ct

ylo

ter f, Innive

06;ne 2

chaide

corresponding cis-acting elements (CAE; putative transcription

[6,7]. Another approach is cross-species comparison of thesequences from orthologous genes, with the assumption that

method is to search for consensus motifs among the regulatory

ces cerevisiae [16] and Homo sapiens [17]. Bussemaker et al.[16] estimated quantitative relationships between gene expres-sion levels and the number of binding sites, selecting putativebinding sites recursively in an add-on manner by looking at

Genomics 88 (2006) 4factor binding motifs). Identifying these CAE is an importantstep toward understanding gene regulation. The combination ofmicroarray technologies that allow measurement of theexpression levels of tens of thousands of genes in a singleexperiment with the availability of genomic DNA sequencesoffers profound opportunities to elucidate the global patterns oftranscriptional regulation [25].

Many computational approaches have been proposed for theidentification of CAE. One method is to search for matches toknown consensus CAE, such as are cataloged in TRANSFAC

sequences of coregulated genes [9,10]; although this finds thebest conserved sites, it does not take into account thecombinatorial effects of CAE. Many algorithms, such asMEME [11], CONSENSUS [12], MDScan [13], and AlignACE[14], have been developed to identify conserved motifs from thepromoter sequences of a set of preselected genes. An extensionof these methods is to look for co-occurrences of severalconserved regions that may denote cis-regulatory modules [15].

A different approach, model-based analysis, was reported topredict regulatory sequences successfully in both Saccharomy-predicted CAE match sites known to be involved in interferon action. MotifModeler demonstrated the potential to computationally identify CAEimportant in gene regulation. 2006 Elsevier Inc. All rights reserved.

Keywords: cis-acting elements; Microarrays; Transcription factors; Gene expression; Gene regulation; Interferon

Understanding regulatory mechanisms of eukaryotic geneexpression is a fundamental goal in modern biology [1].Eukaryotic gene expression is regulated by combinatorialinteractions among many transcription factors bound to their

evolutionarily conserved regions are more likely to serveregulatory functions [8]. Neither of these approaches selectssites that are relevant to a particular physiological orpharmacological manipulation (e.g., drug response). Anothermotifs. Motifs were extended and compared to known binding sites ingiven condition and (2) estimate the effects of the CAE on gene expression. MotifModeler repeatedly tests random subsets of all possible motifs ofa given size and selects those that best fit a combinatorial model of the expression levels. We tested MotifModeler using data from a microarrayexperiment on the effects of interferon- in peripheral blood monocytes. Focusing on 6-bp motifs, we predicted 16 stimulatory and 4 inhibitory

the TRANSFAC database using position-specific scoring matrices. ManyModel-based identification of cis-a

Yunlong Liu a, Milton W. Taa Department of Biochemistry and Molecular Biology and Cen

635 Barnhill Drive MS 4063b Department of Biology, Indiana U

Received 26 January 20Available onli

Abstract

Identification of transcriptional regulatory motifs continues to be aprocedure, MotifModeler, that uses global gene expression data to (1)Abbreviations: CAE, cis-acting element; TCS, transcriptional contributionscore; HAS, highest alignment score; EMS, extended matching score. Corresponding author. Fax: +1 317 274 4686.

E-mail address: edenberg@iupui.edu (H.J. Edenberg).

0888-7543/$ - see front matter 2006 Elsevier Inc. All rights reserved.doi:10.1016/j.ygeno.2006.04.006ing elements from microarray data

r b, Howard J. Edenberg a,

or Medical Genomics, Indiana University School of Medicine,dianapolis, IN 46202, USArsity, Bloomington, IN 47405, USA

accepted 15 April 20063 May 2006

llenging problem in computational biology. We report a model-basedntify cis-acting elements (CAE) that regulate gene expression under a

52461www.elsevier.com/locate/ygenowhich individual sequence reduced the model error to thelargest degree. Although the approach is promising, therecursive strategy is not optimum because the performances

icsof motifs tested later were highly influenced by the previousselections. To avoid this potential bias, we propose an improvedmodel-based procedure, MotifModeler.

MotifModeler is designed to predict a set of CAE thatcoregulate global gene expression. MotifModeler is based uponthe idea that gene expression is controlled by the combinatorialactions of multiple transcription factors present at differentlevels and by the numbers of their binding motifs in theregulatory region of the gene. A set of genes is chosen basedupon their being regulated by a particular condition, such as thepresence of a drug. Any method that generates expression dataon a reasonably sized set of coregulated genes can serve as thestarting point; we used data from a microarray experiment.From a pool of all possible motifs of a fixed size, a set of motifsis selected randomly and mapped onto the selected (putativeregulatory) regions of the set of genes. A linear model isestablished to fit the expression data according to the numberand estimated efficacy of selected motifs. The stimulatory orinhibitory roles of the motifs are estimated using a least-squaresapproach. Each motif is evaluated based on how well itspresence improves the model performance. This is iteratedmany times with different random sets of all possible motifs.The accumulated contribution of each motif (defined as thetranscriptional contribution score, TCS) is used to select the setof motifs that best explain the data and to estimate the relativeeffect of each motif. Because each individual motif is tested incombinations with many other motifs, the approach is notsensitive to starting choice of motifs and can identify multipleportions of extended or bipartite motifs. Our approach identifiesCAE that contribute to the global expression data in a particularsituation, for example, those genes turned on or off by a drug.

