Genome signatures, self-organizing maps and higher order phylogenies: A parametric analysis

Evolutionary Bioinformatics

Volumn 3, Issue , 2007, Pages 211-236

  Gatherer, Derek ^a  

^a INSTITUTE OF VIROLOGY   (United Kingdom)

Author keywords

Genome signature; Herpesvirus; Jack Knife method; Metagenomics; Microarray; Phylogeny; Self Organizing map; Viruses

Indexed keywords

DNA;

ARTICLE; ARTIFICIAL NEURAL NETWORK; CLASSIFICATION; DNA SEQUENCE; GENOME; GENOME SIGNATURE; MICROARRAY ANALYSIS; NONHUMAN; PARAMETRIC TEST; PHYLOGENY; SELF ORGANIZING MAP; STATISTICAL ANALYSIS;

EID: 69249150433     PISSN: None     EISSN: 11769343     Source Type: Journal    
DOI: 10.1177/117693430700300001     Document Type: Article

References (51)

1. Abe, T., Kanaya, S., Kinouchi, M., Ichiba, Y., Kozuki, T. and Ikemura, T. 2003a. Informatics for unveiling hidden genome signatures. Genome. Res., 13:693-702.
2. Abe, T., Kanaya, S., Kinouchi, M., Ichiba, Y., Kozuki, T. and Ikemura, T. 2002. A novel bioinformatic strategy for unveiling hidden genome signatures of eukaryotes: Self-organizing map of oligonucleotide frequency. Genome Informatics, 13:13-20.
3. Abe, T., Kanaya, S., Kinouchi, M., Ishihara, N., Kosaka, Y., Kozuki, T., Ohyama, A. and Ikemura, T. 2003b. Self-organizing maps reveal hidden genome characteristics on a single map analysing 90 genomes of prokaryotes and eukaryotes. In: Human Genome Mapping 2003. Human Genome Organization.
4. Andrade, M.A., Casari, G., Sander, C. and Valencia, A. 1997. Classification of protein families and detection of the determinant residues with an improved self-organizing map. Biol Cybern, 76:441-50.
5. Arrigo, P., Giuliano, F., Scalia, F., Rapallo, A. and Damiani, G 1991. Identification of a new motif on nucleic acid sequence data using Kohonen's self-organizing map. Comput. Appl. Biosci., 7:353-7.
6. Blaisdell, B., Campbell, A. and Karlin, S. 1996. Similarities and dissimilarities of phage genomes. Proc. Natl. Acad. Sci., U.S.A., 93:5854-9.
7. Brocchieri, L. 2001. Phylogenetic inferences from molecular sequences: review and critique. Theoretical Population Biology, 59:27-41.
8. Campanaro, S., Vezzi, A., Vitulo, N., Lauro, F., D'Angelo, M., Simonato, F., Cestaro, A., Malacrida, G., Bertoloni, G., Valle, G. et al. 2005. Laterally transferred elements and high pressure adaptation in Photobacterium profundum strains. BMC Genomics, 6:122-36.
9. Campbell, A., Mrazek, J., Karlin, S. 1999. Genome signature comparisons among prokaryotic, plasmid and mitochondrial DNA. Proc. Natl. Acad. Sci., U.S.A., 96:9184-9.
10. Coenye, T. and Vandamme, P. 2004. Use of the genomic signature in bacterial classification and identification. Systematic and Applied Microbiology, 27:175-85.
11. Covell, D.G., Wallqvist, A., Rabow, A.A. and Thanki, N. 2003. Molecular classification of cancer: unsupervised self-organizing map analysis of gene expression microarray data. Mol. Cancer Ther, 2:317-32.
12. Davison, A. 2002. Evolution of the herpesviruses. Veterinary Microbiology, 86:69-88.
13. Davison, A., Eberle, R., Ehlers, B., Hayward, G., McGeoch, D., Minson, A., Pellett P, Roizman, B., Studdert, M. and Thiry, E. 2005. A planned order: Herpesvirales. In: ICTV. San Francisco.
