1. Biological Motif Discovery Concepts Motif Modeling and Motif Information EM and Gibbs Sampling Comparative Motif Prediction Applications Transcription Factor Binding Site Prediction Epitope Prediction Lab Practical DNA Motif Discovery with MEME and AlignAce Co-regulated genes from TB Boshoff data set

2. Regulatory Motifs Find promoter motifs associated with co-regulated or functionally related genes

3. Transcription Factor Binding Sites Very Small Highly Variable ~Constant Size Often repeated Low-complexity-ish Slide Credit: S. Batzoglou

4. Other “Motifs” Splicing Signals –Splice junctions –Exonic Splicing Enhancers (ESE) –Exonic Splicing Surpressors (ESS) Protein Domains –Glycosylation sites –Kinase targets –Targetting signals Protein Epitopes –MHC binding specificities

5. Modeling Motifs –How to computationally represent motifs Visualizing Motifs –Motif “Information” Predicting Motif Instances –Using the model to classify new sequences Learning Motif Structure –Finding new motifs, assessing their quality Essential Tasks

6. Modeling Motifs

7. Consensus Sequences Useful for publication IUPAC symbols for degenerate sites Not very amenable to computation Nature Biotechnology 24, 423 - 425 (2006)

8. Probabilistic Model.2.5.1.7.2.1.3.1.2.4.5.4.1.2.1.4.1.2.1 ACGTACGT M1M1 MKMK M1M1 P k (S|M) Position Frequency Matrix (PFM) 1 K Count frequencies Add pseudocounts

9. Scoring A Sequence To score a sequence, we compare to a null model Background DNA (B).2.5.1.7.2.1.3.1.2.4.5.4.1.2.1.4.1.2.1 ACGTACGT Log likelihood ratio -0.3 1 -1.3 1.4-0.3 -1.30.3 -1.3-0.30.61 -1.3-0.30.3-0.3 -1.30.6-1.3-0.3-1.3 ACGTACGT Position Weight Matrix (PWM) PFM

10. Scoring a Sequence MacIsaac & Fraenkel (2006) PLoS Comp Bio Common threshold = 60% of maximum score

11. Visualizing Motifs – Motif Logos Represent both base frequency and conservation at each position Height of letter proportional to frequency of base at that position Height of stack proportional to conservation at that position

12. Motif Information The height of a stack is often called the motif information at that position measured in bits Information Why is this a measure of information?

13. Uncertainty and probability “The sun will rise tomorrow” “The sun will not rise tomorrow” Uncertainty is inversely related to probability of event Not surprising (p~1) Very surprising (p<<1) Uncertainty is related to our surprise at an event

14. Average Uncertainty A “The sun will rise tomorrow” B “The sun will not rise tomorrow” P(A)=p 1 P(B)=p 2 Two possible outcomes for sun rising = Entropy What is our average uncertainty about the sun rising

15. Entropy Entropy measures average uncertainty Entropy measures randomness If log is base 2, then the units are called bits

16. Entropy versus randomness 00.10.20.30.40.50.60.70.80.91 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Entropy is maximum at maximum randomness P(heads) Example: Coin Toss Entropy P(heads)=0.1 Not very random H(X)=0.47 bits P(heads)=0.5 Completely random H(X)=1 bits

17. Entropy Examples 1234 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 P(x) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1234 P(x) ATGC ATGC

18. Information Content Information is a decrease in uncertainty Once I tell you the sun will rise, your uncertainty about the event decreases H before (X)H after (X)-Information = Information is difference in entropy after receiving information

19. Motif Information 2- Motif Position Information = H background (X)H motif_i (X) Prior uncertainty about nucleotide Uncertainty after learning it is position i in a motif H(X)=2 bits ATGC 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 P(x) H(X)=0.63 bits ATGC 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 P(x) Uncertainty at this position has been reduced by 0.37 bits

