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Zusammenfassung

Bioinformatik ist eine neue wissenschaftliche Disziplin, die sich mit dem Einsatz von Methoden aus der Informatik in den Biowissenschaften beschäftigt, vorwiegend um Struktur und Funktion von Genen und Proteinen aufzuklären.

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Literatur

  1. Dayhoff MO, Schwartz RM, Orcutt BC (1978) A model of evolutionary change in proteins. in: Atlas of Protein Sequence and Structure, ed. Dayhoff MO, Washington, National Biomedical Research Foundation, pp. 345–352

    Google Scholar 

  2. Dayhoff MO, Barker WC, Hunt LT (1983) Establishing Homologies in Protein Sequences. Methods Enzymol. 91:524–545

    Article  PubMed  CAS  Google Scholar 

  3. Henikoff S, Henikoff JG (1992). Amino acid substitution matrices from protein blocks. PNAS 89:10915–10919

    Article  PubMed  CAS  Google Scholar 

  4. Thompson JD, Higgins DG, Gibson TJ (1994) CLUSTAL W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucl Acids Res 22:4673–4680

    Article  PubMed  CAS  Google Scholar 

  5. Higgins DG, Sharp PM (1988) CLUSTAL: a package for performing multiple sequence alignment on a microcomputer. Gene 73:237–244

    Article  PubMed  CAS  Google Scholar 

  6. Thompson JD, Plewniak F, Poch O (1999) A comprehensive comparison of multiple sequence alignment programs. Nucleic Acids Res 27:2682–2690

    Article  PubMed  CAS  Google Scholar 

  7. Thompson JD, Plewniak F, Poch O (1999) BAliBASE: A benchmark alignments database for the evaluation of multiple sequence alignment programs. Bioinformatics 15:87–88

    Article  PubMed  CAS  Google Scholar 

  8. Vingron M, von Haeseler A (1997) Towards Integration of Multiple Alignment and Phylogenetic Tree Construction. Journal of Computational Biology 4:23–34

    Article  PubMed  CAS  Google Scholar 

  9. Tönges U, Perrey WS, Stoye J, Dress A (1996) A General Method for Fast Multiple Sequence Alignment. Gene 172:GC33–GC41

    Article  PubMed  Google Scholar 

  10. Stoye J (1998) Multiple sequence alignment with the Divide-and-Conquer method. Gene 211:GC45–56

    Article  PubMed  CAS  Google Scholar 

  11. Morgenstern B (2000) A space-efficient algorithm for aligning large genomic sequences. Bioinformatics 16:948–949

    Article  PubMed  CAS  Google Scholar 

  12. Morgenstern B, Dress A, Werner T (1996) Multiple DNA and protein sequence alignment based on segment-to-segment comparison. Proc Nad Acad Sei USA 93:12098–12103

    Article  CAS  Google Scholar 

  13. Gupta SK, Kececioglu JD, Schaffer AA (1995) Improving the practical space and time efficiency of the shortest-paths approach to sum-of-pairs multiple sequence alignment. J Comput Biol 2:459–472

    Article  PubMed  CAS  Google Scholar 

  14. Gribskov M, McLachlan AD, Eisenberg D (1987) Profile analysis: detection of distantly related proteins. Proceedings of the National Academy of Sciences, 84:4355–4358

    Article  CAS  Google Scholar 

  15. Brenner SE (1995)Sequence Logos

    Google Scholar 

  16. Schneider TD, Stephens RM (1990) Sequence Logos: A New Way to Display Consensus Sequences. Nucl Acids Res 18:6097–6100

    Article  PubMed  CAS  Google Scholar 

  17. Lipman DJ, Pearson WR (1985) Rapid and sensitive protein similarity searches. Science 227:1435–1441

    Article  PubMed  CAS  Google Scholar 

  18. Pearson WR, Lipman DJ (1988) Improved tools for biological sequence comparison. PNAS 85:2444–2448

    Article  PubMed  CAS  Google Scholar 

  19. Agarwal P, States DJ (1998) Comparative accuracy of methods for protein sequence similarity search. Bioinformatics 14:40–47

    Article  PubMed  CAS  Google Scholar 

  20. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1990) Basic local alignment search tool. J Mol Biol 215:403–410

    PubMed  CAS  Google Scholar 

  21. Karlin S, Altschul SF (1990) Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc Nad Acad Sci 87:2264–2268

    Article  CAS  Google Scholar 

  22. Gish W, States DJ (1993) Identification of protein coding regions by database similarity search. Nature Genedcs 3:266–272

    Article  CAS  Google Scholar 

  23. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ (1994) Issues in searching molecular sequence databases. Nat Genet 6:119–129

    Article  PubMed  CAS  Google Scholar 

  24. Waterman MS, Vingron M (1994) Rapid and accurate esdmates of statistical significance for sequence data base searches. Proc Nad Acad Sci USA 91:4625–4628

    Article  CAS  Google Scholar 

  25. Altschul SF, Madden TL, Schaffer AA, Zhang J, Zhang Z, Miller W, Lipman DJ (1997) Gapped blast and psi-blast: a new generadon of protein database search programs. Nucl Acids Res 25:3389–3402

