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Published online before print
June 12, 2006, 10.1101/gr.4717506 Genome Res. 16:855-863, 2006 ©2006 by Cold Spring Harbor Laboratory Press; ISSN 1088-9051/06 $5.00
Letter Close sequence comparisons are sufficient to identify human cis-regulatory elements1 Genomics Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA; 2 U.S. Department of Energy Joint Genome Institute, Walnut Creek, California 94598, USA
Cross-species DNA sequence comparison is the primary method used to identify functional noncoding elements in human and other large genomes. However, little is known about the relative merits of evolutionarily close and distant sequence comparisons. To address this problem, we identified evolutionarily conserved noncoding regions in primate, mammalian, and more distant comparisons using a uniform approach (Gumby) that facilitates unbiased assessment of the impact of evolutionary distance on predictive power. We benchmarked computational predictions against previously identified cis-regulatory elements at diverse genomic loci and also tested numerous extremely conserved human–rodent sequences for transcriptional enhancer activity using an in vivo enhancer assay in transgenic mice. Human regulatory elements were identified with acceptable sensitivity (53%–80%) and true-positive rate (27%–67%) by comparison with one to five other eutherian mammals or six other simian primates. More distant comparisons (marsupial, avian, amphibian, and fish) failed to identify many of the empirically defined functional noncoding elements. Our results highlight the practical utility of close sequence comparisons, and the loss of sensitivity entailed by more distant comparisons. We derived an intuitive relationship between ancient and recent noncoding sequence conservation from whole-genome comparative analysis that explains most of the observations from empirical benchmarking. Lastly, we determined that, in addition to strength of conservation, genomic location and/or density of surrounding conserved elements must also be considered in selecting candidate enhancers for in vivo testing at embryonic time points.
The majority of long-range cis-regulatory elements in the human genome have yet to be identified. These gene regulatory modules, which likely number in the tens or hundreds of thousands, are hard to detect, since they lack any obvious distinguishing features analogous to codon structure, splicing motifs, open reading frames, or other hallmarks of protein-coding genes. Furthermore, functional sequences of this class are mostly unique in the genome (Bejerano et al. 2004a  ), which largely rules out paralogy-based identification. The possibility that regulatory elements could lie more than 1 Mb from their target genes (Lettice et al. 2003; Nobrega et al. 2003) presents another challenge. Consequently, cross-species sequence comparisons, which rely upon the slow substitution rate of many categories of functional DNA relative to neutral sequence, have emerged as the pre-eminent means of identifying candidate cis-regulatory elements in large genomes such as human (Pennacchio and Rubin 2001; Nobrega et al. 2003; Ahituv et al. 2004; Chapman et al. 2004; de la Calle-Mustienes et al. 2005; Hughes et al. 2005; King et al. 2005; Woolfe et al. 2005).
Distant comparisons, such as human–fugu (Brenner et al. 1993
Here, we present an empirical and genomic evaluation of the relative merits of close and distant sequence comparisons at detecting functional noncoding regions in the human genome. Our study complements recent theoretical analyses of this problem (Eddy 2005
Assessment of whole-genome noncoding conservation among mammals and more distant species Coding exons are known to retain sequence similarity across great evolutionary distances, such as that between human and pufferfish (Aparicio et al. 2002 ). In contrast, intronic and intergenic conserved elements "evaporate" much more rapidly, thus limiting the sensitivity of distant noncoding sequence comparisons. To quantify the decay of noncoding sequence conservation with evolutionary distance, we generated whole-genome CNS sets through Gumby analysis (see Methods) of the following three-way genome alignments: human–mouse–rat (HMR), human–mouse–chicken (HMG), human–mouse–frog (HMX), and human–mouse–fish (HMF) (see Methods). In the human–rodent alignment, we identified 171,853 CNSs representing 2.2% of the human genome (Gumby P-value  1e-3). At the same P-value threshold, the corresponding CNS statistics were 40,033 and 0.37% for human–mouse–chicken, 14,568 and 0.13% for human–mouse–frog, and 5668 and 0.044% for human–mouse–fish. As