20. Motif Logo Conserved Residue Reduction of uncertainty of 2 bits Little Conservation Minimal reduction of uncertainty

21. Background DNA Frequency 2- Motif Position Information = H motif_i (X) H(X)=1.9 bits ATGC 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 P(x) Some motifs could have negative information! = -0.2 bits The definition of information assumes a uniform background DNA nucleotide frequency What if the background frequency is not uniform? H background (X) H(X)=1.7 bits ATGC 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 P(x) (e.g. Plasmodium) 1.7

22. A Different Measure Relative entropy or Kullback-Leibler (KL) divergence Divergence between a “true” distribution and another “True” DistributionOther Distribution D KL is larger the more different P motif is from P background Same as Information if P background is uniform Properties

23. Comparing Both Methods Information assuming uniform background DNA KL Distance assuming 20% GC content (e.g. Plasmodium)

24. Online Logo Generation http://weblogo.berkeley.edu/http://biodev.hgen.pitt.edu/cgi-bin/enologos/enologos.cgi

25. Finding New Motifs Learning Motif Models

26. A Promoter Model Length K Motif The same motif model in all promoters.2.5.1.2.1.3.2.4.5.4.2.3.4.1.2.1 ACGTACGT M1M1 MKMK M1M1 Background DNA P(S|B) P k (S|M)

27. Probability of a Sequence 16065100 Given a sequence(s), motif model and motif location S i = nucleotide at position i in the sequence A T A T G C.2.5.1.2.1.3.2.4.5.4.2.3.4.1.2.1 ACGTACGT M1M1 MKMK M1M1

28. Parameterizing the Motif Model Given multiple sequences and motif locations but no motif model ACGTACGT M1M1 M6M6 M1M1 3/4 ETC… AATGCG ATATGG ATATCG GATGCA Count Frequencies Add pseudocounts

29. Finding Known Motifs Given multiple sequences and motif model but no motif locations x x x x P(Seq window |Motif) window Calculate P(Seq window |Motif) for every starting location Choose best starting location in each sequence

30. Discovering Motifs Given a set of co-regulated genes, we need to discover with only sequences We have neither a motif model nor motif locations Need to discover both How can we approach this problem? (Hint: start with a random motif model)

31. Expectation Maximization (EM) Remember the basic idea! 1.Use model to estimate (distribution of) missing data 2.Use estimate to update model 3.Repeat until convergence Model is the motif model Missing data are the motif locations

32. EM for Motif Discovery 1. Start with random motif model 2. E Step: estimate probability of motif positions for each sequence 3. M Step: use estimate to update motif model 4. Iterate (to convergence).2.5.1.2.1.3.2.4.5.4.2.3.4.1.2.1 ACGTACGT.2.5.1.2.1.3.1.5.4.5.4.1.2.3.2.3.1 ACGTACGT ETC… At each iteration, P(Sequences|Model) guaranteed to increase

33. MEME http://meme.sdsc.edu/meme/ MEME - implements EM for motif discovery in DNA and proteins MAST – search sequences for motifs given a model

34. P(Seq|Model) Landscape P(Sequences|params1,params2) EM searches for parameters to increase P(seqs|parameters) Useful to think of P(seqs|parameters) as a function of parameters Parameter1 Parameter2 EM starts at an initial set of parameters And then “climbs uphill” until it reaches a local maximum Where EM starts can make a big difference

35. Search from Many Different Starts P(Sequences|params1,params2) To minimize the effects of local maxima, you should search multiple times from different starting points Parameter1 Parameter2 MEME uses this idea Start at many points Run for one iteration Choose starting point that got the “highest” and continue

36. Gibbs Sampling A stochastic version of EM that differs from deterministic EM in two key ways 1. At each iteration, we only update the motif position of a single sequence 2. We may update a motif position to a “suboptimal” new position

37. 1. Start with random motif locations and calculate a motif model 2. Randomly select a sequence, remove its motif and recalculate tempory model 3. With temporary model, calculate probability of motif at each position on sequence 4. Select new position based on this distribution 5. Update model and Iterate Gibbs Sampling.2.5.1.2.1.3.2.4.5.4.2.3.4.1.2.1 ACGTACGT.2.5.1.2.1.3.1.5.4.5.4.1.2.3.2.3.1 ACGTACGT ETC… “Best” Location New Location

