Discovery and Analysis of Regulatory Regions in the Human Genome - - PowerPoint PPT Presentation

Discovery and Analysis of Regulatory Regions in the Human Genome

Wyeth Wasserman

Centre for Molecular Medicine and Therapeutics Children’s and Women’s Hospital University of British Columbia

Acknowledgements

Wasserman Group – CMMT Dave Arenillas Jochen Brumm Danielle Kemmer Jonathan Lim Wasserman Group - Karolinska Albin Sandelin Raf Podowski Wynand Alkema Collaborating Trainees Malin Andersson (KTH) Öjvind Johansson (UCSD) Stuart Lithwick (U.Toronto)

Support: CIHR, CGDN, Merck-Frosst, BC Children’s Hospital Foundation, Pharmacia, EC–Marie Curie, KI-Funder

Collaborators Chip Lawrence (Wadsworth) William Thompson (Wadsworth) Jens Lagergren (SBC/KTH) Christer Höög (K.I.) Brenda Gallie (OCI) Jacob Odeberg (KTH) Niclas Jareborg (AZ) William Hayes (AZ)

Boris Lenhard (K.I.)

Group Alumni Elena Herzog Annette Höglund William Krivan Luis Mendoza

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Overview

• Basics of promoter analysis

– Bioinformatics for detection of transcription factor binding sites

• The Specificity Problem
• Phylogenetic Footprinting

– Pattern recognition for discovery of novel regulatory mechanisms

• A signal-to-noise problem
• Discrimination of Regulatory Regions

– Given binding models for relevant TFs, identify potential regulatory sequences – Analyze potentially important genetic variation within predicted regulatory regions

• Pattern discovery (as time permits)

– Given a set of co-regulated genes, predict important classes of TFBS – Given a newly discovered binding profile, predict candidate regulon members

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Transcription Simplified

TATA URE

URF Pol-II

Teaching a computer to find TFBS…

Representing Binding Sites for a TF

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

A 14 16 4 0 1 19 20 1 4 13 4 4 13 12 3 C 3 0 0 0 0 0 0 0 7 3 1 0 3 1 12 G 4 3 17 0 0 2 0 0 9 1 3 0 5 2 2 T 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 site
• AAGTTAATGA

Set of binding sites AAGTTAATGA CAGTTAATAA GAGTTAAACA CAGTTAATTA GAGTTAATAA CAGTTATTCA GAGTTAATAA CAGTTAATCA AGATTAAAGA AAGTTAACGA AGGTTAACGA ATGTTGATGA AAGTTAATGA AAGTTAACGA AAATTAATGA GAGTTAATGA AAGTTAATCA AAGTTGATGA AAATTAATGA ATGTTAATGA AAGTAAATGA AAGTTAATGA AAGTTAATGA AAATTAATGA AAGTTAATGA AAGTTAATGA AAGTTAATGA AAGTTAATGA Set of binding sites AAGTTAATGA CAGTTAATAA GAGTTAAACA CAGTTAATTA GAGTTAATAA CAGTTATTCA GAGTTAATAA CAGTTAATCA AGATTAAAGA AAGTTAACGA AGGTTAACGA ATGTTGATGA AAGTTAATGA AAGTTAACGA AAATTAATGA GAGTTAATGA AAGTTAATCA AAGTTGATGA AAATTAATGA ATGTTAATGA AAGTAAATGA AAGTTAATGA AAGTTAATGA AAATTAATGA AAGTTAATGA AAGTTAATGA AAGTTAATGA AAGTTAATGA

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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 DNA
• 2. Weighting for the confidence (depth) in the pattern
• 3. Convert to log-scale probability for easy arithmetic

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 matrix w matrix Log(

)

f(b,i)+ s(N) p(b)

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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 for better specificity 70,000,000 years of evolution reveals most regulatory regions.

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SIDENOTE: Global Progressive Alignments (ORCA Algortihm)

• 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

Actin gene compared between human and mouse by ORCA.

