Protein interaction networks from yeast to human

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Danish Proteomics Society and Danish Society for Biochemistry and Molecular Biology Symposium, University of Southern Denmark, Odense, Denmark, December 8, 2005

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Protein interaction networks from yeast to human

  1. 1. Protein interaction networks from yeast to human Lars Juhl Jensen EMBL Heidelberg
  2. 2. Current Opinion in Structural Biology, 2005
  3. 3. STRING
  4. 5. integrate diverse evidence
  5. 6. functional interactions
  6. 7. Bork et al., Current Opinion in Structural Biology, 2005
  7. 8. hundreds of proteomes
  8. 9. evolution
  9. 12. statistics
  10. 13. (the original sin)
  11. 14. prokaryotes
  12. 15. genomic context methods
  13. 16. gene fusion
  14. 18. gene neighborhood
  15. 20. phylogenetic profiles
  16. 25. Cell Cellulosomes Cellulose
  17. 26. eukaryotes
  18. 27. integrate diverse datasets
  19. 28. Jensen et al., Drug Discovery Today: Targets, 2004
  20. 29. curated knowledge
  21. 30. MIPS Munich Information center for Protein Sequences
  22. 31. KEGG Kyoto Encyclopedia of Genes and Genomes
  23. 32. STKE Signal Transduction Knowledge Environment
  24. 33. Reactome
  25. 34. literature mining
  26. 35. co-mentioning
  27. 36. NLP Natural Language Processing
  28. 37. M EDLINE
  29. 38. SGD Saccharomyces Genome Database
  30. 39. The Interactive Fly
  31. 40. OMIM Online Mendelian Inheritance in Man
  32. 41. primary experimental data
  33. 42. microarray expression data
  34. 43. GEO Gene Expression Omnibus
  35. 44. physical protein interactions
  36. 45. BIND Biomolecular Interaction Network Database
  37. 46. MINT Molecular Interactions Database
  38. 47. GRID General Repository for Interaction Datasets
  39. 48. DIP Database of Interacting Proteins
  40. 49. HPRD Human Protein Reference Database
  41. 50. problems
  42. 51. many sources
  43. 52. (different gene identifiers)
  44. 53. many types of evidence
  45. 54. questionable quality
  46. 55. not directly comparable
  47. 56. spread over many species
  48. 57. huge synonyms lists
  49. 58. calculate raw quality scores
  50. 59. calibrate vs. gold standard
  51. 60. KEGG Kyoto Encyclopedia of Genes and Genomes
  52. 61. von Mering et al., Nucleic Acids Research, 2005
  53. 62. transfer based on orthology
  54. 63. combine all evidence
  55. 64. Bork et al., Current Opinion in Structural Biology, 2005
  56. 65. Networ-Kin™ Phospho-peptide data (from MS) Predict the kinase class (NetPhosK and Scansite)
  57. 66. the human interactome
  58. 67. yeast two-hybrid
  59. 68. 1936 13 4 4 1385 65 18465 Stelzl et al. Rual et al. Small-scale studies
  60. 69. 32 0 3 4 18 4 23 Stelzl et al. Rual et al. Small-scale studies
  61. 70. 62 8 39 Small-scale studies Stelzl et al. Rual et al. 852 17 473 432 69 260
  62. 71. 3.5% and 21% sensitivity
  63. 72. in a couple of years
  64. 73. the human interactome
  65. 74. 100% = 1/5?
  66. 75. the yeast interactome
  67. 76. five years ago
  68. 77. yeast two-hybrid
  69. 78. 1150 117 117 72 4053 118 4469 Uetz et al. Ito et al. Small-scale studies
  70. 79. 162 53 34 72 180 29 338 Uetz et al. Ito et al. Small-scale studies
  71. 80. 511 189 616 Small-scale studies Uetz et al. Ito et al. 439 178 759 897 190 1347
  72. 81. 19% and 12% sensitivity
  73. 82. three years ago
  74. 83. complex purification and MS
  75. 84. 14186 784 1178 287 19492 230 3475 Gavin et al. Ho et al. Small-scale studies
  76. 85. 2341 125 656 287 2725 42 149 Gavin et al. Ho et al. Small-scale studies
  77. 86. 4431 1465 5041 Small-scale studies Gavin et al. Ho et al. 14913 1071 632 8047 517 746
  78. 87. 63% and 41% sensitivity
  79. 88. today
  80. 89. combining all available data
  81. 90. scoring and filtering
  82. 92. de Lichtenberg et al., Science, 2005
  83. 93. de Lichtenberg et al., Science, 2005
  84. 94. de Lichtenberg et al., Science, 2005
  85. 95. de Lichtenberg et al., Science, 2005
  86. 96. de Lichtenberg et al., Science, 2005
  87. 97. the challenge
  88. 98. how to get from here …
  89. 99. 1936 13 4 4 1385 65 18465 Stelzl et al. Rual et al. Small-scale studies
  90. 100. … to there …
  91. 101. de Lichtenberg et al., Science, 2005
  92. 102. yeast two-hybrid
  93. 103. complex purification and MS
  94. 104. scoring and filtering
  95. 105. Acknowledgments <ul><li>The STRING team (EMBL) </li></ul><ul><ul><li>Christian von Mering </li></ul></ul><ul><ul><li>Berend Snel </li></ul></ul><ul><ul><li>Martijn Huynen </li></ul></ul><ul><ul><li>Sean Hooper </li></ul></ul><ul><ul><li>Mathilde Foglierini </li></ul></ul><ul><ul><li>Julien Lagarde </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><li>Literature mining project (EML Research) </li></ul><ul><ul><li>Jasmin Saric </li></ul></ul><ul><ul><li>Rossitza Ouzounova </li></ul></ul><ul><ul><li>Isabel Rojas </li></ul></ul><ul><li>Cell cycle studies (CBS) </li></ul><ul><ul><li>Ulrik de Lichtenberg </li></ul></ul><ul><ul><li>Thomas Skøt Jensen </li></ul></ul><ul><ul><li>Søren Brunak </li></ul></ul><ul><li>Networ-Kin™ (Mt. Sinai) </li></ul><ul><ul><li>Rune Linding </li></ul></ul><ul><ul><li>Tony Pawson </li></ul></ul><ul><li>Inspiration for presentation </li></ul><ul><ul><li>Lawrence Lessig </li></ul></ul><ul><ul><li>Dick Clarence Hardt </li></ul></ul><ul><ul><li>Anders Gorm Pedersen </li></ul></ul>
  96. 106. Thank you!

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