38. Gibbs Sampling and Climbing P(Sequences|params1,params2) Because gibbs sampling does always choose the best new location it can move to another place not directly uphill Parameter1 Parameter2 In theory, Gibbs Sampling less likely to get stuck a local maxima

39. AlignACE http://atlas.med.harvard.edu/cgi-bin/alignace.pl Implements Gibbs sampling for motif discovery –Several enhancements ScanAce – look for motifs in a sequence given a model CompareAce – calculate “similarity” between two motifs (i.e. for clustering motifs)

40. Assessing Motif Quality Scan the genome for all instances and associate with nearest genes Category Enrichment – look for association between motif and sets of genes. Score using Hypergeometric distribution –Functional Category –Gene Families –Protein Complexes Group Specificity – how restricted are motif instances to the promoter sequences used to find the motif? Positional Bias – do motif instances cluster at a certain distance from ATG? Orientation Bias – do motifs appear preferentially upstream or downstream of genes?

41. Comparative Motif Prediction

42. Kellis et al. (2003) Nature

43. Scer TTATATTGAATTTTCAAAAATTCTTACTTTTTTTTTGGATGGACGCAAAGAAGTTTAATAATCATATTACATGGCATTACCACCATATACA Spar CTATGTTGATCTTTTCAGAATTTTT-CACTATATTAAGATGGGTGCAAAGAAGTGTGATTATTATATTACATCGCTTTCCTATCATACACA Smik GTATATTGAATTTTTCAGTTTTTTTTCACTATCTTCAAGGTTATGTAAAAAA-TGTCAAGATAATATTACATTTCGTTACTATCATACACA Sbay TTTTTTTGATTTCTTTAGTTTTCTTTCTTTAACTTCAAAATTATAAAAGAAAGTGTAGTCACATCATGCTATCT-GTCACTATCACATATA * * **** * * * ** ** * * ** ** ** * * * ** ** * * * ** * * * Scer TATCCATATCTAATCTTACTTATATGTTGT-GGAAAT-GTAAAGAGCCCCATTATCTTAGCCTAAAAAAACC--TTCTCTTTGGAACTTTCAGTAATACG Spar TATCCATATCTAGTCTTACTTATATGTTGT-GAGAGT-GTTGATAACCCCAGTATCTTAACCCAAGAAAGCC--TT-TCTATGAAACTTGAACTG-TACG Smik TACCGATGTCTAGTCTTACTTATATGTTAC-GGGAATTGTTGGTAATCCCAGTCTCCCAGATCAAAAAAGGT--CTTTCTATGGAGCTTTG-CTA-TATG Sbay TAGATATTTCTGATCTTTCTTATATATTATAGAGAGATGCCAATAAACGTGCTACCTCGAACAAAAGAAGGGGATTTTCTGTAGGGCTTTCCCTATTTTG ** ** *** **** ******* ** * * * * * * * ** ** * *** * *** * * * Scer CTTAACTGCTCATTGC-----TATATTGAAGTACGGATTAGAAGCCGCCGAGCGGGCGACAGCCCTCCGACGGAAGACTCTCCTCCGTGCGTCCTCGTCT Spar CTAAACTGCTCATTGC-----AATATTGAAGTACGGATCAGAAGCCGCCGAGCGGACGACAGCCCTCCGACGGAATATTCCCCTCCGTGCGTCGCCGTCT Smik TTTAGCTGTTCAAG--------ATATTGAAATACGGATGAGAAGCCGCCGAACGGACGACAATTCCCCGACGGAACATTCTCCTCCGCGCGGCGTCCTCT