995) 843-848
17. Krzywicki A., and Slonimski P.P. Formal analysis of protein sequences. I. Specific long range constraints in pair associations of amino acids. J. Theor. Biol. 17 (1967) 136-158
18. Radomski J.P., and Slonimski P.P. Genomic style of proteins: concepts, methods and analysis of ribosomal proteins from 16 microbial species. FEMS Microbiol. Rev. 25 (2001) 425-435
19. Rosato V., Pucello N., and Giuliano G. Evidence for cysteine clustering in thermophylic proteomes. Trends Genet. 18 (2002) 278-281
20. Phillips G.J., Arnold J., and Ivarie R. The effect of codon usage on the oligonucleotide composition of the E. coli genome and identification of over- and underrepresented sequences by Markov chain analysis. NAR 15 6 (1987) 2627-2638
21. Phillips G.J., Arnold J., and Ivarie R. Mono-through hexanucleotide composition of the Escherichia coli genome: a Markov chain analysis. NAR 15 6 (1987) 2611-2626
22. Colosimo A., Morante S., Parisi V., and Rosi G.C. An improved method for detection of words with unusual occurrence frequency in nucleotide sequences. J. Theor. Biol. 165 (1993) 659-672
23. Schbath S., Prum B., and de Turckheim E. Exceptional motifs in different Markov chain models for a statistical analysis of DNA sequences. J. Comp. Biol. 2 (1995) 417-437
24. Gelfand M.S., and Koonin E.V. Avoidance of palindromic words in bacterial and archaeal genomes: a close connection with restriction enzymes. NAR 25 (1997) 2430-2439
25. Karlin S., Mrazek J., and Campbell A.M. Compositional biases of bacterial genomes and evolutionary implications. J. Bacteriol. 179 (1997) 3899-3913
26. Karlin S., and Burge C. Dinucleotide relative abundance extremes: a genomic signature. Trends Genet. 11 (2000) 283-290
27. Rocha E.P.C., Viari A., and Danchin A. Oligonucleotide bias in Bacillus subtilis: general trends and taxonomic comparisons. NAR 26 (1998) 2971-2980
28. Pevzner P.A., Borodovsky M.Y., and Mironov A.A. Linguistics of nucleotide sequences. I. The significance of deviation from mean statistical characteristics and prediction of the frequencies of occurrence of words. J. Biomol. Struct. Dynam. 6 (1989) 1013-1026
29. Karlin S., Burge C., and Campbell A.M. Statistical analysis of counts and distributions of restriction sites in DNA sequences. NAR 20 (1992) 1363-1370
30. Burge C., Campbell A.M., and Karlin S. Over- and under-representation of short oligonucleotides in DNA sequences. PNAS 89 (1992) 1358-1362
31. Schbath S. An efficient statistic to detect over- and under- represented words in DNA sequences. J. Comp. Biol. 4 (1997) 189-192
32. Elhai J. Determination of bias in the relative abundance of oligonucleotides in DNA sequences. J. Comp. Biol. 8 (2001) 151-175
33. Reinert G., Schbath S., and Waterman M.S. Probabilistic and statistical properties of words: an overview. J. Comput. Biol. 7 (2000) 1-46
34. Blaisdall B.E. A measure of the similarity of sets of sequences not requiring sequence alignment. PNAS 83 (1986) 5155-5159
35. Blaisdall B.E. Effectiveness of measures requiring and not requiring prior sequence alignment for estimating the dissimilarity of natural sequences. J. Mol. Evol. 29 (1989) 526-537
36. Blaisdall B.E. Average values of a dissimilarity measure not requiring sequence alignment are twice the averages of conventional mismatch counts requiring sequence alignment for computer generated system model. J. Mol. Evol. 29 (1989) 538-547
37. Karlin S. Statistical significance of sequence patterns in proteins. Curr. Opin. Struct. Biol. 5 (1995) 360-371
38. Lippert R.A., Huang H.Y., and Waterman M.S. Distributional regimes for the number of k-word matches between two random sequences. PNAS 99 (2002) 13980-13989
39. Radomski J.P., and Slonimski P.P. Primary sequences of proteins from complete genomes display a singular periodicity: alignment-free N-gram analysis. C. R. Biol. 330 (2007) 33-48
40. Stuart G.W., Moffett K., and Baker S. Integrated gene and species phylogenies from unaligned whole genome protein sequences. Bioinformatics 18 (2002) 100-108
41. Edgar R.C. Local homology recognition and distance measures in linear time using compressed amino acid alphabets. NAR 32 (2004) 380-384
42. Qi J., Wang B., and Hao B.I. Whole proteome prokaryote phylogeny without sequence alignment: a k-string composition approach. J. Mol. Evol. 58 (2004) 2924-2935
43. Solovyev V.V., and Makarova K.S. A novel method of protein sequence classification based on oligopeptide frequency analysis and its application to search for functional sites and to domain localization. Comput. Appl. Biosci. 9 (1993) 17-24
44. Cheng B.Y., Carbonell J.G., and Klein-Seetharaman J. Protein classification based on text document classification techniques. Proteins 58 (2005) 955-970
