1. Computational Genomics and Proteomics Lecture 8 Motif Discovery C E N T R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U E

2. Outline Gene Regulation DNA Transcription factors Motifs What are they? Binding Sites Combinatoric Approaches Exhaustive searches Consensus Comparative Genomics Example Probabilistic Approaches Statistics EM algorithm Gibbs Sampling

3. www.accessexcellence.org

6. Four DNA nucleotide building blocks G-C is more strongly hydrogen-bonded than A-T

7. Degenerate code Four bases: A, C, G, T Two-fold degenerate IUB codes: R=[AG] -- Purines Y=[CT] -- Pyrimidines K=[GT] M=[AC] S=[GC] W=[AT] Four-fold degenerate: N=[AGCT]

8. Transcription Factors Required but not a part of the RNA polymerase complex Many different roles in gene regulation  Binding  Interaction  Initiation  Enhancing  Repressing Various structural classes (eg. zinc finger domains) Consist of both a DNA-binding domain and an interactive domain

9.  Short sequences of DNA or RNA (or amino acids)  Often consist of 5- 16 nucleotides  May contain gaps  Examples include:  Splice sites  Start/stop codons  Transmembrane domains  Centromeres  Phosphorylation sites  Coiled-coil domains  Transcription factor binding sites (TFBS – regulatory motifs) Motifs

10. TFBSs  Difficult to identify  Each transcription factor may have more than one binding site  Degenerate  Most occur upstream of translation start site (TSS) but are known to also occur in:  introns  exons  3’ UTRs  Usually occur in clusters, i.e. collections of sites within a region (modules)  Often repeated  Sites can be experimentally verified

11. Why are TFBSs important?  Aid in identification of gene networks/pathways  Determine correct network structure  Drug discovery  Switch production of gene product on/off Gene A Gene B

12. Consensus sequences  Matches all of the example sequences closely but not exactly  A single site TACGAT  A set of sites: TACGAT TATAAT GATACT TATGAT TATGTT  Consensus sequence: TATAAT or TATRNT  Trade-off: number of mismatches allowed, ambiguity in consensus sequence and the sensitivity and precision of the representation.

13. Information Content and Entropy

14. Sequence Logos

15.  Given a collection of motifs, TACGAT TATAAT GATACT TATGAT TATGTT  Create the matrix: Frequency Matrices TACGTACG

16. Position weight matrices

17.  Two problems:  Given a collection of known motifs, develop a representation of the motifs such that additional occurrences can reliably be identified in new promoter regions  Given a collection of genes, thought to be related somehow, find the location of the motif common to all and a representation for it.  Two approaches:  Combinatorial  Probabilistic Finding Motifs

18. Combinatorial Approach

19. Exhaustive Search

20. Sample-driven here refers to trying all the words as they occur in the sequences, instead of trying all possible (4 W ) words exhaustively

21. Greedy Motif Clustering

24.  Main Idea: Conserved non coding regions are important  Align the promoters of orthologous co-expressed genes from two (or more) species e.g. human and mouse  Search for TFBS only in conserved regions  Problems:  Not all regulatory regions are conserved  Which genomes to use? Comparative Genomics

25. Phylogenetic Footprinting Phylogenetic Footprinting refers to the task of finding conserved motifs across different species. Common ancestry and selection on these motifs has resulted in these “footprints”.

26. Xie et al. 2005  Genome-wide alignments for four species (human, mouse, rat, dog)  Promoter regions and 3’UTRs then extracted for 17,700 well-annotated genes  Promoter region taken to be (-2000, 2000)  This set of sequences then searched exhaustively for motifs Phylogenetic Footprinting An Example Nature 434, 338-345, 2005

27. The Search Xie et al. 2005

28. Expected Rate

29. Probabilistic Approach

30. Gibbs Sampling (applied to Motif Finding)

31. Gibbs Sampling Algorithm

32. Gibbs Sampling – Motif Positions

33. AlignACE - Gibbs Sampling

34. Remainder of the lecture: Maximum likelihood and the EM algorithm The remaining slides are for your information only and will not be part of the exam

35. Basic Statistics

36. Maximum Likelihood Estimates

37. EM Algorithm

38. Basic idea (MEME) http://meme.nbcr.net/meme/meme-intro.html

39. Basic idea (MEME) MEME is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letter- probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs. MEME takes as input a group of DNA or protein sequences (the training set) and outputs as many motifs as requested. MEME uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif. http://meme.nbcr.net/meme/meme-intro.html

40. Basic MEME Model

41. MEME Background frequencies

42. MEME – Hidden Variable

43. MEME – Conditional Likelihood

44. EM algorithm

45. Example

46. E-step of EM algorithm

47. Example

48. M-step of EM Algorithm

49. Example

50. Characteristics of EM

51. Gibbs Sampling (versus EM)