Unraveling signal transduction networks through data integration
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The document discusses methods for integrating different types of biological data to build networks that model signal transduction pathways. It describes using protein sequence motifs to predict kinase-substrate relationships, and combining this with protein interaction and expression data to provide context. Validation studies on ATM and Cdk1 signaling pathways showed this approach could accurately predict phosphorylation sites and the kinases that target them. Future work involves improving scoring methods and expanding to other types of post-translational modifications and model organisms.
Unraveling signal transduction networks through data integration
- 1. Unraveling signal transduction networks through data integration Lars Juhl Jensen
- 2. the problem
- 4. Linding, Jensen, Ostheimer et al., Cell , 2007
- 5. in vivo phosphosites
- 10. Miller, Jensen et al., Science Signaling , 2008
- 11. domain-specific
- 12. in vitro
- 13. no context
- 15. not what does happen
- 17. sequence motifs
- 18. Miller, Jensen et al., Science Signaling , 2008
- 19. domain-specific
- 20. group-specific
- 21. no context
- 23. not what does happen
- 24. in vitro
- 25. in vivo
- 26. context
- 27. co-activators
- 33. Linding, Jensen, Ostheimer et al., Cell , 2007
- 34. the idea
- 35. Linding, Jensen, Ostheimer et al., Cell , 2007
- 37. NetPhorest
- 39. Miller, Jensen et al., Science Signaling , 2008
- 41. Miller, Jensen et al., Science Signaling , 2008
- 46. motif atlas
- 48. 179 kinases
- 49. 89 SH2 domains
- 50. 8 PTB domains
- 51. BRCT domains
- 52. WW domains
- 53. 14-3-3 proteins
- 54. use cases
- 55. Miller, Jensen et al., Science Signaling , 2008
- 59. STRING
- 60. Jensen, Kuhn et al., Nucleic Acids Research , 2009
- 62. data integration
- 65. Jensen & Bork, Science , 2008
- 69. Korbel et al., Nature Biotechnology , 2004
- 70. Korbel et al., Nature Biotechnology , 2004
- 74. Letunic & Bork, Trends in Biochemical Sciences , 2008
- 75. too easy …
- 76. … to be true
- 79. variable quality
- 80. spread over 630 genomes
- 81. parsers
- 82. thesaurus
- 84. benchmarking
- 85. von Mering et al., Nucleic Acids Research , 2005
- 87. von Mering et al., Nucleic Acids Research , 2005
- 89. context network
- 90. Linding, Jensen, Ostheimer et al., Cell , 2007
- 91. the results
- 92. NetworKIN
- 93. Linding, Jensen, Ostheimer et al., Cell , 2007
- 94. benchmarking
- 95. Phospho.ELM
- 96. Linding, Jensen, Ostheimer et al., Cell , 2007
- 98. much context still missing
- 100. Linding, Jensen, Ostheimer et al., Cell , 2007
- 101. ATM signaling
- 102. Linding, Jensen, Ostheimer et al., Cell , 2007
- 105. Linding, Jensen, Ostheimer et al., Cell , 2007
- 107. Linding, Jensen, Ostheimer et al., Cell , 2007
- 110. Linding, Jensen, Ostheimer et al., Cell , 2007
- 111. the future
- 112. new scoring scheme
- 113. two separate scores
- 114. one combined score
- 115. path length penalty
- 116. model organisms
- 117. S. cerevisiae
- 118. D. melanogaster
- 119. C. elegans
- 120. ( S. pombe )
- 121. other modifications
- 122. phosphatases
- 123. ubiquitylation
- 124. acetylation
- 125. glycosylation
- 126. Acknowledgments NetPhorest.info Rune Linding Martin Lee Miller Francesca Diella Claus Jørgensen Michele Tinti Lei Li Marilyn Hsiung Sirlester A. Parker Jennifer Bordeaux Thomas Sicheritz-Pontén Marina Olhovsky Adrian Pasculescu Jes Alexander Stefan Knapp Nikolaj Blom Peer Bork Shawn Li Gianni Cesareni Tony Pawson Benjamin E. Turk Michael B. Yaffe Søren Brunak STRING-DB.org Christian von Mering Michael Kuhn Manuel Stark Samuel Chaffron Chris Creevey Jean Muller Tobias Doerks Philippe Julien Alexander Roth Milan Simonovic Jan Korbel Berend Snel Martijn Huynen Peer Bork NetworKIN.info Rune Linding Gerard Ostheimer Heiko Horn Martin Lee Miller Francesca Diella Karen Colwill Jing Jin Pavel Metalnikov Vivian Nguyen Adrian Pasculescu Jin Gyoon Park Leona D. Samson Rob Russell Peer Bork Michael B. Yaffe Tony Pawson
- 127. larsjuhljensen
Editor's Notes
- #48: Atlas of human kinases Atlases for phospho-binding proteins Atlases for model organisms Ubiquitination would be welcome