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Multiple Sequence Alignment. 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:
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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 …… scan DB query Question: Using an idea from overlap detection, better way to find all local alignments between two genomes?
Chaining local alignments • Find local alignments • Chain -O(NlogN) L.I.S. • Restricted DP
Progressive Alignment x • When evolutionary tree is known: • Align closest first, in the order of the tree • In each step, align two sequences x, y, or profiles px, py, to generate a new alignment with associated profile presult Weighted version: • Tree edges have weights, proportional to the divergence in that edge • New profile is a weighted average of two old profiles y z Example Profile: (A, C, G, T, -) px = (0.8, 0.2, 0, 0, 0) py = (0.6, 0, 0, 0, 0.4) s(px, py) = 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:pxy= (0.7, 0.1, 0, 0, 0.2) s(px, -) = 0.8*1.0*s(A, -) + 0.2*1.0*s(C, -) Result:px-= (0.4, 0.1, 0, 0, 0.5) w
Threaded Blockset Aligner HMR – CD Restricted Area Profile Alignment Human–Cow
Reconstructing the Ancestral Mammalian Genome Human: C C Baboon: C C Dog: G C or G Cat: C
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) A C • 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 A G
Phylo HMMs HMM Phylogenetic Tree Model Phylo HMM
Statistical Power to Detect Constraint N L C: cutoff # mutations D: neutral mutation rate : constraint mutation rate relative to neutral
Statistical Power to Detect Constraint N L C: cutoff # mutations D: neutral mutation rate : constraint mutation rate relative to neutral