In this study, we aimed to detect regulatory motifs that have been retained over long periods in evolution; in our test case, this applied to mammals to ray-finned fishes such as Fugu. The Fugu genome, however, is very compact and approximately eight or nine times smaller than the human one, although both genomes are assumed to contain a similar repertoire of genes. The compactness of the genome of Fugu is the result of shorter intergenic regions and introns 222342. On the other hand, the preliminary and still often erroneous annotation of the Fugu genome sometimes results in the selection of very long intergenic regions. Such heterogeneous sizes of the intergenic regions that need to be compared complicate identification of regulatory motifs. Widely used alignment algorithms, such as AVID, LAGAN and others, will usually fail when the sequences that need to be aligned differ too drastically in length. This problem is exacerbated when the sequences have a low overall percent identity. To cope with this, motif detection procedures could offer a solution. However, because regulatory motifs are typically only 6 to 30 bp long, whereas intergenic sequences of vertebrate genes range up to tens of kilobases 43, this results in a low signal-to-noise ratio that complicates the immediate use of de novo motif detection procedures. Therefore, we developed a two-step procedure to combine the advantages of the alignment and motif detection procedures.

We included a first data reduction step based on an alignment method prior to the second motif detection step (see Materials and methods and Figure 1). This data reduction step increases the signal-to-noise ratio in the input set used for motif detection. Data reduction is based on the assumption that longer regions conserved in the orthologs of closely related species are more likely to contain biologically relevant motifs compared to non-conserved regions 21. Therefore, in our benchmark study, regions conserved among closely related orthologous intergenic sequences of comparable size were preselected as input for motif detection. The mammalian intergenic sequences showed a relatively high overall percent identity and were comparable in length. Subsequently, these selected conserved mammalian subsequences were subjected to motif detection, together with the full-length Fugu intergenic region.

The data reduction procedure preselects subsequences conserved in closely related (mammalian) sequences. It requires a multiple alignment procedure that combines a pairwise alignment (AVID) and a clustering algorithm (Tribe-MCL). Details on this procedure can be found in the Materials and methods section. A resulting cluster consists of unique, non-overlapping subsequences, corresponding to a specific region conserved among the different related orthologs (human, chimp, mouse and rat).

In our benchmark study, we were primarily interested in finding DNA motifs conserved among all input sequences (orthologs). Therefore, only clusters containing conserved subsequences of all mammalian orthologs included in this study (human, chimp, rat and mouse) were retained for further analysis (supplementary website 44).

The motif detection step aims at identifying motifs that are statistically over-represented in the reduced set of orthologous intergenic sequences. To this end, we extended a previously developed Gibbs sampling based motif detection approach, MotifSampler 373839 (see Materials and methods). The adapted implementation allows the user to choose a core sequence. A potential motif is only retained when it occurs in this core sequence. Indeed, the input data for motif detection consists of a set of (mammalian) subsequences and a complete Fugu intergenic sequence. This Fugu sequence shows a relatively low overall percent of identity with the other sequences. Due to the high sequence conservation (strong data dependence) between the mammalian subsequences, the original implementation of MotifSampler is not appropriate for detecting motifs in the most divergent sequence: the cost function (log likelihood score) that is optimized in the original MotifSampler offers a trade-off between the degree of conservation of the motif and the number of occurrences of the motif 45. This results in the detection of motifs that are highly conserved between the highly similar (mammalian) sequences but that show little or no conservation with the Fugu intergenic sequence. Therefore, to ensure the detection of motifs conserved among all sequences, we introduced the concept of a core sequence. By selecting the most divergent ortholog (the Fugu sequence) as the core sequence, the algorithm is forced to only detect motifs that are also present in the most distantly related organism.

The adapted implementation was also redesigned to search for long conserved blocks instead of searching for short conserved motifs only. In datasets consisting of orthologs, not only the motif itself is conserved but also the local context of the motif 2145. For this reason, we designed BlockSampler to extend motifs and search for the longest conserved blocks. A motif is thus used as a seed to generate ungapped multiple local alignments. Looking for longer motifs/blocks also increases the specificity of motif detection (less false positives). Finally, since it was previously shown that choosing a background model increases the performance of motif detection 37, we adapted the algorithm such that it uses for each ortholog in the dataset an organism-specific background model.

