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STRING/STITCH tutorial
- 1. Exploring proteins, chemicals and their interactions with STRING and STITCH Michael Kuhn
- 2. (talk and practical session)
- 3. interactions of proteins and chemicals
- 11. example Tryptophan synthase beta chain E. Coli K12
- 16. example aspirin Homo sapiens
- 19. STRING: version 8.3 soon: version 9 interactions of proteins STITCH: version 2 interactions of proteins and chemicals
- 20. content STRING 8
- 21. 630 genomes only completely sequenced genomes STRING 9: >1100 genomes
- 22. 2.5 million genes (not proteins)
- 23. 74,000 chemicals (including 2200 drugs)
- 24. many sources of interactions
- 27. gene fusion
- 31. experimental evidence T
- 32. co-expression GEO: Gene Expression Omnibus
- 34. literature
- 35. variable quality different “raw scores”
- 36. benchmarking calibrate against “gold standard” (KEGG)
- 38. probabilistic scores e.g. “70% chance for an association”
- 41. e.g.: two scores of 0.7 combined probability: ?
- 42. e.g.: two scores of 0.7 combined probability: 0.91 1- (1-0.7) 2 = 0.91
- 43. evidence spread over many species
- 45. transfer by orthology (or “fuzzy orthology”)
- 46. von Mering et al., Nucleic Acids Research, 2005
- 47. von Mering et al., Nucleic Acids Research, 2005
- 48. two modes
- 51. proteins mode
- 52. von Mering et al., Nucleic Acids Research, 2005
- 53. maximum specificity lower coverage information will be relevant for selected species
- 54. COG mode “clusters of orthologous groups”
- 55. von Mering et al., Nucleic Acids Research, 2005
- 56. higher coverage lower specificity includes all available evidence some orthologous groups are too large to be meaningful
- 57. STRING plans • next big release (9.0): • coming end of 2010 / early 2011 • more genomes • allow users to add more data to the network
- 58. STITCH plans • next minor release (2.1): • add ChEMBLdb • next big release (3.0): • “zoom” into stereo-isomers, salt forms
- 59. Acknowledgements STRING Christian von Mering STITCH Lars Juhl Jensen Damian Szklarczyk Manuel Stark Andrea Franceschini Samuel Chaffron Monica Campillos Chris Creevey Christian von Mering Jean Muller Lars Juhl Jensen Tobias Doerks Andreas Beyer Philippe Julien Peer Bork Alexander Roth Milan Simonovic Peer Bork
- 60. string-db.org Jensen et al., NAR Database Issue 2009 stitch-db.org Kuhn et al., NAR Database Issue 2010
Notas do Editor
- next: neighborhood view
- next: text mining
- next: actions aspirin -- PTGS1
- four different areas of sources
- especially for procaryotes
- In this mode, STRING predicts interaction partners for one protein in a specific species. This allows for maximum specificity, but has slightly lower coverage. Why? Because, in protein mode, STRING does not precisely know about orthologs in other species - instead, it resorts to estimating orthology through sequence similarity searches. In short, interaction information is transferred between species based on 'degree of orthology' (whereby 'degree of orthology' is a measure of how confident STRING is that two proteins are orthologs. The measure is derived from all-against-all similarity searches, and takes into account putative paralogs in both species. The fewer paralogs there are, the more confident STRING is about orthology).
- In this mode, STRING predicts interaction partners for a group of orthologous proteins. This generally has higher coverage, but may result in slightly lower specificity. Again, the reason is in how STRING derives orthologs. In COG-Mode, information about orthology is derived from the database 'Clusters of Orthologous Groups' (Tatusov & Koonin, NCBI). There, orthology is an 'all-or-nothing' decision, and all proteins considered orthologous are grouped into a single entity. Therefore, a prediction made for one protein applies to all proteins in the group - which is why STRING shows its predictions at the level of the groups. Coverage is higher, because the groups are partly based on manual curation and contain orthology assignments which are difficult to derive through an automated procedure. Specificity is lower, however, because some groups are (for technical reasons) relatively 'inclusive' - i.e. they contain a large number of proteins which cannot be resolved further. For example, almost all Serine/Threonine kinases are grouped into one COG - making predictions for a specific subset impossible. Nevertheless, COGs are very powerful and are the first choice for proteins which do not show much lineage-specific expansions, especially in prokaryotes.