Introduction to STRING

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Lars Juhl Jensen

SEMM, IFOM, Milan, Italy, June 15-16, 2006

Introduction to STRING Lars Juhl Jensen EMBL Heidelberg

STRING

integrate diverse evidence

functional interactions

hundreds of proteomes

Ensembl

SWISS-PROT

prokaryotes

genomic context methods

gene fusion

gene neighborhood

phylogenetic profiles

Cell Cellulosomes Cellulose

eukaryotes

data integration

curated knowledge

MIPS Munich Information center for Protein Sequences

Reactome

KEGG Kyoto Encyclopedia of Genes and Genomes

STKE Signal Transduction Knowledge Environment

literature mining

co-mentioning

NLP Natural Language Processing

M EDLINE

SGD Saccharomyces Genome Database

The Interactive Fly

OMIM Online Mendelian Inheritance in Man

primary experimental data

microarray expression data

GEO Gene Expression Omnibus

SMD Stanford Microarray Database

physical protein interactions

BIND Biomolecular Interaction Network Database

MINT Molecular Interactions Database

GRID General Repository for Interaction Datasets

DIP Database of Interacting Proteins

HPRD Human Protein Reference Database

problems

many sources

different gene identifiers

many types of evidence

questionable quality

not directly comparable

spread over many species

parsers

synonyms lists

quality scores

benchmarking

orthology

how is it actually done?

gene fusion

Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

gene neighborhood

Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species

phylogenetic profiles

literature co-occurrence

Associate abstracts with species Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Infer associations in other species

physical interaction data

Make binary representation of complexes Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

calibrate against KEGG

transfer by orthology

orthologous groups

fuzzy orthology

? Source species Target species

combine all evidence

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

Thank you!

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Introduction to STRING

1. Introduction to STRING Lars Juhl Jensen EMBL Heidelberg

3. integrate diverse evidence

4. functional interactions

5.

6. hundreds of proteomes

10. genomic context methods

11. gene fusion

12.

13. gene neighborhood

14.

15. phylogenetic profiles

16.

17.

18.

19.

20. Cell Cellulosomes Cellulose

22. data integration

23.

24. curated knowledge

25. MIPS Munich Information center for Protein Sequences

27. KEGG Kyoto Encyclopedia of Genes and Genomes

28. STKE Signal Transduction Knowledge Environment

29. literature mining

30. co-mentioning

31. NLP Natural Language Processing

33. SGD Saccharomyces Genome Database

34. The Interactive Fly

35. OMIM Online Mendelian Inheritance in Man

36. primary experimental data

37. microarray expression data

38. GEO Gene Expression Omnibus

39. SMD Stanford Microarray Database

40. physical protein interactions

41. BIND Biomolecular Interaction Network Database

42. MINT Molecular Interactions Database

43. GRID General Repository for Interaction Datasets

44. DIP Database of Interacting Proteins

45. HPRD Human Protein Reference Database

47. many sources

48. different gene identifiers

49. many types of evidence

50. questionable quality

51. not directly comparable

52. spread over many species

54. synonyms lists

55. quality scores

56. benchmarking

58. how is it actually done?

59. gene fusion

60. Find in A genes that match a the same gene in B Exclude overlapping alignments Calibrate against KEGG maps Calculate all-against-all pairwise alignments

61. gene neighborhood

62. Identify runs of adjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species

63. phylogenetic profiles

64. Align all proteins against all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps

65. literature co-occurrence

66. Associate abstracts with species Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Infer associations in other species

67. physical interaction data

68. Make binary representation of complexes Yeast two-hybrid data sets are inherently binary Calculate score from number of (co-)occurrences Calculate score from non-shared partners Calibrate against KEGG maps Infer associations in other species Combine evidence from experiments

69. calibrate against KEGG

70.

71. transfer by orthology

72.

73. orthologous groups

74.

75. fuzzy orthology

76. ? Source species Target species

77. combine all evidence

78.

79.