To evaluate its performance, we applied our two-step motif detection procedure to several benchmark datasets. Since we were primarily interested in detecting regulatory motifs over large evolutionary distances, that is, conserved between Fugu and mammalian genomes, we compiled sets of evolutionarily divergent vertebrate orthologs that had been described to contain conserved motifs.

In vertebrate organisms, large conserved regions tend to be associated with genes encoding regulators of development 2021. Since our strategy aims at detecting such conserved blocks, we tested the methodology on three sets of orthologous genes that function in the regulation of development, containing motifs described in the literature: hoxb2 46, pax6 47 and scl 48. We also included in the analysis one gene, cfos, not related to developmental processes 26.

All the benchmark sets consisted of orthologous genes that contain evolutionarily retained motifs described in the literature that have, to a large extent, been experimentally verified. These known motifs were used to evaluate the performance of our approach and to compare it to other algorithms. Additionally, we monitored whether our procedure was capable of detecting as yet unknown motifs.

Using the two-step procedure we detected 8 significant blocks for hoxb2, 13 for pax6, 1 for scl and none for the cfos dataset (Table 1). The consensus scores of each of these 22 blocks are given in Tables 2, 3, 4 for each benchmark dataset, respectively. The location of these blocks on the complete intergenic region of the respective Fugu orthologs is shown in Figure 2; alignments can be found in 44.

As a first validation step, we compared our results with the alignments and conserved regions identified by well-established genome browsers, namely the UCSC genome browser 49 and the UCR browser 20 (Table 1).

The UCSC genome browser 50 enables access to current genome assemblies; it offers visualizations of several genomic features, such as cross-species homologies 4951. The latter can be viewed as multiple alignments over several species, ranging from closely related mammals to more distantly related species, such as chicken, zebrafish and pufferfish. The multiple alignments were generated with MULTIZ 52. Of the conserved 22 blocks we identified by aligning intergenic regions of mammals and Fugu, 16 could also be retrieved from the USCS genome browser (Table 1); these are indicated in Tables 2, 3, 4. The remaining six blocks could only be identified using our two-step approach.

The set up of the UCR browser 53 is slightly different from the UCSC browser in that it focuses on the detection of ultra-conserved regions (UCRs) only, that is, regions conserved between human, mouse and Fugu. These regions were identified using sequence alignment strategies (BLAT) applied to complete genome sequences without prior data reduction 2054. Although our strategy also identifies regions highly conserved among the species under study, no overlap was detected between our conserved blocks and the UCRs (Table 1); that is, in the regions we studied (up to 40 kb intergenic plus 5' untranslated region), no UCRs were located according to the analysis of Sandelin et al. 20. The regions the UCR browser identified as ultra-conserved were located much more upstream of the gene compared to the regions we used for our analysis.

To further validate the detected blocks, we tested whether they contain the motifs that were originally reported by Scemama et al. 46, Kammandel et al. 47 and Göttgens et al. 48 for hoxb2, pax6 and scl, respectively (no significant blocks were detected for cfos). The previously described motifs present in the respective blocks are listed in Tables 2, 3, 4 (marked with an asterisk). Of the 17 motifs reported by Scemama et al. 46, 8 were present in the significant hoxb2-blocks (Table 2). Five other motifs were present in non-significant blocks. The latter are blocks with scores that fell below the threshold we chose based on the random analysis (see Materials and methods). The four remaining motifs could not be recovered. All motifs described by Kammandel et al. 47 as conserved among mammalian and Fugu pax6 intergenic regions were recovered by our methodology (Table 3). The conserved block detected in the scl dataset contains three of the five motifs previously identified by Göttgens et al. 48 (Table 4); a fourth motif was picked up in a non-significant block. One motif was not detected in any of the blocks.

Besides these blocks containing known motifs, we identified several blocks (three for hoxb2 and eight for pax6) that correspond to conserved regions not previously described in the literature. To validate these blocks, we checked whether they were enriched for yet undescribed regulatory motifs. Hence, we screened all blocks with the Transfac database of vertebrate transcription factor binding sites 55. The result of this screening is summarized in Tables 2, 3, 4. As expected 4156, the conserved blocks we identified contain many potential binding sites; remarkably they tend to be specifically enriched for homeodomain binding sites (in blocks hoxb2 1.1, hoxb2 2.1, hoxb2 2.3, hoxb2 2.4, pax6 1.1, pax6 1.4, pax6 3.1, pax6 3.3 and scl 1.1, homeodomain binding sites were significantly over-represented, with a p value < 10^-8). For a more detailed description of both the previously described and the new potential regulatory motifs present in the detected blocks, please refer to the Supplementary website 44.

