Interaction networks - Prediction, data integration and text mining

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Interaction networks - Prediction, data integration and text mining

  1. 1. Interaction networksPrediction, data integration and text mining Lars Juhl Jensen
  2. 2. the cell cycle
  3. 3. essential process
  4. 4. grow and divide
  5. 5. one cell
  6. 6. two cells
  7. 7. four phases
  8. 8. G1 phase
  9. 9. growth
  10. 10. S phase
  11. 11. DNA replication
  12. 12. G2 phase
  13. 13. growth
  14. 14. M phase
  15. 15. cell division
  16. 16. regulation
  17. 17. gene expression
  18. 18. phosphorylation
  19. 19. targeted degradation
  20. 20. protein interactions
  21. 21. exercise 1
  22. 22. http://string-db.org
  23. 23. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
  24. 24. association networks
  25. 25. guilt by association
  26. 26. STRING
  27. 27. >1100 genomes
  28. 28. genomic context
  29. 29. gene fusion
  30. 30. Korbel et al., Nature Biotechnology, 2004
  31. 31. conserved neighborhood
  32. 32. Korbel et al., Nature Biotechnology, 2004
  33. 33. phylogenetic profiles
  34. 34. Korbel et al., Nature Biotechnology, 2004
  35. 35. protein interactions
  36. 36. Jensen & Bork, Science, 2008
  37. 37. genetic interactions
  38. 38. Beyer et al., Nature Reviews Genetics, 2007
  39. 39. gene coexpression
  40. 40. curated knowledge
  41. 41. Letunic & Bork, Trends in Biochemical Sciences, 2008
  42. 42. >10 km
  43. 43. text mining
  44. 44. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
  45. 45. co-mentioning
  46. 46. NLPNatural Language Processing
  47. 47. different sources
  48. 48. Ensembl
  49. 49. RefSeq
  50. 50. BINDBiomolecular Interaction Network Database
  51. 51. BioGRIDGeneral Repository for Interaction Datasets
  52. 52. DIPDatabase of Interacting Proteins
  53. 53. IntAct
  54. 54. MINTMolecular Interactions Database
  55. 55. HPRDHuman Protein Reference Database
  56. 56. PDBProtein Data Bank
  57. 57. GEOGene Expression Omnibus
  58. 58. MIPSMunich Information center for Protein Sequences
  59. 59. Gene Ontology
  60. 60. BioCyc
  61. 61. KEGGKyoto Encyclopedia of Genes and Genomes
  62. 62. PIDNCI-Nature Pathway Interaction Database
  63. 63. Reactome
  64. 64. different formats
  65. 65. different names
  66. 66. CDC2
  67. 67. CDK1
  68. 68. P06493
  69. 69. not comparable
  70. 70. variable quality
  71. 71. confidence scores
  72. 72. calibrate to gold standard
  73. 73. von Mering et al., Nucleic Acids Research, 2005
  74. 74. transfer by orthology
  75. 75. von Mering et al., Nucleic Acids Research, 2005
  76. 76. combine scores
  77. 77. exercise 2
  78. 78. changing parameters
  79. 79. high confidence only
  80. 80. experiments only
  81. 81. evidence viewers
  82. 82. cell cycle analysis
  83. 83. gene expression
  84. 84. cell cultures
  85. 85. synchronization
  86. 86. microarrays
  87. 87. time courses
  88. 88. Gauthier et al., Nucleic Acids Research, 2007
  89. 89. cycling genes
  90. 90. time of peak expression
  91. 91. protein interactions
  92. 92. temporal network
  93. 93. de Lichtenberg, Jensen et al., Science, 2005
  94. 94. just-in-time assembly
  95. 95. de Lichtenberg, Jensen et al., Cell Cycle, 2007
  96. 96. evolutionary flexibility
  97. 97. orthologs and paralogs
  98. 98. protein complexes
  99. 99. exercise 3
  100. 100. http://string-db.org
  101. 101. network expansion
  102. 102. what is known
  103. 103. external data
  104. 104. save network
  105. 105. open in Cytoscape
  106. 106. layout
  107. 107. clustering
  108. 108. project data onto network
  109. 109. de Lichtenberg, Jensen et al., Science, 2005
  110. 110. very flexible
  111. 111. lose the STRING interface
  112. 112. payload mechanism
  113. 113. show external data
  114. 114. nodes
  115. 115. edges
  116. 116. hosted on your server
  117. 117. exercise 4
  118. 118. http://cyclebase-string.jensenlab.org
  119. 119. network expansion
  120. 120. CDK–cyclin complexes
  121. 121. chemical networks
  122. 122. STITCH
  123. 123. STRING + chemicals
  124. 124. PubChem compounds
  125. 125. >74,000 small molecules
  126. 126. experimental data
  127. 127. BindingDB
  128. 128. ChEMBL
  129. 129. PDSP KiPsycoactive Drug Screening Program
  130. 130. PDBProtein Data Bank
  131. 131. drug targets
  132. 132. CTDComparative Toxicogenomics Database
  133. 133. DrugBank
  134. 134. GLIDAGPCR-Ligand Database
  135. 135. Matador
  136. 136. TTDTherapeutic Target Database
  137. 137. metabolic pathways
  138. 138. BioCyc
  139. 139. KEGGKyoto Encyclopedia of Genes and Genomes
  140. 140. Reactome
  141. 141. text mining
  142. 142. co-mentioning
  143. 143. NLPNatural Language Processing
  144. 144. same issues as for proteins
  145. 145. only worse
  146. 146. exercise 5
  147. 147. http://stitch-db.org
  148. 148. chemical network for TYMS
  149. 149. Kuhn et al., Nucleic Acids Research, 2012
  150. 150. network expansion
  151. 151. interpretation
  152. 152. disease networks
  153. 153. human proteins
  154. 154. >8,000 disease terms
  155. 155. text mining
  156. 156. co-mentioning
  157. 157. exercise 6
  158. 158. http://diseases.jensenlab.org
  159. 159. TYMS disease associations
  160. 160. inspect the evidence
  161. 161. colorectal cancer network
  162. 162. conclusions
  163. 163. know your question
  164. 164. know what is possible
  165. 165. know the tools
  166. 166. shameless self-promotion
  167. 167. CONFIRMED SPEAKERS: Ivan Dikic Steve Jackson Jiri Lukas Andre Nussenzweig Philippe Bastiens Tony Pawson Forest White Eric Verdin Tim Hunt Brenda Schulman Michael Yaffe Matthias Mann Gerand Hart Søren Brunak Henrik Semb Juleen ZierathREGISTRATION FEE, ACCOMMODATIONAND LOCAL COSTS FOR ALL ATTENDEES CHAIRS:ARE COVERED BY THE NOVO NORDISK Jesper Velgaard OlsenFOUNDATION. Chuna Choudhary Niels MailandAPPLICATION DEADLINE SEPTEMBER 14, Lars Juhl Jensen
  168. 168. larsjuhljensen
  169. 169. thank you!

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