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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Dev Cell. Author manuscript; available in PMC Oct 1, 2010.
Published in final edited form as:
PMCID: PMC2768654

Motif-blind, genome-wide discovery of cis-regulatory modules in Drosophila and mouse


We present new approaches to cis-regulatory module (CRM) discovery in the common scenario where relevant transcription factors and/or motifs are unknown. Beginning with a small list of CRMs mediating a common gene expression pattern, we search genome-wide for CRMs with similar functionality, using new statistical scores, and without requiring known motifs or accurate motif discovery. We cross-validate our predictions on 31 regulatory networks in Drosophila and through correlations with gene expression data. Five predicted modules tested using an in vivo reporter gene assay all show tissue-specific regulatory activity. We also demonstrate our methods’ ability to predict mammalian tissue-specific enhancers. Finally, we predict human CRMs that regulate early blood and cardiovascular development. In vivo transgenic mouse analysis of two predicted CRMs demonstrates that both have appropriate enhancer activity. Overall, 7/7 predictions were validated successfully in vivo, demonstrating the effectiveness of our approach for insect and mammalian genomes.


In metazoans, much of transcriptional regulation is mediated by cis-regulatory modules (CRMs; also “modules” or “enhancers”), that form the building blocks of gene regulatory networks (Carroll et al., 2001). CRM identification formerly had been possible only through a dedicated empirical approach of testing sequence fragments for regulatory activity in a reporter gene assay. The genomics era has led to the development of new genome-wide techniques to screen for potential gene-regulatory regions, such as chromatin immunoprecipitation coupled to genomic tiling arrays (ChIP-chip) (Li et al., 2008) or ultra high-throughput sequencing (ChIP-Seq) (Visel et al., 2009). Although they show great promise, even these empirical methods do not identify regulatory elements or predict their tissue-specific activity with complete accuracy. Importantly, the fact that it is impossible to assay all tissue types under all conditions means that potential CRMs will be missed by these techniques.

In recent years, computational methods have provided an attractive complementary approach for module identification. However, their effectiveness has been limited to a few well-understood biological systems, where prior knowledge of the requisite TFs and their binding sites could be exploited. This paper examines CRM prediction in the much more common scenario where knowledge of the relevant TFs and/or their binding specificities (motifs) is missing. We tackle here a common variant of this problem:

Suppose a small set of modules participating in a transcriptional sub-network is known a priori. The task is to use such information as “training data” to guide the search for other modules in that sub-network. We call this task the “supervised CRM prediction problem”. The term sub-network refers here to a group of genes that are coordinately expressed as a result of having common regulatory inputs. We define a successful CRM prediction as identification of a sequence that drives an expression pattern commensurate with a nearby gene’s endogenous expression. Depending on how specific the expression patterns of the training CRMs are, a successfully predicted CRM may recapitulate their common expression pattern, or may not.

Computational methods for CRM prediction (Berman et al., 2002; Halfon et al., 2002) (Frith et al., 2003) (Sinha et al., 2003) (Philippakis et al., 2005) (Donaldson et al., 2005) typically scan the genome for clusters of putative binding sites that are defined by sequence similarity to known motifs. This approach may fail on at least two grounds. First, motif information is sparse as of now; e.g., motif databases for Drosophila catalog only ~12% of the estimated number of TFs. Intense efforts are being made to characterize the binding specificities of all TFs in mouse (Berger et al., 2006) and fruitfly (Noyes et al., 2008) and may in the long term alleviate the problem. However, these efforts are labor-intensive and relatively expensive and the problem may thus persist for scientists studying organisms other than human/mouse and fruitfly. A second, more serious problem facing CRM discovery stems from the fact that most computational tools need prior knowledge of the TFs relevant to the specific regulatory network of interest. For less studied regulatory systems, such knowledge may not be available. Admittedly, even if the relevant TFs and/or their motifs are unknown, computational motif finding tools may be used to discover position-weight-matrix (PWM) motifs from the training data. However, the modest success rate of motif-finding programs, as suggested by a recent survey (Tompa et al., 2005), casts doubts upon the prospect of CRM discovery based on computational motif finding.

Here, we address simultaneously both problems by undertaking supervised CRM discovery in the absence of motif knowledge and without relying upon accurate motif finding. We propose and examine various statistics to capture the functional similarity (due to shared binding sites) between a candidate CRM and the given set of modules. These statistics belong to the realm of “alignment-free” sequence comparison, since the similarity to be detected is not due to orthology. The statistics are based on frequencies of short words, akin to many motif-finding programs, but without the usual objective of finding the most specific (biochemically accurate) characterization of the TF’s binding sites. All new methods developed here are made publicly available as source code at http://veda.cs.uiuc.edu/scrm/index.htm

Previous attempts at solving the supervised CRM prediction problem (Chan and Kibler, 2005; Grad et al., 2004; Nazina and Papatsenko, 2003) have been primarily tested on a single data set, the anterior-posterior patterning sub-network in D. melanogaster. We now evaluate two existing and seven new scoring schemes on 31 data sets in Drosophila and eight data sets in mammals, and perform in vivo validation in both species. In our previous work (Ivan et al., 2008), we proposed computational methods for CRM discovery without prior knowledge of motifs or modules, where the search was constrained to regions around co-regulated genes. Here, we relax that constraint and enable genome-wide search by leveraging the prior knowledge of related CRMs where available. The methods of (Ivan et al., 2008) are not applicable in this setting. An unsupervised version of our problem was also addressed in (Rajewsky et al., 2002) through the use of Poisson statistics on short word counts.

