Language engineering and information theoretic methods in protein sequence similarity studies

  • A. Bogan-Marta
  • A. Hategan
  • I. Pitas
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 85)

The representation of biological data as text information opened new perspectives in the evolution of biological research. Many biological sequence databases are providing detailed information about sequences allowing investigations like searches, comparison, the establishment of relations between different sequences and species. The algorithmic procedures used for data sequence analysis are coming from many areas of computational sciences. Within this book chapter, we are bringing together a diversity of language engineering techniques and those involving information theoretic principles in analyzing protein sequences from similarity perspective. After we are proposing a state of the art in the subject, presenting a survey of the different approaches identified, the attention is oriented to the two methods we experimented. The description of these methods and the experiments performed open discussions addressed to the interested reader that may think about new ideas of improvement.

Keywords

Latent Semantic Analysis Biological Sequence Kolmogorov Complexity Protein Sequence Similarity Protein Secondary Structure Prediction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in to check access.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Metfessel BA and Saurugger PN (1993) Pattern recognition is the prediction of protein structural class. In: Proceedings of the Twenty-Sixth Hawaii INternational Conference on System Science 1:679–688CrossRefGoogle Scholar
  2. 2.
    Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN,BournePE (2000) The Protein Data Bank. Nucleic Acids Research (28):235–242CrossRefGoogle Scholar
  3. 3.
    Boeckmann B, Bairoch A, Apweiler R, Blatter MC, Estreicher A, Gasteiger E, Martin MJ, Michoud K, O’Donovan C, Phan I, Pilbout S, Schneider M (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acids Res. 31:365–370CrossRefGoogle Scholar
  4. 4.
    Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological Sequence Analysis: probabilistic models of proteins and nucleic acids. Cambridge University PressGoogle Scholar
  5. 5.
    Koonin EV and Galperin MY (2002) Sequence-Evolution-Function Computational approaches in comparative genomics. Kluwe, BostonGoogle Scholar
  6. 6.
    Pearson WR and Lipman DJ (1988) Improved tools for biological sequence comparison. PNAS 85(8): 2444–2448CrossRefGoogle Scholar
  7. 7.
    Altschul SF, Gish W, Miller W, Myers EW and Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215(3):403–410Google Scholar
  8. 8.
    G.J. Barton, (1996) Protein Sequence Alignment and Database Scanning. M.J. E. Sternberg (eds), IN: Protein Structure Prediction - a practical approach, IRL Press at Oxford University PressGoogle Scholar
  9. 9.
    Bogan-Marta A, Laskaris N, Gavrielides M, Pitas I, Lyroudia K (2005) A novel efficient protein similarity measure based on n-gram modeling. In: IEEE, IEE Second International Conference on Intelligence in Medicine and Healthcare 122–127Google Scholar
  10. 10.
    Ganapathiraju M, Balakrishnan N, Reddy R, Klein-Seetharaman J, (2005) Computational Biology and Language. Ambient Intelligence for Scientific Discovery, Springer-Verlag Berlin Heidelberg, Lecture Notes in Computer Science LNAI 3345:25–47Google Scholar
  11. 11.
    Searls DB (2002) The Language of Genes. Nature 420(6912):211-7CrossRefGoogle Scholar
  12. 12.
    Bolshoi A (2003) DNA Sequence Analysis Linguistic Tools: Contrast Vocabularies, Compositional Spectra and Linguistic Complexity. Appl. Bioinformatics 2(2):103–12Google Scholar
  13. 13.
    Wu K-P, Lin H-N, Sung T-Y and Su W-L (2003) A new Similarity Measure among Protein Sequences. IEEE Computer Society Bioinformatics Conference (CSB’03) Proceedings 347–352Google Scholar
  14. 14.
    Henikoff S and Henikoff JG (1992) Amino acid substitution matrices from protein block. In: Proceedings of the National Academy of Science USA 89(22):10915–10919CrossRefGoogle Scholar
  15. 15.
    Lachlan Coin, Alex Bateman, and Richard Durbin (2003) Enhanced protein domain discovery by using language modeling techniques from speech recognition, Proc Natl Acad Sci U S A 100(8): 4516–4520CrossRefGoogle Scholar
  16. 16.
    Lord PD, Stevens RD, Brass A and Goble CA (2003) Semantic similarity measures as tools for exploring the gene ontology. In: Pacific Symposium on Biocomputing, PubMed 601–612Google Scholar
  17. 17.
    Sarkar I, Rindflesch T (2002) Discovering Protein Similarity using Natural Language Processing, Proc AMIA Symp :677-81Google Scholar
  18. 18.
    The Gene Ontology Consortium (2001) Creating the gene ontology resource: design and implementation. Genome Res 11(8):1425–33Google Scholar
  19. 19.
