Prediction of Regulatory Elements Controlling Gene Expression

1. Prediction of Regulatory Elements Controlling Gene Expression Martin Tompa Computer Science & Engineering Genome Sciences University of Washington Seattle, Washington, U.S.A.

2. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

3. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

4. DNA, Genes, and Proteins DNA: program for cell processes Proteins: execute cell processes DNA TCCAACGGTGCTGAGGTGCAC Protein Gene

5. Regulation of Genes • What turns genes on (producing a protein) and off? • When is a gene turned on or off? • Where (in which cells) is a gene turned on? • At what rate is the gene product produced?

6. Regulation of Genes Transcription Factor (Protein) RNA polymerase (Protein) DNA Gene Regulatory Element

7. Regulation of Genes Transcription Factor (Protein) RNA polymerase (Protein) DNA Gene Regulatory Element

8. Regulation of Genes Transcription Factor (Protein) RNA polymerase (Protein) New protein DNA Gene Regulatory Element

9. Goal • Identify regulatory elements in DNA sequences. These are: • Binding sites for proteins • Short sequences (5-25 nucleotides) • Up to 1000 nucleotides (or farther) from gene • Inexactly repeating patterns (“motifs”)

10. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

11. 2 Types of Motif Discovery • Motif discovery by overrepresentation • One species • Multiple (co-regulated) genes • Motif discovery by phylogenetic footprinting • Multiple species • One gene

12. Overrepresentation: Daf-19 Binding Sites in C. elegans GTTGTCATGGTGAC GTTTCCATGGAAAC GCTACCATGGCAAC GTTACCATAGTAAC GTTTCCATGGTAAC che-2 daf-19 osm-1 osm-6 F02D8.3 -150 -1

13. Phylogenetic Footprinting:Regulatory Element of Growth Hormone Gene AGGGGATA AGGGTATA AGGGTATA AGGGTATA AGGGTATA Chicken Rat Human Dog Sheep -200 -1

14. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

15. MEME • (Multiple EM for Motif Elicitation) Bailey & Elkan, 1995 • Very general iterative method based on Expectation Maximization • Available at meme.sdsc.edu/meme/website/intro.html

16. Overrepresented Motifs • Given sequences X = {X1, X2, …, Xn}, find statistically overrepresented motifs of length k • For simplicity, assume • Exactly one motif instance per sequence • Sequences over DNA alphabet

17. Hidden Information • Z = {Zij}, where 1, if motif instance starts at Zij = position j of Xi 0, otherwise • Iterate over probabilistic models that could generate X and Z, trying to converge on this solution {

18. Model Parameters • Motif profile: 4×k matrix θ = (θrp), • r {A,C,G,T} • 1  p  k • θrp = Pr(residue r in position p of motif) • Background distribution: • θr0 = Pr(residue r in random nonmotif position)

19. Profile Example GTTGTC 0 0 0 .4 0 0 GTTTCC 0 .2 0 0 .8 1 GCTACC 1 0 0 .2 0 0 GTTACC 0 .8 1 .4 .2 0 GTTTCC profile θ

20. Overview: Expectation Maximization • Goal: Find profile θ and motif positionsZ that have maximum likelihood • At each iteration: • E-step: From θ predict likely motif positions Z • M-step: From sequences at positions Zcompute new profile θ

21. Expectation Maximization • Goal: Find θ,Z that maximize Pr (X, Z | θ) • At iteration t: • E-step: Z(t) = E (Z | X, θ(t)) • M-step: Find θ(t+1) that maximizes Pr (X, Z(t)| θ(t+1))

22. j Xi Use θ0(t) Use θ1(t), θ2(t), …, θk(t) E-step Details Zij(t) = Pr(Xi| Zij=1, θ(t)) Σj Pr(Xi| Zij=1, θ(t))

23. M-step Details • If Zij(t) {0,1} it would be straightforward: Calculate profile θ1, θ2, …, θk from motif instances and θr0 from frequency of r outside of motif instances. • But Zij(t) [0,1], so weight these frequencies by the appropriate values of Zij(t) .

24. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

25. Gibbs Sampler • Lawrence et al., 1993 • Very general iterative method, related to Markov Chain Monte Carlo (MCMC) • Available at bayesweb.wadsworth.org/gibbs/gibbs.html

26. One Iteration of Gibbs Sampler GGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAGCACGGGGGAGCCTGGAGGGGATCCGGAGGGGTGGGCCGTGGGGAACCTGGGGGGAGCTGGGCTCAGGGAGCGTGGAGGTGGGGTGGGAGCTGAGGGTGGGGCTGGGGTGGCGGTGGGAGCCCAGGACGTTG • n motif instances each of length k

27. One Iteration of Gibbs Sampler GGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAGCACGGGGGAGCCTGGAGGGGATCCGGAGGGGTGGGCCGTGGGGAACCTGGGGGGAGCTGGGCTCAGGGAGCGTGGAGGTGGGGTGGGAGCTGAGGGTGGGGCTGGGGTGGCGGTGGGAGCCCAGGACGTTG • n motif instances each of length k • Remove one at random • Form profile of remaining n-1 • Let pi be the probability with which g[i .. i+k-1] fits profile i

28. GGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAGCACGGGGGAGCCTGGAGGGGATCCGGAGGGGTGGGCCGTGGGGAACCTGGGGGGAGCTGGGCTCAGGGAGCGTGGAGGTGGGGTGGGAGCTGAGGGTGGGGCTGGGGTGGCGGTGGGAGCCCAGGACGTTGGGGTCACGGGGTGGGAGCTGAGAAGGGGTGGAGCACGGGGGAGCCTGGAGGGGATCCGGAGGGGTGGGCCGTGGGGAACCTGGGGGGAGCTGGGCTCAGGGAGCGTGGAGGTGGGGTGGGAGCTGAGGGTGGGGCTGGGGTGGCGGTGGGAGCCCAGGACGTTG i One Iteration of Gibbs Sampler • n motif instances each of length k • Remove one at random • Form profile of remaining n-1 • Let pi be the probability with which g[i .. i+k-1] fits profile • Choose to start replacement at i with probability proportional to pi

29. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

30. FootPrinter • Blanchette & Tompa, 2002 • First algorithm explicitly designed for phylogenetic footprinting • Available at bio.cs.washington.edu/software.html

31. Phylogenetic Footprinting(Tagle et al. 1988) Functional regions of DNA evolve slower than nonfunctional ones.

32. Phylogenetic Footprinting(Tagle et al. 1988) • Functional regions of DNA evolve slower than nonfunctional ones. • Consider a set of orthologous (i.e., corresponding) sequences from different species • Identify unusually well conserved substrings (i.e., ones that have not changed much over the course of evolution)