    Article  PubMed  CAS  Google Scholar 

  26. Sean E (2002) HMMER 2.2 Profile hidden Markov models for biological sequence analysis http://hmmer.wustl.edu/

  27. Rabiner LR (1989) A tutorial on Hidden Markov Models and selected apphcations in speech recognidon. Proceedings of the IEEE 77:257–285

    Article  Google Scholar 

  28. Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press. ISBN 0521629713

    Google Scholar 

  29. Brown M, Hughey R, Krogh A, Mian I, Haussler D (1993) Using Dirichlet Mixture Priors to Derive Hidden Markov Models for Protein Families. ISMB 1993: Proceedings of the First International Conference on Intelligent Systems for Molecular Biology

    Google Scholar 

  30. Kawabata T, Ota M, Nishikawa K (1999) The protein mutant database. Nucleic Acids Research 27:355–357

    Article  PubMed  CAS  Google Scholar 

  31. Sanger F, Nickten S, Coulson AR (1977) DNA sequencing with chain terminator inhibitors. PNAS 74:5463–5467

    Article  PubMed  CAS  Google Scholar 

  32. Huang X (1992) Contig assembly program (cap) ftp://ftp.bio.indiana.edu/molbio/align/huang/

  33. Bonfield JK, Smith KF, Staden R (1995) A new DNA sequence assembly program. Nucleic Acids Res 23:4992–4999

    Article  PubMed  CAS  Google Scholar 

  34. Ewing B, Green P (1998) Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8:186–194

    PubMed  CAS  Google Scholar 

  35. Kececioglu J, Myers E (1995) Combinatorial algorithms for DNA sequence assembly. Algorithmica 13:7–51

    Article  Google Scholar 

  36. Sutton G, White O, Adams M, Kerlavage A (1995) TIGR Assembler: A new tool for assembling large shotgun sequencing projects. Genome Sei. Technol. 1:9–19

    Article  CAS  Google Scholar 

  37. Scholler P, Karger AE, Meier-Ewert S, Lehrach H, Delius H, Hoheisel JD (1995) Fine-mapping of shotgun template-libraries; an efficient strategy for the systematic sequencing of genomic DNA. Nucleic Acids Res 23:3842–3849

    Article  PubMed  CAS  Google Scholar 

  38. Tatusov RL, Koonin EV, Lipman DJ (1997) A genomic perspective on protein families. Science 24:631–637

    Article  Google Scholar 

  39. Frishman D, Albermann K, Hani J, Heumann K, Metanomski A, Zollner A, Mewes HW (2001) Functional and structural genomics using PEDANT. Bioinformatics 17:44–57

    Article  PubMed  CAS  Google Scholar 

  40. Mewes HW, Frishman D, Guidener U, et al. (2002) MIPS: a database for genomes and protein sequences. Nucleic Acids Res. 30:31–34

    Article  PubMed  CAS  Google Scholar 

  41. Saitou N, Nei M (1987) The Neighbor-joining Method: A New Method for Reconstructing Phylogenetic Trees. Mol. Biol. Evol. 4:406–425

    PubMed  CAS  Google Scholar 

  42. Fitch WM, Margoliash E (1967) Construction of phylogenetic trees. Science 155:279–84

    Article  PubMed  CAS  Google Scholar 

  43. Rzhetsky A, Nei M (1992) A simple method for estimating and testing minimum evolution trees. Mol Biol Evol 9:945–967

    CAS  Google Scholar 

  44. Swofford DL, Olsen GJ, Waddel PJ, Hillis DM (1996) Phylogenetic Inference, in: Molecular Systematics (ed. Hillis DM, Moritz C, Mable BK), Sinauer Associates, Sunderland, MA, pp. 407–514

    Google Scholar 

  45. Adachi J, Hasegawa M (1996) MOLPHY Version 2.3. Programs for Molecular phylogenetics based on maximum likelihood (Tokyo: Institute of Statistical Mathematics)

    Google Scholar 

  46. Breiman L, Friedman J, Stone C, Olshen R (1984) Classification and Regression Trees. Chapman & Hall. ISBN 0412048418

    Google Scholar 

  47. Ott J (1999) Analysis of Human Genetic Linkage. Johns Hopkins University Press, Baltimore. ISBN 0801861403

    Google Scholar 

  48. Rannala B, Slatkin M (1998) Likelihood analysis of disequilibrium mapping, and related problems. Am J Hum Genet 62:459–473

    Article  PubMed  CAS  Google Scholar 

  49. Rannala B, Slatkin M (1998) Linkage Disequilibrium Mapping and Parkinson’s Disease. Science 280:175a

    Article  Google Scholar 

  50. CEPH: Dausset J, Cann H, Cohen D, Lathrop M, Lalouel JM, White R (1990) Centre d’Étude du Polymorphism Humain (CEPH): Collaborative genetic mapping of the human genome. Genomics 6:575–577

    Article  PubMed  CAS  Google Scholar 

  51. CHLC Map: Murray JC, Buetow, KH, Weber JL et al. (1994) A comprehensive human linkage map with centimorgan density. Science 265:2049–2054

