5.1: Gene Regulation and Promoter Analysis

1
5.1 Gene Regulation and Promoter Analysis

• Wyeth Wasserman
• Centre for Molecular Medicine and Therapeutics
• Childrens and Womens Hospital
• Department of Medical Genetics
• University of British Columbia

www.cisreg.ca
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Overview

• 5.1.0 Bioinformatics for detection of
  transcription factor binding sites
• The Specificity Problem
• 5.1.1 Discrimination of regulatory control
  sequences
• Based on knowledge of established TFBS
• 5.1.2 Discovery of regulatory mechanisms
• Based on de novo pattern discovery
• 5.1.3 Impending advances

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Layers of Complexity in Metazoan Transcription
4
Transcription Simplified
URF
Pol-II
TATA
URE
5
5.1.0 Profile Models for Prediction of TF Binding
Sites
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Representing Binding Sites for a TF
Set of binding sites AAGTTAATGA CAGTTAATAA GAGTT
AAACA CAGTTAATTA GAGTTAATAA CAGTTATTCA GAGTTAATAA
CAGTTAATCA AGATTAAAGA AAGTTAACGA AGGTTAACGA ATGTTG
ATGA AAGTTAATGA AAGTTAACGA AAATTAATGA GAGTTAATGA A
AGTTAATCA AAGTTGATGA AAATTAATGA ATGTTAATGA AAGTAAA
TGA AAGTTAATGA AAGTTAATGA AAATTAATGA AAGTTAATGA AA
GTTAATGA AAGTTAATGA AAGTTAATGA

• A single site
• AAGTTAATGA

• A set of sites represented as a consensus
• VDRTWRWWSHD (IUPAC degenerate DNA)

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PFMs to PWMs
One would like to add the following features to
the model 1. Correcting for the base
frequencies in DNA 2. Weighting for the
confidence (depth) in the pattern 3. Convert to
log-scale probability for easy arithmetic
w matrix
f matrix
A 5 0 1 0 0 C 0 2 2 4 0 G 0 3 1 0
4 T 0 0 1 1 1
A 1.6 -1.7 -0.2 -1.7 -1.7 C -1.7 0.5
0.5 1.3 -1.7 G -1.7 1.0 -0.2 -1.7 1.3 T
-1.7 -1.7 -0.2 -0.2 -0.2
f(b,i) s(N)
Log ( )
p(b)
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JASPAR OPEN-ACCESS DATABASE OF TF BINDING
PROFILES (Some other databases with TF profiles
include Transfac, TRRD, mPromDB, SCPD (yeast),
dbTBS and EcoTFS (bacteria))
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Performance of Profiles

• 95 of predicted sites bound in vitro (Tronche
  1997)
• MyoD binding sites predicted about once every 600
  bp (Fickett 1995)
• Futility Theorem
• Nearly 100 of predicted TFBS have no function in
  vivo
• Brazma claims it should be called the futility
  conjunction

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1000bp promoter screened with collection of TF
profiles (beta-globin)
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5.1.1 Pattern Discrimination

• Overcoming the specificity problem by
  incorporating biological knowledge into
  computational algorithms

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Phylogenetic Footprinting

• 70,000,000 years of evolution reveals most
  regulatory regions.

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SIDENOTE Global Progressive Alignments(e.g.
ORCA, AVID, LAGAN)

• Global alignments memory product of sequence
  lengths
• Progressive alignment by banding with local
  alignments (e.g. BLAST) and running global method
  on banded sub-segments
• Recursion with decreasingly stringent parameters

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Phylogenetic Footprinting to Identify Functional
Segments
Identity
200 bp Window Start Position (human sequence)
Actin gene compared between human and mouse.
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Phylogenetic Footprinting (cont)
FoxC2
100 80 60 40 20 0
Identity
Start Position of 200bp Window
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Recall...
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1000bp beta-globin promoter screened with
phylogenetic footprinting
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Choosing the right species...Genes evolve at
different rates make gene-specific choice
CHICKEN
HUMAN
MOUSE
HUMAN
COW
HUMAN
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Performance Human vs. Mouse Pairwise
SELECTIVITY
SENSITIVITY

• Testing set 40 experimentally defined sites in
  15 well studied genes (Replicated with 100 site
  set)
• 85-95 of defined sites detected with
  conservation filter, while only 11-16 of total
  predictions retained

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ConSite
Now driven by the ORCA Aligner
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Selected Emerging Issues

• Multiple sequence comparisons
• Incorporate phylogenetic distances into a scoring
  metric
• Visualization (see dcode service and Sockeye)
• Analysis of many closely related species
• Phylogenetic shadowing
• Genome rearrangements
• Inversion compatible alignment algorithm
• LAGAN
• Higher order models of TFBS

