Transcript Document
T I C S V U N F O R M A B I O I C E N T R E F I V E R A T O R I N T E G
Computational Genomics and Proteomics
Lecture 8
Motif Discovery
Outline Gene Regulation DNA Transcription factors Motifs What are they?
Binding Sites Combinatoric Approaches Exhaustive searches Consensus Comparative Genomics Example Probabilistic Approaches Statistics EM algorithm Gibbs Sampling
www.accessexcellence.org
www.accessexcellence.org
www.accessexcellence.org
Four DNA nucleotide building blocks
G-C is more strongly hydrogen-bonded than A-T
Degenerate code
Four bases: A, C, G, T Two-fold degenerate IUB codes: R=[AG] -- Purines Y=[CT] -- Pyrimidines K=[GT] M=[AC] S=[GC] W=[AT] Four-fold degenerate: N=[AGCT]
Transcription Factors
•Required but not a part of the RNA polymerase complex •Many different roles in gene regulation Binding Interaction Initiation Enhancing Repressing •Various structural classes (eg. zinc finger domains) •Consist of both a DNA-binding domain and an interactive domain
Motifs
Short sequences of DNA or RNA (or amino acids) Often consist of 5- 16 nucleotides May contain gaps Examples include: Splice sites Start/stop codons Transmembrane domains Centromeres Phosphorylation sites Coiled-coil domains Transcription factor binding sites (TFBS – regulatory motifs)
TFBSs
Difficult to identify Each transcription factor may have more than one binding site Degenerate Most occur upstream of translation start site (TSS) but are known to also occur in: introns exons 3’ UTRs Usually occur in clusters, i.e. collections of sites within a region (modules) Often repeated Sites can be experimentally verified
Why are TFBSs important?
Aid in identification of gene networks/pathways Determine correct network structure
Gene A Gene B
Drug discovery Switch production of gene product on/off
Consensus sequences
Matches all of the example sequences closely but not exactly A single site TACGAT A set of sites: TACGAT TATAAT TATAAT GATACT TATGAT TATGTT Consensus sequence: TATAAT or TATRNT Trade-off: number of mismatches allowed, ambiguity in consensus sequence and the sensitivity and precision of the representation.
Information Content and Entropy
Sequence Logos
Frequency Matrices
Given a collection of motifs, TACGAT TATAAT TATAAT GATACT TATGAT TATGTT Create the matrix: T A C G
Position weight matrices
Finding Motifs
Two problems: Given a collection of known motifs, develop a representation of the motifs such that additional occurrences can reliably be identified in new promoter regions Given a collection of genes, thought to be related somehow, find the location of the motif common to all and a representation for it.
Two approaches: Combinatorial Probabilistic
Combinatorial Approach
Exhaustive Search
Exhaustive Search
Sample-driven here refers to trying all the words as they occur in the sequences, instead of trying all possible (4 W ) words exhaustively
Greedy Motif Clustering
Greedy Motif Clustering
Greedy Motif Clustering
Comparative Genomics Main Idea: Conserved non coding regions are important Align the promoters of orthologous co-expressed genes from two (or more) species e.g. human and mouse Search for TFBS only in conserved regions Problems: Not all regulatory regions are conserved Which genomes to use?
Phylogenetic Footprinting Phylogenetic Footprinting refers to the task of finding conserved motifs across different species. Common ancestry and selection on these motifs has resulted in these “footprints”.
Phylogenetic Footprinting An Example
Xie et al. 2005
Genome-wide alignments for four species (human, mouse, rat, dog) Promoter regions and 3’UTRs then extracted for 17,700 well-annotated genes Promoter region taken to be (-2000, 2000) This set of sequences then searched exhaustively for motifs Nature 434 , 338-345, 2005
Xie et al. 2005
The Search
Expected Rate
Probabilistic Approach
Gibbs Sampling (applied to Motif Finding)
Gibbs Sampling Algorithm
Gibbs Sampling – Motif Positions
AlignACE - Gibbs Sampling
Remainder of the lecture: Maximum likelihood and the EM algorithm The remaining slides are for your information only and will not be part of the exam
Basic Statistics
Maximum Likelihood Estimates
EM Algorithm
Basic idea (MEME) http://meme.nbcr.net/meme/meme-intro.html
Basic idea (MEME) MEME is a tool for discovering motifs in a group of related DNA or protein sequences. A motif is a sequence pattern that occurs repeatedly in a group of related protein or DNA sequences. MEME represents motifs as position-dependent letter probability matrices which describe the probability of each possible letter at each position in the pattern. Individual MEME motifs do not contain gaps. Patterns with variable-length gaps are split by MEME into two or more separate motifs. MEME takes as input a group of DNA or protein sequences (the
training set
) and outputs as many motifs as requested. MEME uses statistical modeling techniques to automatically choose the best width, number of occurrences, and description for each motif. http://meme.nbcr.net/meme/meme-intro.html
Basic MEME Model
MEME Background frequencies
MEME – Hidden Variable
MEME – Conditional Likelihood
EM algorithm
Example
E-step of EM algorithm
Example
M-step of EM Algorithm
Example
Characteristics of EM
Gibbs Sampling (versus EM)