A Statistical Method for Finding Transcriptional Factor Binding Sites Authors: Saurabh Sinha and Martin Tompa Presenter: Christopher Schlosberg CS598ss
Regulation of Gene Expression
Difficulties of Motif Finding Regulatory sequences don’t follow same orientation as the coding sequence or each other Multiple binding sites might exist for each regulated gene Large variation in the binding sites of a single factor. Variations are not well understood.
Previous & Proposed Methods for Finding Motifs Previous Methods: Find longer, general motifs Use local search algorithms (Gibbs sampling, Expectation Maximization, greedy algorithms) Proposed Method: TFBS is small enough to use enumerative methods Enumerative statistical methods guarantee global optimality and affordability
Proposed Method Highlights Allows variations in the binding site instances of a given transcription factor Allows for motifs to include “spacers” Allows for overlapping occurrences (in both orientations), which lends to complex dependencies Statistical significance of a motif (s) is based on the frequencies of shorter (more frequent) oligonucleotides Use of Markov chain to model background genomic distribution Use of z-score to measure statistical significance Allows for multiple binding sites
Characteristics of a Motif Any single TFBS has significant variation Many motifs have spacers from 1-11bp Variation often occurs as a transition (e.g. purine purine) rather than a transversion (e.g. pyrimidine purine) Variation occurs less between a pair of complementary bases. Indels are uncommon
Proposed Motif Definition Motif will be a string with Σ= {A,C,G,T, R,Y,S,W,N} A,C,G,T (DNA bp), R (purine), Y (pyrimidine), S (strong), W (weak), N (spacer) TF database (SCPD) confirms this model of variation Of 50 binding site consensi, 31 exact fits (62%) Another 10 fit if slight variations allowed
Measure of Statistical Significance Given set of corregulated S. cerevisiae genes, the input to the problem is corresponding set of 800bp upstream sequences having 3’ end on start site of gene translation. Model must measure from input sequences: Absolute number of occurrences (N s ) of motif (s) Background genomic distribution X is a set of random DNA sequences in the same number and lengths of the input sequences Generated by Markov chain of order m Transition probabilities determined by (m+1)-mer frequencies in fully complement of (800bp in length) Background model chooses m=3
z-score X s – r.v. is number of occurrences of motif (s) in X E(X s ) – expectation, σ(X s ) – standard deviation z s – number of S.D. by which observed value N s exceeds expectation
Implications Possibility of overlap of a motif with itself (in either orientation) Previous study of pattern autocorrelation Generalized computation of SD, treating motif as a finite set of strings Higher order Markov chains Spacers handled at no extra computational cost Handles motif in either orientation
Algorithm Enumerates over each input sequence Tabulates number N s of occurrences of each motif in either direction Compute expectation and SD for each motif s.t. N s >0 Calculate z-score Rank motifs by z-score
Algorithm Analysis For single motif, complexity is O(c 2 k 2 ) k – # of nonspacer characters in motif c – # of instantiations of R, Y, S, W in motif Only modest values of k Linear dependence on genome size Can trim variance calculation to optimize
Number of Occurrences Convert motif s into a multiset W Add reverse complements for each string in W Motif s only occurs at position in X iff some string in W occurs at same position X s - # of occurrences (in X) of each member of W Handling Palindromes W i – member of W |W| = T
Number of Occurrences Con’t
Expectation Linearity of Expectation
Variance B term C term
C Term A term
A Term
Overlapping Concatenation CW (like W) is potentially a multiset One-to-one correspondence
C Term Simplification
A Term Revisited
S i1 S i2 Term & Approximation Kleffe and Borodovsky (1992) Approximation
B Term
B Term Con’t
Summary
Higher Order Markov Models Variance calculations remain the same except for S i1 S i2 term Experimental m = 3
Experimental Results & Future Considerations 17 coregulated sets of genes Known TF with known binding site consensus In 9 experiments, known consensus was one of 3 highest scoring motifs Future Topics: Non-centered spacers Enumeration Loop optimization Filtering repeats
Question E(X s ) is more straight-forward to calculate compared to σ(X s ). Under the assumptions given in the paper, name one of the reasons for this complication.