We performed extensive cross-validation tests with our scores on 31 data sets representing a broad spectrum of regulatory sub-networks in D. melanogaster, exploiting known modules catalogued in the REDfly database (Halfon et al., 2008). Our tests established the feasibility of supervised CRM prediction for about half of the examined data sets, and also identified data sets that are not amenable to our scores. We then predicted modules genome-wide for each amenable regulatory sub-network, and found their neighboring genes to be highly enriched for the expected expression patterns. We filtered our predicted module collection based on gene expression data, producing a high confidence set of putative CRMs belonging to a regulatory sub-network. We tested five predicted modules in vivo and found each of the five to drive reporter gene expression that recapitulates aspects of the endogenous gene expression (although not always in the expected pattern).

Assessment of the supervised prediction pipeline on eight data sets in mammals, comprising 244 tissue-specific enhancers, led to ~60% of the enhancers being recovered. We finally applied this pipeline to predict CRMs with roles in mammalian blood and cardiovascular development. In vivo validation in transgenic mice allowed us to demonstrate successful identification of two regulatory regions with the predicted activity and demonstrates the extensibility of our computational approach beyond Drosophila.


Scoring schemes

Given a genomic region in which to search, each of our CRM prediction schemes scans the sequence with a shifting window of fixed size, and scores the window for similarity to a (given) training set of CRMs. Thus, the crucial component of each of these schemes is its unique scoring system for matching a candidate module (“test CRM”) to the set of known “training modules”. These scoring schemes are described next at a general level; details are available in Methods and Supplementary Note S1.

1. Markov chain-based score

The “HexMCD” score trains separate generative models (5th order Markov chains) for training modules and background sequences, and quantifies which model matches the test sequence better. This score was originally proposed by (Grad et al., 2004).

2. Dot product-based scores, with statistical significance estimation (“D2z”)

These scores are based on the dot-product of k-mer frequency distributions of training and test sequences. Importantly, the scores are made to reflect the statistical significance of this dot-product, by analytical computation of “z-scores” under suitable null models; hence the name “D2z”. For technical reasons described in Methods, we had to develop and implement this analytical calculation differently from our previous work (Ivan et al., 2008; Kantorovitz et al., 2007) on the D2z score. We developed three scoring schemes in this category (Methods).

3. Selection of representative k-mers, followed by Poisson statistics

In the new score called “Poisson Additive Conditional” or “PAC”, words that are even weakly associated with the training CRMs are identified and their additive contributions to the similarity score are based on overrepresentation of those words in the test sequence, relative to background sequences, calculated using Poisson statistics (Methods).

4. Selection of representative k-mers, followed by weighted sum of counts

The two scores in this category are generically defined as wWσ(w)n(w) where n(w) is the number of occurrences of word w in the test sequence, σ(w) is a weight reflecting its association with the training modules, and the set W comprises the top ranking words based on σ(w). In the “HexDiff” score of (Chan and Kibler, 2005), re-implemented here, the weight of a word is the ratio of its frequency in training and background sequences. In the “HexYMF” scheme we have designed, the weight of w is the “z-score” (see Methods) of the count of w in the training CRMs,.

5. Motif database-driven score

We developed a program, called Stubb-MDB (“Stubb based on Motif Database”), that begins with a large compendium of experimentally validated motifs (Matys et al., 2003) (Noyes et al., 2008) (Halfon et al., 2008), determines the motifs that are relevant to the regulatory sub-network of interest and runs the Stubb program (Sinha et al., 2003) with these short-listed motifs to score the test sequence for matches to the motifs. This motif-based approach provides a useful point of comparison to the motif-blind approaches outlined above (1-4).

