Data integration - Integration of functional associations using STRING
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The document discusses data integration for functional associations, focusing on various methodologies and databases used to interpret gene interactions across 630 genomes. It highlights challenges such as variable quality and different formatting of data, while referencing multiple studies that explore genomic contexts and experimental validations. Additionally, the document acknowledges contributors and outlines several interaction databases that facilitate the analysis of protein interactions and gene expression.
![Gene and protein names Cue words for entity recognition Verbs for relation extraction [ nxgene The GAL4 gene ] [ nxexpr T he expression of [ nxgene the cytochrome genes [ nxpg CYC1 and CYC7 ]]] is controlled by [ nxpg HAP1 ]](https://image.slidesharecdn.com/jen09talk12-091008172256-phpapp02/85/Data-integration-Integration-of-functional-associations-using-STRING-74-320.jpg)
Data integration - Integration of functional associations using STRING
- 1. Data integration Integration of functional associations using STRING Lars Juhl Jensen
- 2. Jensen, Kuhn et al., Nucleic Acids Research , 2009
- 6. 630 genomes
- 8. Ensembl
- 9. RefSeq
- 11. two modes
- 12. protein mode
- 13. von Mering et al., Nucleic Acids Research , 2005
- 14. COG mode
- 15. von Mering et al., Nucleic Acids Research , 2005
- 16. genomic context
- 17. gene fusion
- 18. Korbel et al., Nature Biotechnology , 2004
- 20. operons
- 21. Korbel et al., Nature Biotechnology , 2004
- 23. Korbel et al., Nature Biotechnology , 2004
- 25. Korbel et al., Nature Biotechnology , 2004
- 26. examples
- 29. Banci et al., PNAS , 2005
- 30. Banci et al., PNAS , 2005
- 38. yeast two-hybrid
- 41. Jensen & Bork, Science , 2008
- 43. Beyer et al., Nature Reviews Genetics , 2007
- 44. BIND Biomolecular Interaction Network Database
- 45. BioGRID General Repository for Interaction Datasets
- 46. DIP Database of Interacting Proteins
- 47. IntAct
- 48. MINT Molecular Interactions Database
- 49. HPRD Human Protein Reference Database
- 50. PDB Protein Data Bank
- 54. GEO Gene Expression Omnibus
- 57. complexes
- 58. MIPS Munich Information center for Protein Sequences
- 59. Gene Ontology
- 60. pathways
- 61. Letunic & Bork, Trends in Biochemical Sciences , 2008
- 62. KEGG Kyoto Encyclopedia of Genes and Genomes
- 63. MetaCyc
- 64. Reactome
- 65. PID NCI-Nature Pathway Interaction Database
- 67. >10 km
- 68. M EDLINE
- 69. SGD Saccharomyces Genome Database
- 71. OMIM Online Mendelian Inheritance in Man
- 72. co-mentioning
- 73. NLP Natural Language Processing
- 74. Gene and protein names Cue words for entity recognition Verbs for relation extraction [ nxgene The GAL4 gene ] [ nxexpr T he expression of [ nxgene the cytochrome genes [ nxpg CYC1 and CYC7 ]]] is controlled by [ nxpg HAP1 ]
- 76. easy in theory …
- 77. … but not in practice
- 78. many data types
- 79. not comparable
- 80. variable quality
- 81. many sources
- 85. spread over 630 genomes
- 86. quality scores
- 87. reproducibility
- 88. von Mering et al., Nucleic Acids Research , 2005
- 91. benchmarking
- 92. calibrate vs. gold standard
- 93. von Mering et al., Nucleic Acids Research , 2005
- 97. protein mode
- 98. von Mering et al., Nucleic Acids Research , 2005
- 99. COG mode
- 100. von Mering et al., Nucleic Acids Research , 2005
- 101. combine all evidence
- 102. Frishman et al., Modern Genome Annotation , 2009
- 103. small molecules
- 104. Kuhn et al., Nucleic Acids Research , 2008
- 105. metametabolomics
- 106. Acknowledgments Christian von Mering Michael Kuhn Manuel Stark Samuel Chaffron Philippe Julien Monica Campillos Tobias Doerks Jan Korbel Berend Snel Martijn Huynen Peer Bork
- 107. larsjuhljensen