14. Deschavanne, P., Giron, A., Vilain, J., Fagot, F. and Fertil, B. 1999. Genomic signature: Characterization and classification of species assessed by chaos game representation of sequences. Mol. Biol. Evol., 16:1391-9.
15. Dufraigne, C., Fertil, B., Lespinats, S., Giron, A. and Deschavanne, P. 2005. Detection and characterization of horizontal transfers in prokaryotes using genomic signature. Nucl. Acids Res., 33:e6.
16. Edwards, S., Fertil, B., Giron, A. and Deschavanne, P. 2002. A genomic schism in birds revealed by phylogenetic analysis of DNA strings. Syst Biol, 51:599-613.
17. Fauquet, C., Mayo, M., Maniloff, M. and Desselburger, U., Ball, L.: Virus Taxonomy: The Eighth Report of the International Committee on Taxonomy of Viruses. : Elsevier; 2005.
18. Gentles, A., Karlin, S. 2001. Genome-scale compositional comparisons in eukaryotes. Genome Res, 11:540-6.
19. Giuliano, F., Arrigo P., Scalia F., Cardo, P.P. and Damiani, G. 1993. Potentially functional regions of nucleic acids recognized by a Kohonen's self-organizing map. Comput Appl Biosci, 9:687-93.
20. Jernigan, R., Baran, R. 2002. Pervasive properties of the genomic signature. BMC Genomics, 3:23-32.
21. Kanaya, S., Kinouchi, M., Abe, T., Kudo, Y., Yamada, Y., Nishi, T., Mori, H. and Ikemura, T. 2001. Analysis of codon usage diversity of bacterial genes with a self-organizing map (SOM): characterization of horizontally transferred genes with emphasis on the E. coli O157 genome. Gene, 276:89-99.
22. Karlin, S. 2001. Detecting anomalous gene clusters and pathogenicity islands in diverse bacterial genomes. Trends in Microbiology, 9:335-43.
23. Karlin, S. 1998. Global dinucleotide signatures and analysis of genomic heterogeneity. Current Opinion in Microbiology, 1:598-610.
24. Karlin, S., Brocchieri, L., Mrazek, J., Campbell, A. and AM, S. 1999. A chimeric prokaryotic ancestry of mitochondrial and primitive eukaryotes. Proc. Natl. Acad. Sci., U.S.A., 96:9190-95.
25. Karlin, S. and Burge, C. 1995. Dinucleotide relative abundance extremes: a genomic signature. Trends in Genetics, 11:283-90.
26. Karlin, S., Campbell, A. and Mrázek, J. 1998. Comparative DNA analysis across diverse genomes. Annual. Review. of Genetics., 32:185-225.
27. Karlin, S. and Ladunga, I. 1994. Comparisons of eukaryotic genomes sequences. Proc. Natl. Acad. Sci. U.S.A., 91:12832-6.
28. Karlin, S. and Mrazek, J. 1996. What drives codon choices in human genes? J. Mol. Biol., 262:459-72.
29. Karlin, S. and Mrázek, J. 1997. Compositional differences within and between eukaryotic genomes. Proc. Natl. Acad. Sci., U.S.A., 94:10227-32.
30. Karlin, S., Mrázek, J., Campbell, A. 1997. Compositional biases of bacterial genomes and evolutionary implications. Journal of Bacteriology, 179:3899-913.
31. Kohonen, T: Self-Organizing Maps, vol. 30, 2 edn. Berlin: Springer; 1997.
32. Li, W., Jaroszewski, L. and Godzik, A. 2001. Clustering of highly homologous sequences to reduce the size of large protein database. Bioinformatics, 17:282-3.
33. Li, W., Jaroszewski, L. and Godzik, A. 2002. Tolerating some redundancy significantly speeds up clustering of large protein databases. Bioinformatics, 18:77-82.
