1. Sequence motifs

2. What are sequence motifs? Sequences are translated into electron densities with different affinities of interacting with other molecules. Motifs represent a short common sequence – Regulatory motifs (TF binding sites) – Functional site in proteins (DNA binding motif)

3. DNA Regulatory Motifs Transcription Factors bind to regulatory motifs with high affinity – TF binding motifs are usually 6 – 20 nucleotides long – Usually located near target gene, mostly upstream the transcription start site Transcription Start Site SBF motif MCM1 motif Gene X MCM1 SBF

4. Identification of Known Motifs within Genomic Sequences Main Motivation: - Identifying the target of regulatory proteins (e.g. Transcription Factors) in the cell In many cancers specific TFs are known to be mutated. How do we identify the genes that are affected downstream?

5. P53 the guardian of the cell

6. How can we start looking for p53 (or any other transcription factor) targets using bioinformatics? Scenario 1 : Binding motif is known (easier case) Scenario 2 : Binding motif is unknown (hard case)

7. Challenges How to recognize a regulatory motif? Can we identify new occurrences of known motifs in genome sequences? Can we discover new motifs within upstream sequences of genes?

8. Scenario 1 : Binding targets are known

9. 1. Motif Representation Consensus: represent only ‘common’ nucleotides NANCATNNCCTTTTTATACAGNNNTTNNNTN N N stands for any nucleotide. Representing only consensus loses information. How can this be avoided? GTTCTTCGTGTTTATTTTTAGGAAATTGATGA TTGTTTCTCCTTTTAAAATAGTACTGCTGTTT TTTACTAACGACACATTGAAGAAATCACTTTG GATACGCTTACCGTTATCCAGAGCTACAGCGC TACTAATATGTAATACTTCAGCTCCCCTTAAT ATTGAGATCTTTTTTAACTAGTTAGGTCTACC TTCTCCCCTTCTTCATTTTAGCCTGTTTGGAC TAACATAACTTATTTACATAGTGCCATTGAAC GATATTTCCCGTTGTGTTAAGGCTGAGAAGAA TTTTCCCGACCATCAAGACAGGTGATTTATCA TGCAAAAACTTTTTTTCACAGGGCTAACTTGC GTTTATTGTGTTTCCACTCAGTTAAAAAACGA AACGTACTTTAATATTTATAGTACTTCATTCG AACATGCTATTTTTCATACAGCAACCTCACAT CTGCACTCATCATTAGATTAGAGGAACATGGA TACTTTTCTTTATCTAAGCAGCTAACTCAACT ATCAACATGCTATTGAACTAGAGATCCACCTA TAACTAACATGACTTTAACAGGGCTAATTTAC AGTACTAACTAATTAACTTAGAACATTAACAT GATCACCGTCACATTTATTAGAATTTCAAACG CAGTGGAATTTTTTTTTCTAGAAATGGTATCG CTCTATGACCAATAAAAACAGACTGTACTTTC AAATGGTATTATTTATAACAGTTGAACATTTC ATAAATATGCGATCAATATAGACCGTTGATAT ATTTTACTTTTTTTTTTTTAGGAGCTCCAAGA ATTTATTTCCTTATAATACAGACACGGTTACA TCGCAATTAATTTTCTAATAGTTTTTCATTTT GACCATCTTTCTTTTCCCCAGTGCTAAACACG AACCTTCTTTCTCATTCGTAGATTACTGTTGC AATTACTAACAGCTGTAATAGCCGACAAATTT CTCTCTGCGCGTCCAATTTAGCTATACTGTTG TTGTTTTGTTTTGTCGTACAGTGTTTGGAGAA AAACTTCCATTTCTTACATAGATCATCGCCAT TCCTTTCCATAATTTATTCAGCGCTTTGGTAT CGATTTACTATTTCCATTTAGACGTTGTTCAA AATTTACTAACAATACTTCAGTTTATAATGGA TCCTATACTAACAATTTGTAGTTCATAAATAA

