Mining large-scale data sets on the eukaryotic cell cycle
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The document discusses mining large-scale data sets related to the eukaryotic cell cycle, focusing on gene expression, protein interactions, and regulatory mechanisms across various organisms. It highlights the importance of data integration and benchmarking to uncover biological discoveries, while addressing challenges such as high false positive rates in data collection methods. The document emphasizes a need for automated data mining solutions to enable reliable analysis of complex biological data.
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Mining large-scale data sets on the eukaryotic cell cycle
- 1. Mining large-scale data sets on the eukaryotic cell cycle Lars Juhl Jensen EMBL Heidelberg
- 4. one cell
- 5. two cells
- 6. four phases
- 7. G 1 phase
- 8. growth
- 9. S phase
- 10. DNA replication
- 11. G 2 phase
- 12. growth
- 13. M phase
- 14. cell division
- 16. regulation
- 17. gene expression
- 18. phosphorylation
- 22. one gene
- 23. one postdoc
- 24. many types of data
- 25. a single gene
- 27. one lab
- 28. one technology
- 29. all the relevant genes
- 30. a single type of data
- 31. systems biology
- 32. many types of data
- 33. all the relevant genes
- 34. data integration
- 35. data mining
- 36. expression data
- 37. cell cultures
- 39. synchronization
- 40. microarrays
- 42. time courses
- 46. list of genes
- 48. peak times
- 49. S. cerevisiae
- 50. expression data
- 51. Cho et al.
- 52. Spellman et al.
- 54. Zhao et al.
- 55. Langmead et al.
- 56. Johansson et al.
- 57. Wichert et al.
- 58. Luan and Li
- 59. Lu et al.
- 60. Ahdesm äki et al.
- 61. Willbrand et al.
- 62. Chen et al.
- 63. Qiu et al.
- 64. Ahnert et al.
- 65. Andersson et al.
- 66. no benchmarking
- 67. reanalysis
- 68. benchmarking
- 70. no progress
- 71. no benchmarking
- 73. S. pombe
- 74. Rustici et al.
- 75. Peng et al.
- 76. Oliva et al.
- 77. no benchmarking
- 78. no integration
- 79. reanalysis
- 80. integration
- 81. benchmarking
- 83. no progress
- 84. no benchmarking
- 85. no integration
- 87. H. sapiens
- 88. Whitfield et al.
- 89. reanalysis
- 90. benchmarking
- 92. A. thaliana
- 93. Menges et al.
- 94. reanalysis
- 95. benchmarking
- 97. four organisms
- 98. list of genes
- 100. peak times
- 101. protein interactions
- 102. S. cerevisiae
- 103. yeast two-hybrid
- 104. Uetz et al.
- 105. Ito et al.
- 106. complex pull-down
- 107. Gavin et al.
- 108. Ho et al.
- 112. yeast two-hybrid
- 113. -log((N 1 +1) · (N 2 +1))
- 114. complex pull-down
- 115. log[(N 12 · N)/((N 1 +1) · (N 2 +1))]
- 118. quality threshold
- 121. expression data
- 122. temporal network
- 124. benchmarking
- 130. 3–5% false positives
- 133. who
- 134. whom
- 135. when
- 136. global statements
- 137. dynamic and static
- 140. CDK–cyclin complexes
- 142. consistent timing
- 148. dynamic and static
- 151. phosphorylation
- 152. Übersax et al.
- 154. 27% of dynamic proteins
- 155. 8% of static proteins
- 156. targeted degradation
- 157. PEST regions
- 159. 44% of dynamic proteins
- 160. 29% of static proteins
- 161. data mining
- 162. undescribed link
- 166. how can we test this?
- 169. orthology detection
- 170. sequence similarity
- 172. not conserved
- 173. individual genes
- 175. protein complexes
- 176. peak times
- 177. not comparable
- 178. time warping
- 180. same color = same phase
- 181. DNA replication
- 182. DNA polymerases
- 186. phosphorylation
- 187. Übersax et al.
- 188. Loog et al.
- 189. Phospho.ELM
- 190. NetPhosK
- 191. correlation
- 194. cell cycle vs. non-cell cycle
- 195. co-evolution
- 200. co-evolution
- 201. summary
- 202. reanalysis
- 203. integration
- 204. high-throughput data
- 206. challenge
- 207. data mining
- 209. beware of the noise
- 210. benchmark!
- 211. Acknowledgments Thomas Skøt Jensen Ulrik de Lichtenberg Søren Brunak Peer Bork