200 bp Window Start Position (human sequence)

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Phylogenetic Footprinting (2)

• 0.2

0.2 0.4 0.6 0.8 1 1000 2000 3000 4000 5000 6000 7000

FoxC2

100% 80% 60% 40% 20% 0%

% Identity Start Position of 200bp Window

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Recall...

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1kbp promoter with phylogenetic footprinting

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Choosing the ”right” species...

COW MOUSE CHICKEN

HUMAN HUMAN HUMAN

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Performance: Human vs. Mouse

• 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

SELECTIVITY SENSITIVITY

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ConSite (www.phylofoot.org)

Now driven by the ORCA Aligner

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Emerging Issues

• Multiple sequence comparisons

– Incorporate phylogenetic trees – Visualization

• Analysis of closely related species

– Phylogenetic shadowing

• Genome rearrangements

– Inversion compatible alignment algorithm

• Higher order models of TFBS

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Improving Pattern Discrimination TFs do NOT act in isolation

Layers of Complexity in Metazoan Transcription

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Biochemical complexity enables greater complexity in regulation

500 bp

Yeast ORF A

GO GO GO

Humans

20 000 bp

EXON 1 EXON 3 2

GO GO GO GO GO GO GO GO GO

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Statistically Significant Clusters of Sites

• Can we identify dense clusters of sites that are

statistically significant?

• Diverse methods have been introduced over the past few

years…Berman; Markstein; Frith; Noble; Wagner;…

• In the best cases, we have enough data to train a

discriminant function

• Rare to have sufficient data
• For general purpose, we identify statistically

significant clusters of TFBS

• Non-trivial to correct for non-random properties of DNA

– Most difficulty comes from local direct repeats

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Liver regulatory modules

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Models for Liver TFs…

(10 second slide for 3 months of work)

HNF1 C/EBP HNF3 HNF4

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Logistic Regression Analysis

∗ α1 ∗ α2 ∗ α3 ∗ α4

Σ

“logit” Optimize α 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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PERFORMANCE

• Liver (Genome Research, 2001)

– At 1 hit per 35 kbp, identifies 60% of modules – Limited to genes expressed late in liver development

LRA Models do not account for multiple sites for the same TF and require significant reference collection

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UDPGT1 (Gilbert’s Syndrome)

• 0.2

0.2 0.4 0.6 0.8 1 100 510 920 1330 1740 2150 2560 2970 3380 3790 4200 4610 5020 5430 5840 Series1 Series2 Wildtype Mutant

Liver Module Model Score “Window” Position in Sequence

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Predicted Muscle Regulatory Module

0.1 0.2 0.3 0.4 0.5 1500 3000 4500 6000 7500 9000

Kcna7 Score

I.23

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MSCAN: A more general method

(w/ Jens Lagergren, Royal Technical University of Sweden)

• MSCAN allows users to submit any set of TF

profiles

• Calculates significance for each site based on local

sequence characteristics

• Calculates cluster significance using a dynamic

programming approach

• Approximately 1 significant liver cluster / 18 000 bp in human

genome sequence

• Filters out “significant” clusters of sites that

contain local repeats

• Identification of non-random characteristics in DNA

http://mscan.cgb.ki.se

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JASPAR (jaspar.cgb.ki.se) OPEN-ACCESS DATABASE OF TF BINDING PROFILES

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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

• Detection of clusters of binding sites offers better

predictive performance, especially through trained discriminant functions

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RAVEN Project: Regulatory Analysis of Variation in ENhancers

Genetic variation in TFBS can result in biomedically important phenotypes

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Sequence Variation in TFBS

TSS AaGT

URF

Koivisto et al., 1994 Familial hypercholesterolemia LDLR I DeVivo et al., 2002 Endometrial cancer PR

• Y. Olswang et al., 2002

Obesity PEPCK J Hager et al., 1998 Leptin levels Ob KY Zwarts et al., 2002 Coronary artery disease ABCA1 H Hackstein et al., 2001 Reduced soluble IL4R IL4Ralpha JC Engert et al., 2002 Elevated Body Mass Resistin JC Knight et al., 1999 Malaria Susceptibility TNFalpha S Otabe et al., 2000 Elevated Body Mass UCP3 PJ Bosma, et al., 1995 Gilbert’s Syndrome –jaundice UDP-GT1 REFERENCE DISEASE/CONDITION (associated) GENE

