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Building 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

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Building synteny maps

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  1. 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

  2. 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?

  3. Local Alignments

  4. After chaining

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

  6. 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

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

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

  9. Neutral Substitution Rates Dataset 3: 4-D sites

  10. 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. 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

  12. Finding Conserved Elements

  13. Finding Conserved Elements (3) GERP

  14. Phylo HMMs HMM Phylogenetic Tree Model Phylo HMM

  15. Finding Conserved Elements (3)

  16. How do the methods agree/disagree?

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

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

  19. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATTTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAAT

  20. TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATTTTTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACATATCCATATCTAATCTTACTTATATGTTGTGGAAATGTAAAGAGCCCCATTATCTTAGCCTAAAAAAACCTTCTCTTTGGAACTTTCAGTAATACGCTTAACTGCTCATTGCTATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCTTCACCGGTCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAATACTAGCTTTTATGGTTATGAAGAGGAAAAATTGGCAGTAACCTGGCCCCACAAACCTTCAAATTAACGAATCAAATTAACAACCATAGGATGATAATGCGATTAGTTTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCGATGATTTTTGATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCACTTTAACTAATACTTTCAACATTTTCAGTTTGTATTACTTCTTATTCAAATGTCATAAAAGTATCAACAAAAAATTGTTAATATACCTCTATACTTTAACGTCAAGGAGAAAAAACTATAATGACTAAATCTCATTCAGAAGAAGTGATTGTACCTGAGTTCAATTCTAGCGCAAAGGAATTACCAAGACCATTGGCCGAAAAGTGCCCGAGCATAATTAAGAAATTTATAAGCGCTTATGATGCTAAACCGGATTTTGTTGCTAGATCGCCTGGTAGAGTCAATCTAATTGGTGAACATATTGATTATTGTGACTTCTCGGTTTTACCTTTAGCTATTGATTTTGATATGCTTTGCGCCGTCAAAGTTTTGAACGATGAGATTTCAAGTCTTAAAGCTATATCAGAGGGCTAAGCATGTGTATTCTGAATCTTTAAGAGTCTTGAAGGCTGTGAAATTAATGACTACAGCGAGCTTTACTGCCGACGAAGACTTTTTCAAGCAATTTGGTGCCTTGATGAACGAGTCTCAAGCTTCTTGCGATAAACTTTACGAATGTTCTTGTCCAGAGATTGACAAAATTTGTTCCATTGCTTTGTCAAATGGATCATATGGTTCCCGTTTGACCGGAGCTGGCTGGGGTGGTTGTACTGTTCACTTGGTTCCAGGGGGCCCAAATGGCAACATAGAAAAGGTAAAAGAAGCCCTTGCCAATGAGTTCTACAAGGTCAAGTACCCTAAGATCACTGATGCTGAGCTAGAAAATGCTATCATCGTCTCTAAACCAGCATTGGGCAGCTGTCTATATGAATTAGTCAAGTATACTTCTTTTTTTTACTTTGTTCAGAACAACTTCTCATTTTTTTCTACTCATAACTTTAGCATCACAAAATACGCAATAATAACGAGTAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAG...TTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTTTCCTACGCATAATAAGAATAGGAGGGAATATCAAGCCAGACAATCTATCATTACATTTAAGCGGCTCTTCAAAAAGATTGAACTCTCGCCAACTTATGGAATCTTCCAATGAGACCTTTGCGCCAAATAATGTGGATTTGGAAAAAGAGTATAAGTCATCTCAGAGTAATATAACTACCGAAGTTTATGAGGCATCGAGCTTTGAAGAAAAAGTAAGCTCAGAAAAACCTCAATACAGCTCATTCTGGAAGAAAATCTATTATGAATATGTGGTCGTTGACAAATCAATCTTGGGTGTTTCTATTCTGGATTCATTTATGTACAACCAGGACTTGAAGCCCGTCGAAAAAGAAAGGCGGGTTTGGTCCTGGTACAATTATTGTTACTTCTGGCTTGCTGAATGTTTCAATATCAACACTTGGCAAATTGCAGCTACAGGTCTACAACTGGGTCTAAATTGGTGGCAGTGTTGGATAACAATTTGGATTGGGTACGGTTTCGTTGGTGCTTTTGTTGTTTTGGCCTCTAGAGTTGGATCTGCTTATCATTTGTCATTCCCTATATCATCTAGAGCATCATTCGGTATTTTCTTCTCTTTATGGCCCGTTATTAACAGAGTCGTCATGGCCATCGTTTGGTATAGTGTCCAAGCTTATATTGCGGCAACTCCCGTATCATTAATGCTGAAATCTATCTTTGGAAAAGATTTACAATGATTGTACGTGGGGCAGTTGACGTCTTATCATATGTCAAAGTCATTTGCGAAGTTCTTGGCAAGTTGCCAACTGACGAGATGCAGTAACACTTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCACAAACTTTAAAACACAGGGACAAAATTCTTGATATGCTTTCAACCGCTGCGTTTTGGATACCTATTCTTGACATGATATGACTACCATTTTGTTATTGTTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATAATGTTTTCAATGTAAGAGATTTCGATTATCTTATAGTTCATACATGCTTCAACTACTTAATAAATGATTGTATGATTT Exons Promoter motifs 3’ UTR motifs Introns

