Integration of diverse large-scale datasets
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The document discusses the integration of diverse large-scale datasets to build comprehensive protein-protein interaction networks. It describes challenges with data from different sources having different identifiers, evidence types and quality. It also discusses methods used by STRING and other databases to combine data from curated databases, literature mining, primary datasets and transfer of interactions based on orthology. Examples are given of cell cycle studies in yeast that have analyzed periodically expressed genes and protein interactions.
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Integration of diverse large-scale datasets
- 1. Integration of diverse large-scale datasets
- 7. Jensen et al., Bioinformatics, 2000
- 10. Pedersen et al., Journal of Molecular Biology, 2000
- 12. Workman et al., Genome Biology, 2002
- 18. STRING
- 22. Bork et al., Current Opinion in Structural Biology, 2005
- 23. 179 proteomes
- 24. evolution
- 27. statistics
- 29. prokaryotes
- 31. gene fusion
- 41. eukaryotes
- 43. Jensen et al., Drug Discovery Today: Targets, 2004
- 45. MIPS Munich Information center for Protein Sequences
- 46. KEGG Kyoto Encyclopedia of Genes and Genomes
- 47. STKE Signal Transduction Knowledge Environment
- 48. Reactome
- 50. M EDLINE
- 51. SGD Saccharomyces Genome Database
- 53. OMIM Online Mendelian Inheritance in Man
- 54. co-mentioning
- 55. NLP Natural Language Processing
- 56. Gene and protein names Cue words for entity recognition Verbs for relation extraction [ nxgene The GAL4 gene ] [ nxexpr T he expression of [ nxgene the cytochrome genes [ nxpg CYC1 and CYC7 ]]] is controlled by [ nxpg HAP1 ]
- 60. GEO Gene Expression Omnibus
- 62. BIND Biomolecular Interaction Network Database
- 63. MINT Molecular Interactions Database
- 64. GRID General Repository for Interaction Datasets
- 65. DIP Database of Interacting Proteins
- 66. HPRD Human Protein Reference Database
- 67. problems
- 68. many sources
- 70. many types of evidence
- 73. spread over many species
- 75. calculate raw quality scores
- 76. calibrate vs. gold standard
- 77. KEGG Kyoto Encyclopedia of Genes and Genomes
- 78. von Mering et al., Nucleic Acids Research, 2005
- 79. transfer based on orthology
- 81. Bork et al., Current Opinion in Structural Biology, 2005
- 82. cell cycle
- 85. Chen et al., Molecular Biology of the Cell, 2004
- 86. Chen et al., Molecular Biology of the Cell, 2004
- 93. S. cerevisiae
- 94. Cho et al.
- 95. Spellman et al.
- 97. Cho et al.
- 98. Spellman et al.
- 99. Zhao et al.
- 100. Johansson et al.
- 101. Luan and Li
- 102. Lu et al.
- 103. Ahdesm äki et al.
- 104. Willbrand et al.
- 105. no benchmarking
- 106. de Lichtenberg et al., Bioinformatics, 2005
- 107. reproducibility
- 108. de Lichtenberg et al., Bioinformatics, 2005
- 110. de Lichtenberg et al., Bioinformatics, 2005
- 111. list of 600 periodic genes
- 112. S. pombe
- 114. reproducibility
- 115. Marguerat et al., Yeast, 2006
- 116. name inconsistencies
- 117. Marguerat et al., Yeast, 2006
- 119. no benchmarking
- 120. Marguerat et al., Yeast, 2006
- 121. Marguerat et al., Yeast, 2006
- 122. too many genes suggested
- 123. Marguerat et al., Yeast, 2006
- 124. Marguerat et al., Yeast, 2006
- 125. averaging better than voting
- 126. Marguerat et al., Yeast, 2006
- 127. S. cerevisiae
- 128. list of 600 periodic genes
- 131. von Mering et al., Nucleic Acids Research, 2005
- 132. de Lichtenberg et al., Science, 2005
- 133. dynamic proteins
- 134. static proteins
- 135. de Lichtenberg et al., Science, 2005
- 136. reproduces what is known
- 137. de Lichtenberg et al., Science, 2005
- 139. de Lichtenberg et al., Science, 2005
- 140. global trends
- 141. dynamic proteins
- 142. de Lichtenberg et al., Science, 2005
- 143. static proteins
- 144. de Lichtenberg et al., Science, 2005
- 146. de Lichtenberg et al., Science, 2005
- 147. de Lichtenberg et al., Science, 2005
- 150. Cdc28p substrates
- 153. yeast two-hybrid
- 154. 1936 13 4 4 1385 65 18465 Stelzl et al. Rual et al. Small-scale studies
- 155. 32 0 3 4 18 4 23 Stelzl et al. Rual et al. Small-scale studies
- 156. 62 8 39 Small-scale studies Stelzl et al. Rual et al. 852 17 473 432 69 260
- 157. 3.5% and 21% sensitivity
- 158. in a couple of years
- 160. 100% = 1/5?
- 162. five years ago
- 163. yeast two-hybrid
- 164. 1150 117 117 72 4053 118 4469 Uetz et al. Ito et al. Small-scale studies
- 165. 162 53 34 72 180 29 338 Uetz et al. Ito et al. Small-scale studies
- 166. 511 189 616 Small-scale studies Uetz et al. Ito et al. 439 178 759 897 190 1347
- 167. 19% and 12% sensitivity
- 168. the challenge
- 169. how to get from here …
- 170. 1936 13 4 4 1385 65 18465 Stelzl et al. Rual et al. Small-scale studies
- 171. … to there …
- 172. de Lichtenberg et al., Science, 2005
- 173. Acknowledgments The STRING team (EMBL) Christian von Mering Berend Snel Martijn Huynen Sean Hooper Mathilde Foglierini Julien Lagarde Peer Bork Literature mining project (EML Research) Jasmin Saric Rossitza Ouzounova Isabel Rojas Cell cycle studies (CBS) Ulrik de Lichtenberg Thomas Skøt Jensen Søren Brunak S. pombe cell cycle (Sanger) Samuel Marguerat J ürg Bähler Inspiration for presentation Lawrence Lessig Dick Clarence Hardt Anders Gorm Pedersen
- 174. Thank you!