The STRING database

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Introduction to Bioinformatics, Heidelberg University, Heidelberg, Germany, November 29, 2006

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The STRING database

  1. 1. The STRING database Lars Juhl Jensen EMBL Heidelberg
  2. 2. data integration
  3. 3. Jensen et al., Drug Discovery Today: Targets, 2004
  4. 4. functional interactions
  5. 5. Bork et al., Current Opinion in Structural Biology, 2005
  6. 6. 373 proteomes
  7. 7. Genome Reviews
  8. 8. RefSeq
  9. 9. Ensembl
  10. 10. model organism databases
  11. 11. genomic context methods
  12. 12. gene fusion
  13. 14. gene neighborhood
  14. 16. phylogenetic profiles
  15. 21. Cell Cellulosomes Cellulose
  16. 22. automation
  17. 23. scoring scheme
  18. 24. correct interactions
  19. 25. wrong associations
  20. 26. gene fusion
  21. 27. sequence similarity
  22. 29. gene neighborhood
  23. 30. sum of intergenic distances
  24. 32. phylogenetic profiles
  25. 33. SVD Singular Value Decomposition
  26. 34. Euclidian distance
  27. 36. raw quality scores
  28. 37. not comparable
  29. 38. sequence similarity
  30. 39. sum of intergenic distances
  31. 40. Euclidian distance
  32. 41. benchmarking
  33. 42. calibrate vs. gold standard
  34. 44. raw quality scores
  35. 45. probabilistic scores
  36. 46. curated knowledge
  37. 47. KEGG Kyoto Encyclopedia of Genes and Genomes
  38. 48. Reactome
  39. 49. MIPS Munich Information center for Protein Sequences
  40. 50. STKE Signal Transduction Knowledge Environment
  41. 51. primary experimental data
  42. 52. many sources
  43. 53. many parsers
  44. 54. physical protein interactions
  45. 55. BIND Biomolecular Interaction Network Database
  46. 56. GRID General Repository for Interaction Datasets
  47. 57. MINT Molecular Interactions Database
  48. 58. DIP Database of Interacting Proteins
  49. 59. HPRD Human Protein Reference Database
  50. 60. merge data by publication
  51. 61. topology-based scores
  52. 62. von Mering et al., Nucleic Acids Research, 2005
  53. 63. co-expression
  54. 64. GEO Gene Expression Omnibus
  55. 65. correlation coefficient
  56. 66. literature mining
  57. 67. different gene identifiers
  58. 68. synonyms lists
  59. 69. M EDLINE
  60. 70. SGD Saccharomyces Genome Database
  61. 71. The Interactive Fly
  62. 72. OMIM Online Mendelian Inheritance in Man
  63. 73. co-mentioning
  64. 74. NLP Natural Language Processing
  65. 75. <ul><li>Gene and protein names </li></ul><ul><li>Cue words for entity recognition </li></ul><ul><li>Verbs for relation extraction </li></ul><ul><li>[ nxgene The GAL4 gene ] </li></ul><ul><li>[ nxexpr T he expression of [ nxgene the cytochrome genes [ nxpg CYC1 and CYC7 ]]] is controlled by [ nxpg HAP1 ] </li></ul>
  66. 76. calibrate vs. gold standard
  67. 78. combine all evidence
  68. 79. spread over many species
  69. 80. transfer by orthology
  70. 81. von Mering et al., Nucleic Acids Research, 2005
  71. 82. two modes
  72. 84. orthologous groups
  73. 85. von Mering et al., Nucleic Acids Research, 2005
  74. 86. fuzzy orthology
  75. 87. von Mering et al., Nucleic Acids Research, 2005
  76. 88. Bayesian scoring scheme
  77. 89. Bork et al., Current Opinion in Structural Biology, 2005
  78. 90. Acknowledgments <ul><li>The STRING team (EMBL) </li></ul><ul><ul><li>Christian von Mering </li></ul></ul><ul><ul><li>Berend Snel </li></ul></ul><ul><ul><li>Martijn Huynen </li></ul></ul><ul><ul><li>Sean Hooper </li></ul></ul><ul><ul><li>Samuel Chaffron </li></ul></ul><ul><ul><li>Julien Lagarde </li></ul></ul><ul><ul><li>Mathilde Foglierini </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><li>Literature mining project (EML Research) </li></ul><ul><ul><li>Jasmin Saric </li></ul></ul><ul><ul><li>Rossitza Ouzounova </li></ul></ul><ul><ul><li>Isabel Rojas </li></ul></ul>

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