This article describes our newly developed computationalprocedure, MotifModeler, and tests it using data from anexperiment that used oligonucleotide microarrays to measurethe global effects of PEGylated interferon-2b (PEG-IFN-) ongene expression in peripheral blood monocytes (PBMC) [18].PEG-IFN- is widely used in the therapy of hepatitis C virus[19]. Interferon triggers several signal transduction pathways,particularly JakSTAT pathways [20,21], and alters expressionlevels of many genes [18]. We compared the CAE that wereidentified by MotifModeler to the known transcription bindingsites in TRANSFAC 9.2 [7], using position-specific scoringmatrices (PSSM) as a systematic approach to find the bestmatch of each predicted CAE to a known binding site. Becausemost binding sites are longer than the predicted motifs, weextended the predicted CAE in the regulatory sequences inwhich they were present and compared these extended motifs tothe known binding sites in TRANSFAC. MotifModeleridentified known interferon-responsive elements along withnovel CAE. We also compared the performances of MotifMo-deler and REDUCE [16] by using both the interferon responsedata and an artificial benchmark dataset.

Results

Y. Liu et al. / GenomThe major assumption of MotifModeler is that geneexpression is controlled by the functional levels of multipletranscription factors and numbers of their binding sites in theregulatory region of the gene. MotifModeler starts from knownlevels of gene expression, which can be measured usingmicroarray experiments or other high-throughput technologies,and from genomic DNA sequences from which all possiblebinding motifs can be generated. For each iteration, MotifMo-deler selects a random set of m sequences (m is user defined) oflength w (also user defined). It then finds how manyoccurrences of each motif there are in the (user-selected)genomic regions to be examined. The effect of transcriptionfactor binding to each individual motif is estimated by fittingexpression data to the number of appearances of the motif in theregulatory region of each gene. We generate a TCS that is largeif the overall model fits the gene expression data well (has lowmodel error), and small if it does not (Materials and methods),and add this TCS to each selected motif. This procedure isiterated many times (user selected, generally in the range of100,000 to 10,000,000) with random sets of m motifs untileach motif is picked at least a defined number of times (e.g.,10,000 times). The motifs that have the highest cumulativeTCS are identified as critical CAE, and their averagecontributions to gene expression are estimated as part of theprocedure.

Biological model system

Affymetrix Human Genome U133A microarrays werepreviously used to investigate the global effects of PEG-IFN- and ribavirin on gene expression in human PBMC [18].Taylor et al. [18] found that ribavirin treatment had little effecton gene expression (fewer differences than expected by chance,and fewer differences between PBMC of one indivi-dual ribavirin than between two individuals treated identical-ly), and therefore pooled the data ribavirin to allow morepowerful analysis of the experiment as a 10-array 10-arraycomparison between controls and interferon-treated cells [18].After removing from consideration genes that were not reliablydetected in at least one of the two conditions [22,31], weselected 198 genes with altered expression, using stringentstatistical criteria (false discovery rate 0.001) and alsorequiring the magnitude of expression difference to be greaterthan or equal to twofold. Among these 198 genes, 151 were up-regulated, and 47 were down-regulated (see supplemental TableS1 for the list of genes).

Selection of putative CAE

For this analysis, we focused on all 2080 possible 6-bpmotifs as candidate CAE. MotifModeler can be set to select anyarbitrary number (m) of CAE in each iteration. To examine theeffects of m on the selection of CAE, we tested the spectra ofTCS (see Materials and methods) of the candidate CAE fromanalyses with m = 10, 15, 20, 25, and 30 (Fig. 1A). Fig. 1Ashows clear similarities among the spectra generated with the

45388 (2006) 452461five different values of m. All of the cross-correlationcoefficients are larger than 0.9. Therefore, the TCS spectra arerelatively insensitive to the choice of m.

ics454 Y. Liu et al. / GenomTo test whether the patterns shown in Fig. 1A might havearisen from intrinsic features of genomic DNA sequence, suchas nonrandom distributions of short sequences, we constructed anegative control by calculating the TCS spectra using 198randomly selected genes whose expression levels were notresponsive to IFN. There were no similarities in the TCS spectraeither among three independent analyses of different sets ofrandomly selected genes or between the randomly selectedgenes and the IFN-responsive genes (Fig. 1B). No cross-correlation coefficients among different spectra are greater than10%, and except for one motif that overlapped between panels 1and 3, all other selected CAE (20 per analysis) were distinct.Therefore, we conclude that the TCS spectra in Fig. 1Awere notdue to intrinsic features of genomic DNA sequences.