Comparison of different scoring schemes and characterization of data sets

We utilized a modified version of the CRM prediction benchmark developed in our previous work (Ivan et al., 2008). The new benchmark consists of 31 data sets, each data set comprising a collection of bona fide CRMs that mediate gene expression patterns with some level of commonality. We employed “leave-one-out cross-validation” (LOOCV) to assess the relative performance of our scoring schemes (see Methods). Fig. 1(A) summarizes the results of our cross-validation tests, with asterisks indicating cases where the CRM-level sensitivity was statistically significant (p ≤ 0.05; see Supplementary Note S2). In such cases, we say that the method is “successful” on the data set. We immediately note that the best motif-blind methods (HexMCD, HexDiff-rc, HexYMF-s200-rc) succeed on close to half of the data sets. In contrast, the method that makes use of a motif database (Stubb-MDB-rc) succeeds on fewer data sets. We also tested an alternative motif-based pipeline - that of using “Clover” (Frith et al., 2004) for motif selection and “ClusterBuster” (Frith et al., 2003) for scanning with selected motifs - and found success levels to be comparable to that of Stubb-MDB (Supplementary Note S3). Nucleotide-level sensitivity measurements show similar trends (Fig. S1). Repeating the cross validation exercise four times (see Methods), we examined which methods succeed on a specific data set in all four LOOCV “instantiations” (Table S3). We find that 15 of the 31 data sets have at least one method (among the eight listed) for which this consistency requirement is met (Fig. 1D). We designate these as the “amenable” data sets, i.e., the CRMs in such a data set have the extent and kind of sequence similarity that we need for supervised prediction. We find that for data sets neuroectoderm and blastoderm, all shown methods succeed consistently, suggesting that these two regulatory sub-networks are among the most easy ones for supervised CRM prediction. A closer look at the top 200 words used by HexYMF for the blastoderm data set reveals matches to six of canonical motifs for this network (Fig. S4). We also find examples, such as cardiac-mesoderm, eye.1 and mesectoderm, where only one of the methods is a consistent performer (white cells in Fig. 1D), indicating that the common sequence features of the CRMs in each of these regulatory sub-networks may be harder to capture.

Figure 1
Assessment of methods

Fig. 1B and 1C show one-on-one comparison of the eight methods. We find the top four methods - HexMCD, PAC-rc, HexYMF-s200-rc and HexDiff-rc - to be competitive with one another. The emerging theme however is that CRMs in different data sets are best predicted by different scoring schemes. For instance, the “D2z-cond-weights” score is the only method consistently successful on the “somatic muscle” data set (Fig. 1D), even though in the final tally over all data sets (Fig. 1B, 1C) it is the least successful score. It is natural to ask if a combination of two or three methods can yield better predictive performance on average. We chose the methods HexYMF-s200-rc, HexDiff-rc and PAC-rc to build what we call a “fusion” method - which scores a candidate sequence by the product of the scores from these three methods. Fig. 1B and 1C show that this naïve combination method is indeed better overall than any individual method. It has consistent performance on 10 data sets (Fig. 1D), the most among all methods. It is also the superior method in one-on-one comparisons (Fig. 1B).

Finally, we attempted to find characteristics of data sets that allow strong performance. We found a significant correlation between prediction accuracy on a data set and (i) the extent of homotypic clustering of short words in the training set (Fig. S2A, P=0.009), (ii) the GC content of the training set (Fig. S2B, P=0.005), and (iii) the extent of nucleotide-level conservation with orthologous sequence (Fig. S2C, P=0.007). We did not however see a significant correlation between the number of training CRMs and the performance on a data set.

Multi-species comparisons improve predictions

We obtained the orthologs of the training CRMs from each of two other Drosophila species, and predicted CRMs in D. melanogaster sequence with each of these training sets. Scores from these runs were then averaged to give us a multi-species score profile. Cross-validation on all 31 data sets showed that the multi-species version of HexMCD substantially outperformed its single species version (Fig. 1E; Table S3).

Role of the training set

We asked if the success of our supervised CRM prediction methods is due to an ability of our scoring schemes to learn the characteristics of the specific regulatory sub-network, or an ability to infer general sequence characteristics of CRMs. We constructed for each data set a “random training set” consisting of REDfly CRMs from outside of that regulatory sub-network, and repeated the above evaluation. Of the 16 data sets on which the “fusion” method performs significantly well (Fig. 1A), nine data sets perform worse when using the random training set, meaning that the CRM predictions are specific to the particular regulatory sub-network. On the other hand, the performance on seven data sets, though significantly strong, is comparable to that with training sets comprising randomly selected CRMs, suggestive of a less specific type of CRM prediction based on sequence characteristics of CRMs in general (Table 1). Interestingly, the data sets that give rise to specific prediction are typically smaller than the non-specific ones (P < 0.02, Table 1) and their expression patterns, as measured by the number of individual anatomy terms defining the pattern (Table S7), more specific (P < 0.002, Table 1). We also assessed the value of the training set by executing an unsupervised CRM prediction method (“CSam”, from our previous work (Ivan et al., 2008)) on each of the 31 data sets. The method was successful on only one of the data sets (data not shown), indicating a major advantage conferred by the use of the training set, and also pointing out that our modified benchmark is substantially “harder” than that of (Ivan et al., 2008). (See Supplementary Note S4.)

