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Sequence Motifs. Motifs Motifs represent a short common sequence –Regulatory motifs (TF binding sites) –Functional site in proteins (DNA binding motif)

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Presentation on theme: "Sequence Motifs. Motifs Motifs represent a short common sequence –Regulatory motifs (TF binding sites) –Functional site in proteins (DNA binding motif)"— Presentation transcript:

1 Sequence Motifs

2 Motifs Motifs represent a short common sequence –Regulatory motifs (TF binding sites) –Functional site in proteins (DNA binding motif)

3 Regulatory Motifs Transcription Factors bind to regulatory motifs –Motifs are 6 – 20 nucleotides long –Activators and repressors –Usually located near target gene, mostly upstream Transcription Start Site SBF motif MCM1 motif Gene X MCM1 SBF

4 E. Coli promoter sequences

5 DNA binding Motif Zn finger C2H2

6 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?

7 1. Motif Representation Exact motif: CGGATATA Consensus: represent only deterministic nucleotides. –Example: HAP1 binding sites in 5 sequences. consensus motif: CGGNNNTANCGG N stands for any nucleotide. Representing only consensus loses information. How can this be avoided? CGGATATACCGG CGGTGATAGCGG CGGTACTAACGG CGGCGGTAACGG CGGCCCTAACGG ------------ CGGNNNTANCGG

8 TTGACA -35 TATAAT -10 Transcription start site Representing the motif as a profile -35-10 A T G C 1 23456 A T G C 1 23456 Based on ~450 known promoters 0.1 0.1 0.1 0.5 0.2 0.5 0.7 0.7 0.2 0.2 0.2 0.2 0.1 0.1 0.5 0.1 0.1 0.2 0.1 0.1 0.2 0.2 0.5 0.1 0.1 0.7 0.2 0.6 0.5 0.1 0.7 0.1 0.5 0.2 0.2 0.8 0.1 0.1 0.1 0.1 0.1 0.0 0.1 0.1 0.2 0.1 0.1 0.1

9 12345 A102557060 C3025801015 T50255105 G 2510 20 PSPM – Position Specific Probability Matrix Represents a motif of length k (5) Count the number of occurrence of each nucleotide in each position

10 12345 A0.10.250.050.70.6 C0.30.250.80.10.15 T0.50.250.050.10.05 G0.10.250.1 0.2 PSPM – Position Specific Probability Matrix Defines P i {A,C,G,T} for i={1,..,k}. –P i (A) – frequency of nucleotide A in position i.

11 Graphical Representation – Sequence Logo Horizontal axis: position of the base in the sequence. Vertical axis: amount of information. Letter stack: order indicates importance. Letter height: indicates frequency. Consensus can be read across the top of the letter columns.

12 Identification of Known Motifs within Genomic Sequences Motivation: –identification of new genes controlled by the same TF. –Infer the function of these genes. –enable better understanding of the regulation mechanism.

13 12345 A0.10.250.050.70.6 C0.30.250.80.10.15 T0.50.250.050.10.05 G0.10.250.1 0.2 PSPM – Position Specific Probability Matrix Each k-mer is assigned a probability. –Example: P(TCCAG)=0.5*0.25*0.8*0.7*0.2

14 12345 A0.10.250.050.70.6 C0.30.250.80.10.15 T0.50.250.050.10.05 G0.10.250.1 0.2 Detecting a Known Motif within a Sequence using PSPM The PSPM is moved along the query sequence. At each position the sub-sequence is scored for a match to the PSPM. Example: sequence = ATGCAAGTCT…

15 The PSPM is moved along the query sequence. At each position the sub-sequence is scored for a match to the PSPM. Example: sequence = ATGCAAGTCT… Position 1: ATGCA 0.1*0.25*0.1*0.1*0.6=1.5*10 -4 12345 A0.10.250.050.70.6 C0.30.250.80.10.15 T0.50.250.050.10.05 G0.10.250.1 0.2 Detecting a Known Motif within a Sequence using PSPM

