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Introduction to STRING
STRING integrates diverse evidence about functional interactions between proteins from hundreds of proteomes. It combines data from genomic context methods, curated databases, experiments, and textmining to generate a global network of protein interactions. The different evidence sources have issues like inconsistent identifiers, variable quality, and coverage of different species that STRING addresses through parsers, orthology transfer, and quality scores to generate a single confidence score for each interaction.
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Introduction to STRING
- 1. Introduction to STRINGLars Juhl Jensen EMBL Heidelberg
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- 25. MIPS Munich Informationcenter for Protein Sequences
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- 27. KEGG Kyoto Encyclopediaof Genes and Genomes
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- 31. NLP Natural LanguageProcessing
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- 33. SGD Saccharomyces GenomeDatabase
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- 35. OMIM Online MendelianInheritance in Man
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- 38. GEO Gene ExpressionOmnibus
- 39. SMD Stanford MicroarrayDatabase
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- 41. BIND Biomolecular InteractionNetwork Database
- 42. MINT Molecular InteractionsDatabase
- 43. GRID General Repositoryfor Interaction Datasets
- 44. DIP Database ofInteracting Proteins
- 45. HPRD Human ProteinReference Database
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- 49. many types ofevidence
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- 52. spread over manyspecies
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- 58. how is itactually done?
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- 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
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- 62. Identify runs ofadjacent genes with the same direction Score each gene pair based on intergenic distances Calibrate against KEGG maps Infer associations in other species
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- 64. Align all proteinsagainst all Calculate best-hit profile Join similar species by PCA Calculate PC profile distances Calibrate against KEGG maps
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- 66. Associate abstracts withspecies Identify gene names in title/abstract Count (co-)occurrences of genes Test significance of associations Calibrate against KEGG maps Infer associations in other species
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- 68. Make binary representationof 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
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- 76. ? Source speciesTarget species
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- 79. Acknowledgments The STRINGteam (EMBL) Christian von Mering Berend Snel Martijn Huynen Sean Hooper Samuel Chaffron Julien Lagarde Mathilde Foglierini Peer Bork Literature mining project (EML Research) Jasmin Saric Rossitza Ouzounova Isabel Rojas
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