    Article  PubMed  CAS  Google Scholar 

  52. Généthon Map: Dib C, Fauré S, Fizames C et al. (1996) A comprehensive genetic map of the human genome based on 5264 microsatellites. Nature 380:152–154

    Article  PubMed  CAS  Google Scholar 

  53. Marshfield Map: Broman KW, Murray JC, Sheffield VC et al. (1998) Comprehensive human genetic maps: individual and sex-specific variation in recombination. Am J Hum Genet 63:861–869

    Article  PubMed  CAS  Google Scholar 

  54. Lawrence JB, Singer RH, NcNeil JA (1990) Interphase and metaphase resolution of different distances within the human dystrophin gene. Science 249:928–932

    Article  PubMed  CAS  Google Scholar 

  55. Slonim D, Kruglyak L, Stein L, Lander E (1997) Building human genome maps with radiation hybrids. J Comput Biol 4:487–504

    Article  PubMed  CAS  Google Scholar 

  56. Gyapay G, Schmitt K, Fizames C et al. (1996) A radiation hybrid map of the human genome. Hum Mol Genet 5:339–358

    Article  PubMed  CAS  Google Scholar 

  57. Stewart EA, McKusick KB, Aggarwal A et al. (1997) An STS-based radiation hybrid map of the human genome. Genome Research 7:422–433

    PubMed  CAS  Google Scholar 

  58. Beasley E, Stewart E, McKusick K et al. (1997) The TNG4 radiation hybrids improve the resolution of the G3 panel. Am J Hum Genet 61(Suppl):A231

    Google Scholar 

  59. Deloukas P, Schuler GD, Gyapay G et al. (1998) A physical map of 30.000 human genes. Science 282:744–746

    Article  PubMed  CAS  Google Scholar 

  60. Harley E, Bonner A, Goodman N (1999) Revealing hidden interval graph structure in STS-content data. Bioinformatics 15:278–285

    Article  PubMed  CAS  Google Scholar 

  61. Uberbacher EC, Mural RJ (1991) Locating Protein Coding Regions in Human DNA Sequences Using a Multiple Sensor-Neural Network Approach. Proc. Natl. Acad. Sci. USA, 88:11261–11265

    Article  PubMed  CAS  Google Scholar 

  62. Solovyev VV, Salamov AA, Lawrence CB (1994) Predicting internal exons by oligonucleotide composition and discriminant analysis of spliceable open reading frames. Nucl Acids Res 22:5156–5163

    Article  PubMed  CAS  Google Scholar 

  63. Zang MQ (1997) Identification of protein coding regions in the human genome by quadratic discriminant analysis. Proc Natl Acad Sci USA 94:565–568

    Article  Google Scholar 

  64. Davuluri RV, Grosse I, Zhang MQ (2001) Computational identification of promoters and first exons in the human genome. Nat Genet 29:412–417

    Article  PubMed  CAS  Google Scholar 

  65. Burge C, Karlin S (1997) Prediction of complete gene structures in human genomic DNA. J MOl Biol 268:78–94

    Article  PubMed  CAS  Google Scholar 

  66. Sherlock G, Hernandez-Boussard T, et al. (2001) The Stanford Microarray Database. Nucleic Acids Res 29:152–155

    Article  PubMed  CAS  Google Scholar 

  67. Golub TR et al. (1999) Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:513–537

    Article  Google Scholar 

  68. Kerr M, Churchill (2000) Analysis of variance for gene expression microarray data. Journal of Computational Biology 7:819–837

    Article  PubMed  CAS  Google Scholar 

  69. Tusher VG, Tibshirani R, Chu G (2001) Significance analysis of microarray applied to the ionizing radiation response. PNAS 98:5116–5121

    Article  PubMed  CAS  Google Scholar 

  70. Chow ML, Moler EJ, Mian IS (2001) Identifying marker genes in transcription profiling data using a mixture of feature relevance experts. Physiol Genomics 5:99–111

    PubMed  CAS  Google Scholar 

  71. Tibshirani R, Hastie T, Narasimhan B, Chu G (2002) Diagnosis of multiple cancer types by shrunken centroids of gene expression. PNAS 99:6567–6572

    Article  PubMed  CAS  Google Scholar 

  72. Bairoch A, Apweiler R (1998) The SWISS-PROT protein sequence databank and its supplement TrEMBL in 1998. Nucl Acids Res 26:38–42

    Article  PubMed  CAS  Google Scholar 

  73. Barker WC, Garavelli JS et al. (2001) Protein Information Resource: a community resource for expert annotation of protein data. Nucl Acids Res 29:29–32

    Article  PubMed  CAS  Google Scholar 

  74. Bleasby AJ, Akrigg D, Attwood TK (1994) OWL — A non-redundant, composite protein sequence database. Nucl Acids Res 22:3574–3577

    PubMed  CAS  Google Scholar 

  75. Bjellqvist B, Hughes GJ, Pasquali Ch, Paquet N, Ravier F, Sanchez JCh, Frutiger S, Hochstrasser DF (1993) The focusing positions of polypeptides in immobilized pH gradients can be predicted from their amino acid sequences. Electrophoresis 14:1023–1031