Besides these well-described benchmark datasets, we applied our method to six additional datasets, differing in composition from the benchmark datasets. They all contained a combination of four mammalian sequences (rat, mouse, human, chimp or dog) to be used in the data reduction step and an additional set of sequences originating from more distantly related orthologs (chicken, Fugu, Tetraodon nigroviridis and zebrafish in different combinations) added in the motif detection step. Four of the six additional datasets were derived from genes functioning in developmental regulation, including three homeobox genes (GSH1, Meis2, HOXB5) and one encoding the zinc finger protein EGR3. Besides these regulators involved in development, two genes, PCDH8 and HIV-EP1, were included, which are, according to our knowledge, unrelated to development. PCDH8 is believed to function as a calcium-dependent cell-adhesion protein and HIV-EP1 binds to enhancer elements present in several viral promoters and in a number of cellular promoters such as those of the class I MHC, interleukin-2 receptor, and interferon-beta genes. In the additional datasets involved in development, we detected several strongly conserved blocks: GSH1 contained four blocks that are conserved among human, chimp, mouse, rat and pufferfish (Fugu and Tetraodon); in Meis2, two blocks were recovered that are retained in all organisms under study except for Fugu; and in HOXB5, six strongly conserved blocks were detected in mammals and pufferfish, while the motif seems to have been lost in chicken. In EGR3, two blocks were found conserved in mammals and fish. In the non-developmental related datasets, only in PCDH8 was one large block detected, conserved in human, chimp, mouse, rat, chicken, Tetraodon and Fugu, but not in zebrafish. This shows that conserved regions might also exist in genes not involved in development, although a possible involvement of this additional gene in developmental processes cannot be ruled out. Detailed results of these analyses can be found in Additional data file 1 and in 44. Because the motifs in these additional datasets have not been studied as extensively as those of the benchmark datasets, we cannot guarantee all detected blocks are biologically functional.

To compare the performance of our newly developed two-step strategy to that of other frequently used algorithms, we evaluated to what extent MotifSampler 39, MAVID 10 and 'Threaded Blockset Aligner' (TBA) 52 could recover known motifs in our benchmark sets.

First, we studied the performance of the alignment algorithms MAVID and TBA in detecting conserved regions within our four benchmark datasets. Since MAVID and TBA were originally developed to perform multiple alignments on long sequences, we applied these algorithms to the initial full-length benchmark datasets, that is, the complete mammalian and Fugu intergenics. We evaluated to what extent motifs or conserved regions described in original articles were correctly aligned using either MAVID or TBA. The results are summarized in Table 5 (MAVID and TBA columns) and in 44.

MAVID alignment of all three cfos datasets (mammalian orthologs combined with each of the three Fugu paralogs) could not recover either of the two motifs previously described by Blanchette and Tompa 26 (Table 5). This is in line with our results showing the overall low homology between the cfos mammalian and Fugu orthologs. The MAVID alignment of most of the hoxb2 blocks containing previously described motifs shows that a conserved region in the mammalian intergenic sequences is broken up into small conserved parts interrupted by gaps when aligned to the longer Fugu sequence, resulting in an incorrect alignment of the regulatory motifs: previously reported motifs were not recovered in the MAVID alignment (Table 5). Our method performs better because the most heterogeneous sequence is only aligned in a second step, using a highly flexible local alignment procedure (BlockSampler). Regarding pax6, most of the blocks containing previously described motifs were correctly aligned by MAVID and all the motifs described by Kammandel et al. 47 could be correctly retrieved over all the orthologs under study (Table 5). This dataset is probably relatively well suited for MAVID because the mammalian sequences are only twice as large as the pufferfish pax6 intergenic region (Table 6). Although the lengths of the intergenic regions in the scl dataset (Table 6) are in the same order of magnitude (ranging from 16.5 to 40 kb), MAVID did not succeed in identifying any of the motifs previously described by Göttgens et al. 48 (Figure 3, Table 5).

Although TBA has been shown to outperform MAVID in aligning more divergent sequences 52, applying this alignment tool to the benchmark datasets generated similar results as MAVID: all known pax6-regulating motifs were detected, while motifs present in the other benchmark datasets were not recovered (Table 5, TBA column).

