1. Transcription Regulation Transcription Factor Motif Finding Xiaole Shirley Liu STAT115, STAT215

2. Imagine a Chef Restaurant DinnerHome Lunch Certain recipes used to make certain dishes 2

3. Each Cell Is Like a Chef 3

4. Infant Skin Adult Liver Glucose, Oxygen, Amino Acid Fat, Alcohol Nicotine Healthy Skin Cell State Disease Liver Cell State Certain genes expressed to make certain proteins 4

5. Understanding a Genome Get the complete sequence (encoded cook book) Observe gene expressions at different cell states (meals prepared at different situations) Decode gene regulation (decode the book, understand the rules) 5

6. ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATTTACCACATCGCATCACAGTTCAGGACTAGACACGGACG GCCTCGATTGACGGTGGTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGACCCGTTTAAATCGAGAG CGCTAGCCCGTCATCGATCTTGTTCGAATCGCGAATTGCCT Information in DNA Milk->Yogurt Beef->Burger Egg->Omelet Fish->Sushi Flour->Cake Coding region 2% What is to be made 6

7. Information in DNA Non-coding region 98% Regulation: When, Where, Amount, Other Conditions, etc ATTTACCGATGGCTGCACTATGCCCTATCGATCGACCTCTC ATTTACCACATCGCATCACTACGACGGACTAGACACGGACG GCCTCGATTGACGGTGGTACAGTTCAATGACAACCTGACTA TCTCGTTAGGACCCATGCGTACGACCCGTTTAAATCGAGAG CGCTAGGTCATCCCAGATCTTGTTCGAATCGCGAATTGCCT Milk->Yogurt Beef->Burger Egg->Omelet Fish->Sushi Flour->Cake Morning Japanese Restaurant 5 Oz 9 Oz Butter Coding region 2% 7

8. Measure Gene Expression Microarray or SAGE detects the expression of every gene at a certain cell state Clustering find genes that are co-expressed (potentially share regulation) 8

9. STAT115, 04/01/2008 Decode Gene Regulation GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATATTTACCACCAGTTCAGACACGGACGGC GCCTCGATTTACCGTGGTACAGTTCAAACCTGACTAAA TCTCGTTAGGACCATATTTACCACCCACATCGAGAGCG CGCTAGCCATTTACCGATCTTGTTCGAGAATTGCCTAT Scrambled Egg Bacon Cereal Hash Brown Orange Juice Look at genes always expressed together: Upstream Regions Co-expressed Genes

10. STAT115, 04/01/2008 Decode Gene Regulation GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATATTTACCACCAGTTCAGACACGGACGGC GCCTCGATTTACCGTGGTACAGTTCAAACCTGACTAAA TCTCGTTAGGACCATATTTACCACCCACATCGAGAGCG CGCTAGCCATTTACCGATCTTGTTCGAGAATTGCCTAT Scrambled Egg Bacon Cereal Hash Brown Orange Juice Look at genes always expressed together: Upstream Regions Co-expressed Genes

11. STAT115, 04/01/2008 Decode Gene Regulation GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATATTTACCACCAGTTCAGACACGGACGGC GCCTCGATTTACCGTGGTACAGTTCAAACCTGACTAAA TCTCGTTAGGACCATATTTACCACCCACATCGAGAGCG CGCTAGCCATTTACCGATCTTGTTCGAGAATTGCCTAT Scrambled Egg Bacon Cereal Hash Brown Orange Juice Look at genes always expressed together: Upstream Regions Co-expressed Genes Morning

12. Biology of Transcription Regulation...acatttgcttctgacacaactgtgttcactagcaacctca...aacagacaccATGGTGCACCTGACTCCTGAGGAGAAGTCT......agcaggcccaactccagtgcagctgcaacctgcccactcc...ggcagcgcacATGTCTCTGACCAAGACTGAGAGTGCCGTC......cgctcgcgggccggcactcttctggtccccacagactcag...gatacccaccgATGGTGCTGTCTCCTGCCGACAAGACCAA......gccccgccagcgccgctaccgccctgcccccgggcgagcg...gatgcgcgagtATGGTGCTGTCTCCTGCCGACAAGACCAA... atttgctt ttcact gcaacct aactccagt actca gcaacct ccagcgccg gcaacct Transcription Factor (TF) TF Binding Motif Hemoglobin Beta Hemoglobin Zeta Hemoglobin Alpha Hemoglobin Gamma Motif can only be computational discovered when there are enough cases for machine learning 12

