.

A Simple Hyper Geometric Approach for Discovering

Putative Transcription Factor Binding Sites

Yoseph Barash Gill Bejerano Nir Friedman

Hebrew University Jerusalem

Transcription Factors Rule

• Enhance/repress/initiate mRNA expression

*Essential Cell Biology; p.268

The “Biological Hypothesis”

Co-Expression

Experiments

Gen

es

Co-Regulation

Binding Sites within Promoters

Gen

es

Input: a set of upstream regions

Output: common & unique binding sites (putative)

Our Approach: HighlightsDiverse Literature:Bailey & Elkan, 94; Buhler & Tompa, 01; Bussemaker et al, 00; Lawrence et al, 93; Pevzner & Sze, 00; Tavazoie et al, 99;van Helden et al, 00; Vilo et al, 00, …

Our Approach: Expressive binding site model

Systematic exploration of search space

Statistical significance evaluation

Integrate biological knowledge

Computationally efficient

=1=2

ball B(l,)

Basic Motifs

Subsequence

ACT

AAT

GCT

ACCCCT AGT

ACA

ATA

ATC

AGA

TGT

TATTCT

TTT

ATT

AAA

ATT

CCT

ACA

ACT

TCTGCT

ACC

ACG

AGTAAT

ATT

CATCTT

CGT

GGT

GTT

GAT

TGTTTTTAT

CCA

CCC

CCG

l=3

Motif Generalizations Richer alphabets

SCTNNNGTAAR

WATNNNGTCAR

General distance function

Random projections (following [Buhler & Tompa, 01])

l –mers projections using k < l positions

SCTATGAGTAR

SCAATGATCARSC*A*GA**AR

Statistical Significance

We found a motif… is it significant? When using a large space of motifs, we expect some

artifacts

Two types of null-hypothesis Generative

Probability of generating a set of promoters containing this motif

Discriminative

Probability of selecting a set of genes that contain this motif?

Selected genes

Discriminative P-value

Start with the promoter regions of all genes

Mark genes that contain the motif

P-value: Probability of selecting as

many marked genes if we select n genes at random

Promotersequences

Selected genes

Discriminative P-value

Start with the promoter regions of all genes

Mark genes that contain the motif

P-value: Probability of selecting as

many marked genes if we select n genes at random

Promotersequences

n

kxn

M

xn

KM

x

K

MKnkPhyper ),,|(

Statistical Significance Evaluation• What if we select n genes V times?

False Discovery Rate (FDR) (Benjamini & Huchberg,95):

The expected ratio of false motifs identified from the

whole set of motifs identified is no more than

Bonferroni p-value limit (union bound)

for a false positive rate :

Motif is significant if p-value(Motif)

V

FDR Example

3}501.0

|{ Here k

PPArgMax kk

P-value index

prob

abili

ty

Algorithm Outline

Define Space of Motifsalphabet,distance function,motif sets

Evaluate All Motifs using hyper-geometric null model

Choose Significant Motifsusing Bonfferoni or FDR criteria

From Discrete Motif to PSSMPosition Specific Score Matrix:

for a motif of length L define

Pi(A,C,G,T) for i={1….S} where S >= L

1 2 3 4 5

A 0.1 0.25 0.05 0.7 0.6

C 0.3 0.25 0.8 0.1 0.05

G 0.5 0.25 0.05 0.1 0.05

T 0.1 0.25 0.1 0.1 0.22 Aims:

•Refine motif

•Extend seed to flanking regions

Learning a PSSM

Initialize: Find all subsequences of length S that contain the

motif Align them & compute probabilities

Iterative EM - like procedure: Score each position in each gene using the PSSM Use the score to refine the PSSM representation Iterate Remove non informative flanking regions

Algorithm Outline (revisited)

Define Space of Motifsalphabet,distance function,motif sets

Evaluate All Motifs using hyper-geometric null model

Choose Significant Motifsusing Bonfferoni or FDR criteria

Refine motifs into PSSMiterative EM-like procedure

Yeast Results

Major differences: Background discrimination Running time (~1 hour Vs. ~1 Week)

Cluster TF Cons. Seed PSSM MEME < 8Rank p-value Rank p-value Rank E-value

Spellman et al.CLN2* MBF ACGCGT 1 4e-26 1 3e-42 1 1e-18

SIC1 SWI5p CCAGCA 1 1e-12 1 1e-12 1 8e-00

Tavazoie et al.3 Putative GATGAG 5 9e-07 5 6e-09 4 1e+06

Putative GAAAAatT 2 4e-07 2 1e-11 23 8e+07

8 STRE aAGGgG 3 6e-07 3 4e-06 20 1e+08

30 MET31 gCCACAgT 1 2e-11 1 2e-11 2 5e+02

Iyer et al.MBF MBF ACGCGT 1 1e-12 1 3e-18 3 1e+04

SBF** SBF CGCGAAA 1 1e-32 1 1e-37 2 1e-17

Sample Yeast PSSM’s

SBF

CLN2

Human Results* - TGF

BSMC NHBE NHLF

PBS TGF PBS TGF PBS TGF

* Research Collaboration with Naftali Kaminski, Sheba Medical Center Jane Lee, Dean Shappard, UCSF Tommy Kaplan, Hebrew University

Measuring response to TGF in three distinct cell-types in lungs

Each cell type/conditionrepeated at least 4 times

Human Results (2)

BSMC-down 12:6 1000 ups

Discrete Motif Search:(Random Projections)

Learn PSSM:

Motif suggests LyF-1 binding site: TTTGGGAGR

Summary

Efficiency & Modularity Systematic coverage of event space Extension of initial seeds to PSSM Good background discrimination Enables usage of biological prior knowledge Promising preliminary results on biological data sets Fast evaluation tool for possible co-regulated genes

* = 18.3)10,5,5|4( NKnxPvalhyper

G1 19.5 *

G7 19.1

G5 19.1 *

G1 18.5 *

G4 18.3 *

G2 17.2

G9 16.8

G3 15.2

G8 15.1 *

G10 14.8

Updating - Example

Score

Statistical Significance?

Two Basic Approaches for Background modeling:

• Random Generation

• Random Selection

We found a cluster of n genes, x of them contain a motif. Genome has M genes, K of them contain the

motif. How significant is this ?

Same thing as choosing n balls (x of them red),from a set of M balls, with K red ones.

Simple Example

ACGTGTATTAAGTGACTCCTGATTGAG

ACGAGTGACTTGCATATCCTGATTGAG

ACGTGTATTAGATGAAAATTATCCCCC

ACGTGTATTAAATTTCCGGTGTAGTAGAGTGTATTAAACCCCTCTAGTGTTGAG

Here: n = 5, x = 4, M = 100, K = 10

410*51.2)100,10,5|4(

n

M

xn

KM

x

K

MKnxPhyper

5

4'

),,|'()10,100,5,4(#-

x

MKnxPKMnmotifvaluep hyper

Statistical Significance Evaluation• What if we select n genes V times?

Bonferroni p-value limit (union bound)

for a false positive rate :

Motif is significant if p-value(Motif)

False Discovery Rate (FDR) (Benjamini & Huchberg,95):

The expected ratio of false motifs identified from the

whole set of motifs identified is no more than

V