    Article  PubMed  CAS  Google Scholar 

  76. Bachmair A, Finley D, Varshavsky A (1986) In vivo half-life of a protein is a function of its amino-terminal residue. Science. 234:179–86

    Article  PubMed  CAS  Google Scholar 

  77. Gumprasad K, Reddy BV, Pandit MW (1990) Correladon between stability of a protein and its dipeptide composition: a novel approach for predicting in vivo stability of a protein from its primary sequence. Protein Eng. 4:155–61

    Article  Google Scholar 

  78. Kyte J, Doolittle RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132

    Article  PubMed  CAS  Google Scholar 

  79. Nakashima H, Nishikawa K (1994) Discrimination of intracellular and extracellular proteins using amino acid composition and residue-pair frequencies. J Mol Biol 238:54–61

    Article  PubMed  CAS  Google Scholar 

  80. Kyte J, Doolitde RF (1982) A simple method for displaying the hydropathic character of a protein. J Mol Biol 157:105–132

    Article  PubMed  CAS  Google Scholar 

  81. Hopp TP, Woods KR (1981) Predicdon of protein antigenic determinants from amino acid sequences. Proc Natl Acad Sci USA 78:3824–3828

    Article  PubMed  CAS  Google Scholar 

  82. Hobohm U, Sander C (1995) A sequence property approach to searching protein databases. J.Mol.Biol. 251:390–399

    Article  PubMed  CAS  Google Scholar 

  83. Bernstein FC, Koetzle TF, Williams GJ, Meyer EF, Brice MD, Rodgers JR, Kennard O, Shimanouchi T, Tasumi M (1977) The Protein Data Bank: a Computer-Based Archival File for Macromolecular Structures. J Mol Biol 112:535–542

    Article  PubMed  CAS  Google Scholar 

  84. Wang Y, Addess KJ, Geer L, et al. (2000) MMDB: 3D structure data in Entrez. Nucleic Acids Research 28:243–245

    Article  PubMed  CAS  Google Scholar 

  85. Hogue CWV (1997) Cn3D: a new generation of three-dimensional molecular structure viewer. Trends Biochem Sci 22:314–316

    Article  PubMed  CAS  Google Scholar 

  86. Orengo CA, Michie AD, Jones S et al. (1997) CATH — A Hierarchie Classification of Protein Domain Structures. Structure. 5:1093–1108

    Article  PubMed  CAS  Google Scholar 

  87. Pearl FMG, Martin N, Bray JE et al. (2001) A rapid classification protocoll for the CATH Domain Database to support structural genomics. Nucl Acids Res 29:223–227

    Article  PubMed  CAS  Google Scholar 

  88. Bairoch A, Bucher P, Hofmann K (1997) The PROSITE database, its status in 1997. Nucl. Acids Res. 25:217–221

    Article  PubMed  CAS  Google Scholar 

  89. Gribskov M, McLachlan AD, Eisenberg D (1987) Profile analysis: detection of distantly related proteins. Proc. Natl. Acad. Sci. USA 84:4355–4358

    Article  PubMed  CAS  Google Scholar 

  90. http://www.sanger.ac.uk/Software/Pfam/ Bateman A, Birney E, Durbin R, Eddy SR, Howe KL, Sonnhammer EL (2000) The Pfam protein families database. Nucleic Acids Res. 28:263–266

    Article  PubMed  CAS  Google Scholar 

  91. Krause A, Stoye J, Vingron M (2000) The SYSTERS Protein Sequence Cluster Set. Nucleic Acids Research 28:270–272

    Article  PubMed  CAS  Google Scholar 

  92. Krause A, Nicodème P, Bornberg-Bauer E, Rehmsmeier M, Vingron M (1999) WWW-Access to the SYSTERS Protein Sequence Cluster Set. Bioinformatics 15:262–263

    Article  PubMed  CAS  Google Scholar 

  93. Krause A, Vingron M (1998) A set-theoretic approach to database searching and clustering. Bioinformatics 14:430–438

    Article  PubMed  CAS  Google Scholar 

  94. http://www.toulouse.inra.fr/prodom.html Corpet F, Servant F, Gouzy J, Kahn D (2000) ProDom and ProDom-CG: tools for protein domain analysis and whole genome comparisons. Nucleic Acids Res. 28:267–269

    Article  PubMed  CAS  Google Scholar 

  95. http://smart.embl-heidelberg.de/ Schultz J, Copley RR, Doerks T, Ponting CP, Bork P (2000) SMART: a web-based tool for the study of genetically mobile domains. Nucl Acids Res 28:231–234

    Article  PubMed  CAS  Google Scholar 

  96. http://blocks.fhcrc.org/ Henikoff S, Henikoff JG (1991) Automated assembly of protein blocks for database searching. Nucleic Acids Res 19:6565–6572

    Article  PubMed  CAS  Google Scholar 

  97. Henikoff S, Henikoff JG, Pietrokovski S (1999) Blocks+: A non-redundant database of protein alignment blocks dervied from multiple compilations. Bioinformatics 15:471–479

    Article  PubMed  CAS  Google Scholar 

  98. http://www.bioinf.man.ac.uk/dbbrowser/PRINTS/ Attwood TK, Croning MDR, Flower DR et al. (2000) PRINTS-S: the database formerly known as PRINTS. Nucleic Acids Research 28:225–227

