GIW2008

Using simple rules on presence and positioningof motifs for promoter structure modeling andtissue-specific expression prediction
Alexis Vandenbon
Laboratory of Functional Analysis in silico
Department of Medical Genome Sciences
University of Tokyo

Outlinez

Introductionz

Materials and Methodsz

Results and DiscussionzConcluding Remarksz

Future Perspectives

1Regulation of transcription
transcription
initiation

set of TFBSs

expression pattern

Can we predict expression patterns fromthe promoter sequence architecture?

C. elegans muscle tissuez

Extensive tissue expression dataz

For muscle− Muscle-specific genes, regulatory regions− Candidate cis-regulatory motifs

2Materials and Methods

Datasets
1. C. elegans tissue expression data:− Expression pattern data (WB188)

2. Sequence data: 2000 bps upstream regions− True positives (Zhao et al., 2007):z

121 muscle-specific geneszorthologs in C. briggsae for 78 sequences− True negatives:z2955 non-muscle genes

3True Positive Datasetsthe full set of 121 TP sequences

78 training sequences
for motif prediction
and rule generation

43 test seqs

for rule selection

De novo motif predictionz

Predictions− Using conservation in C. briggsae− In the entire sequences, and in sub-regions ofdifferent lengths− Using MEME, AlignACE, MotifSampler, Weederzz

Get an over-representation measure for eachmotif
Remove redundancies

4ATG-2000

About 10predicted motifs

Are there anysignificantpatterns??

Pattern Typesz

positioning relative tothe translation startsite

5Pattern Typesz

positioning relative tothe translation startsitez

relative positioningof pairs of motifs

Pattern Typesz

positioning relative tothe translation startsitez

relative positioningof pairs of motifszpresence of a motif

6What are Useful Patterns?
zPatterns that are over-represented in theinput sequences vs non-inputs− in total number of occurrences, and− in the number of sequences containing at least1 occurrenceOver-Representation Index
ORI i =

density TP , i
density genomic , i

×

proportion
proportion

TP , igenomic , i

Total number ofAt least 1 occurrence

occurrences of pattern iof pattern iBajic et al., 2004

7

Rule generation

Rule generation

8

Rule generation
ORI-log(P)Promoter region

Rule generation
ORI-log(P)Promoter region

9Rule generation
ORI-log(P)Promoter region

Rule generation
ORI-log(P)Promoter region

10Rule generation-log(P)Promoter region

Rule generation

Thresholdp value(0.001)-log(P)

Promoter region

11True Positive Datasetsthe full set of 121 TP sequences

78 training sequences
for motif prediction
and rule generation

43 test seqs

for rule selection

Genetic Algorithm (GA)generation = 0

Create a populationof chromosomes
Determine thefitness of eachindividualnext
generation

Select nextgeneration

Get
results

Perform reproduction
using crossover
Performmutation

12Genetic Algorithm (GA)generation = 0

Create a populationof chromosomes
Determine thefitness of eachindividual

Each gene = a rule
1: rule is present
0010010010000010...
0: rule is not present

Select next
generation

next
generation

Getresults

Perform reproduction
using crossover
Performmutation

Genetic Algorithm (GA)generation = 0

Create a populationof chromosomes

Fitness = AUC score

Determine thefitness of eachindividual

next
generation

Select next
generation+ penalty for complexity

Getresults

Perform reproduction
using crossover
Perform
mutation

13

Genetic Algorithm (GA)generation = 0

Create a populationof chromosomes
Determine thefitness of eachindividual

Select next
generation

next
generation

Based on theirfitnessGet
results

Perform reproduction
using crossover
Perform
mutation

Genetic Algorithm (GA)generation = 0

Create a populationof chromosomes- single point crossovers- mutations

Determine thefitness of eachindividual

next
generation

Select next
generation

Get
results

Perform reproduction
using crossover
Perform
mutation

14

Genetic Algorithm (GA)generation = 0

Create a populationof chromosomes

If no sufficientchange in best AUC

Determine thefitness of eachindividual

next
generation

Select next
generation

Getresults

Perform reproduction
using crossover= a small set of
informative rules

Performmutation

True Positive Datasetsthe full set of 121 TP sequences

78 training sequences
for motif prediction
and rule generation

43 test seqs

for rule selection

15

Final Performance Evaluation
zzz

True positives: test set (remaining 43 seqs)“True negatives” (2955 seqs with no reported
expression in muscle)
Measures of performance:− ROC curve AUC− enrichment of test seqs in top scoring seqs
− etc

