Large-scale integration of data and text

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Lars Juhl Jensen

Large-scale integration of data and text

Large-scale integration of data and
text
Lars Juhl Jensen

three parts

association networks

guilt by association

protein networks

STRING

2000+ genomes

genomic context

gene fusion

Korbel et al., Nature Biotechnology, 2004

gene neighborhood

Korbel et al., Nature Biotechnology, 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology, 2004

a real example

Cell
Cellulosomes
Cellulose

experimental data

gene coexpression

physical interactions

Jensen & Bork, Science, 2008

genetic interactions

Beyer et al., Nature Reviews Genetics, 2007

curated knowledge

pathways

Letunic & Bork, Trends in Biochemical Sciences, 2008

many databases

different formats

different identifiers

variable quality

not comparable

not same species

hard work

(students)

parsers

mapping files

quality scores

phylogenetic profiles

affinity purification

von Mering et al., Nucleic Acids Research, 2005

score calibration

gold standard

von Mering et al., Nucleic Acids Research, 2005

implicit weighting by quality

common scale

homology-based transfer

orthologous groups

Franceschini et al., Nucleic Acids Research, 2013

Exercise 1
Query STRING for human TYMS
Show network in confidence mode
Show up to 20 interaction partners
Show only experimental evidence
Show also low-confidence links

text mining

>10 km

too much to read

exponential growth

~40 seconds per paper

computer

as smart as a dog

teach it specific tricks

named entity recognition

comprehensive lexicon

cyclin dependent kinase 1

CDC2

orthographic variation

expansion rules

prefixes and suffixes

CDC2

hCdc2

flexible matching

spaces and hyphens

cyclin dependent kinase 1

cyclin-dependent kinase 1

“black list”

SDS

text corpus

~22 million abstracts

Medline

~2 million full-text articles

restricted access

information extraction

co-mentioning

counting

within documents

within paragraphs

within sentences

scoring scheme

score calibration

NLP
Natural Language Processing

grammatical analysis

part-of-speech tagging

what you learned in school
pronoun pronoun verb preposition noun

semantic tagging

words of special interest

sentence parsing

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]
Saric et al., Proceedings of ACL, 2004

more precise

worse recall

web resources

general approach

text mining

curated knowledge

experimental data

computational predictions

common identifiers

quality scores

score calibration

visualization

STRING

protein networks

Szklarczyk et al., Nucleic Acids Research, 2015string-db.org

STITCH

chemical networks

Kuhn et al., Nucleic Acids Research, 2014stitch-db.org

PubChem

pathways

drug targets

high-throughput screens

COMPARTMENTS

subcellular localization

Binder et al., Database, 2014compartments.jensenlab.org

Gene Ontology

GO annotations

UniProtKB

model organism databases

sequence-based prediction

PSORT

YLoc

TISSUES

tissue expression

tissues.jensenlab.org Santos et al., submitted, 2015

Brenda Tissue Ontology

high-throughput studies

EST libraries

microarrays

RNA-Seq

mass spectrometry

immunohistochemistry

DISEASES

disease associations

diseases.jensenlab.org Frankild et al., Methods, 2015

Disease Ontology

genetics studies

Genetics Home Reference

NHGRI GWAS Catalog

DistiLD

cancer mutation data

COSMIC

Exercise 2
Find TYMS-related diseases
http://diseases.jensenlab.org
Find some inhibitors of TYMS
http://stitch-db.org
Assess their tissue specificity
http://tissues.jensenlab.org

thank you!

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Large-scale integration of data and text

1. Large-scale integration of data and text Lars Juhl Jensen

3. association networks

4. guilt by association

5.

6. protein networks

8. 2000+ genomes

9. genomic context

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. physical interactions

25. Jensen & Bork, Science, 2008

26. genetic interactions

27. Beyer et al., Nature Reviews Genetics, 2007

28. curated knowledge

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

31. many databases

32. different formats

33. different identifiers

34. variable quality

35. not comparable

36. not same species

40. mapping files

41. quality scores

42. phylogenetic profiles

43.

44.

45. affinity purification

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

47. score calibration

48. gold standard

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

50. implicit weighting by quality

51. common scale

52. homology-based transfer

53. orthologous groups

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

55. Exercise 1 Query STRING for human TYMS Show network in confidence mode Show up to 20 interaction partners Show only experimental evidence Show also low-confidence links

56. text mining

58. too much to read

59. exponential growth

60. ~40 seconds per paper

62. as smart as a dog

63. teach it specific tricks

64.

65.

66. named entity recognition

67. comprehensive lexicon

68. cyclin dependent kinase 1

70. orthographic variation

71. expansion rules

72. prefixes and suffixes

75. flexible matching

76. spaces and hyphens

77. cyclin dependent kinase 1

78. cyclin-dependent kinase 1

79. “black list”

81. text corpus

82. ~22 million abstracts

84. ~2 million full-text articles

85. restricted access

86. information extraction

87. co-mentioning

89. within documents

90. within paragraphs

91. within sentences

92. scoring scheme

93.

94.

95. score calibration

96. NLP Natural Language Processing

97. grammatical analysis

98. part-of-speech tagging

99. what you learned in school pronoun pronoun verb preposition noun

100. semantic tagging

101. words of special interest

102. sentence parsing

103. 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] Saric et al., Proceedings of ACL, 2004

104. more precise

105. worse recall

106. web resources

107. general approach

108. text mining

109. curated knowledge

110. experimental data

111. computational predictions

112. common identifiers

113. quality scores

114. score calibration

115. visualization

117. protein networks

118. Szklarczyk et al., Nucleic Acids Research, 2015string-db.org

120. chemical networks

121. Kuhn et al., Nucleic Acids Research, 2014stitch-db.org

124. drug targets

125. high-throughput screens

126. COMPARTMENTS

127. subcellular localization

128. Binder et al., Database, 2014compartments.jensenlab.org

129. Gene Ontology

130. GO annotations

132. model organism databases

133. sequence-based prediction

137. tissue expression

138. tissues.jensenlab.org Santos et al., submitted, 2015

139. Brenda Tissue Ontology

140. high-throughput studies

141. EST libraries

142. microarrays

144. mass spectrometry

145. immunohistochemistry

147. disease associations

148. diseases.jensenlab.org Frankild et al., Methods, 2015

149. Disease Ontology

150. genetics studies

151. Genetics Home Reference

152. NHGRI GWAS Catalog

154. cancer mutation data

156. Exercise 2 Find TYMS-related diseases http://diseases.jensenlab.org Find some inhibitors of TYMS http://stitch-db.org Assess their tissue specificity http://tissues.jensenlab.org

157. thank you!