BACKGROUND
restriction landmark genomic scanning  has been used to study aberrant cpg island methylation in cancer for more than ten years.
this approach remains one of the most reliable ways to characterize cpg island hypermethylation in cancer and has been used both to characterize differences in aberrant methylation phenotypes and also to identify tumor suppressor genes.
not only have known tumor suppressor genes like cdkn2a , itga <dig>   <cit> , and igfbp <dig>  <cit>  been identified as targets of aberrant methylation in cancer by rlgs, but also novel tumor suppressor genes such as tcf <dig>  <cit> , slc5a <dig>  <cit> , id <dig>  <cit> , bmp3b  <cit> , and socs <dig>  <cit>  have been identified by rlgs.
rlgs is a two-dimensional gel electrophoresis method  <cit>  that allows detection of dna methylation if a methylation sensitive landmark enzyme such as noti is used.
up to  <dig>  end-labeled landmark sites are displayed in a single rlgs profile.
the labeling of the sites is based on incorporation of radionucleotides into the noti half-site by dna polymerase.
methylated sites are not digested and are therefore not labeled, thus they do not contribute to the two-dimensional pattern of rlgs fragments.
spots present in a normal profile, but absent from a tumor profile are indicative of methylation of the landmark site.
furthermore, the profiles are quantitative such that spot intensity directly correlates with the degree of methylation of a locus, with partial methylation representing the cellular heterogeneity of the cancer both among the malignant cells and the associated non-malignant cells.
rlgs profiles are highly reproducible allowing for comparison of different tissues  or of different individuals.
although this approach does not cover every cpg island in the genome, as microarray-based approaches may achieve  <cit> , rlgs provides highly reliable data due to the molecular simplicity of the assay.
unlike microarray-based techniques that depend upon molecularly complex manipulations such as whole genome pcr, hybridization, and immunoprecipitation of methylated dna, rlgs simply relies upon restriction digestion of dna and gel electrophoresis.
crucial to the successful implementation of rlgs for a cpg island methylation profiling study is the ability to identify the sequence of the targets of aberrant methylation, which has been a bottleneck in the flow of these types of studies.
significant improvements were made in rlgs spot cloning ability with the advent of arrayed boundary libraries, where restriction fragments from the arrayed library were mapped to the pattern of rlgs spots  <cit> .
although highly successful, this method is limited by the tremendous investment in effort required for each genome of interest and each enzyme combination of interest.
with the completion of the human and mouse genome sequences, a bioinformatics approach to rlgs fragment identification became possible where each restriction fragment's migration in both dimensions could be predicted to create a virtual rlgs profile.
such an approach could be applied to any sequenced genome, using any enzyme combination, and is unaffected by any potential tissue specific methylation.
furthermore, such an approach should be far less labor intensive to develop and use than the arrayed boundary library spot cloning method.
the relative ease of setup and use, plus the incredible flexibility in the virtual approach, makes such a development highly attractive, allowing any researcher the ability to identify rlgs spots without the creation of specialized reagents.
this idea was partially realized in  <dig> with a first generation version of a virtual rlgs gel that was called "virtual genome scan" and applied to the human genome  <cit> .
in this system, the restriction fragment migration prediction formula was based solely on fragment length.
based on known fragment lengths and spot positions in both dimensions of rdna and ebv genome derived spots, a cubic polynomial was derived giving fragment length as a function of spot location.
by comparing the known positions of  <dig> previously identified spots with the positions of their corresponding fragments on the virtual gel, it was reported that 75%  of fragments were in a  <dig> ×  <dig> pixel window  around the predicted location, with the remaining fragments in a  <dig> ×  <dig> pixel window   <cit> .
this approach was used to identify one previously unknown rlgs spot methylated in neuroblastoma and found to be in the 5' end of the alx <dig> gene  <cit> .
taking a similar approach, but incorporating the known chromosome of origin of the rlgs spots  <cit>  into the predictions, zardo et al   <cit>  determined  <dig> previously unknown spot identities, and hong et al   <cit>  determined approximately  <dig> additional new spot identities.