Besides detecting the blocks with previously described motifs, our two-step methodology also discovered blocks (block pax6 2.4, for instance) that could not be recovered when aligning the intergenic sequences with MAVID or TBA 4457.

Overall, based on our benchmark analysis, the two-step method performs better than MAVID or TBA in identifying conserved blocks in distantly related orthologs: the proposed method is able to recover in our benchmark sets all the known motifs identified by MAVID and TBA but, in addition, finds several previously described motifs ignored by these algorithms (Table 5, two-step BS, MAVID and TBA columns). Using the two-step procedure, first selecting strongly conserved orthologous sequences, clearly facilitates alignment with the more divergent (lower overall similarity) sequence.

We also tested the performance of MotifSampler as an example of a probabilistic motif detection procedure on the unreduced dataset. In this case, only one previously described motif was detected (Table 5, MS column). This was to be expected as in unreduced datasets the signal to noise ratio is too high for standard motif detection procedures to give reliable and interpretable results.

Our two-step procedure includes two adaptations over previous existing methods: first, it allows for a data reduction step; and secondly, we developed a motif detection procedure specifically adapted to the purpose of detecting large conserved blocks (BlockSampler). To assess the relative contribution of each of these adaptations to the overall result, we set up the following experiment: to study the specific influence of the data reduction step, we compared the results of applying BlockSampler to both the unreduced benchmark datasets and the datasets obtained after data reduction. Table 5 (BS and two-step BS columns) shows the results of this comparison. Overall, the results seem comparable: application of BlockSampler to the complete intergenic sequences results in recovery of 15 of the 30 previously reported motifs (in all four datasets), while the two-step method identified 17. Thus, at first sight, there does not seem to be a major contribution from the data reduction step. A closer look at Table 5, however, shows that the positive contribution of the data reduction (increasing the signal-to-noise ratio) is strongly dependent on the lengths of the intergenic sequences to be aligned. A major positive effect is observed for the large pax6 and scl datasets, whereas for the hoxb2 set, in which the sequences under study are rather short, the data reduction does not offer a clear advantage. To assess the specific improvements of using BlockSampler instead of standard motif detection approaches, we compared the results of BlockSampler to those of MotifSampler when both were applied to the reduced datasets. A reduced dataset thus consists of a subcluster of mammalian sequences (Figure 4) and a complete Fugu ortholog. The performance of MotifSampler was far below that of BlockSampler: MotifSampler only detected two previously described motifs (Table 5, two-step MS column), both in the hoxb2 set, while BlockSampler recovered 17 previously described motifs (Table 5, two-step BS column). Moreover, because MotifSampler searches for short motifs (default eight nucleotides (nt)), it detects many false positive hits. These results show that independent of the data reduction step, BlockSampler is clearly more suited for detecting large conserved blocks than MotifSampler.

The benchmark datasets were generated as follows. First, a set of orthologous genes was defined using the Ensembl genome browser version 23 62. In this study, the benchmark datasets included genes from human (Homo sapiens), mouse (Mus musculus), rat (Rattus norvegicus), chimp (Pan troglodytes) and pufferfish (Fugu rubripes). Regarding the cfos dataset, Ensembl identified three Fugu paralogs - SINFRUG00000132418, SINFRUG00000132419 and SINFRUG00000143787 - that were all included in the analysis. The additional datasets EGR3, GSH1, HIV-EP1, HOXB5, Meis2, PCDH8 contain multiple distantly related orthologs (see 44).

Subsequently, the intergenic regions of these orthologs were selected using the Ensembl mart database release 21.1. The region upstream of the transcription start (as defined by Ensembl) was limited to 40 kb. Additionally, the 5' untranslated region was included. Lengths of the respective intergenics are given in Table 6; the benchmark datasets, cfos, hoxb2, pax6 and scl can be found in 44. The rat cfos ortholog ENSRNOG00000008015, Fugu hoxb2 ortholog SINFRUG00000136637, chimp pax6 ortholog ENSPTRG00000003474, and scl chimp ENSPTRG00000003474 contain long N-stretches, probably as a result of incomplete preliminary annotation.

Remarkably, where Fugu is known to have a very compact genome 23, the Fugu hoxb2 mentioned above is very long compared to the mammalian hoxb2 intergenic sequences (Table 6). This is probably due to the presence of a pseudogene (SINFRUG00000157209) in the intergenic region of SINFRUG00000136637 at circa 5.9 kb from the transcription start site of hoxb2, which was not yet annotated in the release version 23 of ENSEMBL.

