Detection and analysis of transcriptional control sequences

Wyeth Wasserman

October VanBUG Seminar

Centre for Molecular Medicine and TherapeuticsChildren’s and Women’s Hospital

University of British Columbia

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Transcription Simplified

TATAURE

URF Pol-II

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Overview of Transcription in Gene Regulation

• At the most basic level, transcriptional regulation is defined by binding of TFs to DNA

• Complexity is increased by TF interactions, chromatin structure and protein modifications

• How can we advance our understanding of regulation by computational analysis?

A short history lesson…

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Representing Binding Sites for a TF (HNF1)

• A set of sites represented as a consensus» VDRTWRWWSHDWVWH

A 14 16 4 0 1 19 20 1 4 13 4 4 13 12 3C 3 0 0 0 0 0 0 0 7 3 1 0 3 1 12G 4 3 17 0 0 2 0 0 9 1 3 0 5 2 2T 0 2 0 21 20 0 1 20 1 4 13 17 0 6 4

• A matrix describing a a set of sites

• A single HNF1 site» AAGTTAATGATTAAC

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TGCTG = 0.9

PFMs to PWMs

One would like to add the following features to the model:1. Correcting for the base frequencies in DNA2. Weighting for the confidence (depth) in the pattern3. Convert to log-scale probability for easy arithmetic

A 5 0 1 0 0C 0 2 2 4 0G 0 3 1 0 4T 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 1.3T -1.7 -1.7 -0.2 -0.2 -0.2

f matrix w matrix

Log ( )f(b,i) +p(b)

4/N

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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)

• The Futility Theorem– Nearly 100% of predicted transcription factor

binding sites have no function in vivo

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A 1 kbp promoter screened with collection of TF profiles

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Phylogenetic Footprinting

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

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Phylogenetic Footprinting to Identify Functional Segments

% Id

en

tity

Actin gene compared between human and mouse with DPB.

200 bp Window Start Position (human sequence)

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Regulatory sites are usually conserved between orthologous genes

HUMAN ACGATACGCATCACAGACT.ACAGACTACGGCTAGCA -|-|||||||||-|---|--|||-------|-|---|MOUSE GCAATACGCATCGCGATCAGACATCAGCACG.TGTGA

HUMAN ACATCAGCATACACGCAACTACACAGACTACGACTA ---|||||-||||---|-|----||-||-||||---MOUSE CGTTCAGCTTACAGCTAGCATAGCATACGACGATAC

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The 1kbp promoter screen with footprinting

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Choosing the ”right” species...(BONUS: What’s the ultimate sin in bioinformatics?)

COW

MOUSE

CHICKEN

HUMAN

HUMAN

HUMAN

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ConSite (www.phylofoot.org)

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Performance: Human vs. Mouse

• Testing set: 40 experimentally defined sites in 15 well studied genes

• 85-95% of defined sites detected with conservation filter, while only 11-16%of total predictions retained

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de novo Discovery of TF Binding Sites

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Unraveling Transcriptional Control Mechanisms

Given a set of ”co-regulated” genes, define motifs over-represented in the regulatory regions

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Pattern Detection Methods

• Exhaustive – e.g. “Moby Dick” (Bussemaker, Li & Siggia)

– Identify over-represented oligomers in comparison of “+” and “-” (or complete) promoter collections

• 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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Yeast Regulatory Sequence Analysis (YRSA) system

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Yeast tests of YRSA System

PDR3-regulated genes from array study

Classic cell-cycle array data re-clustered by Getz et al

DNA-damage responsepartially mediating by MCB

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10

12

14

16

18

0 100 200 300 400 500 600

THE PROBLEM:

Pattern Detection in Long Sequences

SEQUENCE LENGTH

RANDOM SET

MEF2 SET

ME

F2 S

IMIL

AR

ITY S

CO

RE

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Four Approaches to Extend Sensitivity

• Phylogenetic Footprinting– Human-Mouse eliminates ~75% of sequence

• Better background models– e.g. AnnSpec

• Better definition of co-regulation– Microarrays occasionally produce noise

• Use biochemical knowledge about TFs– TFBS patterns are NOT random

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Some characteristics have been explored…• Segmentation: informative positions separated

by variable positions (proteins bind as dimers)• Positional Variance: subset of positions

contain most of the info• Palindromes are common in the patterns

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Our Hypothesis

• Point 1: Structurally-related DNA binding domains interact with similar target sequences

• Exceptions exist (e.g. Zn-fingers)

• Point 2: There are a finite number of binding domains used in human TFs

• Approximately 20-25

• Idea: We could use the shared binding properties for each family to focus pattern detection methods

• Constrain the range of patterns sought

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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

Score

Fre

que

ncy

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Prediction of TF Class

TF Database(JASPAR)

COMPARE

Match to bHLH

Jackknife Test 87% correct

Independent Test Set 93% correct

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FBPs enhance sensitivity of pattern detection

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APPLICATION:

Cancer 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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Gibbs Sampling

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.