expected, the more distantly related genomes exhibit markedly less conservation relative to human, suggesting a reduction in sensitivity that offsets their increased specificity in detecting functional noncoding regions. In order to correlate mammalian and more ancient noncoding conservation, we classified whole-genome CNSs into four primary categories. Category 1 consists of human–rodent CNSs that overlap human–mouse–chicken, human–mouse–frog, and human–mouse–fish CNSs. Category 2 extends only to human–mouse–frog, Category 3 to human–mouse–chicken, and Category 4 is restricted to human–rodent alone. Median length and –log(P-value) of CNSs within a given category and phylogenetic scope (for instance, Category 2 and human–mouse–frog) were represented by the dimensions of a single rectangular block. Building on such blocks, we define shapes generated by stacking blocks of the same category as evolutionary stacking patterns (ESPs, Fig. 1A–D). Although there is considerable variation within each category, the four ESPs characterizing the four CNS categories illustrate two general trends:
The funnel principle suggests that highly conserved human–rodent CNSs should be enriched for conservation in nonmammals. To quantify this enrichment, we sorted whole-genome human–rodent CNSs by P-value and calculated the fraction of human–mouse–chicken, human–mouse–frog, and human–mouse–fish CNSs overlapped by various quantiles of the human–rodent set (Fig. 2A). Remarkably, we see that the top 10% of the human–rodent CNSs overlap 60% of the CNSs in the human–mouse–fish set, 46% of the human–mouse–frog set, and 30% of the human–mouse–chicken set, indicating that a large fraction of deeply conserved CNSs can paradoxically be identified through shallow evolutionary comparisons. Similarly, almost half (47%) of the human–mouse–fish CNSs are contained in the top 5% of the human–rodent set. In terms of probabilities, the highest-scoring human–rodent CNSs have an 86% chance of being significantly conserved in chicken, while the lowest-scoring ones have less than a 4% chance of being in the human–mouse–chicken set (Fig. 2B). The corresponding likelihoods are 81% and 0.75% in human–mouse–frog and 64% and 0.2% in human–mouse–fish (see Supplemental Methods for analyses of potential artifacts).
Although we see a significant correlation between ancient and recent noncoding conservation, there are also important differences between the two phenomena. While half (2834) of the whole-genome human–mouse–fish CNSs have a human–rodent P-value < 1.9e-28, there are 7686 human–rodent CNSs that meet the same P-value criterion and yet lack significant conservation in fish. These data indicate that human–rodent analysis is capable of identifying a much larger set of unambiguously constrained noncoding elements than are obtainable from mammal–fish comparison. It is conceivable that the funnel principle could merely reflect biases in the algorithm used to identify CNSs, as would indeed be the case if close and distant comparisons were not performed uniformly, or if the CNS sets were corrupted with significant numbers of false-positive predictions. We therefore performed evolutionary simulations and further statistical analyses, which confirmed that such artifacts are unlikely (see Supplemental Methods).
In vivo experimental validation and assessment: Four human-genome loci
We first assessed the overlap between the most conserved human–rodent CNSs and human–fish CNSs in the DACH1 locus. Consistent with the above-described funnel principle, we found that 22 of the 36 strongest human–mouse–rat CNSs in this locus (P-value
To demonstrate the independent predictive power of extreme human–rodent conservation, we focused on the 14 CNSs within the aforementioned set of 36 that exhibited no conservation in fish and randomly selected six of them to assay for transcriptional enhancer activity in vivo. Our assay fuses the human conserved element to a
To test the predictive power of human–rodent conservation P-values on a larger scale, we retrospectively analyzed 133 human–fugu and ultra-conserved (Bejerano et al. 2004b ) CNSs that were tested for in vivo enhancer function as part of a separate whole-genome survey (L. Pennacchio, N. Ahituv, A. Moses, M. Nobrega, S. Prabhakar, M. Shoukry, S. Minovitsky, A. Visel, I. Dubchak, A. Holt, et al., in prep.). Each of the 133 CNSs was assigned a Gumby human–rodent conservation score (–log(P-value)). We found that positive enhancers had significantly higher conservation scores than negatives (t-test P-value = 0.0001), which further confirms the validity of using human–rodent P-values to prioritize candidate embryonic enhancers.