45. Daeyaert F., Moereels H., and Lewi P.J. Classification and identification of proteins by means of common and specific amino acid n-tuples in unaligned sequences. Comput. Methods Prog. Biomed. 56 (1998) 221-233
46. Ganapathiraju M., Weisser D., Klein-Seetharaman J., Rosenfeld R., Carbonell J., and Reddy R. Comparative n-gram analysis of whole-genome sequences, HLT'02. Human Language Technologies Conference. San Diego (2002)
47. Ganapathiraju M., Klein-Seetharaman J., Rosenfeld R., Carbonell J., and Reddy R. Rare and frequent amino acid n-grams in whole-genome protein sequences, RECOMB'02. The Sixth Annual International Conference on Research in Computational Molecular Biology. Washington DC, USA (April 2002)
48. King B.R., and Guda C. ngLOC: an n-gram-based Bayesian method for estimating the subcellular proteomes of eukaryotes. Genome Biol. 8 5 (2007) R68
49. Vinga S., Gouveia-Oliveira R., and Almeida J.S. Comparative evaluation of word composition distances for the recognition of SCOP relationships. Bioinformatics 20 (2004) 206-215
50. Volkovich Z., Kirzhner V., Bolshoy A., Nevo E., and Korol A. The method of N-grams in large-scale clustering of DNA texts. Pattern Recogn. 38 (2005) 1902-1912
51. Kirzhner V., Nevo E., Korol A., and Bolshoy A. A large-scale comparison of genomic sequences: one promising approach. Acta Biotheor. 51 (2003) 73-89
52. Tomović A., Janičić P., and Kešelj V. N-Gram-based classification and unsupervised hierarchical clustering of genome sequences. Comput. Methods Prog. Biomed. 81 (2006) 137-153
53. Rajan I., Aravamuthan S., and Mande S.S. Identification of compositionally distinct regions in genomes using the centroid method. Bioinformatics 23 20 (2007) 2672-2677
54. Srividhya K.V., Alaguraj V., Poornima G., Kumar D., Singh G.P., Raghavenderan L., Katta A.V.S.K.M., Mehta P., and Krishnaswamy S. Identification of prophages in bacterial genomes by dinucleotide relative abundance difference. PLoS One 2 11 (2007) e1193 10.1371/journal.pone.0001193
55. Tobi D., and Bahar I. Recruitment of rare 3-grams at functional sites: Is this a mechanism for increasing enzyme specificity?. BMC Bioinform. 8 (2007) 226
56. Pavlović-Lažetić G., Mitić N., Beljanski M., and Tomović A. Mutational analysis of SARS CoV genome, MCCMB'05. Moscow Conference on Computational Molecular Biology. Moscow, Russia (2005)
57. D. Vitas, G. Pavlović-Lažetić, C. Krstev, About word length counting in Serbian, in: P. Grzybek (Ed.), Word Length Studies and Related Issues, Contributions to the Science of Text and Language, Series: Text, Speech and Language Technology, Springer-Verlag, Heidelberg, 2006, pp. 279-295.
58. Mitić N., Pavlović-Lažetić G., and Beljanski M. Could N-gram analysis contribute to genomic island determination?. J. Biomed. Inform. 41 (2008) 936-943 10.1016/j.jbi.2008.03.007
59. Hsiao W., Wan I., Jones S.J., and Brinkman F.S.L. IslandPath: aiding detection of genomic islands in prokaryotes. Bioinformatics 19 3 (2003) 418-420
60. Mantri Y., and Williams K.P. Islander: a database of integrative islands in prokaryotic genomes, the associated integrases and their DNA site specificities. NAR 32 DB issue (2004) D55-D58
61. Merkl R. SIGI: score-based identification of genomic islands. BMC Bioinform. 5 22 (2004) 1-14
62. Yoon S.H., Hur C.-G., Kang H.-Y., Kim Y.H., Oh T.K., and Kim J.F. A computational approach for identifying pathogenicity islands in prokaryotic genomes. BMC Bioinform. 6 184 (2005) 1-11
63. Yoon S.H., Park Y.-K., Lee S., Choi D., Oh T.K., Hur C.-G., and Kim J.F. Towards pathogenomics: a web based resource for pathogenicity islands. NAR 35 DB issue (2007) D395-D400
64. PubMed NCBI Entrez, http://www.ncbi.nlm.nih.gov/entrez.
65. Schmidt H., and Hensel M. Pathogenecity islands in bacterial pathogenesis. Clin. Microbiol. Rev. 17 (2004) 14-56
66. Zhang R., and Zhang C.-T. A systematic method to identify genomic islands and its applications in analyzing the genomes of Corynebacterium glutamicum and Vibrio vulnificus CMCP6 chromosome I. Bioinformatics 20 (2004) 612-622
67. Levesque R. SPSS Programming and Data Management: A Guide for SPSS and SAS Users (2007), SPSS Inc., Chicago, IL, USA
68. Manning C.D., Raghavan P., and Schütze H. An Introduction to Information Retrieval (2007), Cambridge University Press, Cambridge, England
69. Welch R.A., Burland V., 3rd Plunkett G., Redford P., Roesch P., Rasko D., Buckles E.L., Liou S.-R., Boutin A., Hackett J., Stroud D., Mayhew G.F., Rose D.J., Zhou S., Schwartz D.C., Perna N.T., Mobley H.L.T., Donnenberg M.S., and Blattner F.R. Extensive mosaic structure revealed by the complete genome sequence of uropathogenic Escherichia coli. PNAS 99 (2002) 17020-17024
70. Genscript Com, http://www.genscript.com/cgi-bin/products/enzyme.cgi?op=all_ez.