Gibbs with FBP PriorClassify New TF Motif

Maf (p<0.02)

Jun (p<0.98)

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Regulatory Modules

TFs do NOT act in isolation

Layers of Complexity in Metazoan Transcription

Chromatin picture used with permission of Zymogenetics.

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Liver Differentiation (data mostly from studies of hepatocytes)

CEBP HNF3 HNF1HNF4

Stem Early Fetal Mature

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Liver regulatory modules

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Models for Liver TFs…(Data that takes 2 months to produce and 10 seconds to present) (Or, what to do with an astrophysicist new to bioinformatics)

HNF1

C/EBP

HNF3

HNF4

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Training predictive models for modules

• Limited by small size of positive training set

• We elected to use logistic regression analysis for the first models

• Your favorite statistical approach would probably do equally well– data limited

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Logistic Regression Analysis

“logit”

Optimize vector to maximize the distance between output values for positive and negative training data.

Output value is:

elogit

p(x)= 1 + elogit

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UDPGT1 (Gilbert’s Syndrome)

WildtypeMutant

Live

r M

odul

e M

odel

Sco

re

“Window” Position in Sequence

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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

• Skeletal Muscle (JMB, 1998)

– Set to 1 prediction per 35 000 bp– Identifies 66% of test set correctly

LRA Models do not account for multiple sites for the same TF*

* Side-track: Newer Methods

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Combining Phylogenetic Footprinting with a Module Model

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Genome Scan

• Screened the available mouse genomic sequences (~300 MB) for modules and discarded hits for which sequence was not conserved with human (BLAST)

• Removed regions for which corresponding human sequence did not score as module

• Of ~100 predicted modules• 20 annotated genes: 5 from training, 3 additional

modules, 5 liver specific, 3 unknown and 4 not liver

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de novo Discovery of Regulatory Modules

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Focus on regulatory modules for pattern detection

Cluster Genes by Expression

Identify and ModelContributing TFs

6 0 0 0 7 0 02 8 4 7 1 0 20 0 4 0 0 8 00 0 0 1 0 0 6

Predictive Models

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Finding binding sites in sets of co-regulated human genes

• Sequence “space” is too large– Narrow with Phylogenetic Footprinting

• Identify patterns in conserved blocks via Gibbs sampling

• Assess quality of patterns based on biological knowledge

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Phylogenetic Footprinting to Identify Conserved Regions

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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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Regulatory regions directing muscle-specific transcription

MyoD/Myf SRF

Mef2 Tef

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de novo Discovery of Skeletal Muscle Transcription Factor Binding Sites

Mef2-Like SRF-Like Myf-Like

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We will soon be able to define modules for many contexts…

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A gene-centric data integration project...

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COMING SOON:

The Integrated Module Sampler

Gene1Gene2Gene3Gene4Gene5

Calls to ensEMBL

Calls to GeneLynx

Calls to BlastZ(Switch to Lagan?)

Module Sampler

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YOU SHOULD HAVE BEEN THERE… THIS

SLIDE EXCLUDED FROM THE POSTED FILE

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Conclusions

• Evolution drives understanding in biology– Phylogenetic Footprinting

• Biochemistry inspires Bioinformatics– Regulatory Modules– Familial Binding Profiles

• Analysis of regulatory sequences is improving– Given sets of orthologous genes, one can predict regulatory regions– Given sets of co-regulated genes, it is possible to infer the binding

profiles for critical transcription factors

• Much more work is needed…

THANKS!Wasserman Group – CMMT

Danielle KemmerSeveral Newcomers

Wasserman Group - SwedenAlbin Sandelin

Raf Podowski (CA)Wynand Alkema

Collaborating StudentsMalin Andersson (Odeberg)

Öjvind Johansson (Lagergren)Hui Gao (Dahlman-Wright)

Emily Hodges (Höög)

Support: Merck-Frosst, C&W, Pharmacia, EU–Marie Curie, CGDN, KI-Funder

CollaboratorsChip Lawrence (Wadsworth)

Boris Lenhard (K.I.)Jens Lagergren (SBC)

Christer Höög (K.I.)Brenda Gallie (OCI)

Jacob Odeberg (KTH)Niclas Jareborg (AZ)William Hayes (AZ)

Group AlumniPer Engström Elena Herzog

Annette HöglundWilliam KrivanBoris LenhardLuis Mendoza

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URLs...

• Group: www.cmmt.ubc.ca

• ConSite/DPB: www.phylofoot.org

• GeneLynx: www.genelynx.org