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  • Integration Automation Collaboration

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  • 1. Advanced bioinformaticsmethods for proteomics Lars Juhl Jensen
  • 2. three parts
  • 3. signaling networks
  • 4. association networks
  • 5. text mining
  • 6. Part 1signaling networks
  • 7. phosphoproteomics
  • 8. Linding, Jensen, Ostheimer et al., Cell, 2007
  • 9. in vivo phosphosites
  • 10. kinases are unknown
  • 11. sequence specificity
  • 12. Miller, Jensen et al., Science Signaling, 2008
  • 13. NetPhorest
  • 14. automated pipeline
  • 15. Miller, Jensen et al., Science Signaling, 2008
  • 16. motif atlas
  • 17. kinases
  • 18. phospho-binding proteins
  • 19. phosphatases
  • 20. protein-specific
  • 21. no context
  • 22. co-activators
  • 23. protein scaffolds
  • 24. localization
  • 25. expression
  • 26. association network
  • 27. Linding, Jensen, Ostheimer et al., Cell, 2007
  • 28. NetworKIN
  • 29. Linding, Jensen, Ostheimer et al., Cell, 2007
  • 30. Part 2association networks
  • 31. guilt by association
  • 32. STRING
  • 33. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
  • 34. >1100 genomes
  • 35. computational predictions
  • 36. genomic context
  • 37. gene fusion
  • 38. Korbel et al., Nature Biotechnology, 2004
  • 39. phylogenetic profiles
  • 40. Korbel et al., Nature Biotechnology, 2004
  • 41. experimental data
  • 42. physical interactions
  • 43. Jensen & Bork, Science, 2008
  • 44. genetic interactions
  • 45. Beyer et al., Nature Reviews Genetics, 2007
  • 46. gene coexpression
  • 47. curated knowledge
  • 48. pathways
  • 49. Letunic & Bork, Trends in Biochemical Sciences, 2008
  • 50. many data types
  • 51. many databases
  • 52. different formats
  • 53. different identifiers
  • 54. variable quality
  • 55. not comparable
  • 56. spread over 1100+ genomes
  • 57. quality scores
  • 58. von Mering et al., Nucleic Acids Research, 2005
  • 59. calibrate vs. gold standard
  • 60. von Mering et al., Nucleic Acids Research, 2005
  • 61. orthology transfer
  • 62. missing most of the data
  • 63. Part 3text mining
  • 64. >10 km
  • 65. too much to read
  • 66. computer
  • 67. as smart as a dog
  • 68. teach it specific tricks
  • 69. named entity recognition
  • 70. identify the concepts
  • 71. proteins
  • 72. small molecules
  • 73. comprehensive lexicon
  • 74. orthographic variation
  • 75. “black list”
  • 76. Reflect
  • 77. augmented browsing
  • 78. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010
  • 79. information extraction
  • 80. formalize the facts
  • 81. co-mentioning
  • 82. global statistical analysis
  • 83. NLPNatural Language Processing
  • 84. parsing individual sentences
  • 85. summary
  • 86. computational biology
  • 87. data mining
  • 88. text mining
  • 89. save you much time
  • 90. AcknowledgmentsNetPhorest NetworKIN STRING ReflectRune Linding Rune Linding Christian von Mering Sune FrankildMartin Lee Miller Heiko Horn Damian Szklarczyk Heiko HornErwin Schoof Gerard Ostheimer Michael Kuhn Evangelos PafilisFrancesca Diella Martin Lee Miller Manuel Stark Michael KuhnClaus Jørgensen Francesca Diella Samuel Chaffron Nigel BrownMichele Tinti Karen Colwill Chris Creevey ReinhardtLei Li Jing Jin Jean Muller SchneiderMarilyn Hsiung Pavel Metalnikov Tobias Doerks Sean O’DonoghueSirlester A. Parker Vivian Nguyen Philippe JulienJennifer Bordeaux Adrian Pasculescu Alexander RothThomas Sicheritz-Pontén Jin Gyoon Park Milan SimonovicMarina Olhovsky Leona D. Samson Jan KorbelAdrian Pasculescu Rob Russell Berend SnelJes Alexander Peer Bork Martijn HuynenStefan Knapp Michael Yaffe Peer BorkNikolaj Blom Tony PawsonPeer BorkShawn LiGianni CesareniTony PawsonBenjamin E. TurkMichael B. YaffeSøren Brunak
  • 91. larsjuhljensen