Results and Discussion

16

ROC curves
10.9

true positive
TP ratioratio

0.8
0.7
0.6

averageaverage + STDEV
average - STDEV

0.5
0.4
0.3
0.2
0.1
00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TN ratio
false
positive ratio

ROC curves
10.9

true positive
TP ratioratio

0.8
0.7
0.6

averageaverage + STDEV
average - STDEV

0.5
0.4
0.3

Average AUC: 0.7175(range: 0.6514 – 0.7601)

0.2
0.1000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TN ratio
false
positive ratio

17ROC curves
10.9

true positive
TP ratioratio

0.8
0.7
0.6

averageaverage + STDEV
average - STDEV

0.5
0.4
0.3

On average, 27.9% of TPs scorehigher than 95% of the TNs

0.2
0.1
00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TN ratio
false
positive ratio

ROC curves
10.9

true positive
TP ratioratio

0.8
0.7
0.6

averageaverage + STDEV
average - STDEV

0.5
0.40.3On average, 36.3% of TPs scorehigher than 90% of the TNs

0.2
0.1
000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1TN ratio
false
positive ratio

18

ROC curves
10.9

true positive
TP ratioratio

0.8
0.7
0.6

averageaverage + STDEV
average - STDEV

0.5
0.4
0.3

On average just 9.2 rules(range 5 to 14)

0.2
0.1
00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TN ratio
false
positive ratio

Focus on the Best Run
10.9

true positive
TP ratioratio0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
000.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1TN ratio
false
positive ratio

19

Focus on the Best Run
10.9

true positive
TP ratioratio

0.8
0.7

AUC: 0.7601

0.6
0.5
0.4
0.3
0.2
0.1
00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TNratio
false
positive ratio

Focus on the Best Run
10.9

true positive
TP ratioratio

0.8
0.7
0.6

AUC: 0.7601

0.5

49.5% of TPs score higher than
90% of TNs0.4
0.3
0.2
0.1
00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TNratio
false
positive ratio

20

Focus on the Best Run
10.9

true positive
TP ratioratio

0.8
0.7
0.6

AUC: 0.7601

0.5

49.5% of TPs score higher than
90% of TNs

0.4
0.3

36.1% of TPs score higher than
95% of TNs

0.2
0.1
00

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

TN
ratio
false
positive ratio

positioning ofpairs of motifs

presence ofcertain motifs

bias to transl.
start site21Similarities to Known Motifs

22Genes expressed in muscle tissues are over-represented
among high scoring genes.

Genes expressed in neuronal tissues are over-represented
among high scoring genes.

23

Match to Experimentally
Verified Regions
Sites fitting the rules show a tendency to be present inexperimentally verified regulatory regions (p-value 0.0017).

GuhaThakurta et al., 2004

unc-89 (UNCoordinated) is expressed in body wall muscle,pharyngeal muscle and some cells in the tail.

Concluding Remarks (1)zSimple, easy to understand, modelzGood performance− high AUC values− strong enrichment of TPs among high scoring seqs
− high scoring genes tend to be expressed in muscle

tissues− similarity to known motifs− match to experimentally verified regions

24

Concluding Remarks (2)zModels more than just clustering of sites− positioning to TSS/translation start site
− proximal positioning of pairs of sites− distal positioning of pairs of siteszGenerally applicable− no species specific or tissue specific information isused

Perspectives
Application in other organisms and tissues

25

Acknowledgments
Prof. Kenta Nakai
The members of the Nakai Lab
Japanese Government (MEXT Scholarship)
GIW student bursary

GIW2008

Using simple rules on presence and positioningof motifs for promoter structure modeling andtissue-specific expression prediction
Alexis Vandenbon
Laboratory of Functional Analysis in silico
Department of Medical Genome Sciences
University of Tokyo26