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3-page Detailed Project Outline & Preliminary Results Due Tuesday, November 7

3-page Detailed Project Outline & Preliminary Results Due Tuesday, November 7. Lab Next week (Nov 2): help with projects. First, representation of motifs: Position-specific Weight Matrices (PWMs aka Position-Specific Scoring Matrix, PSSM). Site 1. A G A T G G A T G G

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3-page Detailed Project Outline & Preliminary Results Due Tuesday, November 7

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  1. 3-page Detailed Project Outline & Preliminary Results Due Tuesday, November 7 Lab Next week (Nov 2): help with projects

  2. First, representation of motifs: Position-specific Weight Matrices (PWMs aka Position-Specific Scoring Matrix, PSSM) Site 1 A G A T G G A T G G T G A T T G A T G T T G A T G G A T G G A G A T T G A T C G T G A T G G A T T G T G A T G G A T T G A G A T G G A T T G Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 PWM represents frequencies of each base at each position in the motif * G 0 1.0 0 0 0.71.0 0 0 0.40.8 A 0.4 0 1.0 0 0 0 1.0 0 0 0 T 0.6 0 0 1.0 0.3 0 0 1.00.40.2 C 0 0 0 0 0 0 0 0 0.2 0 * These days, PWM/PSSM can correspond to the frequency matrix or a likelihood matrix

  3. Information content IC The least variable positions likely are important for specifying the protein-DNA interaction Therefore high information content = low sequence variation at that position. G 0 1.0 0 0 0.71.0 0 0 0.40.8 A 0.4 0 1.0 0 0 0 1.0 0 0 0 T 0.6 0 0 1.0 0.3 0 0 1.00.40.2 C 0 0 0 0 0 0 0 0 0.2 0 IC 1.0 2.0 2.0 2.0 1.1 2.0 2.0 2.0 0.5 1.3 = bit score of 15.9 Information Profile: bits Position

  4. Pseudo-counts: protecting against overfitting due to small sample sizes Add 1 count to each base at each position, then divide by n + 4 Site 1 A G A T G G A T G G T G A T T G A T G T T G A T G G A T G G A G A T T G A T C G T G A T G G A T T G T G A T G G A T T G A G A T G G A T T G Site 2 Site 3 Site 4 Site 5 Site 6 Site 7 With pseudo-counts (rounded values): G 0.1 0.7 0.1 0.1 0.40.7 0.1 0.1 0.3 0.7 A 0.3 0.1 0.7 0.1 0.1 0.1 0.7 0.1 0.1 0.1 T 0.4 0.1 0.1 0.70.25 0.1 0.1 0.70.30.2 C 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.2 0.1 Various programs for finding instances of a matrix (PATSER, MAST, ScanAce)

  5. Finding matches to (instances of) a PWM G 0 1.0 0 0 0.7 1.0 0 0 0.4 0.8 A 0.4 0 1.0 0 0 0 1.0 0 0 0 T 0.6 0 0 1.00.3 0 0 1.0 0.4 0.2 C 0 0 0 0 0 0 0 0 0.2 0 Is the sequenceA G A T T G A T C Ta match to this matrix? Joint probability: assuming each position is independent, P(motif) = PPb(i) Background model: P(G,A,T,C) = 0.25 b = G,A,T,C i P(sequence | matrix model ) = (0.4)(1.0)(1.0)(1.0)(0.3)(1.0)(1.0)(1.0)(0.2)(0.2) = 0.0048 P(sequence | background model ) = (0.25)(0.25)(0.25)(0.25)(0.25)(0.25)(0.25)(0.25)(0.25)(0.25) = 6.8e-24

  6. Motif finding methods and algorithms Given a set of n promoters of n coregulated genes, find a motif common to the promoters. Both the PWM and the motif sequences are unknown. Common methods: 1. Enumeration: Simplest case: look at the frequency of all n-mers 2. EM algorithms (MEME): Iteratively hone in on the most likely motif model 3. Gibbs sampling methods (AlignAce, BioProspector) Iteratively replace (‘sample’) sites to retrain the matrix