33. CLUSTALW multiple sequence alignment (rbcS gene) Cotton ACGGTT-TCCATTGGATGA---AATGAGATAAGAT---CACTGTGC---TTCTTCCACGTG--GCAGGTTGCCAAAGATA-------AGGCTTTACCATT Pea GTTTTT-TCAGTTAGCTTA---GTGGGCATCTTA----CACGTGGC---ATTATTATCCTA--TT-GGTGGCTAATGATA-------AGG--TTAGCACA Tobacco TAGGAT-GAGATAAGATTA---CTGAGGTGCTTTA---CACGTGGC---ACCTCCATTGTG--GT-GACTTAAATGAAGA-------ATGGCTTAGCACC Ice-plant TCCCAT-ACATTGACATAT---ATGGCCCGCCTGCGGCAACAAAAA---AACTAAAGGATA--GCTAGTTGCTACTACAATTC--CCATAACTCACCACC Turnip ATTCAT-ATAAATAGAAGG---TCCGCGAACATTG--AAATGTAGATCATGCGTCAGAATT--GTCCTCTCTTAATAGGA-------A-------GGAGC Wheat TATGAT-AAAATGAAATAT---TTTGCCCAGCCA-----ACTCAGTCGCATCCTCGGACAA--TTTGTTATCAAGGAACTCAC--CCAAAAACAAGCAAA Duckweed TCGGAT-GGGGGGGCATGAACACTTGCAATCATT-----TCATGACTCATTTCTGAACATGT-GCCCTTGGCAACGTGTAGACTGCCAACATTAATTAAA Larch TAACAT-ATGATATAACAC---CGGGCACACATTCCTAAACAAAGAGTGATTTCAAATATATCGTTAATTACGACTAACAAAA--TGAAAGTACAAGACC Cotton CAAGAAAAGTTTCCACCCTC------TTTGTGGTCATAATG-GTT-GTAATGTC-ATCTGATTT----AGGATCCAACGTCACCCTTTCTCCCA-----A Pea C---AAAACTTTTCAATCT-------TGTGTGGTTAATATG-ACT-GCAAAGTTTATCATTTTC----ACAATCCAACAA-ACTGGTTCT---------A Tobacco AAAAATAATTTTCCAACCTTT---CATGTGTGGATATTAAG-ATTTGTATAATGTATCAAGAACC-ACATAATCCAATGGTTAGCTTTATTCCAAGATGA Ice-plant ATCACACATTCTTCCATTTCATCCCCTTTTTCTTGGATGAG-ATAAGATATGGGTTCCTGCCAC----GTGGCACCATACCATGGTTTGTTA-ACGATAA Turnip CAAAAGCATTGGCTCAAGTTG-----AGACGAGTAACCATACACATTCATACGTTTTCTTACAAG-ATAAGATAAGATAATGTTATTTCT---------A Wheat GCTAGAAAAAGGTTGTGTGGCAGCCACCTAATGACATGAAGGACT-GAAATTTCCAGCACACACA-A-TGTATCCGACGGCAATGCTTCTTC-------- Duckweed ATATAATATTAGAAAAAAATC-----TCCCATAGTATTTAGTATTTACCAAAAGTCACACGACCA-CTAGACTCCAATTTACCCAAATCACTAACCAATT Larch TTCTCGTATAAGGCCACCA-------TTGGTAGACACGTAGTATGCTAAATATGCACCACACACA-CTATCAGATATGGTAGTGGGATCTG--ACGGTCA Cotton ACCAATCTCT---AAATGTT----GTGAGCT---TAG-GCCAAATTT-TATGACTATA--TAT----AGGGGATTGCACC----AAGGCAGTG-ACACTA Pea GGCAGTGGCC---AACTAC--------------------CACAATTT-TAAGACCATAA-TAT----TGGAAATAGAA------AAATCAAT--ACATTA Tobacco GGGGGTTGTT---GATTTTT----GTCCGTTAGATAT-GCGAAATATGTAAAACCTTAT-CAT----TATATATAGAG------TGGTGGGCA-ACGATG Ice-plant GGCTCTTAATCAAAAGTTTTAGGTGTGAATTTAGTTT-GATGAGTTTTAAGGTCCTTAT-TATA---TATAGGAAGGGGG----TGCTATGGA-GCAAGG Turnip CACCTTTCTTTAATCCTGTGGCAGTTAACGACGATATCATGAAATCTTGATCCTTCGAT-CATTAGGGCTTCATACCTCT----TGCGCTTCTCACTATA Wheat CACTGATCCGGAGAAGATAAGGAAACGAGGCAACCAGCGAACGTGAGCCATCCCAACCA-CATCTGTACCAAAGAAACGG----GGCTATATATACCGTG Duckweed TTAGGTTGAATGGAAAATAG---AACGCAATAATGTCCGACATATTTCCTATATTTCCG-TTTTTCGAGAGAAGGCCTGTGTACCGATAAGGATGTAATC Larch CGCTTCTCCTCTGGAGTTATCCGATTGTAATCCTTGCAGTCCAATTTCTCTGGTCTGGC-CCA----ACCTTAGAGATTG----GGGCTTATA-TCTATA Cotton T-TAAGGGATCAGTGAGAC-TCTTTTGTATAACTGTAGCAT--ATAGTAC Pea TATAAAGCAAGTTTTAGTA-CAAGCTTTGCAATTCAACCAC--A-AGAAC Tobacco CATAGACCATCTTGGAAGT-TTAAAGGGAAAAAAGGAAAAG--GGAGAAA Ice-plant TCCTCATCAAAAGGGAAGTGTTTTTTCTCTAACTATATTACTAAGAGTAC Larch TCTTCTTCACAC---AATCCATTTGTGTAGAGCCGCTGGAAGGTAAATCA Turnip TATAGATAACCA---AAGCAATAGACAGACAAGTAAGTTAAG-AGAAAAG Wheat GTGACCCGGCAATGGGGTCCTCAACTGTAGCCGGCATCCTCCTCTCCTCC Duckweed CATGGGGCGACG---CAGTGTGTGGAGGAGCAGGCTCAGTCTCCTTCTCG