Sbay TCTTATTGTCCATTACTTCGCAATGTTGAAATACGGATCAGAAGCTGCCGACCGGATGACAGTACTCCGGCGGAAAACTGTCCTCCGTGCGAAGTCGTCT ** ** ** ***** ******* ****** ***** *** **** * *** ***** * * ****** *** * *** Scer TCACCGG-TCGCGTTCCTGAAACGCAGATGTGCCTCGCGCCGCACTGCTCCGAACAATAAAGATTCTACAA-----TACTAGCTTTT--ATGGTTATGAA Spar TCGTCGGGTTGTGTCCCTTAA-CATCGATGTACCTCGCGCCGCCCTGCTCCGAACAATAAGGATTCTACAAGAAA-TACTTGTTTTTTTATGGTTATGAC Smik ACGTTGG-TCGCGTCCCTGAA-CATAGGTACGGCTCGCACCACCGTGGTCCGAACTATAATACTGGCATAAAGAGGTACTAATTTCT--ACGGTGATGCC Sbay GTG-CGGATCACGTCCCTGAT-TACTGAAGCGTCTCGCCCCGCCATACCCCGAACAATGCAAATGCAAGAACAAA-TGCCTGTAGTG--GCAGTTATGGT ** * ** *** * * ***** ** * * ****** ** * * ** * * ** *** Scer GAGGA-AAAATTGGCAGTAA----CCTGGCCCCACAAACCTT-CAAATTAACGAATCAAATTAACAACCATA-GGATGATAATGCGA------TTAG--T Spar AGGAACAAAATAAGCAGCCC----ACTGACCCCATATACCTTTCAAACTATTGAATCAAATTGGCCAGCATA-TGGTAATAGTACAG------TTAG--G Smik CAACGCAAAATAAACAGTCC----CCCGGCCCCACATACCTT-CAAATCGATGCGTAAAACTGGCTAGCATA-GAATTTTGGTAGCAA-AATATTAG--G Sbay GAACGTGAAATGACAATTCCTTGCCCCT-CCCCAATATACTTTGTTCCGTGTACAGCACACTGGATAGAACAATGATGGGGTTGCGGTCAAGCCTACTCG **** * * ***** *** * * * * * * * * ** Scer TTTTTAGCCTTATTTCTGGGGTAATTAATCAGCGAAGCG--ATGATTTTT-GATCTATTAACAGATATATAAATGGAAAAGCTGCATAACCAC-----TT Spar GTTTT--TCTTATTCCTGAGACAATTCATCCGCAAAAAATAATGGTTTTT-GGTCTATTAGCAAACATATAAATGCAAAAGTTGCATAGCCAC-----TT Smik TTCTCA--CCTTTCTCTGTGATAATTCATCACCGAAATG--ATGGTTTA--GGACTATTAGCAAACATATAAATGCAAAAGTCGCAGAGATCA-----AT Sbay TTTTCCGTTTTACTTCTGTAGTGGCTCAT--GCAGAAAGTAATGGTTTTCTGTTCCTTTTGCAAACATATAAATATGAAAGTAAGATCGCCTCAATTGTA * * * *** * ** * * *** *** * * ** ** * ******** **** * Scer TAACTAATACTTTCAACATTTTCAGT--TTGTATTACTT-CTTATTCAAAT----GTCATAAAAGTATCAACA-AAAAATTGTTAATATACCTCTATACT Spar TAAATAC-ATTTGCTCCTCCAAGATT--TTTAATTTCGT-TTTGTTTTATT----GTCATGGAAATATTAACA-ACAAGTAGTTAATATACATCTATACT Smik TCATTCC-ATTCGAACCTTTGAGACTAATTATATTTAGTACTAGTTTTCTTTGGAGTTATAGAAATACCAAAA-AAAAATAGTCAGTATCTATACATACA Sbay TAGTTTTTCTTTATTCCGTTTGTACTTCTTAGATTTGTTATTTCCGGTTTTACTTTGTCTCCAATTATCAAAACATCAATAACAAGTATTCAACATTTGT * * * * * * ** *** * * * * ** ** ** * * * * * *** * Scer TTAA-CGTCAAGGA---GAAAAAACTATA Spar TTAT-CGTCAAGGAAA-GAACAAACTATA Smik TCGTTCATCAAGAA----AAAAAACTA.. Sbay TTATCCCAAAAAAACAACAACAACATATA * * ** * ** ** ** GAL10 GAL1 TBP GAL4 MIG1 TBP MIG1 Factor footprint Conservation island slide credits: M. Kellis Conservation of Motifs