The functional effect of each CAE (x in Eq. (1); Materialsand methods) was estimated by least-squares regression (Eq.(2)). Positive and negative values of x indicate stimulatory and

Fig. 1. Transcription contribution score (TCS) spectra. (A) Effect of choice of numberbp CAE are represented in alphabetical order along the x axis, with the correspondingenes, with m = 20, to test whether the spectrum of TCS was related to intrinsic seq88 (2006) 452461inhibitory roles, respectively. Among 2080 6-bp CAEcandidates, 1637 (78.7%) were predicted to be positiveelements and 443 (21.3%) were predicted to be negativeelements; this parallels the number of genes stimulated orrepressed. The histograms of TCS are shown in Fig. 2. Weselected the 1% of CAE candidates with the highest scoresfrom each category, 16 positive sites and 4 negative sites.Table 1 shows the TCS of these CAE and their estimatedfunctional levels.

Correspondence of predicted CAE to known transcriptionfactor binding sites

Because the test data were on interferon-stimulated and-repressed genes, we expected that some of the CAE foundshould match known interferon response elements. We system-atically compared the 20CAEwith the top TCS (16 positive and 4

of CAE (m) on TCS spectra in PEG-INF--treated cells [18]. All 2080 unique 6-g TCS on the y axis. (B) TCS calculated from three different random sets of 198uences rather than to regulation by interferon.

icsY. Liu et al. / Genomnegative motifs, Table 1) to the known binding sites in theTRANSFAC database 9.2 [7]. PSSM were derived for eachknown binding site according to its alignment matrices (seeMaterials andmethods). A givenCAEmight align in several wayswith a known site, because their lengths usually differ, so wechose the highest alignment score (HAS) to indicate howsignificant the correspondence was. Most of the CAE identifiedby MotifModeler match known transcription factor binding sites.Tables 2 and 3 show those with HAS 3 standard deviationsabove the mean that was calculated from alignment scores ofrandomly selected known binding sites and all potential 6-bpsequences (the distribution of scores is shown in Fig. 3).Interestingly, 6 of the 16 predicted stimulatory CAE (AAAGTG,GTGAAA, CGAAAC, GAAACC, AGTTTC, and AAAAGC)match interferon regulatory factor (IRF) binding sites (Table 2).The consensus sequences of IRF1, IRF2, interferon-stimulatedregulatory element (ISRE), and interferon consensus sequencebinding protein (ICSBP) are all variations of the sameconsensus sequence, so high HAS scores were observedbetween predicted 6-mers and these binding sites (Interferonregulatory factorc in Table 2). Together, these 6 stimulatory

Fig. 2. Frequency distribution of TCS in the interferon experiment. (A) Positive CAcutoffs above which are 1% of all the positive and negative CAE candidates, respec45588 (2006) 452461CAE cover 12 of 13 nucleotides of the IRF1 consensussequence [G/C]AAAAG[C/T]GAAACC (Fig. 4). These 6CAE have similar positive functional levels (Eq. (2) andTable 1), ranging from 0.41 104 to 0.85 104, reinforcingthe interpretation that they were selected because they allidentify IRF sites. This suggests a very strong effect of theIRF binding site on global gene expression levels in theinterferon-stimulated PBMC.

We compared the predictions made by MotifModeler andREDUCE [16], another model-based procedure. Of the 20strongest motifs selected by REDUCE, 4 match with the top20 selections by the MotifModeler (labeled with a superscriptb in Table 2). Two of the 4 sites selected by bothapproaches match IRFs in the TRANSFAC database.MotifModeler selected 4 additional motifs that matchedIRFs and selected overlapping motifs within longer IRFsequences; REDUCE did not.

To compare the two approaches further, a benchmark datasetwas designed (see Materials and methods and supplementarydata). Both MotifModeler and REDUCE were applied to thisbenchmark dataset. Although both predicted 6 of the real

E, 1637 total. (B) Negative CAE, 443 total. Arrows P and N indicate the scoretively.

motifs, a much higher fraction of the motifs identified byMotifModeler (11 of 20) overlapped these real motifs, andMotifModeler identified dimeric motifs (Supplementary Fig.

S1). Only 6 of the 20 motifs identified by REDUCE matchedthe real motifs, and REDUCE identified only one part of thedimeric motifs (Supplementary Fig. S1).

Table 1Transcriptional contribution scores and estimated functional levels of selectedCAE

Positive CAE Negative CAE

CAE TCS (1010) x (104) CAE TCS (1010) x (104)

AATGCT 3.89 0.99 GCGTAA 3.18 2.34AATAAT 3.52 0.71 AATCGG 2.84 0.99AAAGTG 3.51 0.65 GGGTTA 2.83 0.78GTGAAA 3.51 0.66 CGTAAA 2.81 1.73AAAAAG 3.46 0.46CGAAAC 3.40 0.85AGGTCA 3.40 0.83GAAACC 3.39 0.52AAACAG 3.31 0.51ACTAGA 3.26 1.21ATTCCG 3.25 1.38AGTTTC 3.23 0.41AAAAGC 3.19 0.55CTGCAA 3.19 0.56CAAAAA 3.17 0.42ACCAAA 3.17 0.60

TCS, transcriptional contribution scores; x, estimated functional levels. Valueswere calculated based on 5,000,000 repeats.