  21. Comparing genomes reveals functional elements • Protein-coding genes • Ultra-conserved elements • Short regulatory motifs

  22. Regulatory Motif Discovery GAL1 Gal4 Gal4 Mig1 • Gene regulation • Genes are turned on / off in response to changing environments • Gene regulatory logic is controlled by sequence motifs • Specialized proteins (transcription factors) recognize motifs • What makes motif discovery hard? • Motifs are short (6-8 bp) and usually degenerate • Act at variable distances upstream (or downstream) of target gene ATGACTAAATCTCATTCAGAAGAAGTGA CGG CCG CGG CCG CCCCW

  23. Overview of Motif Discovery Algorithms

  24. Motif Representation GTATAA CTATAA GTCTTA ATATAC GTAATA TTGTAC GTATTA GTATTC ATCTAA GTATAM IUPAC Complex Dependency Graphical Models ATATAC GTAATA ATCTAA GTATTC GTATAA GTATAA CTATAA GTATTA Consensus PSSM TTGTAC GTCTTA Nonparametric – Graph or Bag of Words

  25. Motif Representation – Pairwise Dependencies Complex Dependency Graphical Models

  26. Motif Representation – MotifScan ATATAC GTAATA ATCTAA GTATTC GTATAA CTATAA GTATTA TTGTAC GTCTTA

  27. Motif Finding • Given a set of promoter sequences • For example, common expression pattern of the respective genes in microarrays ACCGAGAGTATAAGCTTACGTGACTTGCATGATCTTGCGATGTGTGTTCAGCT ATCGTACGTTGAGGAGAGGCGGTAATAGAAGTACGTCGATGTCGTCGTACAT TTCCTATAAGATCGACTGTAGGGAGAGTCTCTGAGAGTATTGCTGGCATGTG ACTTCGAGGAGAGATTCTCTAGATCTATGCTGTGGTATTAAGAGATCTCTAG ATCGATGCGCTGATCGCTATAATATATCGGCGGTATCTGGTTGATCTGGTGT GACTGATGTATCGTATCTGATCTGTCGGTATAATATAGCTGTCTGATTAGTTG TCTCTAGATGCTGTGCTGATGGTCTTATCGATGTGCGACGGTAATAGTATCCT • Find a common motif that they share GTATAA GTAATA CTATAA GTATTA CTATAA GTATAA GTAATA

  28. Most Popular Approaches • Expectation Maximization – MEME • Sequences are mixtures of • Motif model M, e.g., a motif PSSM • Background model B, e.g., 3rd order model of promoters • Learn model by • Starting from random M, learned B from promoters • Assign each position in input to M or B, accordingly • Re-estimate M and B based on current assignments • Gibbs Sampling – AlignACE, BioProspector • Update 1-seq x at a time • Remove from M • Pick a new location in x based on M x M

