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In silico systems biology:network reconstruction, analysis and network based modelling

In silico systems biology:network reconstruction, analysis and network based modelling. EMBO practical course 10-13 April 2010, Hinxton, UK. Integration of genomic data with biological networks state of the art and future challenges. Laura I. Furlong

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In silico systems biology:network reconstruction, analysis and network based modelling

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  1. In silico systems biology:network reconstruction, analysis and network based modelling EMBO practical course 10-13 April 2010, Hinxton, UK

  2. Integration of genomic data with biological networksstate of the art and future challenges Laura I. Furlong Integrative Biomedical Informatics Group, Research Unit on Biomedical Informatics (GRIB)

  3. Network modelling Network visualization Integration of SNPs and their effects with networks Phenotypic effect Functional effect (e.g. loss of binding site) SNP Disease association Bauer-Mehren A, Furlong L, Rautschka M, Sanz F: From SNPs to pathways: integration of functional effect of sequence variations on models of cell signalling pathways. BMC Bioinformatics 2009, 10(Suppl 8):S6.

  4. Prediction of pathogenic effect of mutations and SNPs

  5. Prediction of pathogenic effect of mutations and SNPs

  6. EntrezGene MySQL DB dbSNP A data integration approach • SNP, mutagenesis information • Association to disease • Functional effect Mapping to NCBI Gene Mapping to dbSNP Identification of GO concepts Cytoscape node attribute file

  7. Biological network data • More than 200 pathway repositories and over 60 specialized on reactions in human • More than 200 curated models

  8. dbSNP Sequence variation data Broad collection of SNPs and short range sequence variants • Manually curated information on nsSNPs, mutations • Association to disease • Results from mutagenesis experiments

  9. Visualization

  10. 28/64

  11. Modelling the impact of sequence variation S->A mutation at position 218 leads to protein inactivation

  12. Modelling the impact of sequence variation Birtwistle MR, Hatakeyama M, Yumoto N, Ogunnaike BA, Hoek JB, Kholodenko BN (2007) Ligand-dependent responses of the ErbB signaling network: experimental and modeling analyses. Mol Syst Biol 3: 144.

  13. Challenges • Concerning sequence variations • Too few have been functionally characterized • Synonymous (“silent”) mutations can also alter function, e.g. through modulation of splicing or altering protein folding • Need of tools for prediction of the impact of coding and non coding SNPs on gene/protein function (and even on biological process)

  14. The IntAct project

  15. IntAct goals & achievements • Define a standard for the representation and annotation of molecular interaction data • provide a public repository • populate the repository with experimental data from project partners and curated literature data • provide modular analysis tools • provide portable versions of the software to allow installation of local IntAct nodes. - Curation manual available from home page- Member of the International Molecular interaction Exchange consortium (IMEx) http://www.ebi.ac.uk/intact ftp://ftp.ebi.ac.uk/pub/databases/intact 4200+ distinct publications, 209,000+ binary interactions, 63,000+ proteins imported from UniProt search & advanced search, hierarchView, pay-as-you-go, MiNe… Known installation: AstraZeneca, GSK, MERCK, MINT, Proteome Center of Shanghai

  16. Sanity Checks(nightly) reject Public web site . abstract annotate exp accept FTP site p2 I p1 IMEx CVs Curation manual check Mint DIP report MatrixDB report Super curator curator IntAct Curation “Lifecycle of an Interaction” Publication (full text) Master headline

  17. Public data • All data is manually curated by expert curators • Curation manual rigorously followed • All curated data is reviewed by a senior curator • All data is made available on FTP site: • (!) data updated every week • (!) format available: Data ftp://ftp.ebi.ac.uk/pub/databases/intact

  18. Controlled vocabularies • Why do we use them ? • e.g. far too many ways to write: yeast two hybrid, Y2H, 2H, two-hybrid, … • Full integration of PSI-MI ontology • Over 1,500 terms, fully defined and cross-referenced

  19. How to deal with Complexes • Some experimental protocol do generate complex data: • Eg. Tandem affinity purification (TAP) • One may want to convert these complexes into sets of binary interactions, 2 algorithms are available: Both are somewhat wrong, spoke is said to generated 3 times less false positive (Bader et al.).

  20. The IntAct web site

  21. IntAct: Home page http://www.ebi.ac.uk/intact

  22. IntAct: Search and results Other PSICQUIC services IMEx data Complex ? Interaction details Interactors UniProt Taxonomy PubMed Method (PSI-MI CV)

  23. IntAct: Search and results Export Custom columns Filters

  24. IntAct: Browse

  25. IntAct: Advanced search: Ontologies

  26. IntAct > Advanced search: Fields Filtering options Add more filtering options

  27. IntAct > Advanced search: MIQL • Molecular Interaction Query Language

  28. IntAct > Chemical search 1. Draw your compound 2. View matching molecules 3. View known interactions

  29. IntAct > Interaction details

  30. IntAct > Interaction details > More ..

  31. IntAct > Interaction details>Find similar interactions We search for similar interaction by looking for interactions sharing the same participants. Interactions having the most in commons are shown first. So far all hits are shown, we will work at speeding up that view as it can be rather slow when many participants exist in the original interaction.

  32. IntAct > List of interactors

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