Table 2Correspondence of 16 positive CAE to the TRANSFAC database

CAE EMS TRANSFAC accession ID a Known binding sites Factors

Table 3Correspondence of four negative CAE to the TRANSFAC database

CAE EMS TRANSFACaccession ID a

Known binding sites Factors

GCGTAA 9.22 M00398 Cell-deathspecification 2

ces-2

8.70 M00097 Pax-6 Pax-68.15 M00260 Hepatic leukemia

factorHlf

AATCGG 8.51 M00287 Nuclear factor Y(Y-box binding factor)

CBF(2),NF-Y

GGGTTA 9.51 M00766,M00647,M00964

LXR direct repeat 4 LXR:RXR

9.36 M00406 Myocyte enhancer factor MEF2A8.09 M00767 FXR inverted repeat 1 FOR1,2,

FXR:RXRCGTAAA 8.87 M00290,

M00291Fork head-relatedactivator-2,3

FOXF2,3

8.19 M00097 Pax-6 Pax-68.16 M00475 Defective dauer

formation 16daf-16 1,2,

a Accession identification number for each alignment matrix in theTRANSFAC database.

456 Y. Liu et al. / Genomics 88 (2006) 452461AATGCTb

AATAAT 8.71 M000958.31 M00619

AAAGTG 9.17 M00062, M00063, M00258, M00972

GTGAAAb 9.46 M00150

9.26 M00062, M00063, M00258, M00699, M00772, M00972AAAAAG 8.61 M00734CGAAACb 9.25 M00062, M00063AGGTCAb 11.33 M00512, M00515, M00518, M00528, M00762, M00763

10.85 M00156, M0015710.51 M00511, M00191, M009599.86 M009799.67 M005269.12 M007279.03 M001588.55 M00767

GAAACC 9.60 M00062, M00063, M007728.69 M003198.61 M006418.43 M00415

AAACAG 8.18 M00292ACTAGAATTCCG 9.29 M00224, M00492AGTTTC 9.98 M00062, M00258, M00699, M00772AAAAGC 8.59 M00062, M00063

8.36 M003558.31 M00238

CTGCAACAAAAA 8.96 M00734ACCAAAa Accession identification number for each alignment matrix in the TRANSFAC db Motifs that were also predicted by REDUCE [15].c IRF, IRF1, IRF2, ISRE, and ICSBP share a consensus (Fig. 4) and are listed asCut-like homeodomain protein CUTL1Alx-4 Alx-4Interferon regulatory factorc IRF cBrachyury BrachyuryInterferon regulatory factorc IRFc

CIZ (Cas-associated zinc finger protein) CIZ6-1, CIZ8Interferon regulatory factorc IRFc

PPAR- (peroxisome proliferator-activated receptor ) PPAR-RAR-related orphan receptor ROREstrogen-related receptor ERR1PAX6 Pax-6GCNF (germ cell nuclear factor) GCNFGene encoding splicing factor SF-1 SF-1COUP-TF, hepatocyte nuclear factor 4 COUP-TF1, HNFFXR inverted repeat 1 FXR:RXRInterferon regulatory factorc IRFc

MEF-3 MEF-3Stress-activated transcription factor, HSF HSFAREB6 (atp1a1 regulatory element binding factor 6) ZEBFork head-related activator-4 FOXD1

Signal transducer and activator of transcription 1 STAT1Interferon regulatory factorc IRFc

Interferon regulatory factorc IRFc

Milk protein binding factor (MPBF) PBFBarbiturate-inducible element

CIZ (Cas-associated zinc finger protein) CIZ6-1, CIZ8

atabase.

IRF.

le 6-

Y. Liu et al. / GenomicsExtension of the motifs

Our analysis used 6-bp sites; many CAE are larger than that.To investigate further the alignment of the predicted CAE to

Fig. 3. Distribution of highest alignment scores (HAS; Eq. (6)) between all possib3.46, and the standard deviation is 1.52.known binding sites, we extended the predicted CAE along thesequences of the genes in which they were found from 6 bp tothe length of the nucleotide matrices in the TRANSFACdatabase. The extended sequences were compared with PSSMof known binding sites, and the extended matching scores(EMS) were calculated. Using the six CAE that have significantsimilarities with IRF1 binding sites (Table 2, Fig. 4) as startingpoints, we found 129 IRF1 binding sites with EMS >8 and 44with EMS >12 within the 1000-bp regulatory sequencesupstream from the 198 IFN-responsive genes. The number ofknown IRF1 binding sites was also counted by scanning theregulatory sequences with the PSSM of IRF1, which found 296

Fig. 4. Alignment of six predicted CAE with five known interferon-relatedalignment matrices in TRANSFAC. The TRANSFAC matrices for IRF1, IRF2,IRF, ISRE, and ICSBP were aligned. The alignments of the 6-bp motifs(AAAGTG, GTGAAA, CGAAAC, GAAACC, AGTTTC, and AAAAGC) areshown as underlined sequences. Three of the 6-bp motifs matched all fiveknown binding sites, as shown by boxes around the sequences.and 60 known IRF1 binding sites with EMS >8 and >12,respectively. The percentage of predicted sites that match theconsensus sites, defined as Pmatch = Nprediced site/Nknown site 100%, increased consistently from 44.6% at EMS 8 to 100% at

bp CAE and known binding sites in the TRANSFAC database. The mean HAS is

45788 (2006) 452461EMS 15 (Fig. 5).