a second virtual rlgs system  was reported in  <dig> and applied to the arabidopsis and mouse genomes  <cit> .
the migration algorithms used were similarly based solely on the length of the dna fragments.
in the mouse virtual profile it was observed that there were  <dig> more spots on the virtual profile than are found on the same region of the actual profile.
given that in the mouse genome, unlike the human genome, many of the landmark sites  are within repetitive elements like ltrs, repeat masker was able to remove nearly  <dig> of these extra spots from the virtual profile  <cit> .
this approach was used to identify tissue specifically methylated rlgs spots in mouse  <cit>  and to identify cpg islands that become hypomethylated in mouse es cells when dna methyltransferases are knocked out  <cit> .
with approximately  <dig> spots on a  <dig> ×  <dig> cm gel in the human noti-ecorv-hinfi enzyme combination most commonly used, the average distance between neighboring spots is  <dig>  cm.
to generate a virtual image that approximates the true image, such that the relative predicted positions of any two neighboring spots does not flip-flop, the window of error for spot prediction should be, at most, half this average distance or  <dig>  cm.
one confounding issue is that others have demonstrated a considerable correlation between dna fragment curvature and gel mobility  <cit> .
thus, it is clear that, while a simple fragment migration model based upon fragment length alone is a good start, it must be improved upon.
in addition, since in the human genome noti sites rarely cut in repetitive elements, use of a repeat masker does not help to remove the extra spots in a human virtual rlgs profile.
we have developed a novel system of virtual rlgs  using first and second dimension migration algorithms that use the sequence characteristics of each fragment along with the fragment length to determine spot position.
these algorithms were determined based on the identification of  <dig> rlgs spots in the human noti-ecorv-hinfi profile.
in addition, we have defined a filter to remove extra spots from both the human and mouse vrlgs profiles based on gc content.
we report greatly improved accuracy of pattern prediction even when the migration algorithms are applied to different enzyme combinations and different genomes from which they were derived.
these improvements are combined with a novel graphic interface that allows for visualization of virtual spots that 1) look like real spots, and 2) allow the user to overlap the virtual and real profiles.
these changes make the bioinformatics approach to identifying rlgs spots of interest a highly viable and effective alternative to more complex rlgs spot cloning methods.
RESULTS
spot location prediction
vrlgs profiles for the human noti-ecorv-hinfi, asci-ecorv-hinfi, and the mouse noti-ecorv-hinfi genome and enzyme combinations were generated as described in the materials and methods.
these virtual profiles were then compared to the laboratory gels that serve as our "master profiles" for each.
fig 1a contains a sample screen shot of conime.
the upper panel shows the overlap of the two gels and is colorized with the laboratory gel contributing blue and the virtual gel contributing red, while areas of overlap appear dark purple.
the strongest spots on the laboratory gel arise from the high copy number rdna sequences.
some of these spots line up quite well with the virtual spots, but others do not due to polymorphism in subsets of these highly repeated units.
the virtual profile does not have polymorphic information for these subsets of the repeats, explaining why some of the darkest spots do not align.
the image in fig 1a is zoomed out to show the entire gel, while in fig 1b the image is focused on a specific region of interest.
this region of the gel is a portion of section 2d from the master pbl profile previously described  <cit> .
spots  <dig>   <dig>   <dig> and  <dig> have been identified and confirmed, and their positions on both the actual rlgs gel  and the virtual gel  are shown.
the overlay in the top panel shows how closely the migration algorithms correctly predict the positions of the spots in both dimensions.
in order to identify a top candidate sequence for the spot adjacent to  <dig> with the arrow, the virtual profile is clickable.
this clickable window presents information about the restriction fragment mapping to that position in the vrlgs profile, as shown in fig 1b.
each vrlgs spot has a unique identifier, giving the chromosome followed by the base position on that chromosome.
the unique identifier for each virtual spot is the position of either the noti site or the hinfi site in the 2nd dimension fragment, depending on the orientation of the sequence in the database with the 3' end of the restriction fragment used.