  29. Whole-genomemotif discovery

  30. Regulatory Motif Discovery Study known motifs Derive conservation rules Discover novel motifs

  31. Known motifs are preferentially conserved Is this enough to discover motifs? Is this enough to discover motifs? No.

  32. human CTCTTAATGGTACACGTTCTGCCT----AAGTAGCCTAGACGCTCCCGTGCGCCC-GGGGdog CTCTTA-CGGGGCACATTCTGCTTTCAACAGTGGGGCAGACGGTCCCGCGCGCCCCAAGGmouse GTCTTAGGAGGCT-CGATCGCC---------------------GCCTGCATTATT-----rat GTCTTAGTTGGCCACGACCTGC---------------------TCATGCATAATT----- ***** * * * * * * human CGGGTAGGCCTGGCCGAAAATCTCTCCCGCGCGCCTGACCTTGGGTTGCCCCAGCCAGGCdog CAGGC---CCGGGCTGCAGACCTGCCCTGAGGGAATGACCTTGGGCGGCCGCAGCGGGGCmouse --------------CACAAGCCTGTGGCGCGC-CGTGACCTTGGGCTGCCCCAGGCGGGCrat --------------CACAAGTTTCTC---TGC-CCTGACCTTGGGTTGCCCCAGGCGAG- * * * ********** *** *** * human TGCGGGCCCGAGACCCCCG-------------------GGCCTCCCTGCCCCCCGCGCCGdog CGCGGGCCCAGGCCCCCCTCCCTCCCTCCCTCCCTCCCTCCCTCCCTGCCCCCCGGACCGmouse TGCAGGCTCACCACCCCGTCTTTTCT---------------------GCTTTTCGAGTCGrat -GCATACACCCCGCCTTTTTTTTTTTTTT---------TTTTTTTTTGCCGTTCAAG-AG ** * * ** ** * * Erra Known motifs are preferentially conserved human CTCTTAATGGTACACGTTCTGCCT----AAGTAGCCTAGACGCTCCCGTGCGCCC-GGGGdog CTCTTA-CGGGGCACATTCTGCTTTCAACAGTGGGGCAGACGGTCCCGCGCGCCCCAAGGmouse GTCTTAGGAGGCT-CGATCGCC---------------------GCCTGCATTATT-----rat GTCTTAGTTGGCCACGACCTGC---------------------TCATGCATAATT----- ***** * * * * * * human CGGGTAGGCCTGGCCGAAAATCTCTCCCGCGCGCCTGACCTTGGGTTGCCCCAGCCAGGCdog CAGGC---CCGGGCTGCAGACCTGCCCTGAGGGAATGACCTTGGGCGGCCGCAGCGGGGCmouse --------------CACAAGCCTGTGGCGCGC-CGTGACCTTGGGCTGCCCCAGGCGGGCrat --------------CACAAGTTTCTC---TGC-CCTGACCTTGGGTTGCCCCAGGCGAG- * * * ********** *** *** * human TGCGGGCCCGAGACCCCCG-------------------GGCCTCCCTGCCCCCCGCGCCGdog CGCGGGCCCAGGCCCCCCTCCCTCCCTCCCTCCCTCCCTCCCTCCCTGCCCCCCGGACCGmouse TGCAGGCTCACCACCCCGTCTTTTCT---------------------GCTTTTCGAGTCGrat -GCATACACCCCGCCTTTTTTTTTTTTTT---------TTTTTTTTTGCCGTTCAAG-AG ** * * ** ** * * human CTCTTAATGGTACACGTTCTGCCT----AAGTAGCCTAGACGCTCCCGTGCGCCC-GGGGdog CTCTTA-CGGGGCACATTCTGCTTTCAACAGTGGGGCAGACGGTCCCGCGCGCCCCAAGGmouse GTCTTAGGAGGCT-CGATCGCC---------------------GCCTGCATTATT-----rat GTCTTAGTTGGCCACGACCTGC---------------------TCATGCATAATT----- ***** * * * * * * human CGGGTAGGCCTGGCCGAAAATCTCTCCCGCGCGCCTGACCTTGGGTTGCCCCAGCCAGGCdog CAGGC---CCGGGCTGCAGACCTGCCCTGAGGGAATGACCTTGGGCGGCCGCAGCGGGGCmouse --------------CACAAGCCTGTGGCGCGC-CGTGACCTTGGGCTGCCCCAGGCGGGCrat --------------CACAAGTTTCTC---TGC-CCTGACCTTGGGTTGCCCCAGGCGAG- * * * ********** *** *** * human TGCGGGCCCGAGACCCCCG-------------------GGCCTCCCTGCCCCCCGCGCCGdog CGCGGGCCCAGGCCCCCCTCCCTCCCTCCCTCCCTCCCTCCCTCCCTGCCCCCCGGACCGmouse TGCAGGCTCACCACCCCGTCTTTTCT---------------------GCTTTTCGAGTCGrat -GCATACACCCCGCCTTTTTTTTTTTTTT---------TTTTTTTTTGCCGTTCAAG-AG ** * * ** ** * * Gabpa Is this enough to discover motifs? No