    Rada R, Mili H, Bicknell E, Blettner M (1989) Development and application of a metric on semantic nets. IEEE Transactions on Systems Management and Cybernetics, 19(1):17–30CrossRefGoogle Scholar
  20. 20.
    Lord PW, Stevens RD, Brass A, Goble CA. (2003) Investigating semantic similarity measures across the Gene Ontology: the relationship between sequence and annotation, Bioinformatics Vol. 19(10):1275–1283CrossRefGoogle Scholar
  21. 21.
    Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. IJCAI 448–453Google Scholar
  22. 22.
    Lin D (1998) An information-theoretic definition of similarity. In Morgan Kaufman (EDS) Proc 15th International Conf. on Machine Learning. San Francisco, CA 296–304Google Scholar
  23. 23.
    Jiang JJ and Conrath DW (1998) Semantic similarity based on corpus statistics and lexical taxonomy. In: Proc.of International Conference on Research in Computational LinguisticsGoogle Scholar
  24. 24.
    Resnik P (1999) Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language” Journal of Artificial Intelligence Research, 11:95-130MATHGoogle Scholar
  25. 25.
    Schlicker A, Domingues FS, Rahnenfhrer J, Lengauer T (2006) A new measure for functional similarity of gene products based on Gene Ontology, BMC Bioinformatics 7: 302CrossRefGoogle Scholar
  26. 26.
    Guo X, Shriver CD, Hu H, Liebman MN (2005) Semantic similarity-based validation of human protein-protein interactions, Computational Systems Bioinformatics Conference :149–150Google Scholar
  27. 27.
    Ganapathiraju MK, Klein-Seetharaman J, Balakrishnan N and Reddy R (2004) Characterization of Protein Secondary Structure. Application of latent semantic analysis using different vocabularies. IEEE Signal Processing Magazine 78–86Google Scholar
  28. 28.
    Bellegarda J (2000) Exploiting latent semantic information in statistical language modeling. In: IEEE Proceedings 88(8):1279–1296CrossRefGoogle Scholar
  29. 29.
    Landauer T, Foltx P and Laham D (1998) Introduction to latent semantic analysis. Discourse Processes 25:259–284CrossRefGoogle Scholar
  30. 30.
    Salton G, Wong A, and Yang CS (1975) A Vector Space Model for Automatic Indexing. Communications of the ACM, 18(11)613–620MATHCrossRefGoogle Scholar
  31. 31.
    Haley D, Thomas P, Nuseibeh B, Tailor J, Lefrere P (2003) E-assesment using Lantent Semantic Analysis, Electronic Workshops in Computing, LeGE-WGGoogle Scholar
  32. 32.
    Yuan Y, Lin L, Dong Q, Wang X, Li M (2005) A Protein Classification Method Based on Latent Semantic Analysis, Engineering in Medicine and Biology Society, 2005. IEEE-EMBS 27th Annual International Conference : 7738–7741Google Scholar
  33. 33.
    Dong Q, Wang X, Lin L (2005) Application of latent semantic analysis to protein remote homology detection, Bioinformatics Advance Access published online, Bioinformatics, doi:10.1093/bioinformatics/bti801Google Scholar
  34. 34.
    Maguitman AG, Rechtsteiner A, Verspoor K, Strauss CE, Rocha LM (2006) Large-Scale Testing of Bibliome. Informatics Using Pfam Protein Families, In: Pacific Symposium on Biocomputing 11:76-87Google Scholar
  35. 35.
    Tueyu F, Mostafa J, Seki K (2003) Protein association discovery in biomedical literature, Proceedings of the 3rd ACM/IEEE-CS joint conference on Digital libraries :113-115Google Scholar
  36. 36.
    Finn RD, Mistry J, Schuster-Bckler B, Griffiths-Jones S, Hollich V, Lassmann T, Moxon S, Marshall M, Khanna A, Durbin R, Eddy SR, Sonnhammer ELL, Bateman A (2006) Pfam: clans, web tools and services, Nucleic Acids Research, 34 Database issue D247-D251Google Scholar
  37. 37.
    Mulder NJ, Fleischmann W, Kanapin A, Apweiler R (2006) InterPro as a new tool for complete genome analysis: An example of comparative analysis, Biofizika 51(4):656-660Google Scholar
  38. 38.
    Ganapathiraju, M., V. Manoharan, et al. (2004) BLMT: Statistical Sequence Analysis using N-grams Applied Bioinformatics 3(2-3): 193-200Google Scholar
  39. 39.
    Benedetto D, Caglioti E, and Loreto V (2002) Language trees and zipping. Physical Review Letters 88(4):048702CrossRefGoogle Scholar
  40. 40.
