Input to IAMMS consisted of the upstream region of 11 rod-specific, 12 cone-specific, and 84 non-photoreceptor genes (see table 1 for a list of rod/cone-specific genes, and additional file 1 online for background genes). The flowchart of the IAMMS algorithm is shown in figure 1 (see methods for details). The first step involved an iterative alignment procedure conducted on all rod, cone, and non-photoreceptor promoters. This step resulted in a dataset of 71,195 conserved motifs between 8 and 150 bp in length. Each entry of the dataset contains nucleotide sequences, the location of motif occurrences with respect to the transcription start site, strand, and promoter from which each occurrence originated. To illustrate the composition of the dataset, we plotted motif length against the number of occurrences of each motif in photoreceptor promoters (figure 2; background occurrences are not shown). The color map represents the number of motifs with each length/frequency combination. As may be expected, motif size has an inverse relationship with the number of occurrences.

Analysis showed that the majority of motifs identified after the first step were repeat sequences. The motifs occurring most frequently (> 25 occurrences) were primarily simple repeats. All longer motifs (> 19 bp) were highly similar to microsatellites and interspersed repeats, as revealed by comparison to a database of known repeats (RepBase). Repeat sequences were filtered out at step 2.

After repeat filtering, the remaining motifs, those inside and immediately above the marked box in figure 2, were evaluated for potential enrichment in rod or cone photoreceptors (step 3). Since we are interested in motifs that occur in the promoters of only one photoreceptor cell type, motifs that have occurrences in both rod and cone promoters were classified as ambiguous and were excluded from consideration during this step. To evaluate enrichment of a motif compared to background, we assume a binomial distribution of k_r rod specific (or k_c cone-specific) promoters drawn from the total number of promoters that contain occurrences. A Bonferroni correction for multiple hypothesis testing (E-value) is applied to the resulting p-value, as described in the Statistical annotation section of methods. The top scoring motifs identified during this step were subjected to phylogenetic analysis (step 6) and compared to known motifs using the

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Figure 3 shows representative examples of top-scoring cone- and rod-enriched motifs identified during step 3, after being subjected to phylogenetic analysis, and compared to TESS. The cone-enriched motif is 13 bp in length, contains 5 occurrences in cone-specific promoters and none in rods (non-photoreceptor occurrences are not shown). The

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The rod-enriched motif (figure 3B) is 12 bp in length and contains 6 occurrences in rod promoters. Cross-species conservation shows that Pde6a, Gnb1, and Nr2e3 occurrences are phylogenetically conserved (a cross-species alignment is not available for the region containing the Pde6g occurrence, and thus no score is reported). According to TESS, this motif is similar to a c-Myb binding site. The prediction that c-Myb may have a function unique to one type of photoreceptor is consistent with publicly available microarray data (see Methods). We found that c-Myb is between 2.6 and 7.6 fold enriched in cones compared to rod photoreceptors.

After step 3, IAMMS identified a total of 6 motifs (3 rod- and 3 cone-enriched) with E < 2.5. Since no position filtering was applied to identify these motifs, we refer to them as position independent. All position independent rod- and cone-enriched motifs, sorted based on E-value, are shown on the top of figure 4. The highest scoring rod prediction at the top of figure 4 contains two 5 bp invariant core regions separated by two ambiguous positions (CCTTTNNGCCCT; rod-enriched position independent, row 1). The position variance of this prediction is remarkably small (± 45) considering that no position-based selection was applied to identify this sequence. The top scoring cone motif contains a core region 5 bp in width (aGGGTTca). It occurs in 8/12 cone promoter sequences with no discernable bias in position. Detailed information on the position and phylogenetic conservation of each occurrence is available as additional data (files 1, 2) online.