Human chromosome 16: Genomic distribution of enhancers active at embryonic day 11.5–12.5
To test this hypothesis, we focused on the 50 top-scoring "non-fish" human–mouse–rat CNSs on chromosome 16 (P-value < 1e-40), of which 36 were located outside the four developmental loci encompassed by human–mouse–fish conservation. We tested 11 of these 36 for in vivo enhancer function through our mouse enhancer transgenesis assay and found to our surprise that not a single one of these 11 CNSs drove a reproducible embryonic expression pattern, in contrast to our experience at the DACH1 locus. Excessive distance between the tested CNSs and their flanking genes is an unlikely explanation for the negative results, since 10 of the 11 CNSs lie within 1 Mb of a RefSeq transcription start site. It is possible that these CNSs are not transcriptional enhancers despite extreme human–rodent conservation, or that their activities are beyond the resolution/sensitivity of this assay or perhaps even that they are enhancers at a different embryonic time point.
To determine if extreme human–mouse–rat CNSs are better able to capture transcriptional enhancers flanking key developmental transcription factor and signaling genes, we tested 13 of the 14 extreme human–rodent CNSs (P-value < 1e-40) that lie within the four gene-poor regions on chromosome 16 containing the aforementioned developmental genes. Of these 13 CNSs, five (38%) drove reproducible
SCL benchmark: Benefits of multiple-eutherian comparison
Given the poor sensitivity of distant sequence comparisons, we aligned the available human, mouse, rat, and dog sequences from this locus (human chr1:47,367,748–47,427,851; NCBI Build 35) using MLAGAN, which yielded a total divergence of 0.79 substitutions/site. At the default P-value threshold of 0.5, Gumby conservation analysis (see Supplemental Methods) detected eight of the 10 experimentally defined noncoding elements, with 11 new predictions (Fig. 5). Thus, as in the case of DACH1, P-value prioritization of CNSs in just a few eutherian genomes is sufficient to identify with acceptable true-positive rate functional elements missed by distant sequence comparison. Of the eight functional regions detected by human–mouse–rat–dog analysis, seven have P-value
 -globin benchmark: Simians, primates, mammalsThe human -globin locus is another well-characterized genomic region, with a recent synthesis of extensive computational and empirical analyses cataloging 17 functional noncoding DNA elements (Hughes et al. 2005). The elements comprise 11 promoters, four nonpromoter DHSs involved in transcriptional regulation, and two putative regulators of alternative splicing. In addition, this locus is well suited to evaluating the relative merits of close and distant sequence comparisons, due to the availability of sequence data from 22 vertebrate species spanning a broad range of evolutionary distances from old world monkeys to teleost fish. The analyzed genomic region (human chr16:48,339–219,839; NCBI Build 35) spans the block of conserved synteny telomeric to the -globin genes, the -globin genes themselves, and the LUC7L gene. To ensure accurate alignment, we split the locus into four subregions and aligned each subregion separately (see Supplemental Methods).