10. Entropy - Definition Claude E. Shannon 1948, “A mathematical theory of communication”.

11. Entropy - Definition

12. Entropy - Example

13. Relative Entropy The Kullback-Leibler distance D

14. Information content

15. Information content

16. Information content

17. GTTCTTCGTGTTTATTTTTAGGAAATTGATGA TTGTTTCTCCTTTTAAAATAGTACTGCTGTTT TTTACTAACGACACATTGAAGAAATCACTTTG GATACGCTTACCGTTATCCAGAGCTACAGCGC TACTAATATGTAATACTTCAGCTCCCCTTAAT ATTGAGATCTTTTTTAACTAGTTAGGTCTACC TTCTCCCCTTCTTCATTTTAGCCTGTTTGGAC TAACATAACTTATTTACATAGTGCCATTGAAC GATATTTCCCGTTGTGTTAAGGCTGAGAAGAA TTTTCCCGACCATCAAGACAGGTGATTTATCA TGCAAAAACTTTTTTTCACAGGGCTAACTTGC GTTTATTGTGTTTCCACTCAGTTAAAAAACGA AACGTACTTTAATATTTATAGTACTTCATTCG AACATGCTATTTTTCATACAGCAACCTCACAT CTGCACTCATCATTAGATTAGAGGAACATGGA TACTTTTCTTTATCTAAGCAGCTAACTCAACT ATCAACATGCTATTGAACTAGAGATCCACCTA TAACTAACATGACTTTAACAGGGCTAATTTAC AGTACTAACTAATTAACTTAGAACATTAACAT GATCACCGTCACATTTATTAGAATTTCAAACG CAGTGGAATTTTTTTTTCTAGAAATGGTATCG CTCTATGACCAATAAAAACAGACTGTACTTTC AAATGGTATTATTTATAACAGTTGAACATTTC ATAAATATGCGATCAATATAGACCGTTGATAT ATTTTACTTTTTTTTTTTTAGGAGCTCCAAGA ATTTATTTCCTTATAATACAGACACGGTTACA TCGCAATTAATTTTCTAATAGTTTTTCATTTT GACCATCTTTCTTTTCCCCAGTGCTAAACACG AACCTTCTTTCTCATTCGTAGATTACTGTTGC AATTACTAACAGCTGTAATAGCCGACAAATTT CTCTCTGCGCGTCCAATTTAGCTATACTGTTG TTGTTTTGTTTTGTCGTACAGTGTTTGGAGAA AAACTTCCATTTCTTACATAGATCATCGCCAT TCCTTTCCATAATTTATTCAGCGCTTTGGTAT CGATTTACTATTTCCATTTAGACGTTGTTCAA AATTTACTAACAATACTTCAGTTTATAATGGA TCCTATACTAACAATTTGTAGTTCATAAATAA Count nucleotides at each position: Convert to frequencies: Frequency-logo: Logo plots - HowTo

18. GTTCTTCGTGTTTATTTTTAGGAAATTGATGA TTGTTTCTCCTTTTAAAATAGTACTGCTGTTT TTTACTAACGACACATTGAAGAAATCACTTTG GATACGCTTACCGTTATCCAGAGCTACAGCGC TACTAATATGTAATACTTCAGCTCCCCTTAAT ATTGAGATCTTTTTTAACTAGTTAGGTCTACC TTCTCCCCTTCTTCATTTTAGCCTGTTTGGAC TAACATAACTTATTTACATAGTGCCATTGAAC GATATTTCCCGTTGTGTTAAGGCTGAGAAGAA TTTTCCCGACCATCAAGACAGGTGATTTATCA TGCAAAAACTTTTTTTCACAGGGCTAACTTGC GTTTATTGTGTTTCCACTCAGTTAAAAAACGA AACGTACTTTAATATTTATAGTACTTCATTCG AACATGCTATTTTTCATACAGCAACCTCACAT CTGCACTCATCATTAGATTAGAGGAACATGGA TACTTTTCTTTATCTAAGCAGCTAACTCAACT ATCAACATGCTATTGAACTAGAGATCCACCTA TAACTAACATGACTTTAACAGGGCTAATTTAC AGTACTAACTAATTAACTTAGAACATTAACAT GATCACCGTCACATTTATTAGAATTTCAAACG CAGTGGAATTTTTTTTTCTAGAAATGGTATCG CTCTATGACCAATAAAAACAGACTGTACTTTC AAATGGTATTATTTATAACAGTTGAACATTTC ATAAATATGCGATCAATATAGACCGTTGATAT ATTTTACTTTTTTTTTTTTAGGAGCTCCAAGA ATTTATTTCCTTATAATACAGACACGGTTACA TCGCAATTAATTTTCTAATAGTTTTTCATTTT GACCATCTTTCTTTTCCCCAGTGCTAAACACG AACCTTCTTTCTCATTCGTAGATTACTGTTGC AATTACTAACAGCTGTAATAGCCGACAAATTT CTCTCTGCGCGTCCAATTTAGCTATACTGTTG TTGTTTTGTTTTGTCGTACAGTGTTTGGAGAA AAACTTCCATTTCTTACATAGATCATCGCCAT TCCTTTCCATAATTTATTCAGCGCTTTGGTAT CGATTTACTATTTCCATTTAGACGTTGTTCAA AATTTACTAACAATACTTCAGTTTATAATGGA TCCTATACTAACAATTTGTAGTTCATAAATAA Multiple alignment of acceptor sites from 268 yeast DNA sequences –What is the biological signal around the site ? –What are the important positions –How can it be visualized ? Biological information Sequence-logo Logo plot with Information Content Exon Intron Exon