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Stage 1: Prediction of Regulatory Regions

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Stage 1: Identify Putative Regulatory Regions

• 0.2

0.2 0.4 0.6 0.8 1 1000 2000 3000 4000 5000 6000 7000

FoxC2

100% 80% 60% 40% 20% 0%

• Retrieves orthologous human and mouse gene

sequences from GeneLynx

• Aligns sequences with ORCA Aligner
• Finds most significant non-coding regions
• Designs primers

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Data/Orthology obtained from GeneLynx (www.genelynx.org)

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Stage 2: Analysis of Polymorphisms

ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAATGAATAACAGAT ACGCATAAGTTAACGAATAACAGAT ACGCATAAGTTAACGAATAACAGAT ACGCATAAGTTAACGAATAACAGAT ACGCATAAGTTAACGAATAACAGAT

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Identify TFs with altered binding predictions overlapping variation 1234567890123456789012345 ACGCATAAGTTAATGAATAACAGAT .............C...........

• 4
• 2

2 4 1 2 3 4 5 6 7 8 9 10 11

Differences in scores

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Stage 3: Prediction of Regulatory “HotSpots”

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“HotSpots” in Muscle Regulatory Module (200bp)

• 0.2
• 0.1

0.1 0.2

Maximum Differential for any potential SNP

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UDPGT1 (Gilbert’s Syndrome)

• 0.2

0.2 0.4 0.6 0.8 1 1 5 1 9 2 1 3 3 1 7 4 2 1 5 2 5 6 2 9 7 3 3 8 3 7 9 4 2 4 6 1 5 2 5 4 3 5 8 4 Series1 Series2

Wildtype Mutant

Liver Module Model Score

“Window” Position in Sequence Liver regulatory module scores overlapping sequence variation

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RAVEN Implementation Status A first look at the alpha-version of the RAVEN service…

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RAVEN Analysis Package

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RAVEN Analysis Package

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RAVEN Analysis Package

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RAVEN Analysis Package

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RAVEN Analysis Package: Primer Design

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RAVEN Analysis Package: Primer Design

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RAVEN Analysis Package – SNP Upload

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RAVEN Analysis Package – TF Specification

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RAVEN Analysis Package – TF Hits

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RAVEN Analysis Package If you wish to try out the alpha version and provide feedback on changes, send email to wyeth@cmmt.ubc.ca

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de novo Discovery

• f TF Binding Sites

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Pattern Discovery

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Pattern Discovery Methods

• Exhaustive

– e.g. YMF (Sinha & Tompa) – 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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Regulatory Analysis Methods for Single-celled Organisms The Proving Ground

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Yeast Regulatory Sequence Analysis (YRSA) system

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Tests of YRSA System

PDR3-regulated genes from array study Classic cell-cycle array data re-clustered by Getz et al DNA-damage response partially mediating by MCB

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rank LEU3 STE12 RLM1 MIG1 OAF1 GAL4 XBP1 CBF1 RPN4 PDR3 ADR1 REB1 ABF1 RAP1 GCN4 PHO4 39.0 17.0 21.0 17.8 na 7.2 0.7 na 1.5 na 1.1 1.0 0.9 1.1 1.1 0.8 7 10 7 16 5 6 28 20 24 10 20 24 27 17 12 18 5 10 15 20 25 30 35 40 comparison rank of correct pattern

+

Rank of found pattern in verified promoters Rank of found pattern in randomly selected promoters

a b

average comparison rank (random promoters) average comparison rank (verified promoters) Number of promoters (sequence depth)

Performance: Hit and Miss

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Applied Pattern Discovery is Acutely Sensitive to Noise

10 12 14 16 18 100 200 300 400 500 600

SEQUENCE LENGTH PATTERN SIMILARITY

• vs. TRUE MEF2 PROFILE

True Mef2 Binding Sites

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Four Approaches to Improve Sensitivity

• Better background models
• Higher-order properties of DNA
• Phylogenetic Footprinting

– Human:Mouse 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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Some profile constraints have been explored…

• Segmentation of informative

columns

• Palindromic 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 Frequency

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Intra-family comparisons more similar than inter-family

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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Conclusions

• Pattern analysis methods have utility
• Combine knowledge from multiple fields
• Statistics and AI methods must be imported

– Gibbs sampling, LRA, neural networks, SVMs, etc

• 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

Thanks!