therefore, even though a single noti site can potentially generate two spots, each will get a unique identifier with the spot generated by the fragment 5' to the noti site named by the position of the noti site, and the spot generated by the fragment 3' to the noti site named by the position of the hinfi site.
additional information concerning the cpg characteristics of the region around the noti site are also included and discussed in more detail below.
using sets of confirmed cloned spots for each gel, we computed the distance between the predicted x and y position and each spots' actual x and y positions on the master profiles.
we also computed the euclidean distance between the spots' predicted and actual location on the gel.
these distances represent the spot prediction error and give an objective measure of the accuracy of the dna fragment migration algorithms in each dimension.
table  <dig> presents the results of the prediction formulas applied to the three gels.
we give the median error, the mean error, and the root mean squared error for the x and y dimensions as well as the euclidean prediction errors, the 90'th percentile error,  and the root mean square error of the 90'th percentile of spots with the least error.
the large improvement in the rms of the 90th percentile spots compared to the entire data set indicates that a relatively few number of outliers are strongly influencing the mean error.
importantly, the maximum difference between actual spot position and virtual spot position for the 75th percentile of spots is  <dig>  cm in the 1st dimension and  <dig>  cm in the 2nd dimension.
this represents a significant improvement over the maximum error for the 75th percentile of spots of  <dig>  cm and  <dig>  cm previously reported for a virtual rlgs approach  <cit> .
asee materials and methods for descriptions of functions according to the number; ball values in cm; croot mean square; d maximum error of the 90th percentile of spots .
e euclidean distance.
factors other than length also contribute to migration
spot locations in laboratory rlgs gels depend not only upon dna sequence properties, but also upon laboratory conditions.
temperature, ionic strength of buffers, gel running times, and the physical handling of rlgs gels all affect the migration of dna fragments.
exact spot locations on different gels vary slightly, even if the gels are produced from the same dna specimen, although their relative positions do not change.
thus, our prediction formulas must model not only migration distance based on sequence properties, but also the effects of warping and distortion in the gel used to create the master profile.
comparing the three formulas  based on fragment length  there is marked improvement from the logl formula to the cubic to the quintic polynomial.
adding a linear curvature term to the cubic and quintic length polynomials improves both formulas , with the quintic polynomial retaining an advantage.
replacing curvature by the gc ratio  gives a slight, but non-significant, improvement .
fragment gc ratio is highly correlated with curvature, suggesting that these terms are interchangeable.
computing gc ratio is much simpler than computing curvature and is far preferable.
each spot location is based on two fragments, one that migrates in the x-direction and a second that migrates in the y-direction.
the x-direction formulas discussed above depend upon the sequence of the first fragment in the pair.
the last x-direction formula  adds a linear term proportional to the log of the length of the second sequence.
this can be viewed as slightly adjusting the x-coordinate based on the y-coordinate of the spot.
similarly, the y-direction formulas depend upon the sequence of the second fragment in the pair.
the last y-direction formula  adds a linear term proportional to the log of the length of the first sequence.
the last formula gives the best fit on all three gels.
the improvement in prediction formula using the quintic polynomial with gc ratio and the log of the other dimension fragment length can be easily seen by visually comparing computer generated vrlgs gels with laboratory gels.
figure  <dig>  shows a region in section 2d of the master human noti-ecorv-hinfi profile and the corresponding regions in virtual profiles generated by various formulas.
spots labeled  <dig> through  <dig> are previously cloned rlgs spots allowing the correct identification of each of the corresponding virtual spots, as shown in fig  <dig>  the swirl shape seen in the master profile is much more evident in fig 2c and 2d.
of particular importance are the relative positions of spots  <dig>   <dig>  and  <dig>  in fig 2a and 2b, the position of spot  <dig> is below spots  <dig> and  <dig> in the virtual profiles, but in the master profile spot  <dig> is above spots  <dig> and  <dig>  this is remedied in fig 2c and 2d where adding in a sequence specific factor  corrects for the slower migration of spot  <dig>  finally in fig 2d, although the absolute positions of spots  <dig>   <dig>   <dig>  and  <dig> are not perfect in the virtual gel, their relative spacing is correct within a distance equal to the height of the spots.
