STRING: Large-scale data and text mining

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Lars Juhl Jensen

Science

Korbel et al., Nature Biotechnology, 2004

Letunic & Bork, Trends in Biochemical Sciences, 2008

von Mering et al., Nucleic Acids Research, 2005

Franceschini et al., Nucleic Acids Research, 2013

Gene and protein names
Cue words for entity
recognition
Verbs for relation extraction
[nxexpr The expression of
[nxgene the cytochrome
genes
[nxpg CYC1 and CYC7]]]
is controlled by
[nxpg HAP1]

Exercise 1
Go to http://string-db.org
Query for Mt H37Rv adhD
(Rv3086)
Change between different
views
Check evidence for adhD–lipR

Exercise 2
Go to the paper PMC2995261
Extract the protein names in
table 1
Create STRING network of
them
Change to “advanced” mode

Work on your own data
string-db.org
stitch-db.org
compartments.jensenlab.org
tissues.jensenlab.org
diseases.jensenlab.org

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STRING: Large-scale data and text mining

1. STRING Large-scale data and text mining Lars Juhl Jensen

2. association networks

3. guilt by association

5. biological systems

6. protein networks

7. STRING

8. 1100+ genomes

9. computational predictions

10. gene fusion

11. Korbel et al., Nature Biotechnology, 2004

12. gene neighborhood

13. Korbel et al., Nature Biotechnology, 2004

14. phylogenetic profiles

15. Korbel et al., Nature Biotechnology, 2004

16. a real example

17.

18.

19.

20. Cell Cellulosomes Cellulose

21. experimental data

22. gene coexpression

23.

24. protein interactions

25. Jensen & Bork, Science, 2008

26. curated knowledge

27. complexes

28. pathways

29. Letunic & Bork, Trends in Biochemical Sciences, 2008

30. many databases

31. different formats

32. different identifiers

33. variable quality

34. not comparable

35. not same species

36. hard work

37. (Ph.D. students)

38. common identifiers

39. quality scores

40. von Mering et al., Nucleic Acids Research, 2005

41. score calibration

42. von Mering et al., Nucleic Acids Research, 2005

43. homology-based transfer

44. Franceschini et al., Nucleic Acids Research, 2013

45. missing most of the data

46. text mining

47. >10 km

48. too much to read

49. computer

50. comprehensive lexicon

51. CDC2

52. cyclin dependent kinase 1

53. expansion rules

54. hCdc2

55. CDC2

56. flexible matching

57. cyclin-dependent kinase 1

58. cyclin dependent kinase 1

59. “black list”

60. SDS

61. co-mentioning

62. counting

63. within documents

64. within paragraphs

65. within sentences

66. natural language processing

67. Gene and protein names Cue words for entity recognition Verbs for relation extraction [nxexpr The expression of [nxgene the cytochrome genes [nxpg CYC1 and CYC7]]] is controlled by [nxpg HAP1]

68. text corpus

69. ~2 million full-text articles

70. ~22 million abstracts

71. Exercise 1 Go to http://string-db.org Query for Mt H37Rv adhD (Rv3086) Change between different views Check evidence for adhD–lipR

72.

73.

74. Exercise 2 Go to the paper PMC2995261 Extract the protein names in table 1 Create STRING network of them Change to “advanced” mode

75. multi-page tables

76. related resources

77. general approach

78. curated knowledge

79. experimental data

80. text mining

81. computational predictions

82. common identifiers

83. quality scores

84. score calibration

85. visualization

86. protein networks

87. string-db.org

88. chemical networks

89. stitch-db.org

90. subcellular localization

91. compartments.jensenlab.org

92. tissue expression

93. tissues.jensenlab.org

94. disease associations

95. Work on your own data string-db.org stitch-db.org compartments.jensenlab.org tissues.jensenlab.org diseases.jensenlab.org

STRING: Large-scale data and text mining

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