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Multiple Sequence Alignment

This text discusses multiple sequence alignment and evolution at the DNA level, including deletion mutations, rearrangements, orthology, and paralogy. It also explores the construction of synteny maps and recommended local aligners for DNA comparison.

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Multiple Sequence Alignment

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  1. Multiple Sequence Alignment

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

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

  4. Orthology, Paralogy, Inparalogs, Outparalogs

  5. Genome Evolution – Macro Events • Inversions • Deletions • Duplications

  6. Synteny maps Comparison of human and mouse

  7. Synteny maps

  8. Synteny maps

  9. Synteny maps

  10. 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

  11. 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?

  12. Local Alignments

  13. After chaining

  14. Chaining local alignments • Find local alignments • Chain -O(NlogN) L.I.S. • Restricted DP

  15. 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 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) z w

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

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

  18. Neutral Substitution Rates

  19. 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

  20. 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

  21. Finding Conserved Elements

  22. Finding Conserved Elements (3) GERP

  23. Phylo HMMs HMM Phylogenetic Tree Model Phylo HMM

  24. Finding Conserved Elements (3)

  25. How do the methods agree/disagree?

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

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

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