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Regulatory Modulesfor better specificity

• TFs do NOT act in isolation

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Layers of Complexity in Metazoan Transcription
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Liver regulatory modules
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PSSMs for Liver TFs
HNF3
HNF1
HNF4
C/EBP
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Detection of Clusters of TFBS

• In the best cases, we have enough data to train a
  discriminant function
• Rare to have sufficient data
• Alternatively, identify dense clusters of sites
  that are statistically significant
• Diverse methods have been introduced over the
  past few yearsBerman Markstein Frith Noble
  Wagner
• Non-trivial to correct for non-random properties
  of DNA
• Most difficulty comes from local direct repeats
• A primary challenge from the biological side is
  the selection of a meaningful grouping of TFs
• Multiple testing problems severe

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TFBS Clusters(MSCAN, MCAST, COMET, etc)

• MSCAN allows users to submit any set of TF
  profiles
• Calculates significance for each site based on
  local sequence characteristics
• G-rich PSSM gets less weight on G-rich region of
  gene
• Calculates cluster significance using a dynamic
  programming approach
• Approximately 1 significant liver cluster / 18
  000 bp in human genome sequence
• Filters to remove significant clusters of sites
  that contain local repeats
• Identification of non-random characteristics in
  DNA

http//mscan.cgb.ki.se
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Training predictive models for modules

• Not every combination of sites is meaningful
• Reality Some factors critical, others secondary
• An alternative is to teach the computer which
  combinations are better
• Limited by small size of positive training set
• Explore an older method based on Logistic
  Regression Analysis

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Recall Liver regulatory modules
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Logistic Regression Analysis
a1 a2 a3 a4
Optimize a vector to maximize the distance
between output values for positive and negative
training data. Output value is
elogit
p(x) 1
elogit
S
logit
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PERFORMANCE

• Liver (Genome Research, 2001)
• At 1 hit per 35 kbp, identifies 60 of modules
• Limited to genes expressed late in liver
  development

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UDPGT1 (Gilberts Syndrome)
Wildtype Mutant
Liver Module Model Score
Window Position in Sequence
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Making better predictions

• Profiles make far too many false predictions to
  have predictive value in isolation
• Phylogenetic footprinting eliminates about 90 of
  false predictions while retaining 70-70 of real
  sites (human vs mouse)
• Detection of clusters of binding sites offers
  better predictive performance, especially through
  trained discrimination functions

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Active Issues

• Significance of clusters of sites
• Segmentation of DNA into regions of different
  composition
• Methods using training to find clusters
• Where to place weights?
• Interaction weighting in the absence of large
  data collections
• Resources
• Limited number of solid PSSMs
• Need a reference database for functional
  regulatory regions
• Validation of predictions for tissues/cells not
  well represented in cell culture

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EMERGING APPLICATIONRegulatory Analysis of
Variation in ENhancers

• Genetic variation in TFBS can result in
  biomedically important phenotypes

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Sequence Variation in TFBS
URF
TSS
AaGT
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Stage 1Prediction of Regulatory Regions
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Stage 1 Predict Regulatory Regions

• Retrieve orthologous human and mouse gene
  sequences
• Align sequences with a global aligner (ORCA)
• Identify regions of conservation
• Designs primers for SNP discovery

FoxC2
100 80 60 40 20 0
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SIDENOTE Data/Orthology obtained from GeneLynx
(www.genelynx.org)
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Stage 2Analysis of Polymorphisms
ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAATGAATAACAGA
T ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAATGAATAACA
GAT ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAACGAATAA
CAGAT ACGCATAAGTTAACGAATAACAGAT ACGCATAAGTTAACGAAT
AACAGAT ACGCATAAGTTAACGAATAACAGAT
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Identify variations that generate allele-specific
binding sites (predicted)
Differences in scores
Pseudo-data for instructional purposes
1234567890123456789012345 ACGCATAAGTTAAtGAATAACAGA
T .............c...........
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RAVEN screenshots
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5.1.2 Discovery of Mediating TFBS for Sets of
Co-Regulated Genes

• Finding characteristics over-represented in a set
  of co-regulated genes

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Pattern Discovery
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Linking co-expressed genes from microarrays to
candidate TFs
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oPOSSUM Project

• A significant subset of TFs are represented by
  existing binding profiles
• Within same structural class, often binding
  specificity retained (more on this later)
• Can we link known TFs to a putative regulon by
  over-representation of predicted binding sites in
  promoters?
• Identical concept to the detection of
  over-represented GO terms from previous session

