STRING - Modeling of biological systems through cross-species data integration

641 views
592 views

Published on

Journée Thematique Biologie des Systèmes, INRA, Jouy-en-Josas, France, February 2, 2006

Published in: Technology
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
641
On SlideShare
0
From Embeds
0
Number of Embeds
8
Actions
Shares
0
Downloads
15
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide
  • STRING - Modeling of biological systems through cross-species data integration

    1. 1. STRING Modeling of biological systems through cross-species data integration
    2. 2. Lars Juhl Jensen
    3. 5. promoter analysis
    4. 6. Jensen et al., Bioinformatics, 2000
    5. 7. genome visualization
    6. 8. Pedersen et al., Journal of Molecular Biology, 2000
    7. 9. protein function prediction
    8. 13. STRING
    9. 15. integrate diverse evidence
    10. 16. functional interactions
    11. 17. Bork et al., Current Opinion in Structural Biology, 2005
    12. 18. 179 proteomes
    13. 19. genomic context methods
    14. 20. phylogenetic profiles
    15. 25. Cell Cellulosomes Cellulose
    16. 26. anti-correlated profiles
    17. 28. analogous enzymes
    18. 29. Morett et al., Nature Biotechnology, 2003
    19. 30. gene neighborhood
    20. 32. bidirectional promoters
    21. 34. Korbel et al., Nature Biotechnology, 2004
    22. 35. gene fusion
    23. 37. evolution
    24. 40. statistics
    25. 41. (the original sin)
    26. 42. scoring and benchmarking
    27. 43. raw quality scores
    28. 44. gene neighborhood
    29. 45. sum of intergenic distances
    30. 47. many types of evidence
    31. 48. not directly comparable
    32. 49. calibrate vs. gold standard
    33. 51. curated knowledge
    34. 52. KEGG Kyoto Encyclopedia of Genes and Genomes
    35. 53. STKE Signal Transduction Knowledge Environment
    36. 54. Reactome
    37. 55. MIPS Munich Information center for Protein Sequences
    38. 56. primary experimental data
    39. 57. Jensen et al., Drug Discovery Today: Targets, 2004
    40. 58. microarray expression data
    41. 59. GEO Gene Expression Omnibus
    42. 60. physical protein interactions
    43. 61. BIND Biomolecular Interaction Network Database
    44. 62. MINT Molecular Interactions Database
    45. 63. GRID General Repository for Interaction Datasets
    46. 64. DIP Database of Interacting Proteins
    47. 65. HPRD Human Protein Reference Database
    48. 66. von Mering et al., Nucleic Acids Research, 2005
    49. 67. literature mining
    50. 68. M EDLINE
    51. 69. SGD Saccharomyces Genome Database
    52. 70. The Interactive Fly
    53. 71. OMIM Online Mendelian Inheritance in Man
    54. 72. co-mentioning
    55. 73. different gene names
    56. 74. curated synonyms lists
    57. 75. NLP Natural Language Processing
    58. 76. <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>
    59. 77. Jensen et al., Nature Reviews Genetics, 2006
    60. 78. combine all evidence
    61. 79. na ïve Bayesian scheme
    62. 80. spread over many species
    63. 81. transfer based orthology
    64. 82. ? Source species Target species
    65. 89. defining functional modules
    66. 92. qualitative modeling
    67. 93. the mitochondrial system
    68. 95. RCCs
    69. 96. predicting “mode of action”
    70. 97. Jensen et al., Drug Discovery Today: Targets, 2004
    71. 98. Jensen et al., Drug Discovery Today: Targets, 2004
    72. 99. 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>Mathilde Foglierini </li></ul></ul><ul><ul><li>Julien Lagarde </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><ul><li>New genomic context methods (EMBL) </li></ul><ul><ul><li>Jan Korbel </li></ul></ul><ul><ul><li>Peer Bork </li></ul></ul><ul><li>Modeling of yeast mitochondria (EMBL) </li></ul><ul><ul><li>Fabiana Perocchi </li></ul></ul><ul><ul><li>Lars Steinmetz </li></ul></ul><ul><li>Inspiration for presentation </li></ul><ul><ul><li>Dick Clarence Hardt </li></ul></ul><ul><ul><li>Anders Gorm Pedersen </li></ul></ul>
    73. 100. Thank you!

    ×