Computational Genomics and Proteomics
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Computational Genomics and Proteomics Lecture 8 Lecture 8 Motif Discovery Motif Discovery C E N T R F O R I N T E G R A T I V E B I O I N F O R M A T I C S V U E
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C. E. N. T. E. R. F. O. R. I. N. T. E. G. R. A. T. I. V. E. B. I. O. I. N. F. O. R. M. A. T. I. C. S. V. U. Computational Genomics and Proteomics. Lecture 8 Motif Discovery. Outline Gene Regulation DNA Transcription factors Motifs What are they? - PowerPoint PPT Presentation
Transcript of Computational Genomics and Proteomics
Computational Genomics and Proteomics
Lecture 8Lecture 8
Motif DiscoveryMotif Discovery
CENTR
FORINTEGRATIVE
BIOINFORMATICSVU
E
OutlineGene Regulation
DNATranscription factors
MotifsWhat are they?Binding Sites
Combinatoric ApproachesExhaustive searchesConsensus
Comparative GenomicsExample
Probabilistic ApproachesStatisticsEM algorithmGibbs 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] -- PurinesY=[CT] -- PyrimidinesK=[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
Short sequences of DNA or RNA (or amino acids)Often consist of 5- 16 nucleotidesMay contain gapsExamples include:
Splice sitesStart/stop codonsTransmembrane domainsCentromeresPhosphorylation sitesCoiled-coil domainsTranscription factor binding sites (TFBS – regulatory motifs)
Motifs
TFBSsDifficult to identifyEach transcription factor may have more than one binding siteDegenerateMost occur upstream of translation start site (TSS) but are known to also occur in:
intronsexons3’ UTRs
Usually occur in clusters, i.e. collections of sites within a region (modules)Often repeatedSites can be experimentally verified
Why are TFBSs important?
Aid in identification of gene networks/pathways
Determine correct network structure
Drug discovery
Switch production of gene product on/off
Gene A Gene B
Consensus sequencesMatches all of the example sequences closely but not exactlyA single site
TACGATA set of sites:
TACGATTATAATTATAATGATACTTATGATTATGTT
Consensus sequence:TATAAT orTATRNT
Trade-off: number of mismatches allowed, ambiguity in consensus sequence and the sensitivity and precision of the representation.
Information Content and Entropy
Sequence Logos
Given a collection of motifs,
TACGATTATAATTATAATGATACTTATGATTATGTT
Create the matrix:
Frequency Matrices
TACG
Position weight matrices
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 regionsGiven 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:CombinatorialProbabilistic
Finding Motifs
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 (4W) words exhaustively
Greedy Motif Clustering
Greedy Motif Clustering
Greedy Motif Clustering
Main Idea: Conserved non coding regions are importantAlign the promoters of orthologous co-expressed genes from two (or more) species e.g. human and mouseSearch for TFBS only in conserved regions
Problems:Not all regulatory regions are conservedWhich genomes to use?
Comparative Genomics
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”.
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
Phylogenetic Footprinting
An Example
Nature 434, 338-345, 2005
The SearchXie et al. 2005
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)