Predictions based upon different lengths of regulatory regions

To examine the effect of using different lengths of regulatoryregions on the model prediction, we reran the procedure using1000-bp regulatory sequences (rather than the 500 bp usedabove) from the same genes, and selected the 20 CAE (16stimulatory sites and 4 inhibitory sites) with the highest TCS.Eight CAE were identical in both 500- and 1000-bp predictions(Fig. 6). In the 1000-bp prediction, two CAE (GAAACC andCGAAAC), which were also identified in the 500-bpprediction, were part of IRF1 binding sites. This might be dueto the larger number of IRF1 sites in the proximal promoterregion (45 IRF1 binding sites with EMS >12) than in the distalregion (15 sites between 501 and 1000 bp).

Discussion

We present an algorithm, MotifModeler, for identifyingputative CAE in the regulatory DNA sequences from groups ofgenes whose expression levels differ between two conditions.We tested it on a set of genes whose expression was altered byinterferon-, as measured in PBMC using oligonucleotidemicroarrays [18]. MotifModeler was able to identify CAE froma set of 198 genes that were strongly influenced by interferon-.A major assumption of MotifModeler is that mRNA expressionlevels are controlled by the combinatorial effects of different

ed a

icstranscription factors binding in the regulatory regions. Here, wefocused on the 500-bp immediately upstream of the transcrip-tion start site and chose to look for 6-bp elements. Both of theseparameters can be adjusted. We also tested whether theidentified CAE correspond to known binding sites, becausewe expected that among the sites would be known interferon-

Fig. 5. Effects of different EMS thresholds on the number of predict

458 Y. Liu et al. / Genomresponsive elements. In fact, we detected many matches toregulatory elements known to be important in the interferonresponse, demonstrating that this strategy provided biologicalinsights into the regulatory mechanisms.

Six of the 16 stimulatory CAE, with similar functional levels(Eq. (2) and Table 1), correspond to overlapping parts of knowninterferon regulatory elements, covering 12 of the 13 nucleo-tides of the IRF1 consensus sequence, [G/C]AAAAG[C/T]GAAACC (Fig. 4). The repeated selection of overlappingportions of a site increases confidence in the prediction of thatsite. Another of the stimulatory CAE we identified, ATTCCG,matches the STAT (signal transducers and activators oftranscription) binding site with a high EMS (9.29); this elementhas the strongest predicted stimulatory effect (highest estimatedfunctional level, x = 1.38 104) among all the CAE candidates.STATs are important transcription factors involved in interferonsignaling [19]. The interaction of IFN with IFN receptors on thecell surface leads to activation of Jak kinases, whichphosphorylate STAT proteins in the cytoplasm [20]. Thephosphorylated STAT proteins are then translocated into thenucleus where they bind to transcription factor binding sitessuch as IRF1, IRF2, ISRE, and STAT1 to regulate geneexpression. Therefore, MotifModeler correctly identified a setof CAE expected to be important in this experiment.

In addition to these interferon-related sites, there werematches to sites such as hepatic leukemia factor, estrogenreceptor, and stress-activated transcription factor, which playroles in the blood and immune systems. These sites arerelevant to the biological experiment because we wereexamining gene expression in peripheral blood mononuclearcells; sites important to gene expression in the cell type underinvestigation are expected to play roles in the combinatorialregulation of the genes stimulated by interferon. Two predicted

nd known IRF1 binding sites and percentage of correct predictions.

88 (2006) 452461CAE, AAACAG and CGTAAA, match fork-head-relatedactivators Freac-2 and -4. Although there is no previousevidence that they function during interferon stimulation, theymay interact with the interferon-specific sites in a combina-torial manner that affects gene expression; their roles shouldbe further explored. Three predicted CAE (ACTAGA,CTGCAA, and ACCAAA) did not correspond to any knownbinding sites in the TRANSFAC database and might be novelelements. Follow-up biological validations on the functionalanalysis of predicted CAE would be very interesting.

MotifModeler is very flexible. Most parameters are adjust-able, including the number of nucleotides in each CAE, theregion of regulatory sequences, the number of CAE that areselected in each iteration, and the number of genes upon whichthe calculations are based. MotifModeler is relatively insensi-tive to the parameter m, which determines the number of CAEselected in each iteration, at least within the range of 10 to 30(Fig. 1A). This stability allowed the implementation of thisapproach without knowing the actual number of critical CAE.The reproducibility of the TCS spectra (Fig. 1A) offers greatconfidence in the fairness of our selection.