Those motifs classified as ambiguous during step 3 were subjected to position-based clustering (step 4). As described previously, we acted under the hypothesis that occurrences of a motif near the transcription start site, and those occurring in clusters around a preferred position, are more likely to be functional. One example of clusters selected by the hierarchical clustering algorithm is shown in figure 5A. This particular motif contains 55 occurrences, plotted as triangles based on their 1-dimensional position relative to the transcription start site. These occurrences are broken into clusters by the algorithm, denoted by blue ovals. A cone-enriched cluster just upstream of the transcription start site is shown in pink. This cluster contains 5/12 occurrences from cone-specific promoters, and only 4/84 occurrences in non-photoreceptor promoter regions.

After motifs were broken into position-dependant clusters, we used the same statistical procedure described above to select those clusters enriched in rod or cone promoters (IAMMS, step 5). Figure 5B–C plots the ratio between cell-specific and total occurrences (vertical axis) against the total number of promoters with at least one occurrence (horizontal axis). Points are colored based on the number of motifs with a given combination, in a similar manner to figure 2. The cone-enriched cluster cAGAAG shown in figure 5A is one of the motifs represented by the point marked in figure 5C. This point lies just inside the gray region representing a statistical threshold of p = 0.005 that was used to classify motifs as enriched in rod (or cone) specific promoter sequences. A motif corresponding to the known cone-specific cis-element ROP2 is also represented by a point in the gray region of figure 5C. Figure 5B shows the same representation as figure 5C for rod-specific motifs. A previously characterized rod-specific motif, NRE, is represented by a point that lies just inside the gray region (marked in figure 5A), indicating the biological relevance of motifs represented in this region.

A detailed view of the NRE-like motif identified after step 5 is shown in the left panel of figure 6. The predicted motif contains a core region (aTGCTGa). The occurrence in the Rho promoter at -88 bp (occurrences are enumerated below the logo in figure 6) has already been validated experimentally 23. Two sample cross-species phylogenetic alignments are shown below the functional alignment in figure 6 (Pde6b and Rho). In this case, these occurrences are very highly conserved relative to the surrounding sequence.

Another known transcription factor binding site detected in this study corresponds to the recently discovered cone-specific sequence ROP2, shown in the right panel of figure 6. This prediction contains an occurrence in the Opn1mw promoter that was recently discovered to be required for cone-specific expression 24. Previously unknown occurrences of ROP2 were predicted in the promoter of Opn1sw,Smug1, and Cnga3. The newly-discovered occurrence in the Opn1sw promoter shows remarkable position-conservation relative to the transcription start site when compared with the known Opn1mw occurrence: -94 and -97 bp, respectively, a difference of only 3 bp. Selected phylogenetic alignments (figure 6, right panel, bottom) show that the occurrences in the Cnga3 and Opn1mw promoters are highly conserved through evolution. In addition to increasing confidence in predictions, the ROP2 detection also provides exciting new targets for a cis-element that is pertinent for cone-specificity.

The 12 highest scoring (E-value) rod- and cone-enriched position dependent predictions are shown on the bottom of figure 4. The example given in figure 5A (cAGAAG) can be found among cone-enriched motifs in row 7. Among the high scoring motifs, 6 rod and 3 cone predictions are similar to known motifs whose specific binding positions (with the exception of NRE) are not known, including four putative initiator (INR-like) elements, NRE, an IL-6 effector, an RXR binding site, ROP2, a putative TATA-like motif, and an Engrailed homeodomain binding site. Phylgoenetic conservation is relatively high for several of the elements, including two conservation scores less than -1 for cone-enriched predictions (TATA-like: -1.08 and En2: -1.35). As we show in the next section, many of these motifs are corroborated by motifs predicted by DME and/or BioProspector.

To increase confidence in our predictions, we compared motifs discovered using IAMMS to those discovered using existing de novo motif discovery algorithms, DME and BioProspector. For both of these algorithms, a smaller section of the upstream region was employed (500 bp of upstream sequence and 100 bp of UTR) for a more similar comparison to IAMMS position clustering implementation. In order to return useful results, promoter regions needed to be repeat masked prior to analysis. Since the rod- and cone-specific sets are too small to be compared directly against each other, cone promoters were compared against the combined set of background and rod promoters to evaluate cone-enrichment. The same approach was used to identify rod-enriched predictions.