As was the case with the SCL locus, conservation of the benchmark functional elements in distant species is extremely limited; frog and fish have no apparent sequence homology with any of the 17 noncoding functional elements, and chicken shows homology with only two of the 17. Sequence conservation is also limited in opossum, and even hedgehog (which is the most diverged of the eutherians considered here) shows no similarity for five of the 17 benchmark sequence elements (see Hughes et al. [2005
To assess the relative power of various eutherian comparisons at this locus, we selected the following three species sets: (1) simians (human, baboon, colobus, squirrel monkey, owl monkey, marmoset, dusky titi), (2) primates (simian group plus the prosimian galago), and (3) eutherians minus nonhuman primates and hedgehog (human, mouse, rat, cat, cow, pig). In the eutherian set, which had a total branch length ranging from 1.24 to 1.55 substitutions/site across the four subregions, Gumby identified 13/17 benchmark elements, with 22 new predictions (P-value
Ancient human–fish noncoding conservation has been the mainstay of searches for enhancers of key developmental genes, whereas mammalian sequence comparisons have been considered insufficiently specific, especially in large, highly conserved intergenic regions harboring hundreds of human–rodent CNSs. This dichotomy disappears in light of the funnel principle established by whole-genome meta-analysis of close and distant sequence comparisons: The longer and more constrained a mammalian CNS, the deeper its evolutionary conservation in distant vertebrates (on average), and vice versa. Thus, extreme human–rodent P-values serve as a proxy for human–fish conservation, and most human–fish CNSs can be identified through human–rodent analysis alone. The correspondence between recent and ancient sequence conservation is likely to grow even stronger when more mammalian genomes are added to the human–mouse–rat trio (Margulies et al. 2005 ). A relationship between ancient and recent noncoding conservation consistent with the funnel principle has been reported earlier (Ovcharenko et al. 2004), though the correspondence described between the two evolutionary scales was significantly weaker than is evident from our results. Although human–fish sequence comparison identifies developmental enhancers with high specificity, only a small fraction of the expected tens or hundreds of thousands of functional noncoding elements in the human genome are conserved in fish. Extreme P-value mammalian CNSs form a superset covering the majority of human–fish as well as a much larger fraction of noncoding functional elements in the human genome, while still maintaining a low false-positive rate. More generally, in identifying candidates for experimental testing, the tradeoff between sensitivity and specificity can be tuned simply by adjusting the threshold conservation score (P-value), in contrast to the more common technique of varying the evolutionary distance between the compared species. In vivo assays of 30 extreme human–rodent CNSs with no conservation in fish yielded eight positive enhancers at e11.5–12.5, close to the success rate of human–fish comparison. This experimentally confirms the theoretical prediction that extreme conservation in relatively close species is on a par with conservation between highly diverged species. In terms of genomic distribution, the success rate was 8/19 among human–rodent CNSs in the neighborhood of developmental genes such as DACH1, SALL1, IRX3, IRX5, IRX6, ATBF1, and WWOX, and it was 0/11 at other loci on human chromosome 16, most likely since the assay is limited to genes with complex tissue-specific expression at e11.5/12.5 and produces false negatives in loci that are silent or regulated in a relatively simple manner at this embryonic time point. Since such in vivo tests are expensive and time-consuming, one strategy for large-scale functional genomics would be to prioritize loci that are known to require tissue-specific regulation at the selected developmental stage. Alternatively, one could prioritize loci of unknown function that nevertheless have a high density of ancient (human–mouse–fish, or perhaps even human–mouse–frog or human–mouse–chicken) CNSs, since the loci on human chromosome 16 yielding high success rates in the embryonic enhancer assay were originally identified solely on the basis of their high human–mouse–fish CNS density (Fig. 4). For instance, the second-largest peak of human–mouse–fish CNS density in the human genome (data not shown) occurs between the first exons of the uncharacterized human genes ZNF503 and C10orf11, making this gene a candidate for deeply conserved and highly specific early developmental regulation.