As a model-based approach, MotifModeler differs fromother CAE identification methods in four ways. First, itincorporates combinatorial effects of different elements.Second, in addition to identification of CAE, MotifModelerestimates functional effects (positive or negative and relativelevels, x) of predicted motifs under the conditions compared.

icsY. Liu et al. / GenomThird, the prediction is based on mRNA expression levels undercontrasting conditions, e.g., presence or absence of drug, so thatit finds CAE relevant to particular biological effects rather thanjust finding consensus motifs across multiple species. It can, forexample, be used to look for CAE sensitive to a drug orhormone, by comparing expression in the presence and absenceof the drug, or tissue-specific elements by contrastingexpression levels in different tissues. Finally, it is not limitedto finding previously reported CAE.

Another model-based approach to identifying CAE, RE-DUCE, was first applied in yeast [16]. This method includedcombinatorial effects in the identification of cis-acting elements.

Fig. 6. Correspondences between CAE predicted based on 500- and 1000-bp regulaGreen and red indicate stimulatory and inhibitory CAE, respectively.45988 (2006) 452461However, because CAE were selected recursively in an add-onmanner, later selections were strongly affected by the earlierselections. This approachmeans that if there are two sites that areredundant (e.g., different parts of a longer motif or two halves ofa dimeric site) REDUCE will pick only one, because the secondwill not improve the model once the first is included.MotifModeler, in contrast, can identify binding sites whosefunctions are redundant because the different parts of the motifare tested independently. This is not trivial, because manytranscription-factor binding sites are redundant [2325].MotifModeler predicted overlapping parts of longer CAE withboth the interferon- data (Fig. 4) and the benchmark data

tory sequences. Eight CAE were identical in both predictions (joined by lines).

ics(Supplementary Fig. S1). This increases confidence in the sitesof which multiple parts are identified and also leads to a higherfraction of predicted sites being relevant. MotifModeler was alsoable to predict both parts of a heterodimer (Supplementary Fig.S1), due to its ability to pick up redundant motifs. Therefore,although based on a simplified linear formulation,MotifModelerhas shown the capability to cope with certain nonlinear effects,such as redundancy. MotifModeler has the same advantage overother approaches, such as MOTIF REGRESSOR [26], that usean iterative motif selection strategy. MOTIF REGRESSOR usesMDScan [27] to find overrepresented motifs in the promotersequences and then tests this reduced pool of sequencesiteratively to select those that best correlate with gene expressionlevels. With an iterative selection strategy, once one part of abipartite motif is identified the other will not further reduce themodel error and will thus be missed. MotifModeler tests allcombinations and can identify both parts.

Recently, there have been efforts to predict cis-regulatorymodules (CRMs), consisting of more than one cis-actingelement, by statistical evaluations of the co-occurrence ofbinding sites within the regulatory regions of a set of genes[15,28]. MotifModeler should identify the component elementsof CRMs, because if two CAE cooperate to influence theexpression of a set of genes, both will be identified as importantand should have similar estimated functional effects. It is simplethen to determine which of the CAE map to nearby regions ofthe same genes.

Materials and methods

Modeling of transcriptional regulatory network

Since eukaryotic gene expression is controlled by the combinatorial effectsof multiple transcription factors binding to multiple regulatory sequences, asimplified quantitative relationship between genes and CAE can be formulatedas [16,29]

gk XiaTk

bk;ixi; 1

where gk represents the logarithmic ratios of the mRNA expression levels inresponse to treatment to the control levels, gk = log2(yk

treatment/ykcontrol); bk,i

denotes the number of CAE i in the regulatory regions of the kth gene; and Tkrepresents all the CAE that have connections to the kth gene.

A least-squares approach was used to estimate the contribution of each CAEnode (xi in Eq. (1)) on the global gene expression

x P BTB1BT

Pg: 2

The model error based on a given selection of CAE was defined as the sumsquare of the differences between observed and predicted mRNA expressionlevels,

E XNk 1

gk XiaTk

bk;ixi

!2; 3

where N is total number of genes.

MotifModeler

460 Y. Liu et al. / GenomA motif searching procedure, called MotifModeler, was developed toevaluate the effects of all the potential CAE present in regulatory sequences. Theuser first selects the genomic region within which CAE will be sought. For mostanalyses reported here we selected the 500-bp genomic region immediatelyupstream of the transcriptional start site of the 198 selected genes, as annotatedin the UCSC Genome Browser (http://genome.ucsc.edu) hg16 assembly. Thenthe user sets the number of CAE to be selected in each iteration, m. For mostanalyses reported here, we set the number of effective CAE (m) at 20. The useralso selects the number of times (Nrep) to repeat the algorithm, generally in therange of 100,000 to 10,000,000.

In this study, we focused on 6-bp DNA segments (w = 6). Consideringcomplementary 6-mers to be the same, (i.e., AACTAG was considered as thesame as CTAGTT), there are 2080 different 6-bp CAE candidates. We used1,000,000 iterations such that each of the 2080 sites was tested approximately4.6 times in combination with every other site.