44. Spar Smik Sbay Scer Evaluate conservation within: Gal4Controls 4:11:3(2) Intergenic : coding 12:01:1(3) Upstream : downstream A signature for regulatory motifs 22%5%(1) All intergenic regions Genome-wide conservation

45. 1.Enumerate all “mini-motifs” 2.Apply three tests 1.Look for motifs conserved in intergenic regions 2.Look for motifs more conserved intergenically than in genes 3.Look for motifs preferentially conserved upstream or downstream of genes CTACGA N slide credits: M. Kellis Finding Motifs in Yeast Genomes M. Kellis PhD Thesis

46. Test 1: Intergenic conservation Total count Conserved count CGG-11-CCG slide credits: M. Kellis

47. Test 2: Intergenic vs. Coding Coding Conservation Intergenic Conservation CGG-11-CCG Higher Conservation in Genes slide credits: M. Kellis

48. Test 3: Upstream vs. Downstream CGG-11-CCG Most Patterns Downstream Conservation Upstream Conservation slide credits: M. Kellis

49. Extend Collapse Full Motifs Constructing full motifs 2,000 Mini-motifs 72 Full motifs 6 CTA CGA R R CTGRC CGAA ACCTGCGAACTGRCCGAACTRAY CGAA Y 5 Extend Collapse Merge Test 1Test 2Test 3 slide credits: M. Kellis

50. RankDiscovered Motif Known TF motif Tissue Enrichment Distance bias 1RCGCAnGCGYNRF-1Yes 2CACGTGMYCYes 3SCGGAAGYELK-1Yes 4ACTAYRnnnCCCRYes 5GATTGGYNF-YYes 6GGGCGGRSP1Yes 7TGAnTCAAP-1Yes 8TMTCGCGAnRYes 9TGAYRTCAATF3Yes 10GCCATnTTGYY1Yes 11MGGAAGTGGABPYes 12CAGGTGE12Yes 13CTTTGTLEF1Yes 14TGACGTCAATF3Yes 15CAGCTGAP-4Yes 16RYTTCCTGC-ETS-2Yes 17AACTTTIRF1(*)Yes 18TCAnnTGAYSREBP- 1 Yes 19GKCGCn(7)TGAY G Yes 174 promoter motifs 70 match known TF motifs 60 show positional bias  75% have evidence Control sequences < 2% match known TF motifs < 3% show positional bias  < 7% false positives slide credits: M. Kellis Results

51. Antigen Epitope Prediction

52. Genome to “Immunome” Looking for a needle… –Only a small number of epitopes are typically antigenic …in a very big haystack –Vaccinia virus (258 ORFs): 175,716 potential epitopes (8-, 9-, and 10-mers) –M. tuberculosis (~4K genes): 433,206 potential epitopes –A. nidulans (~9K genes): 1,579,000 potential epitopes Can computational approaches predict all antigenic epitopes from a genome? Pathogen genome sequences provide define all proteins that could illicit an immune response

53. Antigen Processing & Presentation

54. Modeling MHC Epitopes Have a set of peptides that have been associate with a particular MHC allele Want to discover motif within the peptide bound by MHC allele Use motif to predict other potential epitopes

55. Motifs Bound by MHCs MHC 1 –Closed ends of grove –Peptides 8-10 AAs in length –Motif is the peptide MHC 2 –Grove has open ends –Peptides have broad length distribution: 10-30 AAs –Need to find binding motif within peptides

56. MHC 2 Motif Discovery Nielsen et al (2004) Bioinf Use Gibbs Sampling! 462 peptides known to bind to MHC II HLA-DR4(B1*0401) 9-30 residues in length Goal: identify a common length 9 binding motif

57. Vaccinia Epitope Prediction Mutaftsi et al (2006) Nat. Biotech. Predict MHC1 binding peptides Using 4 matrices for H-2 Kb and Db Top 1% predictions experimentally validated 49 validated epitopes accounting for 95% of immune response