  7. Motif finding using the EM algorithm MEME (Bailey & Elkan 1995) http://meme.sdsc.edu/meme/intro.html • EM algorithm: Expectation-Maximization • In one run, trains the matrix model and identifies examples of the matrix MEME works by iteratively refining matrix and identifying sites: 1. Estimate motif model a. Start with an n-mer seed (random or specified) b. Build a matrix by incorporating some of background frequencies 2. Identify examples of the model a. For every n-mer in the input set, identify its probability given the matrix model 3. Re-estimate the motif model a. Calculate a new matrix, based on the weighted frequencies of all n-mers in the set 4. Iteratively refine the matrix and identify sites until convergence.

  8. Motif finding using the EM algorithm MEME (Bailey & Elkan 1995) http://meme.sdsc.edu/meme/intro.html • EM algorithm: Expectation-Maximization • In one run, trains the matrix model and identifies examples of the matrix Choice of parameters significantly affects the algorithm -- motif width w -- motif model: - “zoops” = zero-or-one motif per promoter sequence* - “oops” = one-or-more motif per promoter sequence* - “tcm” = (“any number of sites”) two-component mixture model (ie. Each w-mer sequence is either an example of the background model or the motif model) -- background model: - simplest case: genomic nucleotide frequencies P(G,A,T,C) - nth-order Markov chain (eg. 2nd order Markov chain = P(A|C) = P(AC) = dinucleotide frequencies) *These models keep track of which input sequence (promoter) the motif came from, whereas tcm throws all “w-mers” into a bag

  9. Gibbs Sampling (AlignAce by Hughes et al. 2000 http://atlas.med.harvard.edu/download/index.html, BioProspector by Liu et al. 2001 http://motif.stanford.edu/distributions/r • Start by randomly choosing sites and creates an initial matrix • Sample other sites • Remove some set of matrix examples • Randomly choose other sites and calculate P given matrix • If they have a high score to the matrix, keep the new site • Iterate to convergence

  10. Gibbs sampling: basic idea Current motif = PWM formed by circled substrings Slides generously and unknowingly provided by S. Sinha, Urbana-Chamaign CS Dept.

  11. Gibbs sampling: basic idea Delete one substring Slides generously and unknowingly provided by S. Sinha, Urbana-Chamaign CS Dept.

  12. Gibbs sampling: basic idea Try a replacement: Compute its score, Accept the replacement depending on the score. Slides generously and unknowingly provided by S. Sinha, Urbana-Chamaign CS Dept.

  13. Gibbs Sampling (AlignAce by Hughes et al. 2000 http://atlas.med.harvard.edu/download/index.html, BioProspector by Liu et al. 2001 http://motif.stanford.edu/distributions/r Start by randomly choosing sites and creates an initial matrix Sample other sites Remove some set of matrix examples Randomly choose other sites and calculate P given matrix If they have a high score to the matrix, keep the new site Iterate to convergence Gibbs sampling is less likely to get stuck in a local minimum, since it randomly samples other sites, whereas MEME is more prone to finding local optima (in theory, anyway)

  14. Assessing the biological relevance of identified motifs Keep an eye on these features: 1. Bit score (or normalized bit score) Bit score = Information Content at each position 2. Information content profile Real TF binding sites typically show smooth IC profiles 3. Number of input sequences that contain the motif Overfitting: great looking motif but found in only few of the input sequences 4. Nucleotide frequencies Eg. In yeast, AT rich sequences are common … doesn’t necessarily mean they’re not real binding sites 5. Enrichment of motif in the training set compared to genomic bg Our old friend, the hypergeometric distribution. 6. Any other nonrandom observation can give you confidence (palindromic motif, nonrandom distribution of motifs in input sequences, etc)

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