Evolution at the DNA level …ACGGTGCAGTTACCA… …AC----CAGTCCACCA… Mutation SEQUENCE EDITS REARRANGEMENTS Deletion Inversion Translocation Duplication

Orthology and Paralogy HB Human WB Worm HA1 Human HA2 Human Yeast WA Worm Orthologs: Derived by speciation Paralogs: Everything else Orthologs: Derived by speciation Paralogs: Everything else

Orthology, Paralogy, Inparalogs, Outparalogs

Synteny maps Comparison of human and mouse

Synteny maps

Building synteny maps Recommended local aligners BLASTZ  Most accurate, especially for genes  Chains local alignments WU-BLAST  Good tradeoff of efficiency/sensitivity  Best command-line options BLAT  Fast, less sensitive  Good for comparing very similar sequences finding rough homology map

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?

Local Alignments

After chaining

Chaining local alignments 1.Find local alignments 2.Chain -O(NlogN) L.I.S. 3.Restricted DP

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)

Threaded Blockset Aligner Human–Cow HMR – CD Restricted Area Profile Alignment

Reconstructing the Ancestral Mammalian Genome Human: C Baboon: C Cat: C Dog: G C C or G G

Neutral Substitution Rates

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

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

Finding Conserved Elements

Finding Conserved Elements (3) GERP

Phylo HMMs HMM Phylogenetic Tree Model Phylo HMM

Finding Conserved Elements (3)

How do the methods agree/disagree?

Statistical Power to Detect Constraint L N C: cutoff # mutations D: neutral mutation rate  : constraint mutation rate relative to neutral

Statistical Power to Detect Constraint L N C: cutoff # mutations D: neutral mutation rate  : constraint mutation rate relative to neutral