Wasserman Group – CMMT Dave Arenillas Jochen Brumm Danielle Kemmer Jonathan Lim Wasserman Group - Karolinska Albin Sandelin Raf Podowski Wynand Alkema Collaborating Trainees Malin Andersson (KTH) Öjvind Johansson (UCSD) Stuart Lithwick (U.Toronto)

Support: CIHR, CGDN, Merck-Frosst, BC Children’s Hospital Foundation, Pharmacia, EC–Marie Curie, KI-Funder

Collaborators Chip Lawrence (Wadsworth) William Thompson (Wadsworth) Jens Lagergren (SBC/KTH) Christer Höög (K.I.) Brenda Gallie (OCI) Jacob Odeberg (KTH) Niclas Jareborg (AZ) William Hayes (AZ)

Boris Lenhard (K.I.)

Group Alumni Elena Herzog Annette Höglund William Krivan Luis Mendoza

CMMT

“Regulog” Analysis Comparative Genomics for Promoters

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Approach

• Define all regulatory sequences in S. aureus.
• Transcription factor binding sites
• RNA structures
• Promoters

=>Phylogenetic footprinting

Find a conserved pattern

• E. coli
• B. subtilis
• S. aureus

clpP

taccgctattgaggta taccccgatcggggta tacccattaaggagta taactctaaagtggta tacctcaatagcggta taccccgatcggggta tactccttaatgggta taccactttagagtta

TACCNCN(A/T)(A/T)NGNGGTA TACCNRWAAYGBGGTA

A [0 8 1 0 1 1 1 5 3 5 0 2 1 0 0 8] C [0 0 7 7 4 7 0 0 0 2 0 1 0 0 0 0] G [0 0 0 0 1 0 2 0 0 0 7 4 7 7 0 0] T [8 0 0 1 2 0 5 3 5 1 1 1 0 1 8 0]

Pattern detection

CMMT

MAP value

1 2 3 4 5

Frequency

0.00 0.05 0.10 0.15 0.20 0.25

Regulatory sequences in S. aureus

1430 patterns

Gibbs sampling Compare to random sequences

1818 sets of orthologs from S. aureus real

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Regulatory sequences in S. aureus

MAP value

1 2 3 4 5

Frequency

0.00 0.05 0.10 0.15 0.20 0.25

1430 patterns 318 significant patterns

Gibbs sampling Compare to random sequences Remove redundancies

1818 sets of orthologs from S. aureus real random

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Regulatory sequences in S. aureus

1818 sets of orthologs from S. aureus 1430 patterns

Gibbs sampling Compare to random sequences

318 significant patterns

Cluster with MatrixAligner (Sandelin et al 2003)

154 unique patterns in S. aureus

Remove redundancies

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Approach

• Define all regulatory sequences in S. aureus.
• Transcription factor binding sites
• RNA structures
• Promoters
• Define sets of genes that are under control
• f these regulatory sequences =>regulons

– Sequence search – Regulog filtering

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Regulon prediction with site search

Site score threshold (p-value)

0.00 0.02 0.04 0.06 0.08 0.10 0.12

Fraction of total ORFS in regulon

0.00 0.02 0.04 0.06 0.08 0.10

175 members in E. coli => Site searches produce too many false positive hits

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Regulon conservation filter

• A predicted regulon member is more

likely a true positive when its

• rtholog(s) is regulated by the same

regulatory sequence.