34. FootPrinter • Inputs: • evolutionary tree T • corresponding regulatory regions at leaves • Output: motifs well conserved w.r.t. T.

35. AGTCGTACGTGAC...(Human) AGTAGACGTGCCG...(Chimp) ACGTGAGATACGT...(Rabbit) GAACGGAGTACGT...(Mouse) TCGTGACGGTGAT... (Rat) Finding Short Motifs Size of motif sought: k = 4

36. Most Parsimonious Solution AGTCGTACGTGAC... AGTAGACGTGCCG... ACGTGAGATACGT... GAACGGAGTACGT... TCGTGACGGTGAT... ACGT ACGT ACGT ACGG “Parsimony score”: 1 mutation

37. Substring Parsimony Problem • Given: • phylogenetic tree T, • set of orthologous sequences at leaves of T, • length k of motif • threshold d • Problem: • Find each set S of k-mers, one k-mer from each leaf, such that the parsimony score of S in Tis at most d. • This problem is NP-hard.

38. … ACGG: +ACGT: 0 ... … ACGG:ACGT :0 ... … ACGG:ACGT :0 ... … ACGG:ACGT :0 ... … ACGG: 1 ACGT: 0 ... 4k entries AGTCGTACGTG ACGGGACGTGC ACGTGAGATAC GAACGGAGTAC TCGTGACGGTG … ACGG: 2ACGT: 1... … ACGG: 1ACGT: 1... … ACGG: 0ACGT: 2 ... … ACGG: 0 ACGT: +... FootPrinter’s Exact Algorithm(with Mathieu Blanchette, generalizing Sankoff and Rousseau 1975) Wu [s] = best parsimony score for subtree rooted at node u, if u is labeled with string s.

39. Wu [s] =  min ( Wv [t] + d(s, t) ) v:child t ofu Average sequence length Number of species Total time O(n k (4k + l )) Motif length Running Time

40. Improvements • Better algorithm reduces time from O(n k (42k + l ))toO(n k (4k + l )) • By restricting to motifs with parsimony score at most d, greatly reduce the number of table entries computed (exponential in d, polynomial in k) • Amenable to many useful extensions (e.g., allow insertions and deletions)

41. Gilthead sea bream (678 bp) Medaka fish (1016 bp) Common carp (696 bp) Grass carp (917 bp) Chicken (871 bp) Human (646 bp) Rabbit (636 bp) Rat (966 bp) Mouse (684 bp) Hamster (1107 bp) Application to -actin Gene