Since a smaller model error implies a more influential CAE, a TCS wasassigned to each CAE candidate according to the formulation

TCSi XcaCm;i

1Eac

; 4

where E is the model error derived in Eq. (3), Cm,i represents all thecombinatorial selections (having m CAE) that include the ith CAE; and is thepower factor that influences the effect of single selections ( > 1). A larger value usually amplifies the effects of motif selections in each iteration (here, weuse = 5).

The MotifModeler algorithm can be summarized as:

1. Randomly pick m w-bp CAE.2. Calculate the predicted model error E (Eq. (3)).3. Calculate the current contribution score of each CAE candidate as reciprocal

to E.4. Add the current contribution score to the cumulative TCS (Eq. (4)).

Repeat the procedure (14) Nrep times.All the customized programs were written using Matlab 7.0 (www.

mathworks.com).

Position-specific scoring matrix

A PSSM was defined to evaluate the similarities between model-selected 6-bp CAE and the 762-nucleotide distribution matrices of aligned bindingsequences in the TRANSFAC 9.2 database (July 1, 2005) [7]. The PSSM wasconstructed based on the logarithmic transformation of the frequency of eachnucleotide in a known binding site. To avoid underrepresentative occurrences ofall the possible nucleotides due to the limited number of motif samples, aBayesian-based prediction was implemented by adding N (N is the totalnumber of sample sequences) pseudocounts. The elements in the PSSM weredefined [30] as

Sic log2fic bic

Nc P4i 1

bic

; 5

where fic and bic are the numbers of real counts and pseudocounts of the ithnucleotide (i = A, C, G, or T) in the cth position; Nc is the total number ofsamples in the cth position. Here, we distributed Nc pseudocounts among fournucleotides according to the AT and GC content in the human genome (60.6%AT and 39.4% GC).

The matching score of a sequence on a PSSM was calculated by adding upall the matching elements in the PSSM at different positions,

MS Xlj 1

SMjj; 6

where Mj corresponds to the matching nucleotide on the jth position in thePSSM.

88 (2006) 452461A HAS was defined to quantitate similarities between a 6-bp motif andknown binding site in the TRANSFAC database. Since the lengths of alignmentmatrices are usually larger than 6, there exist multiple potential alignments. The

HAS was defined as the highest scores among all the alignments. To check thedistribution of HAS, we conducted a Monte Carlo analysis by calculating

and 6-bp segments. The calculation was conducted 1,000,000 times.

literature review, J. Comput. Biol. 9 (2002) 67103.

genes using evolutionary computation, Nucleic Acids Res. 32 (2004)38263835.

[10] M.Q. Zhang, Large-scale gene expression data analysis: a new challenge to

461Y. Liu et al. / Genomics 88 (2006) 452461[3] D.J. Lockhart, E.A. Winzeler, Genomics, gene expression and DNAarrays, Nature 405 (2000) 827836.

[4] J.C. Venter, A part of the human genome sequence, Science 299 (2003)11831184.

[5] R.B. Altman, Building successful biological databases, Brief Bioinform. 5(2004) 45.

[6] T. Heinemeyer, et al., Databases on transcriptional regulation: TRANS-FAC, TRRD and COMPEL, Nucleic Acids Res. 26 (1998) 362367.

[7] E. Wingender, P. Dietze, H. Karas, R. Knuppel, TRANSFAC: a databaseon transcription factors and their DNA binding sites, Nucleic Acids Res. 24(1996) 238241.

[8] W. Thompson, M.J. Palumbo, W.W. Wasserman, J.S. Liu, C.E.Lawrence, Decoding human regulatory circuits, Genome Res. 14 (2004)19671974.

[9] G.B. Fogel, et al., Discovery of sequence motifs related to coexpression ofBecause many binding sites in TRANSFAC are larger than 6 bp, weextended the predicted motifs from 6 bp to the length of individual matrices,using the sequences of the individual genes analyzed. An EMS was defined tocalculate the similarity between an extended motif and an alignment matrix.

Comparison between MotifModeler and REDUCE

To compare the predictions of MotifModeler with those of REDUCE [16],another model-based procedure, we tested both against the interferon data andalso against a benchmark dataset that was designed by embedding CAE ofvarying lengths (from 6 to 10 bp and dimers of 6-bp motifs), each assigned afunctional expression coefficient, into 200 random sequences (see supplemen-tary data for details). Both programs were run against this dataset to determinehow well they identified the known, embedded CAE.

Acknowledgments

We thank Ronald E. Jerome and Chunxiao Zhu for expertassistance with the microarray studies, which were carried outusing the facilities of the Center for Medical Genomics atIndiana University School of Medicine. The Center for MedicalGenomics is supported in part by grants from the Indiana 21stCentury Research and Technology Fund and the IndianaGenomics Initiative of Indiana University (supported in partby the Lilly Endowment, Inc.).

Appendix A. Supplementary data

Supplementary data associated with this article can be found,in the online version, at doi:10.1016/j.ygeno.2006.04.006.