Results from whole-genome conservation analysis, from benchmarking using pre-existing functional data sets from three diverse genomic loci and from systematic in vivo characterization of 30 new enhancer predictions, all indicate the considerable statistical power of sequence comparisons involving just a few (three to six) eutherian mammals. Another consistent theme is the loss of sensitivity when more distant species such as marsupials, chicken, frog, and fish are compared with human, either because they have diverged in their cis-regulatory programs or because of stabilizing selection that allows the regulatory sequence to diverge while retaining the same tissue and temporal specificity (Ludwig et al. 2000
It is common in analyses of the statistical power of sequence comparisons to fix the size of an individual constrained block as the total branch length (neutral divergence) is varied (Margulies et al. 2003
In this study, we have focused on the identification of large sequence elements with empirical evidence of cis-regulatory function, such as enhancers, promoters, and DNaseI-hypersensitive sites, which have typical lengths of 100 bp or more. Previous theoretical studies (Cooper et al. 2005
Development of a uniform statistical scoring scheme for sequence conservation (Gumby) In order to uniformly evaluate the benefits and limitations of close versus distant sequence comparisons, we sought a computational algorithm general enough to process alignments at all evolutionary distances, identify conserved regions of any size, and, most importantly, quantify their statistical significance (P-value). For generality and convenience, we further stipulated that the method should require no training data, no prior estimates of evolutionary rates (branch lengths), and only one arbitrary parameter, which could remain fixed across all evolutionary distances. The Gumby program meets these design goals by making minimal assumptions about the statistical features of conserved noncoding regions and treating the sequence alignment as its own training set. Gumby takes its name from the Gumbel distribution, which is the extreme value distribution underlying Karlin-Altschul statistics. The input data are an alignment, a phylogenetic tree (topology only, without branch lengths), and annotations of coding regions (optional). The algorithm proceeds through five steps:
One pitfall in applying Karlin-Altschul statistics to global alignments is the fact that column scores are not identically distributed. For example, the distribution of scores at positions that are aligned in, say, five of 10 species is different from that at positions that are unique to a single species. Also, the number of aligned species is highly correlated between neighboring alignment columns, due to the block-like structure generated by long indels. The fictitious randomly permuted alignments modeled by Karlin-Altschul statistics do not have this correlation structure and are, therefore, less likely to contain local high-scoring segments than are neutral regions in real alignments. Thus, a straightforward application of the permutation-based null model generates unrealistically strong P-values. To compensate for this effect, Gumby takes the conservative approach of estimating the Karlin-Altschul K and
Gumby availability
Generation of whole-genome CNS sets
DACH1 and human chromosome 16: Identifying candidate embryonic enhancers
We thank Alexander Poliakov and Inna Dubchak for incorporating Gumby into the VISTA web server, Nadav Ahituv for the Gateway-HSP68-LacZ vector, Jim Hughes for feedback regarding -globin sequence analysis, Qian-fei Wang, Jan-Fang Cheng, and Michael Brudno for numerous productive conversations during development of Gumby, and James Noonan, Nadav Ahituv, Qian-fei Wang, and Dario Boffelli for comments on the manuscript. F.P. was supported by a fellowship from the Canadian Institutes of Health Research. Research was conducted at the E.O. Lawrence Berkeley National Laboratory, supported by the Grant # HL066681, Berkeley-PGA, under the Programs for Genomic Application, funded by National Heart, Lung, and Blood Institute, USA, and performed under Department of Energy Contract DE-AC02-05CH11231, University of California.