42. Common carp ACGGACTGTTACCACTTCACGCCGACTCAACTGCGCAGAGAAAAACTTCAAACGACAACATTGGCATGGCTTTTGTTATTTTTGGCGCTTGACTCAGGATCTAAAAACTGGAACGGCGAAGGTGACGGCAATGTTTTGGCAAATAAGCATCCCCGAAGTTCTACAATGCATCTGAGGACTCAATGTTTTTTTTTTTTTTTTTTCTTTAGTCATTCCAAATGTTTGTTAAATGCATTGTTCCGAAACTTATTTGCCTCTATGAAGGCTGCCCAGTAATTGGGAGCATACTTAACATTGTAGTATTGTATGTAAATTATGTAACAAAACAATGACTGGGTTTTTGTACTTTCAGCCTTAATCTTGGGTTTTTTTTTTTTTTTGGTTCCAAAAAACTAAGCTTTACCATTCAAGATGTAAAGGTTTCATTCCCCCTGGCATATTGAAAAAGCTGTGTGGAACGTGGCGGTGCAGACATTTGGTGGGGCCAACCTGTACACTGACTAATTCAAATAAAAGTGCACATGTAAGACATCCTACTCTGTGTGATTTTTCTGTTTGTGCTGAGTGAACTTGCTATGAAGTCTTTTAGTGCACTCTTTAATAAAAGTAGTCTTCCCTTAAAGTGTCCCTTCCCTTATGGCCTTCACATTTCTCAACTAGCGCTTCAACTAGAAAGCACTTTAGGGACTGGGATGC Chicken ACCGGACTGTTACCAACACCCACACCCCTGTGATGAAACAAAACCCATAAATGCGCATAAAACAAGACGAGATTGGCATGGCTTTATTTGTTTTTTCTTTTGGCGCTTGACTCAGGATTAAAAAACTGGAATGGTGAAGGTGTCAGCAGCAGTCTTAAAATGAAACATGTTGGAGCGAACGCCCCCAAAGTTCTACAATGCATCTGAGGACTTTGATTGTACATTTGTTTCTTTTTTAATAGTCATTCCAAATATTGTTATAATGCATTGTTACAGGAAGTTACTCGCCTCTGTGAAGGCAACAGCCCAGCTGGGAGGAGCCGGTACCAATTACTGGTGTTAGATGATAATTGCTTGTCTGTAAATTATGTAACCCAACAAGTGTCTTTTTGTATCTTCCGCCTTAAAAACAAAACACACTTGATCCTTTTTGGTTTGTCAAGCAAGCGGGCTGTGTTCCCCAGTGATAGATGTGAATGAAGGCTTTACAGTCCCCCACAGTCTAGGAGTAAAGTGCCAGTATGTGGGGGAGGGAGGGGCTACCTGTACACTGACTTAAGACCAGTTCAAATAAAAGTGCACACAATAGAGGCTTGACTGGTGTTGGTTTTTATTTCTGTGCTGCGCTGCTTGGCCGTTGGTAGCTGTTCTCATCTAGCCTTGCCAGCCTGTGTGGGTCAGCTATCTGCATGGGCTGCGTGCTGGTGCTGTCTGGTGCAGAGGTTGGATAAACCGTGATGATATTTCAGCAAGTGGGAGTTGGCTCTGATTCCATCCTGAGCTGCCATCAGTGTGTTCTGAAGGAAGCTGTTGGATGAGGGTGGGCTGAGTGCTGGGGGACAGCTGGGCTCAGTGGGACTGCAGCTGTGCT Human GCGGACTATGACTTAGTTGCGTTACACCCTTTCTTGACAAAACCTAACTTGCGCAGAAAACAAGATGAGATTGGCATGGCTTTATTTGTTTTTTTTGTTTTGTTTTGGTTTTTTTTTTTTTTTTGGCTTGACTCAGGATTTAAAAACTGGAACGGTGAAGGTGACAGCAGTCGGTTGGAGCGAGCATCCCCCAAAGTTCACAATGTGGCCGAGGACTTTGATTGCATTGTTGTTTTTTTAATAGTCATTCCAAATATGAGATGCATTGTTACAGGAAGTCCCTTGCCATCCTAAAAGCCACCCCACTTCTCTCTAAGGAGAATGGCCCAGTCCTCTCCCAAGTCCACACAGGGGAGGTGATAGCATTGCTTTCGTGTAAATTATGTAATGCAAAATTTTTTTAATCTTCGCCTTAATACTTTTTTATTTTGTTTTATTTTGAATGATGAGCCTTCGTGCCCCCCCTTCCCCCTTTTTGTCCCCCAACTTGAGATGTATGAAGGCTTTTGGTCTCCCTGGGAGTGGGTGGAGGCAGCCAGGGCTTACCTGTACACTGACTTGAGACCAGTTGAATAAAAGTGCACACCTTAAAAATGAGGCCAAGTGTGACTTTGTGGTGTGGCTGGGTTGGGGGCAGCAGAGGGTG Parsimony score over 10 vertebrates: 0 1 2