The top 10 motifs for each motif length between 6 and 10 bp (DME) or 6 and 12 bp (BioProspector) were compared with the top IAMMS predictions. This comparison is shown in Figure 7. Predictions made by IAMMS and confirmed by DME or BioProspector are highlighted in yellow (DME), blue (BioProspector), or orange (both DME and BioProspector). It is interesting to note that rod predictions for DME and BioProspector were in agreement with IAMMS a much higher proportion of the time (nearly 80%) compared to cone predictions (just under 50%). This difference between the numbers results from a much lower rate of agreement between IAMMS and BioProspector in cone sequences. Compared to BioProspector, the rate of agreement between IAMMS and DME in rods and cones is similar (47% in cones, 57% in rods). We conclude that although they use different underlying algorithms, results obtained using DME are more similar to IAMMS compared with BioProspector.

Overall, of 40 rod- and cone-specific predictions, 25 (over 60%) are confirmed by either DME or BioProspector and 11 (nearly 30%) were confirmed by both. Major predictions, including the ROP2 binding site, Initiator, TATA-like, and IL-6 (discussed in detail below) were corroborated by at least one motif discovery algorithm. The initiator-like and TATA-like predictions were identified by all 3 algorithms, increasing our confidence in these predictions.

Previous microarray studies suggest that at least 4 transcription factors (RXRγ, En2, Sall3, and Prdm1) are more active in cone photoreceptors than rods 2. The role of RXR is supported by additional biochemical studies which demonstrate that RXRγ plays a vital role in patterning cone photoreceptors in response to signaling by thyroid hormone receptor β2 2526. The RXR prediction is shown in figure 4, position independent, row 3. Functional RXR cis-elements often contain a degenerate repeat of the invariant core in close proximity 27. Therefore, we examined the promoter sequences surrounding predicted RXR sites for degenerate variations of the putative core selected by IAMMS. Out of 5 sites, 4 contain an additional occurrence of G(N [0–2])TCA within 4 bp of the recognized site (see the image in additional file 3 online). This is very unlikely to occur by chance (p~3.7 × 10^-4), further increasing our confidence that the predicted motif binds RXR-family transcription factors.

The En2-like motif is shown in figure 4 cone-enriched position dependent row 9. The prediction includes the central portion of the optimal En2 homeodomain transcription factor consensus (T

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We were unable to find binding sites for Sall3 or Prdm1 in the experimental literature. Some of the unidentified motifs predicted in this study (group 1) may correspond to binding sties for these transcription factors. Future experimental studies will be required to discover any correspondence between these transcription factors and motifs predicted in this study.

One of the rod-specific predictions, detected by both IAMMS and BioProspector, is similar to an IL-6 effector (figure 7, row marked IL-6). This is interesting in the context of recent findings that in rodents, signaling by IL-6 family members CNTF and LIF can block the formation of rod photoeceptors during development 3031. According to the literature, peak IL-6 effector activity is obtained by the invariant core (CTGGGAA) and another degenerate occurrence (CTGGAA) appearing nearby 32. Our prediction corresponds to the first invariant core (CTGGGA). To determine if a degenerate occurrence appeared nearby, we took rod-enriched IL-6-like predictions andsearched nearby promoter sequence to find if any elements similar to the core were present. We found that 4 rod-specific promoter sequences, including Pde6b, Gnat1, Pde6d, and Rho contain an exact copy of either the degenerate sequence or the high-affinity core binding sequence within 50 bp of a predicted occurrence (see the image in additional file 4, as well as additional files 1 and 2 for more IL-6 like predictions). It is interesting that in chick, where artificial IL-6 stimulation increases the number of rod photoreceptors 33, only one orthologous promoter (of all those in table 1) contains both the invariant core and a degenerate consensus within 50 bp of one another. The correspondence between empirical evidence and occurrences of IL-6-like motifs lends support for the biological relevance of the IL-6 prediction.