Table 1
Categorization of data sets amenable to the Fusion method into “specific” and “non-specific” (column 4). “Specific“ indicates that nucleotide-level sensitivity in LOOCV tests was better (≥0.05) when ...

Genome-wide prediction of Drosophila CRMs

For each of the 15 amenable data sets, taking all known modules as training data, we scanned the non-coding genome of D. melanogaster for the highest scoring modules, using the “fusion” method as well as the best individual scoring scheme from the cross-validation results for that data set. We then used gene expression databases (FlyBase, BDGP) to test the quality of these genome-wide predictions. First, we defined a set of genes (“expression gene set”) with expression patterns commensurate with those of the CRMs in the data set (see Methods). We next took the modules predicted for that data set and extracted their nearest neighboring genes (“predicted gene set”). Finally, we performed hypergeometric tests of enrichment between the expression gene set and the predicted gene set (Table 2). Of the 15 data sets, eight had enrichment p-value ≤ 10-10, and 14 had p-value ≤ 0.01 either with fusion or the best individual scoring scheme. (Given that 15×2 tests were done, 13 are significant at p < 0.05 with Bonferroni correction.) The strongest enrichments were observed for the data sets blastoderm, imaginal disc, and wing. In a negative control experiment, we “predicted” random genes for each data set and performed the same test for enrichment; the enrichment p-value was not significant on any data set (Table 2). Thus, intersecting the predictions with gene expression data provides strong support for our genome-wide CRM predictions. Furthermore, taking this intersection gives us a high confidence set of module predictions for each of the 15 data sets, which are made available in an online interface with “Genome Browser” integration (Supplementary Note S5). This interface also lists associations with motifs from existing databases, for each data set.

Table 2
Genome-wide CRM predictions for the 15 amenable data sets, using the “Fusion” method and the best individual method from LOOCV tests. The Hypergeometric p-value of enrichment (PF,PL,PR) between the predicted CRMs for a data set and the ...

In vivo validation of predictions for the blastoderm data set

The blastoderm dataset performed strongly in the cross-validation exercises using each of the scoring methods and had the strongest gene expression pattern enrichment in the genome wide search. We therefore focused our follow-up efforts on this high-performing dataset. Our compendium lists 113 modules as being related to the training CRMs in this data set (see Methods and Table S2). 33 of these top modules belong to the training set itself, while an additional 26 are known modules that were not included in the training set (Table S2). The remaining 54 (= 113 - 33 - 26) modules are novel CRM predictions. (Also see Supplementary Note S6.)

Although the expression pattern enrichment results and successful prediction of known modules not included in the training set suggest that a large fraction of our predictions are correct, only in vivo testing of the predicted elements can confirm that they have regulatory function. We therefore chose five putative modules, located near the genes edl, srp, odd, SoxN, and cas, for in vivo validation using a GFP-based reporter construct in transgenic Drosophila embryos. (None of these was predicted by the motif-based “Ahab” program in (Schroeder et al., 2004). Remarkably, all five tested constructs showed reporter gene expression in a pattern consistent with the expression of their predicted associated gene (Fig. 2). Interestingly, although the five modules were selected using the blastoderm data set as training data, and all five associated genes are expressed during the blastoderm stage of development, only two of the identified CRMs, those for edl and srp, appear to regulate blastoderm-stage gene expression (Fig. 2A,B,G,H). Both of these also regulate expression at later stages in development. In the case of edl, this later expression is a subset of the complete expression pattern of the gene (Fig. 2C-F), consistent with the notion of modular gene regulation through the action of multiple CRMs for each gene (Arnone and Davidson, 1997). Expression driven by the srp CRM more comprehensively covers the range of endogenous srp expression, in addition to driving apparently ectopic reporter gene expression in the midgut of later stage embryos (Fig. 2I, 2J and data not shown). This may represent actual ectopic expression, but may also simply be perdurance of GFP expression in tissues that developed from earlier srp-expressing progenitors. The SoxN and cas CRMs closely recapitulate subsets of the central nervous system expression of their respective associated genes from mid-embryogenesis on (Fig. 2M-2T), while the odd CRM recapitulates native odd expression in a number of tissues including mid-stage mesodermal progenitor cells and late-stage fat body (Fig. 2K, 2L and data not shown). Ectopic reporter gene expression is observed in cardioblasts (data not shown). This may again merely represent perdurance of the GFP reporter in a subset of mesodermal cells, or may indicate improper activity of the CRM in descendants of a common cell lineage (a subset of Odd-negative cardioblasts are sibling cells to Odd-positive pericardial cells, arising from a common progenitor (Ward and Skeath, 2000)). Detailed experimental analysis will be needed to distinguish between these possibilities.