    Article  PubMed  CAS  Google Scholar 

  99. http://www.cryst.bioc.cam.ac.uk/~homstrad/ Mizuguchi K, Deane CM, Blundell TL, Overington JP (1998) HOMSTRAD: a database of protein structure alignments for homologous families. Protein Sci 7:2469–2471

    Article  PubMed  CAS  Google Scholar 

  100. http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml Marchler-Bauer A, Panchenko AR, Shoemaker BA et al. (2002) CDD: a database of conserved domain alignments with links to domain three-dimensional structure. Nucleic Acids Res 30:281–283

    Article  PubMed  CAS  Google Scholar 

  101. http://www.ebi.ac.uk/interpro/ Apweiler R, Attwood TK, Bairoch A et al. (2001) The InterPro database, an integrated documentation resource for protein families, domains and functional sites. Nucl Acids Res 29:37–40

    Article  PubMed  CAS  Google Scholar 

  102. http://pir.georgetown.edu/iproclass/ Wu C, Xiao C, Hou Z, Huang H, Barker WC (2001) iProclass: an integrated, comprehensive and annotated protein classification database. Nucleic Acids Res 29: 52–54

    Article  PubMed  Google Scholar 

  103. Barker WC, Pfeiffer F, George D (1996) Superfamily classification in PIR-international protein sequence database. Methods Enzymol 266:59–71

    Article  PubMed  CAS  Google Scholar 

  104. http://www.jura.ebi.ac.uk:8765/ext-genequiz/ Andrade MA, Brown NP, Leroy C et al. (1999) Automated genome sequence analysis and annotation. Bioinformatics 15:391–412

    Article  PubMed  CAS  Google Scholar 

  105. Kabsch W, Sander C (1983) Dictionary of protein secondary structure: pattern recognition of hydrogen-bonded and geometrical features. Biopolymers 22:2577–2637

    Article  PubMed  CAS  Google Scholar 

  106. Richards FM, Kundrot CE (1988) Identificadon of structural motifs from protein coordinate data: secondary structure and first-level supersecondary structure. Proteins 3:71–84

    Article  PubMed  CAS  Google Scholar 

  107. Frishman D, Argos P (1995) Knowledge-based secondary structure assignment. Proteins: structure, function and genetics 23:566–579

    Article  CAS  Google Scholar 

  108. Chou PY, Fasman 6G (1978) Prediction of secondary structure of proteins from their aminoacid sequence. Adv Enzymol 47:45–148

    PubMed  CAS  Google Scholar 

  109. Garnier J, Osguthorpe DJ, Robson B (1978) Analysis of the accuracy and implications of simple methods for predicting the secondary structure of globular proteins. J Mol Biol 120:97–120

    Article  PubMed  CAS  Google Scholar 

  110. Yi TM, and Lander S (1993) Protein secondary structure prediction using neirest-neighbor methods. J Mol Biol 232:1117–1129

    Article  PubMed  CAS  Google Scholar 

  111. http://cubic.bioc.columbia.edu/predictprotein/ Rost B (1996) PHD: predicting one-dimensional protein structure by profile based neural networks. Methods in Enzymology 266:525–539

    Article  PubMed  CAS  Google Scholar 

  112. http://www.npsa-pbil.ibcp.fr/cgi-bin/npsa_automat.pl?page=npsa_gor4.html Garnier J, Gibrat JF, Robson B (1996) GOR method for predicting protein secondary structure from amino acid sequence. Methods in Enzymology 266:540–553

    Article  PubMed  CAS  Google Scholar 

  113. http://biolnf.cs.ucl.ac.uk/psipred/ Jones DT (1999) Protein secondary structure prediction based on position-specific scoring matrices. J Mol Biol 292:195–202

    Article  PubMed  CAS  Google Scholar 

  114. http://jura.ebi.ac.uk:8888/jnet/ Cuff JA, Barton GJ (2000) Application of multiple sequence alignment profiles to improve protein secondary structure prediction. Proteins 40:502–511

    Article  PubMed  CAS  Google Scholar 

  115. http://jpred.ebi.ac.uk Cuff JA, Barton GJ (1999) Evaluation and improvement of multiple sequence methods for protein secondary structure prediction. PROTEINS: Structure, Function and Genetics. 34:508–519

    Article  CAS  Google Scholar 

  116. http://www.ch.embnet.org/software/TMPRED_form.html Hofmann K, Stoffel W (1993) TMbase — A database of membrane spanning proteins segments. Biol. Chem. Hoppe-Seyler 374, 166

    Google Scholar 

  117. http://www.enzim.hu/hmmtop/ Tusnády GE, Simon I (2001) The HMMTOP transmembrane topology prediction server. Bioinformatics 17:849–850

    Article  PubMed  Google Scholar 

  118. http://cubic.bioc.columbia.edu/predictprotein/ Rost B, Fariselli P, Casadio R (1996) Topology prediction for helical transmembrane proteins at 86% accuracy Protein Science 7:1704–1718

