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Evolution at the DNA Level: Insights into Mutations, Orthology, and Synteny Mapping

This document explores the evolutionary transformations at the DNA level, focusing on deletion mutations, sequence edits, and rearrangements such as inversions and translocations. It delves into orthologs, paralogs, and the methodologies for constructing synteny maps in various organisms, including humans and mice. Local alignment strategies using tools like BLASTZ, WU-BLAST, and BLAT are highlighted for their effectiveness in genomic comparisons. The statistical approaches for detecting conserved elements through methods like the binomial approach and GERP are discussed, providing a comprehensive overview of DNA evolution and analysis.

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Evolution at the DNA Level: Insights into Mutations, Orthology, and Synteny Mapping

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

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

  3. Orthology, Paralogy, Inparalogs, Outparalogs

  4. Synteny maps Comparison of human and mouse

  5. Synteny maps

  6. Synteny maps

  7. Synteny maps

  8. 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

  9. 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?

  10. Local Alignments

  11. After chaining

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

  13. 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

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

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

  16. Neutral Substitution Rates

  17. 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

  18. 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

  19. Finding Conserved Elements

  20. Finding Conserved Elements (3) GERP

  21. Phylo HMMs HMM Phylogenetic Tree Model Phylo HMM

  22. Finding Conserved Elements (3)

  23. How do the methods agree/disagree?

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

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

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