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oPOSSUM Procedure
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Reference Gene Sets
Fisher p-values plt1e-05, plt1e-02
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MICROARRAY APPLICATIONNF-kB Inhibitor-sensitive
genes (326)
plt1e-30, plt1e-10, plt1e-05, plt1e-02
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oPOSSUM Server
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Over-represented Site Combinations (Kreiman 2004)

• Based on our understanding of CRMs, likely that
  combinations of sites would be more distinguished
  than individual sites (better signal-to-noise?)
• Kreiman has introduced a system to assess
  clusters of neighbouring conserved sites based on
  counting
• Hypergeometric distribution, simply compare the
  frequency of the cluster occurrence vs.
  expectation

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What if the TFBS is novel?
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de novo Pattern Discovery Methods

• String-based
• e.g. Moby Dick (Bussemaker, Li Siggia)
• Identify over-represented oligomers in comparison
  of and - (or complete) promoter collections
• Profile-based
• Monte Carlo/Gibbs Sampling
• e.g. AnnSpec (Workman Stormo)
• Identify strong patterns in promoter
  collection vs. background model of expected
  sequence characteristics

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String-base Exhaustive Methods
Word-based methods How likely are X words in a
set of sequences, given sequence characteristics?
CCCGCCGGAATGAAATCTGATTGACATTTTCC gtEP71002 ()
CeIV msp-56 B range -100 to -75
TTCAAATTTTAACGCCGGAATAATCTCCTATT gtEP63009 () Ce
Cuticle Col-12 range -100 to -75
TCGCTGTAACCGGAATATTTAGTCAGTTTTTG gtEP63010 () Ce
Cuticle Col-13 range -100 to -75
TATCGTCATTCTCCGCCTCTTTTCTT gtEP11013 () Ce
vitellogenin 2 range -100 to -75
GCTTATCAATGCGCCCGGAATAAAACGCTATA gtEP11014 () Ce
vitellogenin 5 range -100 to -75
CATTGACTTTATCGAATAAATCTGTT gtEP11015 (-) Ce
vitellogenin 4 range -100 to -75
ATCTATTTACAATGATAAAACTTCAA gtEP11016 () Ce
vitellogenin 6 range -100 to -75
ATGGTCTCTACCGGAAAGCTACTTTCAGAATT gtEP11017 () Ce
calmodulin cal-2 range -100 to -75
TTTCAAATCCGGAATTTCCACCCGGAATTACT gtEP63007 (-) Ce
cAMP-dep. PKR P1 range -100 to -75
TTTCCTTCTTCCCGGAATCCACTTTTTCTTCC gtEP63008 () Ce
cAMP-dep. PKR P2 range -100 to -75
ACTGAACTTGTCTTCAAATTTCAACACCGGAA gtEP17012 () Ce
hsp 16K-1 A range -100 to -75 TCAATGCCGGAATTCTGAA
TGTGAGTCGCCCT gtEP55011 (-) Ce hsp 16K-1 B range
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Exhaustive methods(2)
Find all words of length 7 in the yeast genome
GTCTTTATCTTCAAAGTTGTCTGTCCAAGATTTGGACTTGAAGGACAAGC
GTGTCTTCTCAGAGTTGACTTCAACGTCCCATTGGACGGTAAGAAGATCA
CTTCTAACCAAAGAATTGTTGCTGCTTTGCCAACCATCAAGTACGTTTTG
GAACACCACCCAAGATACGTTGTCTTGTTCTCACTTGGGTAGACCAAACG
GTGAAAGAAACGAAAAATACTCTTTGGCTCCAGTTGCTAAGGAATTGCAA
TCATTGTTGGGTAAGGATGTCACCTTCTTGAACGACTGTGTCGGTCCAGA
AGTTGAAGCCGCTGTCAAGGCTTCTGCCCCAGGTTCCGTTATTTTGTTGG
AAAACTGCGTTACCACATCGAAGAAGAAGGTTCCAGAAAGGTCGATGGTC
AAAAGGTCAAGGCTCAAGGAAGATGTTCAAAAGTTCAGACACGAATTGAG
CTCTTTGGCTGATGTTTACATCACGATGCCTTCGGTACCGCTCACAGAGC
TCACTCTTCTATGGTCGGTTTCGACTTGCCAACGTGCTGCCGGTTTCTTG
TTGGAAAAGGAATTGAAGTACTTCGGTAAGGCTTTGGAGAACCCAACCAG
ACCATTCTTGGCCATCTTAGGTGGTGCCAAGGTTGCTGACAAGATTCAAT
TGATTGACAACTTGTTGGACAAGGTCGACTCTATCATCATTGGTGGTGGT
ATGGCTTTCCCTTCAAGAAGGTTTTGGAAAACACTGAAATCGGTGACTCC
ATCTTCGACAAGGCTGGTGCTGAAATCGTTCCAAAGTTGATGGAAAAGGC
CAAGGCCAAGGGTGTCGAAGTCGTCTTGCAGTCGACTTCATCATTGCTGA
TGCTTTCTCTGCTGATGCCAACACCAAGACTGTCACTGACAAGGAAGGTA
TTCCAGCTGGCTGGCAAGGGTTGGACAATGGTCCAGAATCTAGAAAGTGT
TTGCTGCTACTGTTGCAAAGGCTAAGACCATTGTCTGGAACGGTCCACCA
GGTGTTTTCGAATTCGAAAAGTTCGCTGCTGGTACTAAGGCTTTGTTAGA
CGAAGTTGTCAAGAGCTCTGCTGCTGGTAACACCGTCATCATTGGTGGTG
GTGACACTGCCA
Make a lookup table TTTTTTTT/aaaaaaa 57788 GATAG
GCA/tgcctatc 589 AAACCTTT/aaaggttt
456 Etc...
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Exhaustive methods(3)
Over-representation How many words of type
AGGAGTGA are found in our sequences?
How likely is this result?
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Exhaustive methods(4)
Modeling Properties of DNA
Simple How likely are single nucleotides?
(extended Bernoulli) Complex Neglect certain
words Locations of TFBS Higher-order
descriptions of DNA
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Exhaustive methods Key items

