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1. Introduction
2. Outline
3. Finding regulatory motifs
4. Motif discovery: the two-step strategy
5. Amadeus
6. Motif search algorithm
7. Scoring over-represented motifs
8. Metazoan motif discovery benchmark
9. Comparing Amadeus to 5 popular motifs
10. Amadeus - global spatial analysis
11. Global analyses
12. Chromosomal preference in C. elegans
13. Finding transcriptional programs
14. Goal (1)
15. Our goal: bypass the two-step approach
16. Yeast osmotic shock pathway
17. 3' UTR analysis: human stem cells
18. Human stem cells: results using binned score
19. A new regulatory motif pair in C. elegans
20. Pair occurs in 154 promoters
21. Motif across the C. rhabditis genus
22. Testing the motif experimentally
23. User interface of Amadeus
24. Analysis of gene expression profiles + a network
25. Overall gene interaction network
26. Goal (2)
27. Matisse algorithm development
28. Probabilistic model
29. Front vs. back nodes
30. Test case: yeast osmotic shock
31. Pheromone response subnetwork
32. Performance comparison
33. GO and promoter analysis
34. Application to stem cells
35. Network found using Matisse
36. Accounting for PPI confidence
37. DNA damage response in S. cerevisiae
38. DNA damage response modules
39. Comparison with prior work
40. Conclusion (1)
41. Conclusion (2)
42. Thanks (1)
43. Thanks (2)

Topics Covered

• Finding regulatory motifs
• Finding motifs using target sets of promoters
• Problem definition
• The Amadeus motif finding algorithm
• Architecture, benchmark results and global analysis
• Finding transcriptional programs and their regulatory motifs
• Problem definition
• The Allegro software for recovering transcriptional program
• Results on yeast osmotic shock expression profiles
• Recovering microRNA binding motifs from 3' UTRs using stem cell expression profiles
• Discovering a novel motif pair in C. elegans
• Utilizing network information
• Using expression and protein interaction data
• Problem definition and the Matisse algorithm
• Demonstration of Matisse on yeast osmotic shock data
• Comparison of Matisse to other algorithms
• Demonstration of Matisse on human stem cell data
• The pluripotency network
• Using expression and protein interaction data with interaction confidence
• The Cezanne algorithm
• DNA damage response in yeast

Links

Series:

• Protein Homeostasis

Categories:

• Biochemistry
• Cell Biology
• Methods

Talk Citation

Shamir, R. (2020, October 19). Computational methods in analysis of gene regulation and protein interactions [Video file]. In The Biomedical & Life Sciences Collection, Henry Stewart Talks. Retrieved April 15, 2025, from https://doi.org/10.69645/WTDF6649.
Export Citation (RIS)

Publication History

• Published on February 2, 2012
• Reviewed on October 19, 2020

Financial Disclosures

• Prof. Ron Shamir has not informed HSTalks of any commercial/financial relationship that it is appropriate to disclose.