The high-affinity core and a degenerate occurrence missing only the final A (i.e. CTGGA, also within 50 bp) was found in the Nrl promoter. This predicted site is likely to be significant considering the important role Nrl plays in rod photoreceptor differentiation 2. This observation suggests that one possible mechanism for IL-6 regulation of rod-differentiation involves suppression of the Nrl gene product.

One of the most striking findings of this study is differences in the core promoter region of rod-and cone-specific genes. We predicted several rod-enriched motifs centered on the transcription start site that are similar to characterized initiator consensus sequences, but no enrichment of degenerate initiator-like sequences were found specific to cones. Conversely, a TATA-like motif was detected in almost all cone promoters near the appropriate position upstream of the transcription start site, whereas it was absent from rod promoters.

Figure 4 depicts 4 unique, rod-enriched motifs whose mean position lies near the transcription start with relatively low position variance. Three of these motifs are similar to portions of experimentally-validated initiators (INR-like), including the motifs in row 4 (aGGTCC) 34, row 6 (TCTGAG) 35, and row 11 (GCACAG) 36. The fourth motif (ACAGTGa), in row 2, is also attributed to the initiator-like group because its antisense mismatches the accepted initiator consensus (YYANWYY) at only one position. More details of the TCTGAG motif are shown in the left panel of figure 8. We detected occurrences of this motif near the transcription start site in 5 rod promoters. The Pde6a, Cnga1, and Sag occurrences are on the -1 strand, and are consequently highly similar to the pyridine rich initiator consensus (YYANWYY). The proximity of the 4 motifs to the annotated transcription start site, their phylogenetic conservation, as well as similarity to portions of experimentally characterized initiators suggests that these motifs may function as degenerate initiator sequences in rod-specific promoters.

In cone promoters we found a different core promoter element, ATAA, a motif similar to the central portion of a TATA box (see Figure 4 and Figure 7). One such prediction is depicted in the right panel of figure 8. Occurrences of this particular motif are found in 4 cone promoters, Arr3,Gnat2,Gnb3, and Pde6c (figure 8, right panel). These occurrences are located between 20 and 45 bp upstream of the transcription start site, close to the typical position of a TATA-box 37, supporting the idea that it is, indeed, a degenerate variation on the TATA consensus. A high degree of phylogenetic conservation of this motif and corroboration by both DME and BioProspector further support the biological relevance of this prediction.

Many of the ATAA occurrences contain an additional T on the beginning of the motif, making them even closer to the classic TATA-consensus. We conducted a search for TATA-like sequences in the core promoter of rod and cone genes. It is interesting that the sequence TATAA (or its antisense) appears in 7 cone promoters (Opn1mw, Opn1sw, Cngb3, Arr3, Pde6c, Smug1, and Cnga3) between -180 to +60 relative to the transcription start site. Conversely, this sequence is only found in two (Rho and Cnga1) out of 11 rod promoters. The enrichment of the TATAA sequence in cones, although not as pronounced as ATAA, lends further support to the idea that the cone promoters studied here are initiated by a TATA box. It is notable that except for Elovl2 and Pde6h, an occurrence of either sATAAgw or TATAA is present near the transcription start site in all cone-specific promoters.

Experimental evidence supports the biological relevance of the ATAA prediction, regardless of whether it is, indeed, a degenerate TATA-box. A recent experimental study deleted two occurrences of TATA-like motifs from the Arr3 promoter 38 and observed that the previously cone-specific promoter drove transgene expression in rods as well. In light of our predictions, we suggest that a TATA or TATA-like motif in the core promoter plays a central role in the differences between rod and cone expression patterns.

The fact that the number of genes specifically expressed in either rod or cone photoreceptors is rather small makes the application of de novo motif discovery approaches that heavily rely on statistical analysis difficult. Because of this consideration, we took two independent approaches designed to increase the accuracy of our results. First, we employed a large number of non-photoreceptor genes as a negative control, and evaluated enrichment of motifs in either rod or cone promoters relative to this large dataset. Second, we applied 3 motif discovery software packages that use different algorithms to identify motifs. While we do not filter motifs that are identified by only one algorithm from our final database, we do provide a table of overlaps (figure 7) as additional information that can be used to evaluate predictions. Together, these two approaches should minimize both false positive and false negative errors.