19. Logo plots - Information Content Sequence-logo Calculate Information Content I =  a  p a log 2 p a + log 2 (4), Maximal value is 2 bits X axis – Relative position. Y axis – Cross Entropy. Total height at a position is the Information Content measured in bits. Height of letter is the proportional to the frequency of that letter. Stack order indicates importance, consensus is read at the top. A Logo plot is a visualization of a multiple alignment. ~0.5 each Completely conserved

20. Pseudocounts

21. PSSM – Position Specific Scoring Matrix Besides Entropy and Information content there are other ways to express a motif -4-3-20 A 0.180.2-1- C 0.050.020.5-- T 0.20.18 0.5-- G 0.020.05--1

22. Example: Predicting the cAMP Receptor Protein (CRP) binding site motif by using a logo plot

23. Extract experimentally defined CRP Binding Sites GGATAACAATTTCACA AGTGTGTGAGCGGATAACAA AAGGTGTGAGTTAGCTCACTCCCC TGTGATCTCTGTTACATAG ACGTGCGAGGATGAGAACACA ATGTGTGTGCTCGGTTTAGTTCACC TGTGACACAGTGCAAACGCG CCTGACGGAGTTCACA AATTGTGAGTGTCTATAATCACG ATCGATTTGGAATATCCATCACA TGCAAAGGACGTCACGATTTGGG AGCTGGCGACCTGGGTCATG TGTGATGTGTATCGAACCGTGT ATTTATTTGAACCACATCGCA GGTGAGAGCCATCACAG GAGTGTGTAAGCTGTGCCACG TTTATTCCATGTCACGAGTGT TGTTATACACATCACTAGTG AAACGTGCTCCCACTCGCA TGTGATTCGATTCACA

24. Create a Multiple Sequence Alignment GGATAACAATTTCACA TGTGAGCGGATAACAA TGTGAGTTAGCTCACT TGTGATCTCTGTTACA CGAGGATGAGAACACA CTCGGTTTAGTTCACC TGTGACACAGTGCAAA CCTGACGGAGTTCACA AGTGTCTATAATCACG TGGAATATCCATCACA TGCAAAGGACGTCACG GGCGACCTGGGTCATG TGTGATGTGTATCGAA TTTGAACCACATCGCA GGTGAGAGCCATCACA TGTAAGCTGTGCCACG TTTATTCCATGTCACG TGTTATACACATCACT CGTGCTCCCACTCGCA TGTGATTCGATTCACA

25. Generate a Logo plot XXXXXTGTGAXXXXAXTCACAXXXXXXX XXXXXACACTXXXXTXAGTGTXXXXXXX

26. http://weblogo.berkeley.edu WebLogo - Input

27. Genes: WebLogo - Outputs Proteins:

28. PROBLEMS… When searching for a motif in a genome using PSSM or other methods – the motif is usually found all over the place – The motif is considered real if found in the vicinity of a gene. Checking experimentally for the binding sites of a specific TF (location analysis) – the sites that bind the motif are in some cases similar to the PSSM and sometimes not!

29. Scenario 2 : Binding targets are unknown

30. Finding new Motifs We are given a group of genes, which presumably contain a common regulatory motif. We know nothing of the TF that binds to the putative motif. The problem: discover the motif.

31. Motif Discovery Motif Discovery

32. Computational Methods This problem has received a lot of attention from CS people. Methods include: – Probabilistic methods – hidden Markov models (HMMs), expectation maximization (EM), Gibbs sampling, etc. – Enumeration methods – problematic for inexact motifs of length k>10. … Current status: Problem is still open.