• Algorithms with high complexity - Large sequences
  and/or many possible word lengths not possible
• Often string-based
• TFBS are not words (fuzzy binding)
• Sensitivity susceptible to noisy indata (e.g.
  microarrays)

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Profile-based Methods(usually probablistic)
Find a local alignment of width x of sites that
maximizes information content in reasonable
time Usually by Gibbs sampling or EM methods
Motivations TFBS are not words Efficiency Can be
intentionally influenced by biological data
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Profile Methods (2)
tgacttcc
The Gibbs Sampling algorithm
tgatctct
agacctca
tgacctct
Two data structures used 1) Current pattern
nucleotide frequencies qi,1,..., qi,4 and
corresponding background frequencies pi,1,...,
pi,4 2) Current positions of site startpoints
in the N sequences a1, ..., aN , i.e. the
alignment that contributes to qi,j. One starting
point in each sequence is chosen randomly
initially.
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Profile Methods (3)
Iteration step Remove one sequence z from the
set. Update the current pattern according to
z
A
tgacttcc
tgatctct
agacctca
tgacctct
Pseudocount for symbol j
Sum of all pseudocounts in column
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Pattern Discovery Across Orthologous Promoters
from Gram-Positive Bacteria
Real sets
random
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EXAMPLEYeast Regulatory Sequence Analysis (YRSA)
system
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Tests of YRSA System
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SIDENOTE Comparison of profiles requires
alignment and a scoring function

• Scoring function based on sum of squared
  differences
• Align frequency matrices with modified
  Needleman-Wunsch algorithm
• Calculate empirical p-values based on simulated
  set of matrices

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How is the Performance Hit and Miss
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Applied Pattern Discovery is Acutely Sensitive to
Noise
True Mef2 Binding Sites
68
Over-coming the sensitivity challenge

• Metazoan genomes are far from ideal

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Biochemical complexity enables greater complexity
in regulation
70
Four Approaches to Improve Sensitivity

• Better background models
• -Higher-order properties of DNA
• Phylogenetic Footprinting
• HumanMouse comparison eliminates 75 of
  sequence
• Regulatory Modules
• Architectural rules
• Limit the types of binding profiles allowed
• TFBS patterns are NOT random

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Phylogenetic Footprinting to Identify Conserved
Regions
Bayes Block Aligner (Lawrence Group)
ORCA
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Skeletal Muscle Genes

• One of the most extensively studied tissues for
  transcriptional regulation
• 45 genes partially analyzed
• 26 genes with orthologous genomic sequence from
  human and rodent
• Five primary classes of transcription factors
• Principal Myf (myoD), Mef2, SRF
• Secondary Sp1 (G/C rich patches), Tef (subset of
  skeletal muscle types)

