Using networks to derive function from multiple data sources

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Lars Juhl Jensen

This document discusses using networks to derive biological function from genomic data. It mentions several types of data that can be used like gene expression, protein-protein interactions, genetic interactions, pathways, literature mining, and co-mentioning in text. It also notes challenges integrating these diverse data sources that have different formats, identifiers, quality, and are spread across many databases and genomes. Lastly, it recommends combining all available evidence to predict functional associations.

Using networks to derive function Lars Juhl Jensen

STRING

Jensen, Kuhn et al., Nucleic Acids Research , 2009

functional associations

Frishman et al., Modern Genome Annotation , 2009

common basis

630 genomes

model organism databases

Ensembl

RefSeq

genomic context methods

gene fusion

Korbel et al., Nature Biotechnology , 2004

conserved neighborhood

operons

Korbel et al., Nature Biotechnology , 2004

bidirectional promoters

Korbel et al., Nature Biotechnology , 2004

phylogenetic profiles

Korbel et al., Nature Biotechnology , 2004

primary experimental data

protein interactions

yeast two-hybrid

affinity purification

fragment complementation

Jensen & Bork, Science , 2008

genetic interactions

Beyer et al., Nature Reviews Genetics , 2007

BIND Biomolecular Interaction Network Database

BioGRID General Repository for Interaction Datasets

DIP Database of Interacting Proteins

IntAct

MINT Molecular Interactions Database

HPRD Human Protein Reference Database

PDB Protein Data Bank

inferred associations

gene coexpression

GEO Gene Expression Omnibus

expression compendia

curated knowledge

complexes

MIPS Munich Information center for Protein Sequences

Gene Ontology

pathways

Letunic & Bork, Trends in Biochemical Sciences , 2008

KEGG Kyoto Encyclopedia of Genes and Genomes

MetaCyc

Reactome

PID NCI-Nature Pathway Interaction Database

literature mining

M EDLINE

SGD Saccharomyces Genome Database

The Interactive Fly

OMIM Online Mendelian Inheritance in Man

co-mentioning

statistical methods

NLP Natural Language Processing

[object Object],[object Object],[object Object],[object Object],[object Object]

easy in theory …

… but not in practice

many data types

not comparable

variable quality

many sources

different file formats

different gene identifiers

partially redundant

spread over 630 genomes

quality scores

reproducibility

von Mering et al., Nucleic Acids Research , 2005

benchmarking

von Mering et al., Nucleic Acids Research , 2005

orthology

von Mering et al., Nucleic Acids Research , 2005

two modes

COG mode

von Mering et al., Nucleic Acids Research , 2005

protein mode

von Mering et al., Nucleic Acids Research , 2005

combine all evidence

Frishman et al., Modern Genome Annotation , 2009

Acknowledgments ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

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Using networks to derive function from multiple data sources

1. Using networks to derive function Lars Juhl Jensen

3. Jensen, Kuhn et al., Nucleic Acids Research , 2009

4. functional associations

5. Frishman et al., Modern Genome Annotation , 2009

6. common basis

8. model organism databases

11. genomic context methods

12. gene fusion

13. Korbel et al., Nature Biotechnology , 2004

14. conserved neighborhood

16. Korbel et al., Nature Biotechnology , 2004

17. bidirectional promoters

18. Korbel et al., Nature Biotechnology , 2004

19. phylogenetic profiles

20. Korbel et al., Nature Biotechnology , 2004

21. primary experimental data

22. protein interactions

23. yeast two-hybrid

24. affinity purification

25. fragment complementation

26. Jensen & Bork, Science , 2008

27. genetic interactions

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

29. BIND Biomolecular Interaction Network Database

30. BioGRID General Repository for Interaction Datasets

31. DIP Database of Interacting Proteins

33. MINT Molecular Interactions Database

34. HPRD Human Protein Reference Database

35. PDB Protein Data Bank

36. inferred associations

37. gene coexpression

38.

39. GEO Gene Expression Omnibus

40. expression compendia

41. curated knowledge

43. MIPS Munich Information center for Protein Sequences

44. Gene Ontology

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

47. KEGG Kyoto Encyclopedia of Genes and Genomes

50. PID NCI-Nature Pathway Interaction Database

51. literature mining

53. SGD Saccharomyces Genome Database

54. The Interactive Fly

55. OMIM Online Mendelian Inheritance in Man

56. co-mentioning

57. statistical methods

58. NLP Natural Language Processing

59.

60.

61. easy in theory …

62. … but not in practice

63. many data types

64. not comparable

65. variable quality

66. many sources

67. different file formats

68. different gene identifiers

69. partially redundant

70. spread over 630 genomes

71. quality scores

72. reproducibility

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

74. benchmarking

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

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

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

81. protein mode

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

83. combine all evidence

84. Frishman et al., Modern Genome Annotation , 2009

85.