    Article  Google Scholar 

  119. http://www.cbs.dtu.dk/services/SignalP/ Nielsen H, Engelbrecht J, Brunak S, von Heijne G (1997) Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites. Protein Engineering 10:1–6

    Article  PubMed  CAS  Google Scholar 

  120. http://cubic.bioc.columbia.edu/predictprotein/ Rost B (1995) TOPITS: Threading One-dimensional Predictions Into Three-dimensional Structures. In: Rawlings C, Clark D, Altman R, Hunter L, Lengauer T, and Wodak S (eds.). The third international conference on Intelligent Systems for Molecular Biology (ISMB), Cambridge, England, Menlo Park, CA: AAAI Press, 314–321

    Google Scholar 

  121. Rost B, Schneider R, Sander C (1997) Protein fold recognition by prediction-based threading. J Mol Biol 270:471–480

    Article  PubMed  CAS  Google Scholar 

  122. http://fold.doe-mbi.ucla.edu/ Salwinski L, Eisenberg D (2001) Motif-Based Fold Assignment. Prot Sci 10:2460–2469

    Article  CAS  Google Scholar 

  123. http://www.sbg.bio.ic.ac.uk/~3dpssm/ Kelley LA, MacCallum RM, Sternberg MJ (2000) Enhanced genome annotation using structural profiles in the program 3D-PSSM. J Mol Biol 299:499–520

    Article  PubMed  CAS  Google Scholar 

  124. Guex N, Peitsch MC (1997) SWISS-MODEL and the Swiss-PdbViewer: An environment for comparative protein modelling. Electrophoresis 18:2714–2723

    Article  PubMed  CAS  Google Scholar 

  125. Peitsch MC (1996) ProMod and Swiss-Model: Internet-based tools for automated comparative protein modelling. Biochem Soc Trans 24:274–279

    PubMed  CAS  Google Scholar 

  126. Peitsch MC, Schwede T, Guex N (2000) Automated protein modelling — the proteome in 3D. Pharmacogenomics 1:257–266

    Article  PubMed  CAS  Google Scholar 

  127. Holm L, Sander C (1996) Mapping the protein universe. Science 273:595–602

    Article  PubMed  CAS  Google Scholar 

  128. Holm L, Sander C (1993) Protein structure Comparison By Alignment Of Distance Matrices. J Mol Biol 233:123–138

    Article  PubMed  CAS  Google Scholar 

  129. http://www.biochem.ucl.ac.uk/~roman/procheck/procheck.html Laskowski RA, MacArthur MW, Moss DS, Thornton JM (1993) PROCHECK: A program to check the stereochemical quality of protein structures. J. Appl. Cryst. 26:283–291

    Article  CAS  Google Scholar 

  130. Guex N, Peitsch MC: Principles of Protein Structure, Comparative Protein Modelling and Visualisation / Secondary structure and backbone conformation http://www.expasy.ch/swissmod/course/text/chapter1.htm

  131. Morris AL, MacArthur MW, Hutchinson EG, Thornton JM (1992) Stereochemical quality of protein structure coordinates. Proteins 12:345–364

    Article  PubMed  CAS  Google Scholar 

  132. Hooft RW, Sander C, Vriend G (1997) Objectively judging the quality of a protein structure from a Ramachandran plot. Comput Appl Biosci 13:425–430

    PubMed  CAS  Google Scholar 

  133. Ramachandran GN, Sassiekharan V (1968) Conformation of polypeptides and proteins. Adv Prot Chem 28:283–437

    Article  Google Scholar 

  134. Vriend G (1990) WHAT IF: A molecular modeling and drug design program. J Mol Graph 8:52–56

    Article  PubMed  CAS  Google Scholar 

  135. Hooft RWW, Vriend G, Sander C, Abola EE (1996) Errors in protein structures. Nature 381:272–272

    Article  PubMed  CAS  Google Scholar 

  136. Pontius J, Richelle J, Wodak, SJ (1996) Quality assessment of protein 3D structures using standard atomic volumes. J Mol Biol 264:121–136

    Article  PubMed  CAS  Google Scholar 

  137. http://www.biochem.ucl.ac.uk/bsm/pdbsum/ Laskowski RA (2001) PDBsum: summaries and analyses of PDB structures. Nucl Acids Res 29:221–222

    Article  PubMed  CAS  Google Scholar 

  138. Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846

    Article  PubMed  CAS  Google Scholar 

  139. Williams KL (1999) Genomes and proteomes: Towards a multidimensional view of biology. Electrophoresis 20:678–688

    Article  PubMed  CAS  Google Scholar 

  140. Gauss C, Kalkum M, Lowe M, Lehrach H, Klose J (1999) Analysis of the mouse proteome. (I) Brain proteins: Separation by two-dimensional electrophoresis and identification by mass spectrometry and genetic variation. Electrophoresis 20:575–600

    Article  PubMed  CAS  Google Scholar 

  141. Page MJ, Amess B, Townsend, RR, et al. (1999) Proteomic Definition of normal human luminal and myoepithelial breast cells purified from reduction mammoplasties. Proc Natl Acad Sci USA 96:12589–12594

    Article  PubMed  CAS  Google Scholar 

  142. Celis JE, Ostergaard M, Rasmussen HH, et al. (1999) A comprehensive protein resource for the study of bladder cancer. Electrophoresis 20:300–309

