Periodic clusters

Non periodic clusters

That was only the beginning…

The human cell cycle

G1-Phase S-Phase

G2-Phase M-Phase

The proliferation cluster genes are cell cycle periodic

5 10 15 20 25 30 35 40 45

4

3

2

1

0

-1

-2

-3

-4

G2/M G1/SCHR

Samples

Gen

e Ex

pres

sion

Disrtribution of cell cycle periodicity

00.10.20.30.40.50.60.70.8

1 2 3 4 5 6 7 8 9 10CCP score

Prop

ortio

n

All genes Proliferation genes

200 150 100 50 TSS

NFYE2F

ELK1

CDE

CHR

The cell cycle motifs are enriched among the periodic genes

Not in the cluster, mutated in cancer

Tabach et al. Mol Sys Biol 2005

Potential regulatory motifs in 3’ UTRs

Finding 3’ UTRs elements associated with high/low transcript stability (in yeast)

AAGCTTCC CCTACAACEntire genome

0 5 10 15-2

-1

0

1

2

3

4

Time/tissues

Expr

essio

n

ClusteringMotif

finding

Diagnosing motifs using expression

Reverse the inference flow

Once we reverse the inference order we can

• Enumerate and score all possible k-mer motifs• Examine the effect of “mutations” on motifs• Examine the effect of motif location within

promoter• Examine the effect of motif combinations,

distances within a combination• More?

• …But the correlation between gene• cluster and motifs is imprecise in both directions:

• there are genes in the cluster without the motif

• and many genes with the motif do not• respond. • If gene control is multifactorial, groups of genes defined by a

common motif will not be mutually disjointed• partitioning• the data into disjoint clusters will cause loss of information.

A k-mer enumeration method: score every possible k-mer for an association with expression level

Ag is expression level of gene gC is a basal expression level (same for all gs)The integer Nμg equals the number of occurrences of motif μ in gene gM a set of motifsFμ is the increase/decrease in expression level caused by the presence of motif μ (same for all gs)

2 4 6 8 10 12 14-2

-1

0

1

2

3

4

Time

Expr

essi

on le

vel

2 4 6 8 10 12 14

-3

-2

-1

0

1

2

3

Time

Expr

essi

on le

vel

EC score = 0.05

EC score = 0.5ScanACE(Hughes et al.)

Motifs characterization through Expression

Coherence (EC)

*

*

**

*

*

** *

*

**

*

** *

** *** **

** *

*****

1 2

3 4

EC1=0 EC2=0.66

EC3=0.2 EC4=0.2

Threshold distance, D

Expression coherence score, intuition

Interaction of motifs

5 10 15

-2

0

2EC=0.05

5 10 15

-2

0

2EC=0.05

TimeTime

Expr

essi

on le

vel

Only M1 Only M2

Expr

essi

on le

vel

Time5 10 15

-2

0

2EC=0.23

M1 AND M2

G2 G2

M1 M2

Synergistic motifs

A combination of two motifs is called ‘synergistic’ if the expression coherence score of the genes that

have the two motifs is significantly higher than the scores of the genes that have either of the motifs

SFFMcm1

A global map of combinatorial expression control

mRPE72

SWI5

SFF '

MCM1

SFFMCM1'

ECB SCB

MCB

PAC

mRRPE

mRRSE3

GCN4

BAS1

LYS14

RAP1

mRPE34

mRPE57

mRPE6mRPE58

STRE

RPN4 ABF1

PDR

CCAPHO4

AFT1

STE12

MIG1

CSRE

HAP234

ALPHA1'

ALPHA1

ALPHA2

mRPE8

mRPE69

Heat-shockCell cycleSporulationDiauxic shiftMAPK signalingDNA damage

*High connectivity*Hubs*Alternative partners in various conditions

Pilpel et al. Nature Genetics 2001

Deduced network Properties.

0

0.5

1

-0.5

-1

0.2

0.4

0.6

0.8

G1G2

Mbp1 Ndt80 Ume6 MCM1'

MCB MSE URS1 SCB MCM1' SFF'

Corre

latio

nEx

pres

sion

Cohe

renc

e

Fkh1

Swi4

Sufficiency

Necessity

Ho et al. Nature. 2002

TF-TF interaction

Hierarchy

Detect the effect of mutations in a motif

.

0

0.5

1

1.5

2

-200

-120

-40

40

120

200

-0.5

-0.4

-0.3

-0.2

-0.1

0

36 19 8 14 20 2 3 7 1 2 0Exp

ress

ion

cohe

renc

e1-

Cor

rela

tion

Dis

tanc

e in

b.p

.

mRRPE is closer

PAC is closer

ATG

ATG

ATG

ATG

ATG

ATG

Distance and orientation of motifsaffect expression profiles

Some typical expression patterns

A Bayesian approach (conditional probability)Xi could “1” to denote denote:

• The presences of motif m

• It’s distance from TSS is < N

• It’s on the coding strand

• It neighbors another motif m’

Or “0” otherwiseei = being expressed in patter i

Example: two rRNA processing motifs

The two motifs Work together

The two motifs’ orientation matters

The procedure

• Given that P(N|D)=P(N)*P(D|N) / P(D):• Search in the space of possible Ns to look for a

one that maximizes the above probability• Impossible to enumerate all possible networks• Use cross validation: partition the data into 5

gene sets, learn the rules based on all but one and test based on the left-out, each time.

For example: what does it take to belong to expression patter (4)?

• Need to have RRPE and PAC

• If PAC is not within 140 bps from ATG , but RRPE is within 240 bps then the probability of pattern 4 is 22%

• If PAC is within 140 and RRPE is within 240 bp then 100% chance

Inferring various logical conditions (“gates”) on motif combinations

The Bayesian network predicts very accurately expression profiles

Can make useful predictions in worm

The modern synthetic approach

Motif discovery from evolutionary conservation data

S. Cerevisiae S. mikatae, S. kudriavzevii, S. bayanus). S. castellii S. Kluyveri

Their intergenicsequences average 59 to 67% identityto their S. cerevisiae orthologs in globalAlignmentsS. castellii and S. Kluyveri~40% identity to Cerevisae

Nucleotide conservation in promoters is highest close to the TSS

TATA-containing genes

All genes

?????

A set of discovered motifs

NATURE | VOL 434 | 17 MARCH 2005

The data• Examined intergenic regions of human mouse rate and dog• ~18,000 genes• “Promoters”: 4kb centered on TSS• 3UTRs based on RNA annotations• 64 Mb, and 15 Mb in total respectively• Negative control: Introns of ~120 Mb• % of alignable sequence:

promoters: 51% (44% upstream and 58% downstream of the TSS),

3’ UTR: 73%, Introns:34%, Entire genome: 28%

The phylogenetic trees

Questions:• How would addition of species affect analyses?• What if the sequences were not only mammalian?

An example: a known binding site of Err-a in the GABPA promoter

Questions:• What is the

“meaning” of the other conserved positions?

Discovery of new motifs: exhaustive enumeration of all 6-mers

Discovery of new motifs: exhaustive enumeration of all 6-mers

Targets of new motifs showed defined expression patterns

Motifs often show clear positional bias – close to TSS

Same methods to look for motifs in 3’ UTRs reveals strand-specific motifs