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Advanced bioinformatics
methods for proteomics
Lars Juhl Jensen
phosphoproteomics
in vivo phosphosites
kinases are unknown
sequence motifs
Miller, Jensen et al., Science Signaling, 2008
NetPhorest
data organization
Miller, Jensen et al., Science Signaling, 2008
automated pipeline
Miller, Jensen et al., Science Signaling, 2008
compilation of datasets
training and evaluation
motif atlas
179 kinases
89 SH2 domains
8 PTB domains
BRCT domains
WW domains
14-3-3 proteins
sequence specificity
protein-specific
no context
association networks
Linding, Jensen, Ostheimer et al., Cell, 2007
guilt by association
STRING
Jensen, Kuhn et al., Nucleic Acids Research, 2009
computational predictions
gene fusion
Korbel et al., Nature Biotechnology, 2004
phylogenetic profiles
Korbel et al., Nature Biotechnology, 2004
experimental data
physical interactions
Jensen & Bork, Science, 2008
genetic interactions
Beyer et al., Nature Reviews Genetics, 2007
gene coexpression
curated knowledge
Letunic & Bork, Trends in Biochemical Sciences, 2008
>10 km
literature mining
Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
co-mentioning
NLP
Natural Language Processing
different formats
different names
variable quality
not comparable
spread over 630 genomes
confidence scores
von Mering et al., Nucleic Acids Research, 2005
transfer by orthology
von Mering et al., Nucleic Acids Research, 2005
Linding, Jensen, Ostheimer et al., Cell, 2007
integration
NetworKIN
Linding, Jensen, Ostheimer et al., Cell, 2007
>2x better accuracy
use case
DNA damage response
Linding, Jensen, Ostheimer et al., Cell, 2007
experimental validation
ATM phosphorylates Rad50
Linding, Jensen, Ostheimer et al., Cell, 2007
summary
computational biology
network analysis
testable predictions
save much time in the lab
thank you!
NetPhorest
– Rune Linding
– Martin Lee Miller
– Francesca Diella
– Claus Jørgensen
– Michele Tinti
– Lei Li
– M...
larsjuhljensen
Advanced bioinformatics methods for proteomics
Advanced bioinformatics methods for proteomics
Advanced bioinformatics methods for proteomics
Advanced bioinformatics methods for proteomics
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Advanced bioinformatics methods for proteomics

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Advanced bioinformatics methods for proteomics

  1. 1. Advanced bioinformatics methods for proteomics Lars Juhl Jensen
  2. 2. phosphoproteomics
  3. 3. in vivo phosphosites
  4. 4. kinases are unknown
  5. 5. sequence motifs
  6. 6. Miller, Jensen et al., Science Signaling, 2008
  7. 7. NetPhorest
  8. 8. data organization
  9. 9. Miller, Jensen et al., Science Signaling, 2008
  10. 10. automated pipeline
  11. 11. Miller, Jensen et al., Science Signaling, 2008
  12. 12. compilation of datasets
  13. 13. training and evaluation
  14. 14. motif atlas
  15. 15. 179 kinases
  16. 16. 89 SH2 domains
  17. 17. 8 PTB domains
  18. 18. BRCT domains
  19. 19. WW domains
  20. 20. 14-3-3 proteins
  21. 21. sequence specificity
  22. 22. protein-specific
  23. 23. no context
  24. 24. association networks
  25. 25. Linding, Jensen, Ostheimer et al., Cell, 2007
  26. 26. guilt by association
  27. 27. STRING
  28. 28. Jensen, Kuhn et al., Nucleic Acids Research, 2009
  29. 29. computational predictions
  30. 30. gene fusion
  31. 31. Korbel et al., Nature Biotechnology, 2004
  32. 32. phylogenetic profiles
  33. 33. Korbel et al., Nature Biotechnology, 2004
  34. 34. experimental data
  35. 35. physical 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. literature mining
  44. 44. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009
  45. 45. co-mentioning
  46. 46. NLP Natural Language Processing
  47. 47. different formats
  48. 48. different names
  49. 49. variable quality
  50. 50. not comparable
  51. 51. spread over 630 genomes
  52. 52. confidence scores
  53. 53. von Mering et al., Nucleic Acids Research, 2005
  54. 54. transfer by orthology
  55. 55. von Mering et al., Nucleic Acids Research, 2005
  56. 56. Linding, Jensen, Ostheimer et al., Cell, 2007
  57. 57. integration
  58. 58. NetworKIN
  59. 59. Linding, Jensen, Ostheimer et al., Cell, 2007
  60. 60. >2x better accuracy
  61. 61. use case
  62. 62. DNA damage response
  63. 63. Linding, Jensen, Ostheimer et al., Cell, 2007
  64. 64. experimental validation
  65. 65. ATM phosphorylates Rad50
  66. 66. Linding, Jensen, Ostheimer et al., Cell, 2007
  67. 67. summary
  68. 68. computational biology
  69. 69. network analysis
  70. 70. testable predictions
  71. 71. save much time in the lab
  72. 72. thank you! NetPhorest – 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 – Christian von Mering – Damian Szklarczyk – 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 Reflect – Sune Frankild – Heiko Horn – Evangelos Pafilis – Michael Kuhn – Nigel Brown – Reinhardt Schneider – Sean O’Donoghue NetworKIN – Rune Linding – Heiko Horn – Gerard Ostheimer – 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 Yaffe – Tony Pawson
  73. 73. larsjuhljensen

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