  33. Erra Erra Erra Dog Mouse Rat Conservation rate: 37% Known motifs are frequently conserved Human • Across the human promoter regions, the Erra motif: • appears 434 times • is conserved 162 times • Compare to random control motifs • Conservation rate of control motifs: 6.8% • Erra enrichment: 5.4-fold • Erra p-value < 10-50 (25 standard deviations under binomial) Motif Conservation Score (MCS)

  34. MCS distribution of all 6-mers shows excess conservation High scoring patterns include known motifs Excess specific to promoters and 3’-UTRs (not introns) For MCS > 6, estimate 97% specificity Select motifs with MCS > 6.0, cluster Motif density Motif density Motif Conservation Score (MCS)

  35. Hill-climbing in sequence space • Seed selection • Three mini-motif conservation criteria (CC1, CC2, CC3) • Motif extension • Non-random conservation of neighbors • Motif collapsing • Merge neighbors using hierarchical clustering, avg-max-linkage • Re-scoring complex motifs • Motif conservation score for full motifs (MCS)

  36. CGG-11-CCG Test 1: Intergenic conservation Conserved count Total count

  37. N r Conservation rate Binomial score Test 1: Selecting mini-motifs • Estimate basal rate of conservation • Expected conservation rate at the evolutionary distances observed • Average conservation rate of non-outlier mini-motifs • Score conservation of mini-motif • k: conserved motif occurrences • n: total motif occurrences • r: basal conservation rate • Evaluate binomial probability of observing k successes out of n trials • Assign z-score to each mini-motif • Bulk of distribution is symmetric • Estimate specificity as (R-L)/R • Select cutoff: 5.0 sigma • 1190 mini-motifs, 97.5% non-random Specificity Cutoff Right tail Left tail

  38. CGG-11-CCG Higher Conservation in Genes Test 2: Intergenic vs. Coding Intergenic Conservation Coding Conservation

  39. CGG-11-CCG Downstream motifs? Most Patterns Test 3: Upstream vs. Downstream Upstream Conservation Downstream Conservation

  40. 5 6 Y R T C G C A C G A Extend Extend Extend Collapse Extend G T C A C A C G A A T C R Y A C G A Collapse Collapse Collapse R T C G C A C G A Merge 72 Full motifs Full Motifs Constructing full motifs 2,000 Mini-motifs Test 1 Test 2 Test 3 R T C A A C G R

  41. Find maximally discriminating neighborhood N1 M1 R T C A G A C G W N2 M2 Y T C x H A x G S • Evaluate non-randomness of neighborhood • chi-square contingency test on [N1,M1], [N2,M2] Extending mini-motifs • Separate conserved and non-conserved instances 6 T C A A C G Causal set 6 T C x A x G Random set

  42. 174 motifs in promoters 106 motifs in 3’ UTRs Systematically test candidate patterns gap S R T G C Y W T A G R • Enumerate • Length between 6 and 15 nt, allow central gap • 11 letter alphabet (A C G T, 2-fold codes, N) • Score • Compute binomial score (conserved vs. total) • Select MCS > 6.0  specificity 97% • Cluster • Sequence similarity • Overlapping occurrences All potential motifs Evaluate MCS Cluster similar motifs Are these real ?

  43. Functions of discovered motifs

  44. Evidence of motif function • Promoter motifs: • Comparison to known motifs • Distance from TSS • Expression enrichment Promoter 3’-UTR Stop ATG 174 motifs 106 motifs

  45. Promoter motifs match known TF binding sites Compare discovered motifs to TRANSFAC database of 125 known motifs 45% of discovered motifs match TRANSFAC motifs (only 2% of control sequences match TRANSFAC motifs) 55% of TRANSFAC motifs match discovered motifs

  46. (2) Promoter motifs show preferred distance to TSS Motif instances in human Conserved motif sites in all four species Motif 4 -81 Each of 174 discovered motifs Motif 8 -63 Distance from TSS Discovered motifs occur preferentially Within 200 bp of Transcription Start Site Individual motifs show strong peaks Regardless of conservation 32% of discovered motifs show strong positional bias

  47. (3) Promoter motifs enriched in specific tissues New motifs Known TFs 70% of motifs show significant enrichment in at least one tissue

  48. New New New New New Summary for promoter motifs • 174 promoter motifs • 70 match known TF motifs • 115 expression enrichment • 60 show positional bias  75% have evidence • Control sequences < 2% match known TF motifs < 5% expression enrichment < 3% show positional bias  < 7% false positives Most discovered motifs are likely to be functional

  49. Summary of Promoter Motifs

  50. Similar analysis in 5% most conserved regions in human 12-22 bp long motifs

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