• Such conserved regulons are called

regulogs

Regulogs

gene geneA geneB geneC geneD geneF geneA geneB geneC geneD geneF geneC geneD geneF

B C D

geneG geneG geneG geneA geneC geneD geneE geneG geneF

A

geneB = regulon 1 1 0.66 0.33 1 gene = regulog geneA geneB geneC geneD geneE geneG geneF

A

Regulon Conservation Filtering (RECF)

= putative binding site

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RECF test: Escherichia coli

10.4 3 21 3 218 4 metR 11 4 7 4 77 4 torR 12.5 3 11 3 137 5

• xyR

15.2 2 12 2 182 2 ilvY 25.5 1 4 1 102 4 pdhR Pos Total Pos Total Efficiency REGULOG REGULON #known TF

Efficiency

RECF

SpecificityREGULOG x SensitivityREGULOG SpecificityREGULON x SensitivityREGULON

RECF test: Escherichia coli

. . . . . . . . . . . . . . . . . . . . . 10.4 3 21 3 218 4 metR 4.2 3.8 20 7.2 174 9.8 AVG. 11 4 7 4 77 4 torR 12.5 3 11 3 137 5

• xyR

15.2 2 12 2 182 2 ilvY 25.5 1 4 1 102 4 pdhR Pos Total Pos Total Efficiency REGULOG REGULON #known TF

Efficiency

RECF

SpecificityREGULOG x SensitivityREGULOG SpecificityREGULON x SensitivityREGULON

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RECF applied to S. aureus

RCS Consensus Members (leftmost members are the members with the highest confidence)

1.00 AACACAATATATAGTG nrdD,SA2409,nrdI,nrdE,cspC,mtlF 1.00 TGTTAGAAAATCTAAC glnR,nrgA,glnA 1.00 AGGTGCTAAATCCTGC SA0011 0.89 GCCAGCGTAGGGAAGT SA0928,SA0929,thiD,thiE,SA1897,gapR,thiM 0.88 ACAGGTCATAAGGGTC SA0929,SA1897,SA0928,thiD,polC,thiE,thiM 0.87 AAGGGTGGAACCACGA thrS,leuS,alaS,cysE,cysS,SA0489,SA0490,SA0491,pheS,pheT,S A1931,aspS,hisS,ileS,tyrS,trpG,valS,serS,SA0331,SA2101,SA148 6,SA1289,SA1290,SA1291,SA2205,SA1392,truncated(radC),SA1 578,murE,SA2102,SA1562,SA1199,trpD,trpC,trpF,trpB,trpA 0.86 TGTGAA?T?TTTCAC? narG,narI,SA2183,narH,pflB,SA2174,lctE,SA1455,narK,SA0293,m smX,adhE,rpsU,fbaA 0.83 AAAAGAGTGCTAACA? crtM,groES,hrcA,SA1747,SA1582,SA1581,SA2305,SA1748,grpE 0.83 TTGAAAATGATTATCA SA0307,SA0116,SA0689,SA0117,SA0690,SA0331,SA0977,SA09 78,SA1329,SA2162,ahpF,SA1979,SA0688,feoB,SA2338,sirA,SA2 079,katA,SA0757,ahpC,fhuA,fhuB,fhuG,SA0335,SA2101,SA0160, SA0170,hemX,sirB,hemL,hemB,hemD,hemC,dapD,hemA,SA2102 ,SA0588,SA0589,SA0115,dps,fer,SA0774,SA1678 0.82 ?A?AAAAGTTATCCAC SA0339,orfX,dnaA,dnaN,SA1419,SA1420,SA1421,SA1422,SA14 23,aroE,SA1425,SA1426,SA0248

The Fur regulog

Known in

• ther bacteria

Known in

• S. aureus

Unknown

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Regulog Conclusions

• Using only sequence data, reliable

predictions for sets of co-regulated genes can be obtained.

– Phylogenetic information is used to obtain a set

• f putative regulatory sequences

– Phylogenetic information is used to improve of predictions of sets of co-regulated genes – Facilitates targetting of genes for experimental studies – Portable to other genomes