Bioinformatics and Genomic Medicine

생명정보학과 유전체의학

  • Kim, Ju-Han (Department of Preventive Medicine, Seoul National University College of Medicine)
  • 김주한 (서울대학교 의과대학 예방의학교실)
  • Published : 2002.06.01

Abstract

Bioinformatics is a rapidly emerging field of biomedical research. A flood of large-scale genomic and postgenomic data means that many of the challenges in biomedical research are now challenges in computational sciences. Clinical informatics has long developed methodologies to improve biomedical research and clinical care by integrating experimental and clinical information systems. The informatics revolutions both in bioinformatics and clinical informatics will eventually change the current practice of medicine, including diagnostics, therapeutics, and prognostics. Postgenome informatics, powered by high throughput technologies and genomic-scale databases, is likely to transform our biomedical understanding forever much the same way that biochemistry did a generation ago. The paper describes how these technologies will impact biomedical research and clinical care, emphasizing recent advances in biochip-based functional genomics and proteomics. Basic data preprocessing with normalization, primary pattern analysis, and machine learning algorithms will be presented. Use of integrated biochip informatics technologies, text mining of factual and literature databases, and integrated management of biomolecular databases will be discussed. Each step will be given with real examples in the context of clinical relevance. Issues of linking molecular genotype and clinical phenotype information will be discussed.