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de novo Discovery of Skeletal Muscle
Transcription Factor Binding Sites
Mef2-Like
SRF-Like
Myf-Like
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Pattern discovery methods using biochemical
constraints
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RECALL Gibbs Algorithm
z
tgacttcc
tgatctct
agacctca
tgacttcc
tgacctct
tgatctct
agacctca
tgacctct
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(No Transcript)
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Intra-family PSSM similarity
TF Database (JASPAR)
COMPARE
Jackknife Test 87 correct Independent Test
Set 93 correct
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(No Transcript)
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FBPs enhance sensitivity of pattern detection
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(No Transcript)
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APPLICATIONCancer Protection Response

• Detoxification-related enzymes are induced by
  compounds present in Broccoli
• Arrays, SSH and hard work have defined a set of
  responsive genes
• A known element mediates the response
  (Antioxidant Responsive Element)
• Controversy over the type of mediating leucine
  zipper TF
• NF-E2/Maf or Jun/Fos

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Application (2)
Problem Given a set of co-regulated genes,
determine the common TFBS. Classify the
mediating TF. We expect a leucine zipper-type
TF.
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Application (3)
Problem Given a set of co-regulated genes,
determine the common TFBS. Classify the
mediating TF. We expect a leucine zipper-type
TF.
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Application (4)
Problem Given a set of co-regulated genes,
determine the common TFBS. Classify the
mediating TF. We expect a leucine zipper-type
TF.
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EMERGING METHODde novo Analysis of Regulatory
Modules
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Focus on regulatory modules for pattern detection
Cluster Genes by Expression
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Analyze co-regulated genes to define circuit
characteristics
General Circuit Properties
Specific Gene Features
Binding Profiles
mi

aij
Neighbor Interactions
mi
mj
mi
mj
0
b
Width Distributions (Sum of Separations)
250
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Discovery performance

• Approximately 50 of annotated TFBS are detected
  in the training set sequences of 25 genes
• Only 40 of predicted TFBS are annotated
• We suspect that most of the un-annotated sites
  will turn out to be functional. This needs to be
  determined.

89
Review of Primary Points

• Second Chance

90
Regulatory regions problem space
Sets of binding sites AATCACCAAATCACCAAATCACCA
AATCACCAAATCTCCCAATCTCCGAATCACACAATCATCAAATC
TCACAATCTCTGAGTCCCCAAATCCCGGAATCTGAGAATCCATA
ATTCAGCCAATAACTTGATAACCTAATTAGACGATTACAGGATTA
GCGATTCTTCCTATGAACAGATTAAAAAGACCCCA
Specificity profiles for binding sites A -2
0 -2 -0.415 0.585 -2 -2 2.088 -2
-2 -1 0.585 C 1 0.585 0 0
-1 -2 -2 -2 2.088 -2 0.585 0.807
G 0.585 0.322 0.807 1.585 1 -2 2
-2 -2 2.088 -2 0 T 0.319 0.322
1 -2 0 2.088 -1 -2 -2 -2
1.459 -0.415
Clusters of binding sites
Transcription factors Transcription factor
binding sites Regulatory nucleotide sequences
91
Detecting binding sites in a single sequence
Scanning a sequence against a PWM
Sp1
Abs_score 13.4 (sum of column scores)
Is 93 better than 82?
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Phylogenetic Footprints
Scanning a single sequence
Scanning a pair orf orthologous sequences for
conserved patterns in conserved sequence regions
A dramatic improvement in the percentage of
biologically significant detections

• Low specificity of profiles
• too many hits
• great majority are not biologically significant

93
Applied Pattern Discovery is Acutely Sensitive to
Noise
True Mef2 Binding Sites
94
Acknowledgements

• Wasserman Group
• Wynand Alkema
• Dave Arenillas
• Jochen Brumm
• Alice Chou
• Shannan Ho Sui
• Danielle Kemmer
• Jonathan Lim
• Raf Podowski
• Dora Pak
• Albin Sandelin
• Chris Walsh
• Collaborating Trainees
• Malin Andersson (KTH)
• Öjvind Johansson (UCSD)
• Stuart Lithwick (U.Toronto)

Collaborators Boris Lenhard (K.I.) Chip Lawrence
(Wadsworth) William Thompson (Wadsworth) Jens
Lagergren (KTH) Christer Höög (K.I.) Brenda
Gallie (OCI) Jacob Odeberg (KTH) Niclas Jareborg
(AZ) William Hayes (AZ) James Mortimer
(MF) Group Alumni Elena Herzog Annette
Höglund William Krivan Luis Mendoza
Support CIHR, CGDN, CFI, Merck-Frosst, BC
Childrens Hospital Foundation, Pharmacia,
ECMarie Curie, KI-Funder
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EXTRA SLIDESWhat will a computational biologist
do with a scoring function?

• Build a similarity tree!

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The matrix tree
97
Compare with consensus for both classes - CANNTG