    Article  PubMed  CAS  Google Scholar 

  143. Rout MP, Aitchison JD, Suprapto A, et al. (2000) The Yeast Nuclear Pore Complex: Composition, Architecture, and Transport Mechanism. J. Cell Biol. 148:635–651

    Article  PubMed  CAS  Google Scholar 

  144. Caprioli R, et al. Mass Spectrometry Tutorial http://nns.mc.vanderbilt.edu/tutorials/ms/ms.htm

  145. Renzel WJ, Billeci TM, Stults JT, Wong SC (1993) Identifying proteins from two-dimensional gels by molecular mass searching of peptide fragments in protein sequence databases. Proc. Natl Acad. Sci. USA 90:5011–5015

    Article  Google Scholar 

  146. Shevchenko A, et al. (1996) Linking genome and proteome by mass spectrometry: large scale identification of yeast proteins from two dimensional gels. Proc. Natl Acad. Sci. USA 93:14440–14445

    Article  PubMed  CAS  Google Scholar 

  147. Berndt P, Robohm U, Langen R (1999) Reliable automatic protein identification from matrix-assisted laser desorption/ionization mass spectrometric peptide fingerprints. Electrophoresis 20:3521–3526

    Article  PubMed  CAS  Google Scholar 

  148. Link AJ, Eng J, Schieltz DM, et al. (1999) Direct analysis of protein complexes using mass spectrometry. Nat. Biotechnol. 17:676–682

    Article  PubMed  CAS  Google Scholar 

  149. Jensen PK, et al. (1999) Probing proteomes using capillary isoelectric focusing-electrospray ionization Fourier transform ion cyclotron resonance mass spectrometry. Anal. Chem. 71:2076–2084

    Article  PubMed  CAS  Google Scholar 

  150. Shevchenko A, Loboda A, Shevchenko A, Ens W, Standing KG (2000) MALDI quadrupole time-of-flight mass spectrometry: a powerful tool for proteomic research. Anal Chem 72:2132–2141

    Article  PubMed  CAS  Google Scholar 

  151. http://www.narrador.embl-heidelberg.de/GroupPages/PageLink/peptidesearchpage.html

  152. Mann M, Wilm M (1994) Error-tolerant identification of peptides in sequence databases by peptide sequence tags. Anal. Chem. 66:4390–4399

    Article  PubMed  CAS  Google Scholar 

  153. http://www.srs.hgmp.mrc.ac.uk/~roman/procheck/procheck.html Pappin DJC, Hojrup P, Bleasby AJ (1993) Rapid Identification of Proteins by Peptide-Mass Fingerprinting. Current Biology 3:327–332

    Article  PubMed  CAS  Google Scholar 

  154. http://prowl.rockefeller.edu/ Zhang W, Chait BT (2000) ProFound: An Expert System for Protein Identification Using Mass Spectrometric Peptide Mapping Information. Anal Chem 72:2482–2489

    Article  PubMed  CAS  Google Scholar 

  155. http://prowl.rockefeller.edu/ Fenyo D, Qin J, Chait BT (1998) Protein identification using mass spectrometric information. Electrophoresis 19:998–1005

    Article  PubMed  CAS  Google Scholar 

  156. http://fields.scripps.edu/sequest/ Eng JK, McCormack AL, Yates JR (1994) An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J. Amer. Soc. Mass Spectrom. 5:976–989

    Article  CAS  Google Scholar 

  157. Yates JR, Eng JK, McCormack AL, Schieltz D (1995) Method to correlate tandem mass spectra of modified peptides to amino acid sequences in the protein database. Anal. Chem. 67:1426–1436

    Article  PubMed  CAS  Google Scholar 

  158. http://prospector.ucsf.edu/ Clauser KR, Baker PR, Burlingame AL (1999) Role of accurate mass measurement (+/− 10 ppm) in protein identification strategies employing MS or MS/MS and database searching. Analytical Chemistry 71:2871–2882

    Article  PubMed  CAS  Google Scholar 

  159. Hoogland C, Sanchez JC, Tonella L, et al (2000) The 1999 SWISS-2DPAGE database update. Nucleic Acids Res. 28:286–288

    Article  PubMed  CAS  Google Scholar 

  160. http://www.lecb.ncifcrf.gov/2dwgDB/ Lemkin PF (1997) The 2DWG meta-database of two-dimensional electrophoretic gel images on the Internet. Electrophoresis 18:2759–2773

    Article  PubMed  CAS  Google Scholar 

  161. Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y (2001) A comprehensive two-hybrid analysis to explore the yeast protein interactome. PNAS 98:4569–4574

    Article  PubMed  CAS  Google Scholar 

  162. Uetz P, Giot L, Cagney G, Mansfield TA, et al. (2000) A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 403:623–627

    Article  PubMed  CAS  Google Scholar 

  163. Gavin AC, Bösche, Krause R, et al. (2002) Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 415:141–147

    Article  PubMed  CAS  Google Scholar 

  164. http://dip.doe-mbi.ucla.edu/ Xenarios I, Fernandez E, Salwinski L, Duan XJ, Thompson MJ, Marcotte EM, Eisenberg D (2001) DIP: The Database of Interacting Proteins: 2001 update. Nucleid Acids Research 29:239–241