    Chen X, Francia B, Ming L, McKinnon B and Seker A (2004) Shared information and program plagiarism detection. IEEE Transactions on Information Theory 50(7):1545–1551CrossRefGoogle Scholar
  41. 41.
    Grozea C (2004) Plagiarism detection with state of the art compression programs. In: CDMTCS Research Report SeriesGoogle Scholar
  42. 42.
    Chen X, Kwong S, and Li M (1999) A compression algorithm for DNA sequences and its applications in genome comparison. In: Genome Informatics. Universal Academy Press, TokyoGoogle Scholar
  43. 43.
    Li M, Badger JH, Chen X, Kwong S, Kearney P, and Zhang H (2001) An information-based sequence distance and its application to whole mitochondrial genome phylogeny. Bioinformatics 17(2):149154CrossRefGoogle Scholar
  44. 44.
    Otu HH and Sayood K (2003) A new sequence distance measure for phylogenetic tree construction. Bioinformatics 19(16):2122–2130CrossRefGoogle Scholar
  45. 45.
    Li M, Chen X, Li X, Ma B, and Vitnyi PMB (2004) The similarity metric. IEEE Transactions on Information Theory 50(12):3250–3264CrossRefGoogle Scholar
  46. 46.
    Hategan A and Tabus I (2004) Protein is compressible. In: NORSIG2005 1992–195Google Scholar
  47. 47.
    Cilibrasi R and Vitanyi PMB (2005) Clustering by compression.IEEE Transactions on Information Theory 51(4):1523–1545CrossRefMathSciNetGoogle Scholar
  48. 48.
    Bennett CH, Li M, and Ma B (2003) Chain letters and evolutionary histories. Scientific American 288(6):76–81CrossRefGoogle Scholar
  49. 49.
    Kocsor A, Kertsz-Farkas A, Kajn L, and Pongor S (2006) Application of compression-based distance measures to protein sequence classification: a methodological study. Bioinformatics 22(4):407–412CrossRefGoogle Scholar
  50. 50.
    Kolmogorov AN (1965) Three approaches to the definition of the concept “quantity of information”. Problemy Peredachi Informatsii 1:3–11MATHMathSciNetGoogle Scholar
  51. 51.
    Bennett CH, Gacs P, Li M, Vitanyi PMB, Zurek WH (1998) Information Distance, IEEE Transacations on Information Theory 44(4):1407–1423MATHCrossRefMathSciNetGoogle Scholar
  52. 52.
    Li M and Vitanyi PMB (1997) An Introduction to Kolmogorov Complexity and its Applications. Springer-Verlag, 2nd EditionGoogle Scholar
  53. 53.
    Apostolico A and Lonardi S (2000) Compression of biological sequences by greedy off-line textual substitution. In: Data Compression Conference. IEEE Computer Society PressGoogle Scholar
  54. 54.
    Chen X, Kwong S and Li M (2001) A compression algorithm for DNA sequences. IEEE-EMB Special Issue on Bioinformatics 20(4):61–66Google Scholar
  55. 55.
    Chen X, Li M, Ma B, and Tromp J (2002) DNACompress: Fast and effective DNA sequence compression. Bioinformatics 18:1696-1698CrossRefGoogle Scholar
  56. 56.
    Grumbach S and Tahi F (1993) Compression of DNA sequences. In: Data Compression Conference. IEEE Computer Society PressGoogle Scholar
  57. 57.
    Korodi G and Tabus I (2005) An efficient normalized maximum likelihood algorithm for DNA sequence compression. ACM Transactions on Information Systems 23(1):3–34CrossRefGoogle Scholar
  58. 58.
    Tabus I, Korodi G and Rissanen J (2003) DNA Sequence Compression Using the Normalized Maximum Likelihood Model for Discrete Regression. In: Data Compression Conference. IEEE Computer Society PressGoogle Scholar
  59. 59.
    Nevill-Manning CG and Witten IH (1999) Protein is incompressible. In: Data Compression Conference. IEEE Computer Society PressGoogle Scholar
  60. 60.
    Wang S, Schuurmans D, Peng F, Zhao F (2005) Combining Statistical Language Models via the Latent Maximum Entropy Principle. Machine Learning, Springer Netherlands 60(1-3):229–250Google Scholar
  61. 61.
    Kang S, Wang S, Greiner R, Schuurmans D, Cheng L (2004) Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields. International Symposium on Chinese Spoken Language Processing : 305–308Google Scholar
  62. 62.
    Wang S, Schuurmans D, Pengun F and Zhao Y (2003) Semantic N-gram Language Modeling With The Latent Maximum Entropy Principle. In IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP-03)Google Scholar
  63. 63.