All intergenic sequences were selected as described above, except the intergenic sequence of the Fugu scl ortholog. Because the putative scl ortholog annotated by ENSEMBL (SINFRUG00000145588) did not contain motifs shown to be present in the Fugu scl ortholog by Göttgens et al. 48, we used the Genbank Fugu scl sequence [Genbank: AJ131019]. This sequence (referring to a cosmid sequence of circa 33 kb) was also used in the original study of Barton et al. 63. To delineate the intergenic region of scl, we aligned the coding sequence from the scl homolog SINFRUG00000145588 with the AJ131019 sequence using 'blast 2 sequences' 64. The coding region was located from positions 20,156 to 22,165; we then selected the upstream region (from positions 1 to 20,155).

A schematic representation of the developed two-step procedure is given in Figure 1.

To set a threshold on the adapted consensus score of the blocks (blocks with a score above the threshold are considered relevant), we compared block scores of the genuine set with those of corresponding random sets. For each genuine dataset, 100 random sets were generated. A corresponding random set contains, besides the different homologous regions of the genuine subcluster under study, a random Fugu intergenic sequence. This additional random sequence was not orthologous with the mammalian sequences and thus is unlikely to contain the same motifs. In each random set, motifs were identified using the same procedure as described for the genuine set. For each random set the best scoring motif was selected, that is, the block with the highest normalized consensus score. This resulted in a group of the best scoring 100 false positive motifs. These scores were approximately normally distributed. As a threshold, we choose the 90th percentile of the best scoring random motifs.

For each block we detected, a BLAT search against the human genome (May 2004 assembly) was performed 5469. This linked to the UCSC genome browser 51, where alignments between multiple vertebrate organisms were generated using MULTIZ 52. Subsequently, we checked in the UCR browser 20 whether UCRs were identified in the intergenic regions under study.

To assess whether known transcription factor binding sites are located in the detected blocks, we compared the consensus sequence of each block with motifs described in the literature. In addition, we scanned the block consensus sequence with the Transfac 6.0 public database of vertebrate transcription factor binding site profiles 55. This scanning was performed using MotifLocator 707172 with a 0^th order vertebrate background model. Hits with a score >0.9 were regarded as potential binding sites. The binding sites are indicated by the Transfac factor name 55.

To calculate the statistical over-representation of homeodomain binding sites, 100 sequences were selected randomly from the Fugu genome and screened to make sure they differed from the genes under study. These random sequences were screened with matrix models from homeodomain binding sites (obtained from TRANSFAC 8.2) using MotifLocator, as described above. We calculated the chi-square statistic with Yates correction of the 2 × 2 contingency table test for the set of homeodomain binding sites 73. Homeobox binding sites were significantly over-represented in a certain block at a p value of 10^-8.

To evaluate our newly developed procedure, we compared its performance to that of two algorithms often used for phylogenetic footprinting, namely the motif detection algorithm MotifSampler 39 and the multiple alignment procedures MAVID 1065 and TBA 52. These three algorithms were applied to the benchmark datasets and the resulting motifs (conserved in all organisms under study) were compared to those detected by the two-step procedure. We aligned the full-length initial datasets (Table 6) 44 using the online MAVID version at 74 with the default parameter setting 9.

Besides MAVID, we used TBA as it has been shown to outperform MAVID 52. All the necessary tools were obtained from the Miller Lab website 75. To generate a multiple alignment using TBA, we first pairwise aligned the initial datasets using blastz. We used the evolutionary tree ((human chimp)(rat mouse) Fugu); the additional blastz parameter file (latest version) was obtained from the E Margulies ftp site 76. The final multiple alignment was obtained by running the TBA executable.

We applied MotifSampler both on the reduced datasets (subcluster + complete Fugu intergenic sequence 44) and on the complete intergenic sequences (initial datasets). For the reduced sets we performed 100 MotifSampler runs, while for the complete datasets MotifSampler was run 1,000 times, each time using the standard parameter settings of the algorithm: the algorithm searches for only one motif (n = 1) of 8 nt (w = 8) on both strands (s = 1) and the prior probability of 1 motif copy (p) is 0.5. A third order vertebrate background model was used.