In the present study, we selected promoters based on ENSEMBL annotated transcription start sites. However, recent reports suggest that two separate ambiguities exist in transcription start site annotations. One, the so-called "borad" class of transcription start sites, represents inherent local variation over 50–100 bp 3739. This complication should not have a major impact on the quality of our predictions. Since hierarchical clustering automatically chooses the mode and variance of a motifs' position distribution relative to the transcription start site separately for each motif, IAMMS should turn up the same predictions with some additional position variance.

A second ambiguity is the recent observation that a majority of genes are driven by two or more alternative promoter sequences 3739. To determine the relevance of this finding for our study, we searched the database of transcription start sites (DBTSS) for genes used in this study, and found only 3 genes (Nr2e3, Gngt2, and Pde6h) that contain potential alternate transcription start sites far from the ENSEMBL annotation, and 4 additional genes (Pde6d, Pde6b, Gnb3, and Elovl2) that contain alternate transcription start sites within 200 bp 40. However, in all of these cases the alternate transcripts were identified in non-retinal tissue, and therefore the alternate start sites do not pertain to our present application. In addition, 8/23 promoters that we selected for analysis are validated experimentally (Arr3 3841, Pde6c42, Opn1mw 2443, Pde6a44, Gnat245, Sag 4647, Rho23, and Nrl48). Those considerations expressed above give us confidence in the promoter regions selected for this study.

This study did not detect motifs corresponding to two transcription factors known to be enriched in rod-photoreceptors compared to cones, including Mef2c 22 and NR2E3 49. One of the causes of this omission could potentially be the multiple severe constraints in our selection criteria that were introduced to maximally reduce the rate of false positive predictions. In the case of NR2E3, another potential reason may be that NR2E3 may not bind DNA directly in vivo. Rather, recent findings suggest that NR2E3 regulates expression indirectly by interactions with CRX 50. If there is no NR2E3 binding directly to DNA, it is not surprising that we do not identify a motif for this transcription factor.

Genes in the photoreceptor-specific list (table 1) were selected as follows. Cone genes (except Cnga3) were selected using microarray data from NRL or NR2E3-knockout mouse retina that are known to produce a rod-deficient phenotype 215. We included Cnga3 which was found to be cone-specific by experimental studies 21. Rod genes Sag 17, Pde6d 20, Nrl 18, Nr2e3 19, and Gnb1 15 were previously observed to be expressed in rod but not cone photoreceptors by biochemical studies 161718192021. The remaining rod genes (Rho, Pde6a, Pde6g, Pde6b, Gnat1, and Cnga1) were selected based on microarray data comparing FACS sorted rods to a model of cone-photoreceptors 22. The latter involves FACS sorting cells expressing GFP by the NRL promoter in NRL knockout mice, and is demonstrated to be a good model for cone photoreceptors 51. To pick the rod genes we obtained, raw CEL files for 4 normal and 4 NRL knockout animals were obtained using the Gene Expression Omnibus website. The data were MAS5 normalized and averaged using the bioconductor package 52. We selected genes involved in the phototransduction pathway that were significantly down-regulated in the Nrl knockout samples (p < 0.02; Student's t-test).

For each gene in table 1, 2 kb of sequence upstream of the annotated transcription start site and the entire 5' UTR of the mouse was obtained from ENSEMBL (Mouse v.36, Aug. 2005). Two genes (Nrl and Gngt2) contained two annotated transcription start sites within 1000 bp of each other, and each promoter contained a UTR. In both cases, the promoter closer to the translation start site was chosen. This choice effectively included the region immediately upstream of each transcription start site; for Nrl, this choice corresponded to an experimental study 48.