Figure 2
In vivo validation of Drosophila CRM predictions

Application to data sets of tissue-specific enhancers from mouse

Encouraged by these strong validation results, we also sought to determine whether our methods would work effectively for prediction of mammalian CRMs. We constructed eight new data sets, comprising a total of 244 CRMs that drive expression in specific tissues in mouse (Pennacchio et al., 2006). We performed leave-one-out cross validation as in the Drosophila analysis above, with each of the seven motif-blind methods. Seven of the eight data sets were amenable to consistent prediction by at least one method, over 10 LOOCV instantiations (Methods, Fig. 3, Table S3); as observed previously, no one method proved universally superior. (Also see results on cross validation with native flanks, Fig. S5, Table S5.) The accuracy is especially noteworthy on the forebrain, hindbrain rhombencephalon, and neural tube data sets, being at 75-85% at the CRM lavel, and 58-76% at the nucleotide level (Fig. 3, Table S6). Overall, roughly 60% of the full complement of enhancers tested were recovered by supervised prediction in a cross-validation setting, making a strong case for the generalizability of the approach.

Figure 3
In silico validation on mammalian enhancers

Application to early blood and cardiovascular development in human/mouse

To follow up on these results in a genome-wide manner, we turned to a data set of ten enhancers known to function in the developing blood and vasculature (Supplementary Note S8). We used the two top-scoring methods from LOOCV, HexYMF-s200-rc (54% sensitivity) and PAC-rc (48% sensitivity), to predict CRMs in the human genome. For efficiency, the search was limited to “evolutionarily conserved regions” (ECRs) (Loots and Ovcharenko, 2007) based on human-mouse conservation. The top 1000 CRM predictions (of each method) were then assessed for enrichment for a set of genes known to be differentially expressed in blood stem cells. Predicted CRMs with a neighboring gene in this set were counted, and were found to be highly statistically significant when using either method (z-scores > 10 and > 6 respectively, see Fig. 3C.)

All ten training CRMs contain consensus binding sites for the Ets and GATA families of transcription factors. We therefore tested the top 1000 CRM predictions for the presence of GATA and Ets motifs (Fig. 3D). We found 234 of the top 1000 predictions from PAC-rc to have the Ets motif; comparing this to a random expectation of 59, we see a four-fold enrichment and a z-score of > 45. Similar enrichment for the GATA motif is observed (z > 13) in the PAC-rc predictions and for both motifs in the HexYMF-200-rc predictions (z > 7, Fig. 3D).

We constructed a high confidence CRM prediction set for each method by taking the 1000 top predictions, requiring that a neighboring gene be in the blood stem-cell gene set, and specifying that either the Ets or GATA motif be present. This led to 75 distinct predicted CRMs based on HexYMF-200-rc and 114 based on PAC-rc (Table S4).

In vivo validation in mouse

To demonstrate that the above approach was indeed able to identify regulatory modules and predict their in vivo biological activity, we generated lacZ reporter constructs for two predicted intronic elements for testing in transgenic mouse embryos. Neither of the two respective gene loci has previously been implicated in either blood or cardiovascular development. The first gene (EBF3) encodes a little-known paralog of early B-cell factor EBF1, a helix-loop-helix transcription factor important during early B-lymphocyte development (Busslinger et al., 2000). (The predicted CRM is ranked at 41 by HexYMF-s200-rc and 63 by PAC-rc.) The second gene (C1ORF164) corresponds to an uncharacterized open reading frame on chromosome 1 predicted to encode a ring-finger-domain containing protein. (The predicted CRM is ranked 12 by HexYMF-s200-rc and 23 by PAC-rc)

Multiple transgenic founders were generated for each construct and day E11.5 embryos were collected and stained for reporter gene activity. As exemplified by a representative Lyl1 promoter transgenic embryo, the 10 elements used as training data all show tissue specific enhancer activity by driving expression in blood vessels, the heart, and developing blood cells in the fetal liver (Fig. 4). The transgenic embryos generated with the EBF3 and C1ORF164 candidate enhancers reproducibly showed transgene expression in two and three (respectively) of these three tissues thus demonstrating that the computational screen not only led to the identification of bona fide transcriptional enhancers, but more importantly was able to predict the tissue specific activity of these elements.

Figure 4
Validation of predicted CRMs for in vivo activity in blood and cardiovascular tissues using transgenic mouse assays


The problem of supervised CRM prediction is easily motivated when we consider that the interactions in a regulatory sub-network involve up to hundreds of genes (Davidson, 2006) and a relatively small set of TFs. Clearly, there is a significant layer of combinatorial regulation in between, implemented through cis-regulatory modules. We thus expect tens to hundreds of modules that share some degree of similarity in their binding site content, and we should be able to predict most of these given a representative subset. Such an initial set of modules typically will be obtained through reporter gene assays, computational methods or high-throughput technologies such as ChIP-chip. Our algorithms will then leverage the initial set to provide much greater coverage of the regulatory sub-network. We have demonstrated here that our methods apply similarly to both the Drosophila and mouse genomes with high accuracy.

