cis/tf discovery for arabidopsis aristotelis tsirigos email: tsirigos@cs.nyu.edu nyu computer...

Cis/TF discovery for Arabidopsis

Aristotelis Tsirigosemail: tsirigos@cs.nyu.edu

NYU Computer Science

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Outline

• Input data

• The proposed model

• Results on yeast

• Results on arabidopsis

• Unsupervised pattern discovery

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Input data

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Input data~

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25 points1,500bp

upstream

gctaagc...

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Normalization~

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25 points1,500bp

upstream

normalize columns(mean=0)

gctaagc...

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Filtering~

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25 points1,500bp

upstream

normalize columns(mean=0, stdev=1)

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25 pointsgctaagc...motif

bitmap

001011…

filter outlow-variance

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The proposed model

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Assumption 1

A single TF binds on a single cis element (motif)

Source: U.S. Department of Energy Genomics (http://doegenomestolife.org)

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Assumption 2

TFs regulate genes sharing a motif only on subset of conditions

TF & regulated genes (group #1)

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nTF & regulated genes (group #2)

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Expression pattern #1

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nExpression pattern #2

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Assumption 2 (cont’d)

TFs regulate genes sharing a motif only on subset of conditions

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Assumption 3The TF expression correlates with the

sum of the partially correlating expression patterns

sum of genes

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Objective

• For each cis element (motif):

– discover groups of co-regulated genes

– compute aggregate motif expression

• For each TF:

– find best correlating motifs

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The algorithm – step 1~

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step 1: clustering

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The algorithm – step 2~

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step 1: clustering

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step 2 for any motif

compute its gene set

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The algorithm – step 3~

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step 1 clustering

25 points

step 2 for any motif

compute its gene set

step 3 compute the distribution of its genes into the clusters.

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The algorithm – step 4~

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step 1 clustering

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step 2 for any motif

compute its gene set

step 3 compute the distribution of its genes into the clusters

step 4 determine overrepresented

clusters using t-test

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The algorithm – final step~

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final stepcompute motif

aggregate expression

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Yeast

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Example TF: BAS1

RANK MOTIF OCCUR corr score 1 gactcg 46 0.6446 66 2 cgagtc 46 0.6446 16 3 gactaa 163 0.6381 66 4 ttagtc 163 0.6381 33 5 tcggct 87 0.6374 33 ... 12 gctagt 110 0.6268 33 13 agtcac 137 0.6262 83 p-value=0.079 ... 27 gagtca 136 0.6192 100 p-value=0.004

Using cis/TF version 1:

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Example TF: BAS1

Using cis/TF version 2:

RANK MOTIF OCCUR signf corr score 1 ctgact 122 0.62 0.66 33 2 agtcag 122 0.62 0.66 83 3 ggttta 187 0.62 0.63 50 4 taaacc 187 0.62 0.63 33 5 gagtca 136 0.68 0.63 100 p-value=0.002 6 tgactc 136 0.68 0.63 33 7 atttga 378 0.64 0.63 33 8 tcaaat 378 0.64 0.63 50 9 agtggc 126 0.66 0.61 50 10 gccact 126 0.66 0.61 50

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Cluster #1: correlation = 0.02

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BAS1

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Cluster #2: correlation = -0.05

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BAS1#2

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Cluster #0: correlation = 0.18

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BAS1#0

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Cluster #4: correlation = -0.35

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BAS1#4

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Cluster #4: correlation = 0.63

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BAS1

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Conclusions

Advantages of version 2:

gives ability to focus on gene cluster that correlates best with a given TF

thus, increases overall correlation and motif rank

offers a measure of motif significance

can be extended to pairs of TFs/motifs

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Arabidopsis

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Procedure• Permute gene cluster assignment

• Compile list of putative motifs

• Compute significance score of known motifs

• Repeat 1000 times

• Compute p-value of the score

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ranking score

# o

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p-val = 0.006

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TF discovery?

Need data for training!

(TFs and their associated binding cites)

Parameters to be estimated: number of clusters

motif size & degeneracy

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Pattern discovery

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TF-driven pattern discovery

• Unsupervised pattern discovery

• Find groups of genes partially correlating with TF

• Apply statistical filter

• Look for over-represented motifs in genes’ upstream regions

• Data for validation?

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AT1G73230 (TF)

AT1G53290

AT5G59880

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Pattern discovery example

TF & regulated genes (group #2)

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“Predicting Gene Expression form Sequence”Beer & Tavazoie, Cell 2004

• Group genes in 49 clusters• Predict gene cluster using motifs discovered in

its upstream region

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freq

uenc

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2,500 genes

PAC

RRPE

PAC&RRPE

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Conclusions

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ConlusionsTwo options:

• Supervised training:

– uses background knowledge to construct model

– needs more training data

• Unsupervised pattern discovery:

– minimal model bias (no prior knowledge)

– needs more ‘expert’ help to filter results