13. Computational Motif Finding Input data: –Upstream sequences of gene expression profile cluster –20-800 sequences, each 300-5000 bps long Output: enriched sequence patterns (motifs) Ultimate goals: –Which TFs are involved and their binding motifs and effects (enhance / repress gene expression)? –Which genes are regulated by this TF, why is there disease when a TF goes wrong? –Are there binding partner / competitor for a TF? 13

14. Challenges: Where/what the signal The motif should be abundant GAAATATGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATATTTACCACCAAATAAGACACGGACGGC GCCTCGAAATAGCCATTTACCGTTCAAACCTGACTAAA TCTCGTATTTACCATATTAAATACCCACATCGAGAGCG CGCTAGCAAATATACGATTTACCTCGAGAATTGCCTAT Water 14

15. The motif should be abundant And Abundant with significance GAAATATGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATATTTACCACCAAATAAGACACGGACGGC GCCTCGAAATAGCCATTTACCGTTCAAACCTGACTAAA TCTCGTATTTACCATATTAAATACCCACATCGAGAGCG CGCTAGCAAATATACGATTTACCTCGAGAATTGCCTAT Coconut Challenges: Where/what the signal 15

16. Challenges: Double stranded DNA Motif appears in both strands GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATGGTAAATACCAGTTCAGACACGGACGGC TCTCAGGTAAATCAGTCATACTACCCACATCGAGAGCG ||||||||||||||||||||||||||||| GTGTAGCGTACCATTTATGGTCAAGTCTG ||||||||||||||||||||||||||||| AGAGTCCATTTAGTCAGTATGATGGGTGT 16

17. Challenges: Base substitutions Sequences do not have to match the motif perfectly, base substitutions are allowed GATGGCTGCACATTTACCTATGCCCTACGACCTCTCGC CACATCGCATATGTACCACCAGTTCAGACACGGACGGC GCCTCGATTTGCCGTGGTACAGTTCAAACCTGACTAAA TCTCGTTAGGACCATATTTATCACCCACATCGAGAGCG CGCTAGCCAATTACCGATCTTGTTCGAGAATTGCCTAT 17

18. Challenges: Variable motif copies Some sequences do not have the motif Some have multiple copies of the motif GATGATGCCTCGGACGGATATGCCCTACGACCTCTCGC CACATCGCAATGCAGCAATGCGTTCAGACACGGACGGC TCATGCTAATGCCAGTCATGCTACATGCATCGAGAGCG GCCTCTAGCTAGGCCGGTGAACATCAGACCTGACTAAA CGCAATATAGCATTAGCAGACAGACGAGAATTGCCTAT 18

19. Challenges: Variable motif copies Some sequences do not have the motif Some have multiple copies of the motif GATGATGCCTCGGACGGATATGCCCTACGACCTCTCGC CACATCGCAATGCAGCAATGCGTTCAGACACGGACGGC TCATGCTAATGCCAGTCATGCTACATGCATCGAGAGCG GCCTCTAGCTAGGCCGGTGAACATCAGACCTGACTAAA CGCAATATAGCATTAGCAGACAGACGAGAATTGCCTAT Sushi Hand Roll Sashimi Tempura Sake Fish 19

20. Challenges: Two-block motifs Some motifs have two parts GACACATTTACCTATGC TGGCCCTACGACCTCTCGC CACAATTTACCACCA TGGCGTGATCTCAGACACGGACGGC GCCTCGATTTACCGTGGTATGGCTAGTTCTCAAACCTGACTAAA TCTCGTTAGATTTACCACCCA TGGCCGTATCGAGAGCG CGCTAGCCATTTACCGAT TGGCGTTCTCGAGAATTGCCTAT AATGCG GCGTAA or palindromic patterns CoconutMilk 20

21. Scan for Known TF Motif Sites Experimental TF sites: TRANSFAC, JASPAR TRANSFACJASPAR Motif representation: –Regular expression: Consensus CACAAAA binary decision Degenerate CRCAAAW IUPAC A/TA/G 21

22. IUPAC for DNA A adenosine Ccytidine Gguanine T thymidine U uridine R G A (purine) Y T C (pyrimidine) K G T (keto) M A C (amino) S G C (strong) W A T (weak) B C G T (not A) D A G T (not C) H A C T (not G) VA C G (not T) NA C G T (any) 22