It is important to contrast our method with the “tissue-specific CRM prediction” approaches undertaken in (Chen and Blanchette, 2007; Hallikas et al., 2006; Pennacchio et al., 2007; Smith et al., 2007; Yu et al., 2007) for the human genome. All of these methods rely upon a large collection of vertebrate TF motifs (from TRANSFAC (Matys et al., 2003) and/or JASPAR (Bryne et al., 2008)), which is their main point of difference from our approach. Moreover, their problem formulation and data assumptions are distinct from ours: their strategy hinges on large scale gene expression data across a large spectrum of tissues (Chen and Blanchette, 2007; Pennacchio et al., 2007; Smith et al., 2007; Yu et al., 2007) or on knowledge of TFs mediating the specific transcriptional response (Hallikas et al., 2006). In many cases, such information may not be available. We require instead the prior knowledge of some of the CRMs involved in a particular regulatory sub-network (which could be a tissue-specific sub-network). The “EDGI” program of (Sosinsky et al., 2007) has a similar objective to ours, i.e., CRM prediction without motif knowledge, relying instead on inter-species conservation and clustering of binding sites, but the only published tests of this method have been on the A/P patterning network in Drosophila, precluding statements about its broader applicability. The most definitive test of any method is its ability to predict CRMs that function in vivo. Current success rates based on in vivo validation top out at approximately 80% in Drosophila and 70% in mouse (Table S8). Although continued validation using a larger number of predictions drawn from a broader selection of datasets is still required, the 100% true-positive rate we have achieved so far in both fly and mouse is highly encouraging and at a minimum puts our method on a par with the top existing approaches.

Recent advances in genome-scale empirical methods represent a promising new means for CRM discovery and will provide an important complement to, although not a replacement for, computational methods. For example, (Visel et al., 2009) have described a ChIP-seq based study in which CRM sequences were enriched through chromatin immunoprecipitation of the common enhancer binding protein p300 using RNA isolated from specific tissues. At present, such approaches require significant amounts of biological material, which represents a particular problem when studying stem cell systems or early developmental programs. Employing a computational strategy not only circumvents the need for pure cell populations but also has the potential to provide information on cis-regulatory elements operating in all cell types. Moreover, while tissue-specific p300-directed ChIP-Seq can reveal that two modules are active in the same tissue, it makes no predictions as to whether they may be related in terms of their control mechanisms or the specific sub-networks in which they participate. Importantly, our methods should be easily adaptable for assessing which empirically-identified CRMs are functioning through related mechanisms, and data from empirical methods will therefore provide valuable input to the computational discovery approaches we have outlined here. Consequently, the approach developed here represents a widely applicable strategy for deciphering transcriptional regulatory networks across a wide range of model systems.

We recommend use of the cross-validation step as a quick method to assess whether a set of modules is amenable to computational prediction, and if so, which scores are good at capturing the essence of these regulatory sequences. If the user finds that his or her data set does not show consistently significant performance with any of our methods, he or she should not proceed further with our pipeline. Although the absolute performance values in cross-validation do not pertain to genome-wide prediction accuracy, the empirical p-values do give us an idea of whether the supervised prediction scheme is feasible for the data set.

Our CRM prediction pipeline encapsulates a broad variety of scoring schemes to capture the essential features of functionally related modules. We implemented two previously reported scoring schemes (HexDiff and HexMCD), designed five novel motif-blind scoring schemes (HexYMF, PAC, and three variants of D2z), and examined statistical issues related to them (such as normalization for background composition). Our results (Fig. 1B, 1C) show that each of the scoring schemes explored has its merits, and there is no universally superior method. At the same time, some general trends may guide us in our search for better scores. For instance, counting words on both strands improved performance of most scores (See Table S1 and Supplementary Note S7). Using a subset of 100-200 6-mers rather than all k-mers was another beneficial choice which presumably increases the signal by removing words unrelated to the true motifs in the data. It is also clear that motif-blind approaches are competitive with or better than the motif-based Stubb-MDB (or Clover-ClusterBuster). In fact, the data sets on which Stubb-MDB is the “best performing” method (neuroectoderm, ventral ectoderm and ectoderm.2) are those on which almost all methods are successful. We believe this will in general be the case for motif-based methods, although a comprehensive test of existing approaches (e.g., (Philippakis et al., 2005)) has not yet been conducted.

We have used slightly different pipelines for genome-wide prediction in Drosophila and human - the fusion method and the best LOOCV method were deployed in the former, while the two best LOOCV methods were used in the human scans. This is meant to show that the underlying statistical scoring schemes may be used in a variety of ways that can be decided be the user. Also, the vertebrate analysis filters the CRM predictions for presence of either the GATA or the Ets motif, while the Drosophila analysis does not impose any motif filter. Again, this demonstrates the flexibility of including optional filters, which could potentially aid in refining the tissue specificity of predictions, based on the user’s prior knowledge of the biological system. Note that even in the vertebrate case the motif filters were used post-search to prioritize the results; the search itself, like the Drosophila search, was conducted in a fully motif-blind fashion.