Keywords

References

  1. Hughes TR, Marton MJ, Jones AR, Roberts CJ, Stoughton R, Armour CD, Bennett HA, Coffey E, Dai H, He YD, Kidd MJ, King AM, Meyer MR, Slade D, Lum PY, Stepaniants SB, Shoemaker DD, Gachotte D, Chakraburtty K, Simon J, Bard M, Friend SH. Functional discovery via a compendium of expression profiles. Cell 2000; 102(1): 109-26 https://doi.org/10.1016/S0092-8674(00)00015-5
  2. Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarmer R, Goodlett DR, Aebersold R, Hood L. Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science 2001; 292(5518): 929-934 https://doi.org/10.1126/science.292.5518.929
  3. Curtius T. Ueber das Glycocoll. Chem Ber 1883; 16: 753-757
  4. Meischer JF. Med-Chem Unter 1871; 441
  5. Zukerkandl E, Pauling L. Molecular disease, evolution, and genic hetero geniety. In Kasha, M. and Pullman B Eds. Horizons in Biochemistry Academic Press, New York; 1962. p. 189-225
  6. Pipas JM, McMahon JE. Method for predicting RNA secondary structure. Proc Natl Acad Sci U S A 1975; 72(6): 2017-2021 https://doi.org/10.1073/pnas.72.6.2017
  7. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a cDNA microarray. Science 1995; 270: 467-470 https://doi.org/10.1126/science.270.5235.467
  8. Shalon D, Smioth SJ, Brown PO. A DNA micro-array system for analysing complex DNA samples using two-color fluorescent probe hybridization. Genome Res 1996; 6: 639-645 https://doi.org/10.1101/gr.6.7.639
  9. Pease AC, Solars D, Sullivan EJ, Cronin MT, Holmes CP, Fodor SP. Light-generated oligonucleotide arrays for rapid DNA sequence analysis. Proc Natl Acad Sci U S A 1994; 91: 5022-5026 https://doi.org/10.1073/pnas.91.11.5022
  10. Lockhart DJ, Dong H, Byrne MC, Follettie MT, Gallo MV, Chee MS, Mittmann M, Wang C, Kobayashi M, Horton H, Brown EL. Expression monitoring by hybridization to high-density oligonucleotide arrays. Nature Biotechnol 1996; 14(13): 1675-1680 https://doi.org/10.1038/nbt1296-1675
  11. DeRisi JL, Iyer V, Brown PO. Exploring the metabolic and genetic control of gene expression on a genomic scale. Science 1997; 278: 680-686 https://doi.org/10.1126/science.278.5338.680
  12. Brown PO, Botstein D. Exploring the new world of the genome with DNA microarrays. Nat Genetics Suppl 1999; 21: 33-37 https://doi.org/10.1038/4462
  13. Risi J, Penland L, Brown PO, Bittner ML, Meltzer PS, Ray M, Chen Y, Su YA, Trent JM. Use of a cDNA microarray to analyse gene expression patterns in human cancer. Nat Genet 1996; 14(4): 457-60 https://doi.org/10.1038/ng1296-457
  14. Heller RA, Schena M, Chai A, Shalon D, Bedilion T, Gilmore J, Woolley DE, Davis RW. Discovery and analysis of inflammatory disease-related genes using cDNA microarrays. Proc Natl Acad Sci USA 1997; 94(6): 2150-2155 https://doi.org/10.1073/pnas.94.6.2150
  15. Spellman PT, Sherlock G, Zhang MQ, Iyer YR, Anders K, Eisen MB, Brown PO, Botstein D, Futcher B. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces Cerevisiae by microarray hybridization. Mol Biol Cell 1998; 9: 3273-3297 https://doi.org/10.1091/mbc.9.12.3273
  16. Eisen MB, Spellman PT, Brown PO, Botstein D. Cluster analysis and display of genome-wide expression patterns. Proc Natl Acad Sci U S A 1998; 95: 14863-14868 https://doi.org/10.1073/pnas.95.25.14863
  17. Butte AJ and Kohane IS. Mutual information relevance networks: functional genomic clustering using pairwise entropy measurements. Pac Symp Biocomput 2000; 418-429
  18. Tavazoie S, Hughes JD, Campbell MJ, Cho RJ, Church GM. Systematic determi nation of genetic network architecture. Nature Genetics 1999; 22: 281-285 https://doi.org/10.1038/10343
  19. Kohonen T. Self-organized formati on of topologically correct feature maps. Biol Cybern 1982 ;43: 59-69 https://doi.org/10.1007/BF00337288
  20. Tamayo P, Slonim D, Mesirov J, Zhu Q, Kitareewan S, Dmitrovsky E, Lander ES, Golub TR. Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoi etic differentiation. Proc Natl Acad Sci U S A 1999; 96: 2907-2919 https://doi.org/10.1073/pnas.96.6.2907
  21. Kim JH, Ohno-Machado L., Kohane IS Unsupervised Learning from complex data: the Matrix Incision Tree Algorithm. Pac Symp Biocomput 2001; 30-41
  22. Kim JH, Ohno-Machado L, Kohane IS. Visualization and Evaluation of Clus tering Structures for Gene Expression Data Analysis. J Biomed Inform 2002 (accepted and in press)
  23. Jenssen TK, Laegreid A, Komorowski J, Hovig E. A literature network of human genes for high-throughput analysis of gene expression. Nat Genet 2001; 28(1): 21-28 https://doi.org/10.1038/88213
  24. Park JC, Kim HS, Kim JJ. Bidirectional Incremental Parsing for Automatic Pathway Identification with Combinatory Categorial Grarumar. Pac Symp Biocom put 2001; 6: 396-407
  25. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, Coller H, Loh ML, Downing JR, Caligiuri MA, Bloomfield CD, Lander ES. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999; 286: 531-537 https://doi.org/10.1126/science.286.5439.531
  26. Alizadeh AA, Eisen MB, Davis RE, Ma C, Lassos IS, Rosenwald A, Boldrick JC, Sabet H, Tran T, Yu X, Powell JI, Yang L, Marti GE, Moore T, Hudson J Jr, Lu L, Lewis DB, Tibshirani R, Sherlock G, Chan WC, Greiner TC, Weisenburger DD, Armitage JO, Warnke R, Staudt LM, et al. Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 2000; 403: 503-511 https://doi.org/10.1038/35000501
  27. Altman RB. The Interactions Between Clinical Informatics and Bioinformatics: A Case Study. J Am Med Inform Assoc 2000; 7(5): 439-443 https://doi.org/10.1136/jamia.2000.0070439
  28. Tomita M. Whole-cell simulation: a grand challenge of the 21st century. Trends Biotechnol 2001; 19(6): 205-210 https://doi.org/10.1016/S0167-7799(01)01636-5
  29. Chicurel M. Databasing the brain. Nature 2000; 406: 822-825 https://doi.org/10.1038/35022659
  30. Brinkley JF. Structural informatics and its applications in medicine and biology. Academic Medicine 1991; 66: 589-591 https://doi.org/10.1097/00001888-199110000-00005
  31. Brown FK. Chemoinformatics: What is it and How does it Impact Drug Discovery. Annual Reports in Medicinal Chemistry 1998; 33: 375-384 https://doi.org/10.1016/S0065-7743(08)61100-8
  32. Hann M, Green R. Chemoinformatics - A new name for an old problem. Curr Opin Chem Biol 1999; 379-383
  33. Degoulet P, Fischi M. Introduction to clinical inforamatics. 1997, Springer, New York
  34. Friede A, Blum HL, McDonald M. Public health informatics: how informa tion-age technology can strengthen public health. Annu Rev Public Health 1995; 16: 239-252 https://doi.org/10.1146/annurev.pu.16.050195.001323