Cis/TF discovery for Arabidopsis Aristotelis Tsirigos NYU Computer Science

2 Outline Input data The proposed model Results on yeast Results on arabidopsis Unsupervised pattern discovery

3 Input data

4 ~23,000 genes 25 points 1,500bp upstream gctaagc...

5 Normalization ~23,000 genes 25 points 1,500bp upstream normalize columns (mean=0) gctaagc...

6 Filtering ~23,000 genes 25 points 1,500bp upstream normalize columns (mean=0, stdev=1) ~5,000 genes 25 points gctaagc... motif bitmap … filter out low-variance

7 The proposed model

8 Assumption 1 A single TF binds on a single cis element (motif) Source: U.S. Department of Energy Genomics (

9 Assumption 2 TFs regulate genes sharing a motif only on subset of conditions

10 Assumption 2 (cont’d) TFs regulate genes sharing a motif only on subset of conditions

11 Assumption 3 The TF expression correlates with the sum of the partially correlating expression patterns

12 Objective For each cis element (motif): –discover groups of co-regulated genes –compute aggregate motif expression For each TF: –find best correlating motifs

13 The algorithm – step 1 ~5,000 genes step 1: clustering 25 points

14 The algorithm – step 2 ~5,000 genes step 1: clustering 25 points step 2 for any motif compute its gene set......

15 The algorithm – step 3 ~5,000 genes 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......

16 The algorithm – step 4 ~5,000 genes 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 step 4 determine overrepresented clusters using t-test......

17 The algorithm – final step ~5,000 genes 25 points final step compute motif aggregate expression 25 points......

18 Yeast

19 Example TF: BAS1 RANK MOTIF OCCUR corr score 1 gactcg cgagtc gactaa ttagtc tcggct gctagt agtcac p-value= gagtca p-value=0.004 Using cis/TF version 1:

20 Example TF: BAS1 Using cis/TF version 2: RANK MOTIF OCCUR signf corr score 1 ctgact agtcag ggttta taaacc gagtca p-value= tgactc atttga tcaaat agtggc gccact

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26 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

27 Arabidopsis

28 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

30 TF discovery? Need data for training! (TFs and their associated binding cites) Parameters to be estimated:  number of clusters  motif size & degeneracy

31 Pattern discovery

32 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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34 Pattern discovery example

35 “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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37 Conclusions

38 Conlusions Two 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