The strong correlations seen between performance and simple properties of data sets—size, GC content, etc. (Fig. S2A-C)—are suggestive of a possible source of “contamination” in the training sets. Experimentally identified modules are not always the minimal sequence required for the specific function, and may include non-regulatory sequences flanking the functionally important core(s). Since CRMs in general demonstrate a higher degree of evolutionary sequence conservation, GC content and, in some cases, homotypic motif clustering than their flanking regions (Li et al., 2007), data sets with relatively lower values of these variables may be the ones with greater “contamination”, which may partially explain their relatively low amenability to prediction.

One may define a successful prediction as either a module with expression consistent with the training set, or as a module capturing some aspect of its associated gene’s expression pattern. While we consider the latter condition to be necessary, the former condition depends largely on how tightly defined the training set is, and on the nature of the TFs that act via the training CRMs. These TFs will frequently also regulate other biological processes, and may lead our supervised prediction framework to report modules related to those processes. For instance, of the five tested Drosophila modules, only two (edl, srp) mediated the predicted blastoderm gene expression, but three drove expression in the brain and midline of the central nervous system (edl, cas, SoxN); suggestively, many of the gap and pair-rule genes that regulate gene expression in the blastoderm also act during nervous system development. It is also possible that a training set spans a very broad spectrum of expression patterns, making it hard to learn the cis-regulatory commonalities from them. In such cases, the supervised prediction may learn generic characteristics of CRMs and predict successfully, but without specificity, or may fail completely. This may explain why smaller datasets with more tightly defined expression characteristics led to more specific CRM detection in our LOOCV experiments.


New scoring schemes


The z-score of a word is calculated by the YMF program (Sinha and Tompa, 2000), based on its count in training CRMs, and mean and standard deviation calculated from a 3rd order Markov chain trained on background sequences. In our implementation, called “HexYMF-s200-rc”, W comprises the 200 top-ranking 6-mers by z-score (hence “-s200”), and n(w) denotes the count of w on both strands of a sequence.

Poisson Additive Conditional (PAC)

This score is defined as: 1WwWF(λ(w),n(w)1) where F(λ,x) is the cumulative Poisson distribution function, λ(w) is the expected count of w in the test sequence, and n(w) is its observed count. W is the set of the most overrepresented words in training CRMs, as defined above. Note that 1-F(λ(w),n(w)-1) represents the p-value of the observed count of w. This score considers the words that are most associated with the training set, and then examines how overrepresented each of these words is in the test sequence, relative to the assumed background. The implementation is called “PAC-rc” since word counts on both strands are considered in identifying the set W.

D2z score

The “D2 statistic” is the number of k-mer matches between two given sequences, and the “D2z score” introduced in our earlier work (Kantorovitz et al., 2007) computes the statistical significance (z-score) of this number. Here, we cannot use the calculations from (Kantorovitz et al., 2007) (also used in (Ivan et al., 2008)) since the model under which the z-score was computed is inapplicable in the supervised prediction setting. In the null model of (Kantorovitz et al., 2007), both sequences are random sequences, while in our setting the training sequence S is known (treated as a fixed sequence) and only the test sequence T is random (in the null model). We show how to analytically calculate the z-score for D2 under this setting and call it conditional-D2z. In addition, we develop the following variations of the conditional D2z score. (All derivations are provided in Supplementary Methods.)

  • Subsets of words: This restricts the summation to k-mers w [set membership] W defined above.
  • Weighted summation: Here, the D2 score is redefined to be a weighted dot product, with z(w) (see HexYMF) as weights, i.e., we re-define D2 as wWz(w)nS(w)nT(w) and compute its z-score.
  • Reverse complement counting: We extended the D2z score of (Kantorovitz et al., 2007) to count words on both strands while ignoring statistical dependencies between the strands.

The three variants of D2z score that are used in our final pipeline include “D2z-cond-mo1-weights-rc”, “D2z-cond-weights”, and “D2z-cond-s100” (“-cond” for conditional z-score, “-mo1” for first order Markov background, “-weights” for weighted summation, “-rc” for counting words on both strands, and “-s100” for subset of top 100 words).


Given a set of experimentally characterized motifs (PWMs) and a set S of training CRMs, the first step determines the relevance of each motif M to S, as follows:

  1. Calculate the “log likelihood ratio score” of M for each sequence s in S as: LLR(s,M)=logPr(sHMM(M,pfree))Pr(sHMM(M,pglobal)) where HMM(M, p) is a two-state zeroth order HMM (Sinha et al., 2003), with motif transition probability p and background transition probability 1-p. The value pglobal in the denominator is the maximum likelihood value of this parameter learned from genomic background, while pfree in the numerator is a free parameter trained on sequence s.
  2. Calculate the empirical p-value of LLR(s,M) based on scores of equal length windows in genomic background.
  3. Declare M to be relevant to S if more than 10% of sequences s in S have p-value(LLR(s,M)) ≤ 0.05.