References

[1] F.S. Collins, E.D. Green, A.E. Guttmacher, M.S. Guyer, A vision for thefuture of genomics research, Nature 422 (2003) 835847.

[2] H. de Jong, Modeling and simulation of genetic regulatory systems: acomputational biologists, Genome Res. 9 (1999) 681688.[11] T.L. Bailey, C. Elkan, in: R. Altman, D. Brutlag, P. Karp, R. Lathrop, D.

Searls (Eds.), Proceedings of the Second International Conference onIntelligent Systems for Molecular Biology, AAAI Press, Menlo Park, CA,1994, pp. 2836.

[12] G.Z. Hertz, G.D. Stormo, Identifying DNA and protein patterns withstatistically significant alignments of multiple sequences, Bioinformatics15 (1999) 563577.

[13] Y. Liu, et al., A suite of Web-based programs to search for transcriptionalregulatory motifs, Nucleic Acids Res. 32 (2004) W204W207.

[14] J.D. Hughes, P.W. Estep, S. Tavazoie, G.M. Church, Computationalidentification of cis-regulatory elements associated with groups offunctionally related genes in Saccharomyces cerevisiae, J. Mol. Biol.296 (2000) 12051214.

[15] M. Gupta, J.S. Liu, De novo cis-regulatory module elicitation foreukaryotic genomes, Proc. Natl. Acad. Sci. USA 102 (2005) 70797084.

[16] H.J. Bussemaker, H. Li, E.D. Siggia, Regulatory element detection usingcorrelation with expression, Nat. Genet. 27 (2001) 167171.

[17] Y. Liu, M.P. Vincenti, H. Yokota, Principal component analysis forpredicting transcription-factor binding motifs from array-derived data,BMC Bioinform. 6 (2005) 276.

[18] M.W. Taylor, et al., Global effect of PEG-IFN-alpha and ribavirin on geneexpression in PBMC in vitro, J. Interferon Cytokine Res. 24 (2004)107118.

[19] M.W. Fried, et al., Peginterferon alfa-2a plus ribavirin for chronic hepatitisC virus infection, N. Engl. J. Med. 347 (2002) 975982.

[20] J.E. Darnell Jr., I.M. Kerr, G.R. Stark, JakSTAT pathways andtranscriptional activation in response to IFNs and other extracellularsignaling proteins, Science 264 (1994) 14151421.

[21] M. Caraglia, et al., Alpha-interferon and its effects on signal transductionpathways, J. Cell. Physiol. 202 (2005) 323335.

[22] J.N. McClintick, R.E. Jerome, C.R. Nicholson, D.W. Crabb, H.J.Edenberg, Reproducibility of oligonucleotide arrays using small samples,BMC Genom. 4 (2003) 4.

[23] E.G. Cole, K. Gaston, A functional YY1 binding site is necessary andsufficient to activate Surf-1 promoter activity in response to serum growthfactors, Nucleic Acids Res. 25 (1997) 37053711.

[24] T.I. Lee, et al., Redundant roles for the TFIID and SAGA complexes inglobal transcription, Nature 405 (2000) 701704.

[25] E. Moyano, J.F. Martinez-Garcia, C. Martin, Apparent redundancy in mybgene function provides gearing for the control of flavonoid biosynthesis inantirrhinum flowers, Plant Cell 8 (1996) 15191532.

[26] E.M. Conlon, X.S. Liu, J.D. Lieb, J.S. Liu, Integrating regulatory motifdiscovery and genome-wide expression analysis, Proc. Natl. Acad. Sci.USA 100 (2003) 33393344.

[27] X.S. Liu, D.L. Brutlag, J.S. Liu, An algorithm for finding proteinDNAbinding sites with applications to chromatin-immunoprecipitation micro-array experiments, Nat. Biotechnol. 20 (2002) 835839.

[28] R. Sharan, A. Ben-Hur, G.G. Loots, I. Ovcharenko, CREME: cis-regulatory module explorer for the human genome, Nucleic Acids Res. 32(2004) W253W256.

[29] Y. Liu, H. Yokota, Modelling and identification of transcription-factorbinding motifs in human chondrogenesis, Syst. Biol. 1 (2004) 8592.

[30] J.G. Henikoff, S. Henikoff, Using substitution probabilities to improveposition-specific scoring matrices, Comput. Appl. Biosci. 12 (1996)135143.

[31] J.N. McClintick, H.J. Edenberg, Effects of filtering by Present call onanalysis of microarray experiments, BMC Bioinformatics 7 (2006) 49.highest alignment scores of randomly selected pairs from known binding sites

Model-based identification of cis-acting elements from microarray dataResultsBiological model systemSelection of putative CAECorrespondence of predicted CAE to known transcription factor binding sitesExtension of the motifsPredictions based upon different lengths of regulatory regions

DiscussionMaterials and methodsModeling of transcriptional regulatory networkMotifModelerPosition-specific scoring matrixComparison between MotifModeler and REDUCE

Acknowledgmentsapp1References