3 Present address: Department of Integrative Biology, University of California, Berkeley, CA 94720, USA. 
E-mail SPrabhakar{at}lbl.gov; fax (510) 486-4229. E-mail LAPennacchio{at}lbl.gov; fax (510) 486-4229. [Supplemental material is available online at www.genome.org.] Article published online before print. Article and publication date are at http://www.genome.org/cgi/doi/10.1101/gr.4717506
Ahituv N., Rubin E.M., Nobrega M.A. 2004. Exploiting human–fish genome comparisons for deciphering gene regulation. Hum. Mol. Genet. 13: R261–R266. Ahituv N., Prabhakar S., Poulin F., Rubin E.M., Couronne O. 2005. Mapping cis-regulatory domains in the human genome using multi-species conservation of synteny. Hum. Mol. Genet. 14: 3057–3063. Altschul S.F., Gish W., Miller W., Myers E.W., Lipman D.J. 1990. Basic local alignment search tool. J. Mol. Biol. 215: 403–410.[CrossRef][Medline] Aparicio S., Chapman J., Stupka E., Putnam N., Chia J.M., Dehal P., Christoffels A., Rash S., Hoon S., Smit A.et al. 2002. Whole-genome shotgun assembly and analysis of the genome of Fugu rubripes. Science 297: 1301–1310. Bejerano G., Haussler D., Blanchette M. 2004a. Into the heart of darkness: Large-scale clustering of human non-coding DNA. Bioinformatics 20:Suppl. 1, I40–I48.[CrossRef][Medline] Bejerano G., Pheasant M., Makunin I., Stephen S., Kent W.J., Mattick J.S., Haussler D. 2004b. Ultraconserved elements in the human genome. Science 304: 1321–1325. Boffelli D., McAuliffe J., Ovcharenko D., Lewis K.D., Ovcharenko I., Pachter L., Rubin E.M. 2003. Phylogenetic shadowing of primate sequences to find functional regions of the human genome. Science 299: 1391–1394. Brenner S., Elgar G., Sandford R., Macrae A., Venkatesh B., Aparicio S. 1993. Characterization of the pufferfish (Fugu) genome as a compact model vertebrate genome. Nature 366: 265–268.[CrossRef][Medline] Brudno M., Chapman M., Göttgens B., Batzoglou S., Morgenstern B. 2003a. Fast and sensitive multiple alignment of large genomic sequences. BMC Bioinformatics 4: 66.[CrossRef][Medline] Brudno M., Do C.B., Cooper G.M., Kim M.F., Davydov E., Green E.D., Sidow A., Batzoglou S. 2003b. LAGAN and Multi-LAGAN: Efficient tools for large-scale multiple alignment of genomic DNA. Genome Res. 13: 721–731. Caubit X., Thangarajah R., Theil T., Wirth J., Nothwang H.G., Ruther U., Krauss S. 1999. Mouse Dac, a novel nuclear factor with homology to Drosophila dachshund shows a dynamic expression in the neural crest, the eye, the neocortex, and the limb bud. Dev. Dyn. 214: 66–80.[CrossRef][Medline] Chapman M.A., Donaldson I.J., Gilbert J., Grafham D., Rogers J., Green A.R., Göttgens B. 2004. Analysis of multiple genomic sequence alignments: A web resource, online tools, and lessons learned from analysis of mammalian SCL loci. Genome Res. 14: 313–318. Cooper G.M., Brudno M., Green E.D., Batzoglou S., Sidow A. 2003. Quantitative estimates of sequence divergence for comparative analyses of mammalian genomes. Genome Res. 13: 813–820. Cooper G.M., Stone E.A., Asimenos G., Green E.D., Batzoglou S., Sidow A. 2005. Distribution and intensity of constraint in mammalian genomic sequence. Genome Res. 15: 901–913. Davis R.J., Shen W., Heanue T.A., Mardon G. 1999. Mouse Dach, a homologue of Drosophila dachshund, is expressed in the developing retina, brain and limbs. Dev. Genes Evol. 209: 526–536.[CrossRef][Medline] de la Calle-Mustienes E., Feijoo C.G., Manzanares M., Tena J.J., Rodriguez-Seguel E., Letizia A., Allende M.L., Gomez-Skarmeta J.L. 2005. A functional survey of the enhancer activity of conserved non-coding sequences from vertebrate Iroquois cluster gene deserts. Genome Res. 15: 1061–1072. Eddy S.R. 2005. A model of the statistical power of comparative genome sequence analysis. PLoS Biol. 3: e10.[CrossRef][Medline] Frazer K.A., Pachter L., Poliakov A., Rubin E.M., Dubchak I. 2004. VISTA: Computational tools for comparative genomics. Nucleic Acids Res. 32: W273–W279. Göttgens B., Barton L.M., Gilbert J.G., Bench A.J., Sanchez M.J., Bahn S., Mistry S., Grafham D., McMurray A., Vaudin M.et al. 2000. Analysis of vertebrate SCL loci identifies conserved enhancers. Nat. Biotechnol. 