34. Mahony, S., McInerney, J.O., Smith, T.J. and Golden, A. 2004. Gene prediction using the Self-Organizing Map: automatic generation of multiple gene models. BMC Bioinformatics, 5:23.
35. McHardy, A.C., Martin, H.G., Tsirigos, A., Hugenholtz, P. and Rigoutsos, I. 2007. Accurate phylogenetic classification of variable-length DNA fragments. Nature Methods, 4:63-72.
36. Oja, M., Sperber, G.O., Blomberg, J. and Kaski, S. 2005. Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups. Int. J. Neural. Syst., 15:163-79.
37. Paz, A., Kirzhner, V., Nevo, E. and Korol, A. 2006. Coevolution of DNAinteracting proteins and genome "dialect". Mol. Biol. Evol., 23:56-64.
38. Reid, J.F., Lusa, L., De Cecco, L., Coradini, D., Veneroni, S., Daidone, M.G., Gariboldi, M. and Pierotti, M.A. 2005. Limits of predictive models using microarray data for breast cancer clinical treatment outcome. J. Natl. Cancer Inst, 97:927-30.
39. Ressom, H., Wang, D. and Natarajan, P. 2003. Clustering gene expression data using adaptive double self-organizing map. Physiol. Genomics., 14:35-46.
40. Russell, G. and Subak-Sharpe, J. 1977. Similarity of the general designs of protochordates and invertebrates. Nature, 266:533-5.
41. Russell, G., Walker, P., Elton, R. and Subak-Sharpe, J. 1976. Doublet frequency analysis of fractionated vertebrate nuclear DNA. J. Mol. Biol., 108:1-23.
42. Russo, J., Moral, R., Balogh, G., Mailo, D. and Russo, I. 2005. The protective role of pregnancy in breast cancer. Breast Cancer Research, 7:131-42.
43. Samsonova, E.V., Kok, J.N. and Ijzerman, A.P. 2006. TreeSOM: Cluster analysis in the self-organizing map. Neural Networks, 19:935-49.
44. Sandberg, R., Bränden, C-I, Ernberg, I. and Cöster, J. 2003. Quantifying the species-specificity in genomic signatures, synonymous codon choice, amino acid usage and G + C content. Gene, 311:35-42.
45. Sandberg, R., Winberg, G., Bränden, C-I, Kaske, A., Ernberg, I. and Cöster, J. 2001. Capturing whole-genome characteristics in short sequences using a naive Bayesian classifier. Genome Res., 11:1404-9.
46. Tamayo, P., Slonim, D., Mesirov, J., Zhu, Q., Kitareewan, S., Dmitrovsky, E., Lander, E. and Golub, T. 1999. Interpreting patterns of gene expression with self-organizing maps: methods and application to hematopoietic differentiation. Proc. Natl. Acad. Sci., U.S.A., 96:2907-12.
47. Teeling, H., Meyerdierks, A., Bauer, M., Amann, R. and Glockner, F.O. 2004. Application of tetranucleotide frequencies for the assignment of genomic fragments. Environ. Microbiol., 6:938-47.
48. Ultsch, A. 1993. Self-organized feature maps for monitoring and knowledge acquisition of a chemical process. In: Gielen, S., Kappen, B, ed. Proc. Intl. Conf. on Artificial Neural Networks (ICANN). Amsterdam: Springer-Verlag, 1993:864-7.
49. Wang, H.C., Badger, J., Kearney, P. and Li, M. 2001. Analysis of codon usage patterns of bacterial genomes using the self-organizing map. Mol. Biol. Evol., 18:792-800.
50. Wang, Y., Hill, K., Singh, S., Kan, L. 2005. The spectrum of genomic signatures: from dinucleotides to chaos game representation. Gene, 346:173-85.
51. Xiao, L., Wang, K., Teng, Y. and Zhang, J. 2003. Component plane presentation integrated self-organizing map for microarray data analysis. FEBS Lett, 538:117-24.