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Microarrays for transcription factor binding location analysis (chIP-chip)

Microarrays for transcription factor binding location analysis (chIP-chip). and the “Active Modules” approach. Protein-DNA interactions: ChIP-chip. Lee et al., Science 2002. Simon et al., Cell 2001. ChIP-chip Microarray Data. Differentially represented intergenic regions

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Microarrays for transcription factor binding location analysis (chIP-chip)

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  1. Microarrays for transcription factor binding location analysis (chIP-chip) and the “Active Modules” approach

  2. Protein-DNA interactions: ChIP-chip Lee et al.,Science 2002 Simon et al., Cell 2001

  3. ChIP-chip Microarray Data Differentially represented intergenic regions provides evidence for protein-DNA interaction

  4. Network representation of TF-DNA interactions

  5. Dynamic role of transcription factors Harbison C, Gordon B, et al. Nature 2004

  6. Mapping transcription factor binding sites Harbison C, Gordon B, et al. Nature 2004

  7. Integrating gene Expression Data with Interaction Networks

  8. Data Integration Need computational tools able to distill pathways of interest from large molecular interaction databases

  9. List of Genes Implicated in an Experiment • What do we make of such a result? Jelinsky S & Samson LD, Proc. Natl. Acad. Sci. USA Vol. 96, pp. 1486–1491,1999

  10. KEGGhttp://www.genome.jp/kegg/

  11. Activated Metabolic Pathways

  12. Types of Information to Integrate • Data that determine the network (nodes and edges) • protein-protein • protein-DNA, etc… • Data that determine the state of the system • mRNA expression data • Protein modifications • Protein levels • Growth phenotype • Dynamics over time

  13. Network Perturbations • Environmental: • Growth conditions • Drugs • Toxins • Genetic: • Gene knockouts • Mutations • Disease states

  14. Finding “Active” Sub-graphs Active Modules

  15. Finding “Active” Modules/Pathways in a Large Network is Hard • Finding the highest scoring subnetwork is NP hard, so we use heuristic search algorithms to identify a collection of high-scoring subnetworks (local optima) • Simulated annealing and/or greedy search starting from an initial subnetwork “seed” • Considerations: Local topology, sub-network score significance (is score higher than would be expected at random?), multiple states (conditions)

  16. Activated Sub-graphs Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signaling circuits in molecular interaction networks.Bioinformatics. 2002;18 Suppl 1:S233-40.

  17. Scoring a Sub-graph Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signaling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S233-40.

  18. Significance Assessment of Active Module Score distributions for the 1st - 5th best scoring modules before (blue) and after (red) randomizing Z-scores (“states”). Randomization disrupts correlation between gene expression and network location. Ideker T, Ozier O, Schwikowski B, Siegel AF. Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics. 2002;18 Suppl 1:S233-40.

  19. Network Regions of Differential Expression After Gene Deletions Ideker, Ozier, Schwikowski, Siegel. Bioinformatics (2002)

  20. Network based classifier of cancer

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