23. Scan for Known TF Motif Sites Experimental TF sites: TRANSFAC, JASPAR Motif representation: –Regular expression: Consensus CACAAAA binary decision Degenerate CRCAAAW –Position weight matrix (PWM): need score cutoff Pos 12345678 ATGGCATG AGGGTGCG ATCGCATG TTGCCACG ATGGTATT ATTGCACG AGGGCGTT ATGACATG ATGGCATG ACTGGATG Motif Matrix Sites Segment ATGCAGCT score = p(generate ATGCAGCT from motif matrix) p(generate ATGCAGCT from background) p 0 A  p 0 T  p 0 G  p 0 C  p 0 A  p 0 G  p 0 C  p 0 T 23

24. A Word on Sequence Logo SeqLogo consists of stacks of symbols, one stack for each position in the sequence The overall height of the stack indicates the sequence conservation at that position The height of symbols within the stack indicates the relative frequency of nucleic acid at that position ATGGCATG AGGGTGCG ATCGCATG TTGCCACG ATGGTATT ATTGCACG AGGGCGTT ATGACATG ATGGCATG ACTGGATG 24

25. JASPAR User defined cutoff to scan for a particular motif 25

26. Drawbacks to Known TF Motif Scans Limited number of motifs Limited number of sites to represent each motif –Low sensitivity and specificity Poor description of motif –Binding site borders not clear –Binding site many mismatches Many motifs look very similar –E.g. GC-rich motif, E-box (CACGTG) 26

27. De Novo Motif Finding 27

28. De novo Sequence Motif Finding Goal: look for common sequence patterns enriched in the input data (compared to the genome background) Regular expression enumeration –Pattern driven approach –Enumerate k-mers, check significance in dataset Position weight matrix update –Data driven approach, use data to refine motifs –EM & Gibbs samplingEMGibbs sampling –Motif score and Markov backgroundMotif score Markov background 28

29. Regular Expression Enumeration Oligonucleotide Analysis: check over- representation for every w-mer: –Expected w occurrence in data Consider genome sequence + current data size –Observed w occurrence in data –Over-represented w is potential TF binding motif Observed occurrence of w in the data p w from genome background size of sequence data Expected occurrence of w in the data 29

30. Suffix Tree for Fast Search Weeder, Pavesi & Pesole 2006 Construction is linear in time and space to length of S. Quickly locating a substring allowing a certain number of mistakes Provides first linear-time solutions for the longest common substring problem Typically requires significantly more space than storing the string itself. 30

31. Regular Expression Enumeration RE Enumeration Derivatives: –oligo-analysis, spaced dyads w 1.n s.w 2 –IUPAC alphabetIUPAC alphabet –Markov background (later)Markov background –2-bit encoding, fast index access –Enumerate limited RE patterns known for a TF protein structure or interaction theme Exhaustive, guaranteed to find global optimum, and can find multiple motifs Not as flexible with base substitutions, long list of similar good motifs, and limited with motif width 31

32. Expectation Maximization and Gibbs Sampling Model Objects: –Seq: sequence data to search for motif –  0 : non-motif (genome background) probability –  : motif probability matrix parameter –  : motif site locations Problem: P( ,  | seq,  0 ) Approach: alternately estimate –  by P(  | , seq,  0 ) –  by P(  | , seq,  0 ) –EM and Gibbs differ in the estimation methods 32

33. Expectation Maximization E step:  | , seq,  0 TTGACGACTGCACGT TTGACp 1 TGACGp 2 GACGAp 3 ACGACp 4 CGACTp 5 GACTGp 6 ACTGCp 7 CTGCAp 8... P 1 = likelihood ratio = P(TTGAC|  ) P(TTGAC|  0 ) p 0 T  p 0 T  p 0 G  p 0 A  p 0 C = 0.3  0.3  0.2  0.3  0.2 33

34. Expectation Maximization E step:  | , seq,  0 TTGACGACTGCACGT TTGACp 1 TGACGp 2 GACGAp 3 ACGACp 4 CGACTp 5 GACTGp 6 ACTGCp 7 CTGCAp 8... M step:  | , seq,  0 p 1  TTGAC p 2  TGACG p 3  GACGA p 4  ACGAC... Scale ACGT at each position,  reflects weighted average of  34

35. M Step TTGACGACTGCACGT 0.8  TTGAC 0.2  TGACG 0.6  GACGA 0.5  ACGAC 0.3  CGACT 0.7  GACTG 0.4  ACTGC 0.1  CTGCA 0.9  TGCAC … 35