43. Motifs Absent from Some Species • Find motifs • with small parsimony score • that span a large part of the tree • Example: in tree of 10 species spanning 760 Myrs, find all motifs with • score 0 spanning at least 250 Myrs • score 1 spanning at least 350 Myrs • score 2 spanning at least 450 Myrs • score 3 spanning at least 550 Myrs

44. Application to c-fos Gene 10 Puffer fish Chicken Pig Mouse Hamster Human 7 2 2 1 2 2 1 0 1 Asked for motifs of length 10, with 0 mutations over tree of size 6 1 mutation over tree of size 11 2 mutations over tree of size 16 3 mutations over tree of size 21 4 mutations over tree of size 26 Found: 0 mutations over tree of size 8 1 mutation over tree of size 16 3 mutations over tree of size 21 4 mutations over tree of size 28

45. Application to c-fos Gene Motif Score Conserved in Known? CAGGTGCGAATGTTC 0 4 mammals TTCCCGCCTCCCCTCCCC 0 4 mammals yes GAGTTGGCTGcagcc 3 puffer + 4 mammals GTTCCCGTCAATCcct 1 chicken + 4 mammals yes CACAGGATGTcc 4 all 6 yes AGGACATCTG 1 chicken + 4 mammals yes GTCAGCAGGTTTCCACG 0 4 mammals yes TACTCCAACCGC 0 4 mammals metK in B. subtilis

46. Outline • Regulation of genes • Motif discovery by overrepresentation • MEME • Gibbs sampling • Motif discovery by phylogenetic footprinting • FootPrinter • MicroFootPrinter

47. MicroFootPrinter • Neph & Tompa, 2006 • Designed specifically for phylogenetic footprinting in prokaryotic genomes • Front end to FootPrinter • Available at bio.cs.washington.edu/software.html

48. Microbial Footprinting • 1454 prokaryotes with genomes completely sequenced (as of 2/17/2011) • For any prokaryotic gene of interest, plenty of close genes in other species available • Relatively simple genomes • MicroFootPrinter • undergraduate Computational Biology Capstone project • Goal: simple interface for microbiologists • User specifies species and gene of interest • Automates collection of orthologous genes, cis-regulatory sequences, gene tree, parameters

49. Demo • MicroFootPrinter home • Examples: Agrobacterium tumefaciens genes regulated by ChvI (with Eugene Nester) • chvI (two component response regulator) • ropB (outer membrane protein )

50. Sample chvI motif Parsimony score: 2Span: 41.10Significance score: 4.22 B. henselae-151 GCTACAATTTR. etli -90 GCCACAATTTR. leguminosarum -106 GCCACAATTTS. meliloti -119 GCCACAATTTS. medicae -118 GCCACAATTTA. tumefaciens -105 GCCACAATTTM. loti -80 GCCACATTTTM. sp. -87 GCCACATTTTO. anthropi -158 GCCACATTTTB. suis -38 GCCACATTTTB. melitensis -156 GCCACATTTTB. abortus -156 GCCACATTTTB. ovis -156 GCCACATTTTB. canis -38 GCCACATTTT