16 The PSPM is moved along the query sequence. At each position the sub-sequence is scored for a match to the PSPM. Example: sequence = ATGCAAGTCT… Position 1: ATGCA 0.1*0.25*0.1*0.1*0.6=1.5*10 -4 Position 2: TGCAA 0.5*0.25*0.8*0.7*0.6=0.042 12345 A0.10.250.050.70.6 C0.30.250.80.10.15 T0.50.250.050.10.05 G0.10.250.1 0.2 Detecting a Known Motif within a Sequence using PSPM

17 Detecting a Known Motif within a Sequence using PSSM Is it a random match, or is it indeed an occurrence of the motif? PSPM -> PSSM (Probability Specific Scoring Matrix) –odds score matrix: O i (n) where n  {A,C,G,T} for i={1,..,k} –defined as P i (n)/P(n), where P(n) is background frequency. O i (n) increases => higher odds that n at position i is part of a real motif.

18 12345 A0.10.250.050.70.6 12345 A0.410.22.82.4 12345 A-1.3220-2.3221.4851.263 PSSM as Odds Score Matrix Assumption: the background frequency of each nucleotide is 0.25. 1.Original PSPM (P i ): 2.Odds Matrix (O i ): 3.Going to log scale we get an additive score, Log odds Matrix (log 2 O i ):

19 12345 A-1.320-2.321.481.26 C0.2601.68-1.32-0.74 T10-2.32-1.32-2.32 G-1.320 -0.32 Calculating using Log Odds Matrix Odds  0 implies random match; Odds > 0 implies real match (?). Example: sequence = ATGCAAGTCT… Position 1: ATGCA -1.32+0-1.32-1.32+1.26=-2.7 odds= 2 -2.7 =0.15 Position 2: TGCAA 1+0+1.68+1.48+1.26 =5.42 odds=2 5.42 =42.8

20 Calculating the probability of a Match ATGCAAG Position 1 ATGCA = 0.15

21 Calculating the probability of a Match ATGCAAG Position 1 ATGCA = 0.15 Position 2 TGCAA = 42.3

22 Calculating the probability of a Match ATGCAAG Position 1 ATGCA = 0.15 Position 2 TGCAA = 42.3 Position 3 GCAAG =0.18

23 Calculating the probability of a match ATGCAAG Position 1 ATGCA = 0.15 Position 2 TGCAA = 42.3 Position 3 GCAAG =0.18 P (i) = S / (∑ S) Example 0.15 /(.15+42.8+.18)=0.003 P (1)= 0.003 P (2)= 0.993 P (3) =0.004

24 Building a PSSM Collect all known sequences that bind a certain TF. Align all sequences (using multiple sequence alignment). Compute the frequency of each nucleotide in each position (PSPM). Incorporate background frequency for each nucleotide (PSSM).

25 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.

26 Example Predicting the cAMP Receptor Protein (CRP) binding site motif

27 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

28 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

29 A C G T 1-0.430.1-0.460.55 21.370.12-1.59-11.2 31.69-1.28-11.2-1.43 4-1.280.12-11.21.32 50.91-11.2-0.460.47 61.53-1.38-1.48-1.43 70.9-0.48-11.20.12 8-1.37-1.28-11.21.68 9-11.2 1.73-0.56 10-11.2-0.51-11.21.72 11-0.48-11.21.72-11.2 121.56-1.59-11.2-0.46 13-0.51-0.38-0.550.88 14-11.20.50.570.13 150.17-0.510.12 160.9-11.20.5-0.48 170.170.160.06-0.48 18-0.4-0.380.82-0.48 19-1.38-1.28-11.21.68 20-1.481.7-11.2-1.38 211.5-1.38-1.43-1.28 Generate a PSSM

30 XXXXXTGTGAXXXXAXTCACAXXXXXXX XXXXXACACTXXXXTXGATGTXXXXXXX

31 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!

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 Tools on the Web MEME – Multiple EM for Motif Elicitation. http://meme.sdsc.edu/meme/website/ http://meme.sdsc.edu/meme/website/ metaMEME- Uses HMM method http://meme.sdsc.edu/meme MAST-Motif Alignment and Search Tool http://meme.sdsc.edu/meme 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 in the protein level. http://motif.stanford.edu/emotif/

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