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Systems biology - Bioinformatics on complete biological systems

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Systems biology - Bioinformatics on complete biological systems

  1. 1. Systems biologyBioinformatics on complete biological systems Lars Juhl Jensen
  2. 2. can a biologist fix a radio?
  3. 3. Lazebnik, Biochemistry, 2004
  4. 4. one gene
  5. 5. one postdoc
  6. 6. knockout phenotype
  7. 7. name the gene
  8. 8. Lazebnik, Biochemistry, 2004
  9. 9. all aspects
  10. 10. one gene
  11. 11. high-throughput biology
  12. 12. one technology
  13. 13. one lab
  14. 14. all genes
  15. 15. one aspect
  16. 16. systems biology
  17. 17. complete systems
  18. 18. all aspects
  19. 19. all genes
  20. 20. systems-level properties
  21. 21. two subfields
  22. 22. mathematical modeling
  23. 23. small systems
  24. 24. data integration
  25. 25. large systems
  26. 26. mathematical modeling
  27. 27. small systems
  28. 28. Chen, Mol. Biol. Cell, 2004
  29. 29. many equations
  30. 30. Chen, Mol. Biol. Cell, 2004
  31. 31. simulation
  32. 32. Chen, Mol. Biol. Cell, 2004
  33. 33. many parameters
  34. 34. Chen, Mol. Biol. Cell, 2004
  35. 35. equires detailed knowledge
  36. 36. data integration
  37. 37. association networks
  38. 38. guilt by association
  39. 39. STRING
  40. 40. ~2.6 million proteins
  41. 41. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
  42. 42. genomic context
  43. 43. gene fusion
  44. 44. Korbel et al., Nature Biotechnology, 2004
  45. 45. operons
  46. 46. Korbel et al., Nature Biotechnology, 2004
  47. 47. bidirectional promoters
  48. 48. Korbel et al., Nature Biotechnology, 2004
  49. 49. phylogenetic profiles
  50. 50. Korbel et al., Nature Biotechnology, 2004
  51. 51. a real example
  52. 52. Cell Cellulosomes Cellulose
  53. 53. experimental data
  54. 54. gene coexpression
  55. 55. protein interactions
  56. 56. Jensen & Bork, Science, 2008
  57. 57. curated knowledge
  58. 58. complexes
  59. 59. pathways
  60. 60. Letunic & Bork, Trends in Biochemical Sciences, 2008
  61. 61. many databases
  62. 62. different formats
  63. 63. different identifiers
  64. 64. variable quality
  65. 65. not comparable
  66. 66. hard work
  67. 67. quality scores
  68. 68. von Mering et al., Nucleic Acids Research, 2005
  69. 69. calibrate vs. gold standard
  70. 70. missing most of the data
  71. 71. text mining
  72. 72. >10 km
  73. 73. too much to read
  74. 74. computer
  75. 75. as smart as a dog
  76. 76. teach it specific tricks
  77. 77. named entity recognition
  78. 78. comprehensive lexicon
  79. 79. cyclin dependent kinase 1
  80. 80. CDK1
  81. 81. CDC2
  82. 82. flexible matching
  83. 83. spaces and hyphens
  84. 84. cyclin dependent kinase 1
  85. 85. cyclin-dependent kinase 1
  86. 86. orthographic variation
  87. 87. CDC2
  88. 88. hCdc2
  89. 89. “black list”
  90. 90. SDS
  91. 91. information extraction
  92. 92. count co-mentioning
  93. 93. within documents
  94. 94. within paragraphs
  95. 95. within sentences
  96. 96. scoring scheme
  97. 97. corpora
  98. 98. ~22 million abstracts
  99. 99. no access
  100. 100. ~4 million full-text articles
  101. 101. augmented browsing
  102. 102. Reflect
  103. 103. browser add-on
  104. 104. real-time text mining
  105. 105. Pafilis, O’Donoghue, Jensen et al., Nature Biotechnology, 2009 O’Donoghue et al., Journal of Web Semantics, 2010
  106. 106. localization and disease
  107. 107. small molecules
  108. 108. proteins
  109. 109. compartments
  110. 110. tissues
  111. 111. diseases
  112. 112. organisms
  113. 113. environments
  114. 114. suite of web resources
  115. 115. common backend database
  116. 116. jensenlab.org
  117. 117. text mining
  118. 118. curated knowledge
  119. 119. experimental data
  120. 120. computational predictions
  121. 121. quality scores
  122. 122. web-centric databases
  123. 123. DISEASES
  124. 124. visualization
  125. 125. COMPARTMENTS
  126. 126. compartments.jensenlab.org
  127. 127. TISSUES
  128. 128. tissues.jensenlab.org
  129. 129. project onto networks
  130. 130. Szklarczyk, Franceschini et al., Nucleic Acids Research, 2011
  131. 131. compartments.jensenlab.org
  132. 132. tissues.jensenlab.org
  133. 133. diseases.jensenlab.org
  134. 134. summary
  135. 135. bioinformatics
  136. 136. more than alignment
  137. 137. data/text mining
  138. 138. save you much time
  139. 139. Acknowledgments Protein Literature mining networks Sune Frankild Evangelos PafilisChristian von Mering Janos Binder Damian Szklarczyk Kalliopi Tsafou Michael Kuhn Alberto Santos Manuel Stark Heiko Horn Samuel Chaffron Michael Kuhn Chris Creevey Nigel Brown Jean Muller Reinhardt Schneider Tobias Doerks Sean O’Donoghue Philippe Julien Alexander Roth Milan Simonovic Jan Korbel Berend Snel Martijn Huynen

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