    Article  CAS  Google Scholar 

  165. Ito T, Tashiro K, Muta S et al. (2000) Toward a protein-protein interaction map of the budding yeast: A comprehensive system to examine two-hybrid interactions in all possible combinations between the yeast proteins. PNAS 97:1143–1147

    Article  PubMed  CAS  Google Scholar 

  166. http://binddb.org/ Bader GD, Donaldson I, Wolting C, Ouellette BF, Pawson T, Hogue CW (2001) BIND — The Biomolecular Interaction Network Database. Nucleic Acids Res 29:242–245

    Article  PubMed  CAS  Google Scholar 

  167. Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:837–846

    Article  PubMed  CAS  Google Scholar 

  168. MacBeath G, Schreiber SL (2000) Printing Proteins as Microarrays for High-Throughput Function Determination. Science 289:1760–1763

    PubMed  CAS  Google Scholar 

  169. de Wildt RM, Mundy CR, Gorick BD, Tomlinson IM (2000) Antibody arrays for high-throughput screening of antibody-antigen interactions. Nat Biotechnol 18:989–994

    Article  PubMed  CAS  Google Scholar 

  170. Ogata H, Goto S, Sato K, Fujibuchi W, Bono H, Kanehisa M (1999) KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res 27:29–34

    Article  PubMed  CAS  Google Scholar 

  171. Kanehisa M, Goto S, Kawashima S, Nakaya A (2002) The KEGG databases at GenomeNet. Nucleic Acids Res 30:42–46

    Article  PubMed  CAS  Google Scholar 

  172. Overbeek R, Larsen N, Pusch GD, et al. (2000) WIT: integrated system for high-throughput genome sequence analysis and metabolic reconstruction. Nucleic Acids Res 28:123–125

    Article  PubMed  CAS  Google Scholar 

  173. Selkov E Jr, Grechkin Y, Mikhailova N, Selkov E (1998) MPW: the Metabolic Pathways Database. Nucleic Acids Res 26:43–45

    Article  PubMed  CAS  Google Scholar 

  174. Selkov E, Basmanova S, Gaasterland T, et al. (1996) The metabolic pathway collection from EMP: the enzymes and metabolic pathways database. Nucleic Acids Res 24:26–28

    Article  PubMed  CAS  Google Scholar 

  175. Goryanin I, Hodgman TC, Selkov E (1999) Mathematical simulation and analysis of cellular metabolism and regulation. Bioinformatics 15:749–758

    Article  PubMed  CAS  Google Scholar 

  176. http://ecocyc.org/ Karp PD, Riley M, Saier M, et al. (2002) The EcoCyc Database. Nucleic Acids Res 30:56–58

    Article  PubMed  CAS  Google Scholar 

  177. Karp PD, Riley M, Paley SM, Pellegrini-Toole A (2002) The MetaCyc Database. Nucleic Acids Res 30:59–61

    Article  PubMed  CAS  Google Scholar 

  178. Karp PD (2001) Pathway databases: a case study in computational symbolic theories. Science 293:2040–2044

    Article  PubMed  CAS  Google Scholar 

  179. http://www.expasy.ch/enzyme/ Bairoch A (2000) The ENZYME database in 2000. Nucleic Acids Res 28:304–305

    Google Scholar 

  180. Tomita M, Hashimoto K, Takahashi K, et al. (1999) E-CELL: software environment for whole-cell simulation. Bioinformatics 15:72–84

    Article  PubMed  CAS  Google Scholar 

  181. Miklos GLG, Maleszka R (2001) Integrating molecular medicine with functional proteomics: Realities and expectations. Proteomics 1:30–41

    Article  PubMed  CAS  Google Scholar 

  182. Rohlff C (2000) Proteomics in molecular medicine: Applications in central nervous systems disorders. Electrophoresis 21:1227–1234

    Article  PubMed  CAS  Google Scholar 

  183. Baxevanis A, Ouellette F (2001) Bioinformatics: A Practical Guide to the Analysis of Genes and Proteins. John Wiley & Sons, New York. ISBN 0471383910

    Google Scholar 

  184. Misener S, Krawetz S, Krawetz S (2000) Bioinformatics Methods and Protocols (Methods in Molecular Biology, Vol 132) Humana Press. ISBN 0896037320

    Google Scholar 

  185. Lesk AM (2002) Introduction to Bioinformatics. Oxford University Press. ISBN 0199251967

    Google Scholar 

  186. Durbin R, Eddy S, Krogh A, Mitchison G (1998) Biological sequence analysis: probabilistic models of proteins and nucleic acids. Cambridge, U.K. New York, Cambridge University Press. ISBN 0521629713

    Book  Google Scholar 

  187. Clote P, Backofen R (2000) Computational molecular biology: an introduction. Chichester; New York, John Wiley. ISBN 0471872520

    Google Scholar 

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Dugas, M., Schmidt, K. (2003). Bioinformatik. In: Medizinische Informatik und Bioinformatik. Springer-Lehrbuch. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-55883-2_4

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