Motif finding - lesson plan

Learning objectives

• Understand concept of motif and why they are important for understanding biology
• Understand difference between motifs and frequent k-mers (chapter 2 vs. chapter 1)
• Understand concept of consensus string/motif
• Able to construct brute force algorithm to find motifs
• Understand motif profiles as probability distributions
• Able to score a sequence against a profile using probabilities
• Understand the necessity of entropy and information content
• Understand need for pseudo-counts
• Understand difference between Las Vegas and Monte Carlo randomized algorithms
• Understand Gibbs sampling procedure
• Understand why Gibbs sampler allows sub-optimal solutions

Class activities

• Describe biological problem
• Compute entropy of figure 2.2
• Score of motif table - brute force algorithm to consensus string
• From consensus string to probabilities - probabilistic scoring
• Randomized search
• Gibbs sampler

Exercises

Rosalind section 3