We constructed a background sequence set from genes that are not expressed in either rods or cones, but are expressed in most tissues, in a tissue independent manner. To construct this background set, we first identified all genes that are not expressed in adult rod or cone photoreceptors. To do this, we used the MAS5 normalized FACS sorted microarray data obtained in the previous section. From this data, we obtained a list of REFSEQ IDs where all probes associated with each REFSEQ ID was marked absent by the Affymetrix perfect match/mismatch designation.

To evaluate tissue specificity, microarray data from the mouse gene-atlas 53 was obtained from NCBI's Gene Expression Omnibus (GSE1133) using R's Bioconductor plugin 52 and GEOquery 54 packages. Average expression of each gene in each tissue was calculated, and probe sets were converted to REFSEQ IDs. Next, we calculated the ratio of maximum expression to the sum of expression in all tissues. This tissue-specificity ratio ranged between 0.02 (nearly equal expression between all tissues) and 0.98 (highly specific to one tissue). For the background set, we selected all REFSEQ IDs for genes with a ratio less than 0.03 that are also absent from both adult rod and cone photoreceptors (n= 84). For all of these genes, 2 kb of upstream sequence and the entire 5' UTR was obtained using ENSEMBL's BioMart.

The flowchart of IAMMS is depicted in figure 1. The input for the algorithm consists of 3 sets of promoter sequences, including 11 rod-specific sequences, 12 cone-specific sequences, and an additional set of background sequences that do not drive expression in photoreceptors. All promoters were passed through an iterative alignment procedure (step 1) that returns a motif for each sequence ≥ 8 bp in length that appears more than once in photoreceptor promoters. The resulting database of potential motifs was scanned for sequences similar to a known simple or interspersed repeat sequence (step 2). Motifs were evaluated for cell-specificity using a binomial model of enrichment (statistical annotation, step 3) to create predictions for cell-specific motifs. Ambiguous motifs were filtered to extract sets of position-enriched occurrences using an agglomerative clustering procedure (step 4). Position-enriched clusters were subsequently analyzed using statistical annotation (step 5) to create a set of position-specific predictions. Both position-enriched and non-enriched predictions were subsequently analyzed by phylogenetic analysis (step 6) and were compared to known cis-elements (step 7).

All sequences ≥ 8 bp in length that appear more than once in rod and cone photoreceptor promoters were identified using the BLAST implementation distributed by Washington University 55. We used scoring parameters that were observed to return short, nearly exact matches: +2 for a match, -3 for a mismatch, and a threshold bit score of 16. Gaps were allowed, but using the default score of -20 gaps rarely appeared (impossible in any sequence pair less than 28 bp match of flanking surrounding a gap). After completion, we separated pairwise alignments into a database of individual sequences. We filtered this database, so that each sequence occurs exactly once. This database contains each sequence ≥ 8 bp in length that occurs at least twice in the photoreceptor promoter set. Next, we constructed a multiple alignment for each sequence returned in the pairwise alignment. In the second iteration, we scanned all promoter sequences (rod, cone, and 84 background promoters) using each sequence identified in the previous database. BLAST was run using the same match and mismatch parameters as the first iteration, but the threshold bit score was changed for each sequence. To calculate the threshold bit score, we multiplied the sequence length by 1.4 (we also tried a variety of constants between 1.3 and 1.7). The output of this step is a series of multiple alignments – one alignment for each sequence occurring twice in photoreceptor promoters.

Longer sequences were evaluated to examine potential similarities to known repeats. BLAST was used to compare each sequence ≥ 20 bp in length to all mouse repeats represented in RepBase 56 v.11.07. A scoring scheme of +2 (match), -3 (mismatch), and 20 (threshold) was used.

Let k_r and k_c be the number of rod- and cone-specific promoters that contain at least one occurrence in a motif with n occurrences. We take the p-value of cell-specific enrichment to be the binomial probability of selecting a list that contains n sequences, of which k_r or k_c are mapped to rod- or cone-specific promoters. The probability of selecting one rod/cone-specific promoter is the number of rod or cone promoters divided by the total number of promoters (11/107 = 0.103 for rods).