The second step is to filter the relevant motifs for redundancies based on relative entropy between two PWMs. The final step is to scan the test sequence for CRMs using Stubb (Sinha et al., 2003), using the top 10 relevant, non-redundant motifs.

Evaluation of CRM prediction by cross-validation

If there are n CRMs in a data set, the cross-validation was done in n “folds”. In each fold, one CRM is the test data and the remaining n-1 CRMs are training data. The former is embedded in a 10 Kbp long non-coding genomic sequence with G/C content similar to the native flank of the CRM, thus creating the “test sequence”. For each data set, the average length L of known modules was computed and the task in each fold was to predict a module of length L in the test sequence. The highest scoring window by each scoring scheme was treated as the prediction of that scheme. We obtain L-length predictions in all n test sequences, which are then evaluated based on overlap with the embedded CRMs. We use two measures of accuracy: (i) “nucleotide level sensitivity”, determined by the base-pair overlap, and (ii) “CRM level sensitivity”, determined by the number of folds where the true and predicted CRMs overlap by 100 bp or more. Due to this experimental design, the sensitivity values are equal to the respective precision (PPV) values. We further compute empirical p-values of the sensitivity, as in (Ivan et al., 2008). Note that the test CRM is embedded in a randomly chosen non-coding region, instead of being kept in its native flank, in order to maximize the odds that there are no other related modules in a test sequence. In different “instantiations” of cross-validation, the randomly chosen flanking sequences are different. Finally, we note that the p-value threshold of 0.05 used in designating success on a data set is meant only as a guide to choose methods, and not for biological discovery. As such, no multiple hypothesis correction is applied here.

Relationship between performance and data set properties

We measured the relative extent of homotypic clustering in a CRM by its “FTT-Z rank” (Li et al., 2007), which computes the FTT (Fluffy-Tail-Test) score (Abnizova et al., 2005) of the sequence, calculates its empirical z-score, and ranks the CRM by this FTT-Z score. Evolutionary conservation with D. pseudoobscura was calculated as in (Li et al., 2007).

Genome-wide scans and validation

The D. melanogaster genome (Release 4.3) was masked for exons and for short tandem repeats (Benson, 1999), and the CRM prediction pipeline was run with 500 bp windows. For each data set, nearest neighboring genes of the top scoring modules genome-wide were extracted until we had a list of 200 distinct genes. This list was tested for enrichment for the corresponding gene set defined from expression data, defined by specifying the appropriate sets of anatomical terms at BDGP (http://www.fruitfly.org/) or at FlyBase (http://flybase.org/) (Table S10). For the blastoderm data set, we further considered the subset of the 200 predicted genes that have an anterior-posterior (A/P) pattern, and obtained 137 modules, of which 113 were within 10 Kbp of the proximal gene.

Drosophila transgenic analysis

Genomic sequences were generated by PCR (Table S9) and subcloned into an EGFP (details available on request). Transgenic flies were created by injection into line ϕX-96E (Bischof et al., 2007). Homozygous transgenic embryos were collected, fixed, and stained with antibodies to GFP (MAb JL-8, Clontech) using standard methods. Labeling for Odd expression used guinea pig anti-Odd (Kosman et al., 1998). Probes for in situ hybridization were generated using clones from the Drosophila Gene Collection (Stapleton et al., 2002).

Mouse reporter constructs and transgenic analysis

Reporter constructs were generated using primers listed in Table S9 and confirmed by sequencing. Detailed information on reporter constructs is available on request. Plasmids were linearised and founder transgenic embryos produced by pronuclear injection as described (Landry et al., 2005). Embryos were harvested at E11.5 and analyzed as described (Pimanda et al., 2006).

Mammalian data sets for cross validation

Tissue specific human enhancers were downloaded from the Vista Enhancer Browser (http://enhancer.lbl.gov/) and enhancers driving expression in a unique tissue were considered. These were grouped by the associated tissue, and groups with at size ≥ 5 were treated as data sets (covering 244 enhancers overall). Cross-validation was performed by “planting” each CRM in 10 Kbp random non-coding sequence (Fig. 3 and Table S6), as well as in local context (Fig. S5 and Table S5). A data set was considered amenable to a method if the sensitivity p-value was ≤ 0.05 on at least eight of 10 instantiations of LOOCV.

Supplementary Material






This research was funded in part by the NIH (grant #1R01GM085233-01, to SS and MSH), the Illinois Sociogenomics Initiative (to GER), and the Leukaemia Research Fund and Leukemia and Lymphoma Society (to BG). We thank Jack Leatherbarrow for technical support, Jim Skeath for anti-Odd antiserum, and the Duke University Model Systems Genomics for fly injections. We thank Ivan Ovcharenko for providing ECRs between human and mouse.


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