18: 181–186.[CrossRef][Medline] Hughes J.R., Cheng J.F., Ventress N., Prabhakar S., Clark K., Anguita E., De Gobbi M., de Jong P., Rubin E., Higgs D.R. 2005. Annotation of cis-regulatory elements by identification, subclassification, and functional assessment of multispecies conserved sequences. Proc. Natl. Acad. Sci. 102: 9830–9835. Karlin S. and Altschul S.F. 1990. Methods for assessing the statistical significance of molecular sequence features by using general scoring schemes. Proc. Natl. Acad. Sci. 87: 2264–2268. King D.C., Taylor J., Elnitski L., Chiaromonte F., Miller W., Hardison R.C. 2005. Evaluation of regulatory potential and conservation scores for detecting cis-regulatory modules in aligned mammalian genome sequences. Genome Res. 15: 1051–1060. Kothary R., Clapoff S., Darling S., Perry M.D., Moran L.A., Rossant J. 1989. Inducible expression of an hsp68-lacZ hybrid gene in transgenic mice. Development 105: 707–714. Lettice L.A., Heaney S.J., Purdie L.A., Li L., de Beer P., Oostra B.A., Goode D., Elgar G., Hill R.E., de Graaff E. 2003. A long-range Shh enhancer regulates expression in the developing limb and fin and is associated with preaxial polydactyly. Hum. Mol. Genet. 12: 1725–1735. Ludwig M.Z., Bergman C., Patel N.H., Kreitman M. 2000. Evidence for stabilizing selection in a eukaryotic enhancer element. Nature 403: 564–567.[CrossRef][Medline] Margulies E.H., Blanchette M., Haussler D., Green E.D. 2003. Identification and characterization of multi-species conserved sequences. Genome Res. 13: 2507–2518. Margulies E.H., Vinson J.P., Miller W., Jaffe D.B., Lindblad-Toh K., Chang J.L., Green E.D., Lander E.S., Mullikin J.C., Clamp M. 2005. An initial strategy for the systematic identification of functional elements in the human genome by low-redundancy comparative sequencing. Proc. Natl. Acad. Sci. 102: 4795–4800. Nobrega M.A., Ovcharenko I., Afzal V., Rubin E.M. 2003. Scanning human gene deserts for long-range enhancers. Science 302: 413. Oda-Ishii I., Bertrand V., Matsuo I., Lemaire P., Saiga H. 2005. Making very similar embryos with divergent genomes: Conservation of regulatory mechanisms of Otx between the ascidians Halocynthia roretzi and Ciona intestinalis. Development 132: 1663–1674. Ovcharenko I., Stubbs L., Loots G.G. 2004. Interpreting mammalian evolution using Fugu genome comparisons. Genomics 84: 890–895.[CrossRef][Medline] Pennacchio L.A. and Rubin E.M. 2001. Genomic strategies to identify mammalian regulatory sequences. Nat. Rev. Genet. 2: 100–109.[CrossRef][Medline] Sandelin A., Bailey P., Bruce S., Engstrom P.G., Klos J.M., Wasserman W.W., Ericson J., Lenhard B. 2004. Arrays of ultraconserved non-coding regions span the loci of key developmental genes in vertebrate genomes. BMC Genomics 5: 99.[CrossRef][Medline] Stone E.A., Cooper G.M., Sidow A. 2005. Trade-offs in detecting evolutionarily constrained sequence by comparative genomics. Annu. Rev. Genomics Hum. Genet. 6: 143–164.[CrossRef][Medline] Woolfe A., Goodson M., Goode D.K., Snell P., McEwen G.K., Vavouri T., Smith S.F., North P., Callaway H., Kelly K.et al. 2005. Highly conserved non-coding sequences are associated with vertebrate development. PLoS Biol. 3: e7.[CrossRef][Medline]
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ABSTRACT
Results
-galactosidase reporter vector and assesses the ability of the conserved fragment to drive tissue-specific expression in transgenic mice at embryonic day 11.5–12.5 (e11.5–12.5) (Kothary et al. 1989
E. Thus, the P-value also doubles as an estimate of the false-positive rate. 
parameters only on the basis of columns that are aligned in at least k species (k 
2). As k is increased, the number of columns in the null model decreases. Gumby sets k to the maximum value such that the number of columns in the null model is at least 40% of the length of the base sequence. Another source of spurious P-values is the suppression of null-model scores by large indels that are artifacts of missing sequence data. Gumby again takes the conservative approach of penalizing indels only after the null model has been generated. 