33. MEME "We need a name for the new replicator, a noun that conveys the idea of a unit of cultural transmission, or a unit of imitation. 'Mimeme' comes from a suitable Greek root, but I want a monosyllable that sounds a bit like 'gene'. I hope my classicist friends will forgive me, if I abbreviate mimeme to meme...“ Richard Dawkins

34. An (unsupervised) machine learning approach to motif discovery. Input: – Set of unaligned sequences. – Possible width of motifs. Output: – A set of gapless motifs. – Classifier for each motif. – Alignment of the occurrences of the motif to the input set. Timothy L. Bailey and Charles Elkan, "Fitting a mixture model by expectation maximization to discover motifs in biopolymers", Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology, pp. 28-36, AAAI Press, Menlo Park, California, 1994.

35. MEME: Expectation Maximization Goal: Find motif profile and positions that have maximum likelihood Iteratively estimates a probabilistic model for a random motif to be statistically overrepresented in the dataset. Converges at local optimum.

36. MEME result example

37. MEME Pros and cons The number of motifs or their occurrences are not required in the input. Only allows exact matches. High time complexity. Very pessimistic, can miss signals.

38. DRIM is a tool for discovering short motifs in a ranked list of nucleic acid sequences. From a mathematical point of view, DRIM identifies subsequences that tend to appear at the top of the list more often than in the rest of the list. –The definition of TOP in this context is flexible and driven by the data. E. Eden, D. Lipson, S. Yogev & Z. Yakhini. Discovering Motifs in Ranked Lists of DNA Sequences, PLoS Computational Biology, 2007.

39. The HyperGeometric (HG) score The HG score estimates the significance of the intersection (of size b) N genes B n b N all genes, ranked according to some criterion B of them contain the motif n of them are located at the top of the list b contain the motif and are located at the top of the list

40. The mHG score DRIM checks all the possibilities for n, in order to optimize the significance of the intersection. –It chooses the n i which has the minimal HG score – denoted as the mHG score. N genes B nini bibi The mHG score reflects the surprise of seeing the observed density of motif occurrences at the top of the list compared with the rest of the list. (STILL NEEDS TO BE CORRECTED FOR MULTIPLE HYPOTHESIS)

41. Puf2 – an RNA binding protein Yeast 3’UTR sequences were ranked according to Puf2 binding affinity. >YDR222W, affinity = 5.962 ACAAAAGCGUGAACACUUCCACAUGAAAUUCGUUUUUUGUCCUUUUUUUUCUCUUC UUUUUCUCUCCUGUUUCU >YLR297W, affinity = 5.937 AAUAAAAAUAGAUAUAAUAGAUGGCACCGCUCUUCACGCCCGAAAGUUGGACAUUUU AAAUUUUAAUUCUCAUGA >YOL109W, affinity = 5.763 UCACACUUGAAUGUGCUGCACUUUACUAGAAGUUUCUUUUUCUUUUUUUAAAAAUA AAAAAAGAGGAGAAAAAUGC >YGR138C, affinity = 5.498 GCUGGUGCAAGUUUCCGGUAAAAAUAAUGAUGUUCUAGUCAUUCAUAUAUACGAUA CAAAAAUAACA >YGL035C, affinity = 5.091 UACGCUGACAAGUUUUUGGCGGUGCAGAUAAAUCAAAAGACAAUAGACAAGAAUUAA UAAUAUUAACAAUUAA... DRIM (mHG p-value= 9.9∙10 -49 )

42. DRIM pros and cons Finds relations between ranking variable and motifs (enrichment). Returns best possible match without the need of a significance threshold. Impossible to build a dictionary for motifs of > ~10-mers.

43. Tools on the Web MEME – Multiple EM for Motif Elicitation. http://meme.sdsc.edu/meme http://meme.sdsc.edu/meme – metaMEME- Uses HMM method – MAST-Motif Alignment and Search Tool – Etc… TRANSFAC - database of eukaryotic cis-acting regulatory DNA elements and trans-acting factors. http://transfac.gbf.de/TRANSFAC/ http://transfac.gbf.de/TRANSFAC/ eMotif - allows to scan, make and search for motifs at the protein level. http://motif.stanford.edu/emotif/ http://motif.stanford.edu/emotif/ DRIM – Finds short motifs enriched in ranked lists. http://bioinfo.cs.technion.ac.il/drim/ http://bioinfo.cs.technion.ac.il/drim/