36. EM Derivatives First EM motif finder (C Lawrence) –Deterministic algorithm, guarantee local optimum MEME (TL Bailey) –Prior probability allows 0-n site / sequence –Parallel running multiple EM with different seed –User friendly results 36

37. Gibbs Sampling Stochastic process, although still may need multiple initializations –Sample  from P(  | , seq,  0 ) –Sample  from P(  | , seq,  0 ) Collapsed form: –  estimated with counts, not sampling from Dirichlet –Sample site from one seq based on sites from other seqs Converged motif matrix  and converged motif sites  represent stationary distribution of a Markov Chain 37

38. 11 22 33 44 55 Gibbs Sampler Initial  1 3131 4141 5151 2121  11 Randomly initialize a probability matrixRandomly initialize a probability matrix n A1 + s A n A1 + s A + n C1 + s C + n G1 + s G + n T1 + s T  estimated with counts p A1 = 38

39. Gibbs Sampler  1 Without  11 Segment Take out one sequence with its sites from current motifTake out one sequence with its sites from current motif 3131 4141 5151 2121  11 39

40. Segment (1-8) Sequence 1 Gibbs Sampler Score each possible segment of this sequenceScore each possible segment of this sequence  1 Without  11 Segment 3131 4141 5151 2121 40

41. Segment (2-9) Sequence 1 Gibbs Sampler Score each possible segment of this sequenceScore each possible segment of this sequence 3131 4141 5151 2121  1 Without  11 Segment 41

42. Segment Score Use current motif matrix to score a segment Pos 12345678 ATGGCATG AGGGTGCG ATCGCATG TTGCCACG ATGGTATT ATTGCACG AGGGCGTT ATGACATG ATGGCATG ACTGGATG Motif Matrix Sites Segment ATGCAGCT score = p(generate ATGCAGCT from motif matrix) p(generate ATGCAGCT from background) p 0 A  p 0 T  p 0 G  p 0 C  p 0 A  p 0 G  p 0 C  p 0 T 42

43. Scoring Segments Motif12345bg A0.40.10.30.40.20.3 T0.20.50.10.20.20.3 G0.20.20.20.30.40.2 C0.20.20.40.10.20.2 Ignore pseudo counts for now… Sequence: TTCCATATTAATCAGATTCCG… score TAATC… AATCA0.4/0.3 x 0.1/0.3 x 0.1/0.3 x 0.1/0.2 x 0.2/0.3 = 0.049383 ATCAG0.4/0.3 x 0.5/0.3 x 0.4/0.2 x 0.4/0.3 x 0.4/0.2 = 11.85185 TCAGA0.2/0.3 x 0.2/0.3 x 0.3/0.3 x 0.3/0.2 x 0.2/0.3 = 0.444444 CAGAT… 43

44.  12 Gibbs Sampler Sample site from one seq based on sites from other seqs 3131 4141 5151 2121 Modified  1  estimated with counts 44

45. Hill Climbing vs Sampling 45 Pos123456789 Score3112589126 SubT3416212938394147 Rand(subtotal) = X Find the first position with subtotal larger than X Pos123456789 Score311258950026 SubT3416212938538540546

46. Gibbs Sampler Repeat the process until motif convergesRepeat the process until motif converges  1 Without  21 Segment 3131 4141 5151  12 2121 46

47. Gibbs Sampler Intuition Beginning: –Randomly initialized motif –No preference towards any segment 47

48. Gibbs Sampler Intuition Motif appears: –Motif should have enriched signal (more sites) –By chance some correct sites come to alignment –Sites bias motif to attract other similar sites 48

49. Gibbs Sampler Intuition Motif converges: –All sites come to alignment –Motif totally biased to sample sites every time 49

50. 11 22 33 44 55 Gibbs Sampler  3i 4i4i  5i  2i  1i Column shift Metropolis algorithm: –Propose  * as  shifted 1 column to left or right –Calculate motif score u(  ) and u(  *) –Accept  * with prob = min(1, u(  *) / u(  )) 50

51. Summary Biology and challenge of transcription regulation Scan for known TF motif sites: TRANSFAC & JASPAR De novo method –Regular expression enumeration Oligonucleotide analysis –Position weight matrix update EM (iterate ,  ;  ~ weighted  average) Gibbs Sampler (sample ,  ; Markov chain convergence) 51