    Ganapathiraju M, Weisser D, Rosenfeld R, Carbonell J, Reddy R, Klein-Seetharaman J (2002) Comparative n-gram analysis of whole-genome protein sequences. Proc. HLT, San Diego 2002Google Scholar
  64. 64.
    Liu Y, Carbonell J, et al. (2004) Context Sensitive Vocabulary And its Application in Protein Secondary Structure Prediction. ACM SIGIR Conference.Google Scholar
  65. 65.
    Cheng, B., J. Carbonell, et al. (2004). A Machine Text-Inspired Machine Learning Approach for Identification of Transmembrane Helix Boundaries. 15th International Symposium on Methodologies for Intelligent Systems, Saratoga Springs, New York, USAGoogle Scholar
  66. 66.
    Cheng, B., J. Carbonell, et al. (2005) Protein Classification based on Text Document Classification Techniques. Proteins - Structure, Function and Bioinformatics 58(4): 955-70CrossRefGoogle Scholar
  67. 67.
    Burkhardt S, Crauser A, Ferragina P, Lenhof HP, Rivals E, Vingron M (1999). q-gram Based Database Searching Using a Suffix Array (QUASAR). Third Annual International Conference on Computational Molecular Biology, RECOMB’99, Lyon, France.Google Scholar
  68. 68.
    Van Compernolle D (2003) Spoken Language Science and Technology, course materialGoogle Scholar
  69. 69.
    Manning CD and Schtze H (2000) Foundations of statistical natural language processing. Massachusetts Institute of Technology Press, Cambridge, Massachusetts London, England 554–588Google Scholar
  70. 70.
    Brown PF, Della Pietra AS, Della Pietra VJ, Mercer Robert LR and Jennifer CL (1992) An estimation of an upper bound for the entropy of English. In Association for Computational Linguistics, Yorktown Heights, NY 10598Google Scholar
  71. 71.
    Jurafsky D and Martin J (2000) Speech and Language Processing. Prentice Hall(EDS)Google Scholar
  72. 72.
    Bogan-Marta A, Gavrielides M, Pitas I and Lyroudia K (2005) A New Statistical Measure of Protein Similarity based on Language Modeling. In: IEEE International Workshop on Genomic Signal Processing and StatisticsGoogle Scholar
  73. 73.
    Bogan-Marta A, Pitas I, Lyroudia K (2006) Statistical Method of Context Evaluation for Biological Sequence Similarity. In: IEEE Conference on ‘Artificial Intelligence in Theory and Practice’, IFIP World Computer Congress 11:1–10Google Scholar
  74. 74.
    Liao L and Noble W S (2003) Combining pairwise sequence similarity and support vector machines for detecting remoteprotein evolutionary and structural relationships. Journal of Computational Biology 10:857–868CrossRefGoogle Scholar
  75. 75.
    Schffer A, Aravind L, Madden L, Shavirin S, Spouge J, Wolf Y, Koonin E, Altschul S (2001). Improving the accuracy of PSI-BLAST protein data-base searches with composition-based statistics and other refinements. Nucleic Acids Res 29(14):2994–3005CrossRefGoogle Scholar
  76. 76.
    Cover TM and Thomas AJ (1991) Elements of information theory, New YorkGoogle Scholar
  77. 77.
    Huffman DA (1952) A method for the construction of minimum redundancy codes. Proceedings of the IRE 40:1098–1101CrossRefGoogle Scholar
  78. 78.
    Rissanen J (1976) Generalized Kraft inequality and arithmetic coding. IBM Journal of Research and Development 20:198–203MATHMathSciNetCrossRefGoogle Scholar
  79. 79.
    Ross SM (1996) Stochastic processes, 2nd Edition, New YorkGoogle Scholar
  80. 80.
    Hategan A and Tabus I (2005) Detecting local similarity based on lossless compression of protein sequences. In: International Workshop on Genomic Signal Processing 95–99Google Scholar
  81. 81.
    Yu YK, Wootton JC and Altschul SF (2003) The compositional adjustment of amino acid subtitution matrices. PNAS 100(26):15688–15693CrossRefGoogle Scholar
  82. 82.
    Cao MD, Dix TI, Allison L, Mears C (2007) A simple statistical algorithm for biological sequence compression. In: DCC’07, 43–52Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • A. Bogan-Marta
    • 1
    • 3
  • A. Hategan
    • 2
  • I. Pitas
    • 3
  1. 1.Department of ComputersUniversity of OradeaOradeaRomania
  2. 2.Institute of Signal ProcessingTampere University of Technology
  3. 3.Department of Informatics, Artificial Intelligence and Information Analysis LaboratoryAristotle University of ThessalonikiThessalonikiGreece

Personalised recommendations