Due to overlap between different motif core regions, we observed underdispersion relative to the binomial model described above. We corrected the p-value by Z-score normalizing across all groups with a given number of occurrences using an empirically derived mean and standard deviation. To perform the Bonferroni correction, we multiplied the corrected p-value by the total number of sequences considered for cell-specific expression, not counting motifs with less than 4 occurrences in photoreceptor promoters or similar to repeat sequences longer than 20 bp (38,779). For the sake of simplicity our calculations do not take into account dependence between motifs with highly similar core regions, and are therefore highly conservative. This Bonferroni corrected expected false positive rate is referred to as the E-value. We select all motifs with an enrichment E-value less than 2.5 in one photoreceptor cell type, and also require that no occurrences are found in the alternate photoreceptor cell type.

Single link hierarchical agglomerative clustering was performed on the distance to the transcription start site for each motif. Motifs are broken into the minimum number of clusters when the mean inter-cluster variance reaches 1% of the total variance. To ensure that the data contains an underlying structure that can be described by clusters, only motifs with an agglomerative coefficient greater than or equal to 0.7 were analyzed using agglomerative clustering. All computations were performed using the R-cluster package 57 (v.2.3.0). Samples of clusters selected by this procedure are shown in figure 5A. In addition to single link, Euclidean and Ward's algorithms in the R-cluster package were tested, though all results are reported on single-link results.

Each position enriched cluster with occurrences in the first 400 bp upstream of the transcription start site was analyzed for rod/cone specificity using the statistical annotation procedure described in step 3.

To compare the conservation of a putative mouse cis-element to other predictions, we applied a recently described model 58 involving a comparison between the actual number of substitutions in the predicted cis-element to the expected rate of conservation between all species in the alignment. First, alignments corresponding to promoters of interest were extracted from existing whole-genome alignments using the UCSC genome browser 59 (mm8 version). Raw alignments corresponding to each mouse promoter were obtained as different regions known as alignment blocks. We defined the cross-species conservation score (CSCS) as the Z-score of the calculated substitutions in our sequence of interest, relative to all surrounding windows (the same size as our comparison sequence) in the same local alignment block. To calculate the mean and standard deviation, we used a sliding window (the same size as the prediction) locally, in the alignment block. Negative results mean less than the average number of mutations are found in the window (i.e. the sequence is conserved); positive values mean that there are more differences. Sequences corresponding to gaps where no alignment is available between the mouse sequence and other species were not included in the analysis. References to these scores were left blank in the figures and additional data files.

Rod/cone-specific motifs were compared to a database of known motif and consensus sequences using the

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Predictions highly similar in the core region, but differing in ambiguous peripheral positions, (for example, see figure 7, rows 8–10 and row 7 antisense) were grouped prior to the creation of figure 4 and counting predictions for the text. Sequences were grouped according to the methods described in 14. A single vector was created by concatenating column vectors from the position-weight-matrix representation of a motif. The maximum Pearson coefficient over each possible alignment between two separate motifs (including sense-antisense) was subsequently calculated. When comparing motifs with a different length, the smaller motif was compared against the larger to emphasize similarities in the invariant core region. During this step, overhangs were filled in with P(A) = P(T) = P(C) = P(G) = 0.25. Motifs were considered highly-similar if the Pearson coefficient was greater than 0.85.

The same promoters used for IAMMS were repeat masked 61 using the following settings: wublast algorithm, DNA source set to mouse, and default sensitivity. After masking, sequences were chopped to include only 500 bp of upstream sequence and 100 bp of UTR, when available. Each software program was run with default settings, except that the number of motifs to return was set to 10, and the motif size was varied between 6 and 10 (for DME), or 6 and 12 (for BioProspector). When identifying cone-specific motifs, cone promoters were compared against the combined set of rod-specific and background promoters (and vice-versa). Results for each run were added into the same table and sorted by the score given by each program. Output motifs were compared to predictions made by IAMMS using the method described in 14, and repeated above (see Comparison between predictions in Methods section).