1. CS273a Lecture 11, Aut 08, Batzoglou Multiple Sequence Alignment

2. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Index-based local alignment Dictionary: All words of length k (~10) Alignment initiated between words of alignment score  T (typically T = k) Alignment: Ungapped extensions until score below statistical threshold Output: All local alignments with score > statistical threshold …… query DB query scan Question: Using an idea from overlap detection, better way to find all local alignments between two genomes?

3. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Local Alignments

4. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 After chaining

5. CS273a Lecture 11, Aut 08, Batzoglou Chaining local alignments 1.Find local alignments 2.Chain -O(NlogN) L.I.S. 3.Restricted DP

6. CS273a Lecture 11, Aut 08, Batzoglou Progressive Alignment When evolutionary tree is known:  Align closest first, in the order of the tree  In each step, align two sequences x, y, or profiles p x, p y, to generate a new alignment with associated profile p result Weighted version:  Tree edges have weights, proportional to the divergence in that edge  New profile is a weighted average of two old profiles x w y z Example Profile: (A, C, G, T, -) p x = (0.8, 0.2, 0, 0, 0) p y = (0.6, 0, 0, 0, 0.4) s(p x, p y ) = 0.8*0.6*s(A, A) + 0.2*0.6*s(C, A) + 0.8*0.4*s(A, -) + 0.2*0.4*s(C, -) Result: p xy = (0.7, 0.1, 0, 0, 0.2) s(p x, -) = 0.8*1.0*s(A, -) + 0.2*1.0*s(C, -) Result: p x- = (0.4, 0.1, 0, 0, 0.5)

7. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Threaded Blockset Aligner Human–Cow HMR – CD Restricted Area Profile Alignment

8. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Reconstructing the Ancestral Mammalian Genome Human: C Baboon: C Cat: C Dog: G C C or G C

9. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Neutral Substitution Rates

10. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Finding Conserved Elements (1) Binomial method  25-bp window in the human genome  Binomial distribution of k matches in N bases given the neutral probability of substitution

11. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Finding Conserved Elements (2) Parsimony Method  Count minimum # of mutations explaining each column  Assign a probability to this parsimony score given neutral model  Multiply probabilities across 25-bp window of human genome A C A A G

12. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Finding Conserved Elements

13. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Finding Conserved Elements (3) GERP

14. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Phylo HMMs HMM Phylogenetic Tree Model Phylo HMM

15. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Finding Conserved Elements (3)

16. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 How do the methods agree/disagree?

17. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Statistical Power to Detect Constraint L N C: cutoff # mutations D: neutral mutation rate  : constraint mutation rate relative to neutral

18. CS273a Lecture 11, Aut 08, Batzoglou CS273a Lecture 11, Fall 2008 Statistical Power to Detect Constraint L N C: cutoff # mutations D: neutral mutation rate  : constraint mutation rate relative to neutral