more broadly, table  <dig> shows that by using the migration formulas shown in fig 2d, the median error between predicted position and actual position for all cloned spots is  <dig>  cm in the 1st dimension and  <dig>  cm in the 2nd.
to address whether these data were due merely to over-fitting of the migration formulas to the spot migration data, we randomly selected half of the cloned spots to be used to derive the migration formulas and measured the accuracy of the other half.
we found a median error in the 1st dimension of  <dig>  cm and of  <dig>  in the second dimension , demonstrating that the data shown in table  <dig> is not due to over-fitting of the data.
such improvement over previous virtual rlgs approaches dramatically improves the utility of virtual rlgs for spot identification since the error is less than half the average distance between spots.
this makes it much more likely that the pattern of spots relative to each other will be faithfully preserved in the virtual image even if some small error remains in their absolute positions.
there are two confounding factors in modeling dna fragment migration in these gels that make using a simple migration model based on the log of the fragment size insufficient.
these are the physical imperfection of these gels as described above and the sequence dependent geometry of the dna fragments.
we hypothesize that the improvement from the logarithmic to cubic to quintic formulas probably reflects the better ability of the quintic polynomial to model physical gel distortion.
the variability in physical gel distortion from one master profile to the other can be accounted for by independent derivation of the coefficients based upon known spots.
the improvements seen by adding in the gc ratio factor reflect the sequence dependent influence of dna fragment geometry on fragment migration.
although this is an indirect measure of geometry, it performed similarly to the use of the geometry prediction program curva.
we believe that this factor is the most difficult to model properly and is the major reason some error remains in these models.
the improvement in the final formula, adding in the log of the length of the other dimension fragment, reflects the better ability of this formula to model physical gel distortion and possibly the influence of dna fragment geometry.
the fact that vertical migration in the y-direction depends somewhat on the size of the horizontal fragment is not so surprising since vertical migration follows horizontal migration.
more remarkable is that the x-location seems to depend upon the size of the vertical fragment, even though horizontal migration precedes vertical migration.
we hypothesize that vertical fragments do not necessarily migrate in a perfect vertical line through a gel, but actually move slightly along the x-direction, explaining why the x-location would depend upon the vertical fragment size.
filtering extra spots from the vrlgs
rlgs is typically applied using a methylation sensitive landmark enzyme such as noti or asci, with the goal of identifying methylation differences at cpg islands between state a and state b .
upon close examination of the vrlgs profile it became clear that there were significantly more virtual spots than real spots.
this can be explained by the fact that certain noti sites are methylated, even in normal tissues, and this may be tissue specific.
the presence of these extra spots on the virtual profile makes the pattern correlations with the actual rlgs profile much more difficult to discern.
we investigated whether a difference in the sequence surrounding the noti sites could be found between real rlgs spots and extra spots seen only on the virtual profile.
we identified  <dig> extra spots from the virtual profile in regions of the gel where most of the actual rlgs spots had already been identified.
we compared the sequence of the noti site +/-  <dig> bp  from the  <dig> extra virtual spots to  <dig> cloned and confirmed actual rlgs spots.
figure  <dig> shows the distributions of gc%, observed over expected cpg ratio, and cpg count in the  <dig> extra virtual spots and the  <dig> actual rlgs spots.
we tested various combinations of these three factors  that could best distinguish the two groups in order to maximize the number of extra spots identified, yet minimize the number of actual spots identified.
we found that if we filtered out noti sites whose surrounding  <dig> bp of sequence had less than 50% gc, a cpg ratio of less than  <dig> , and a cpg count of less than  <dig>  we could eliminate 46% of our  <dig> extra virtual spots, yet retain 96% of our  <dig> actual spots.
the results of this filtering can be seen in figure 4a and 4b.
in each, the lower left is the master rlgs profile zoomed in to section 3d, with the corresponding region of the virtual gel shown in the right panel.
the numbered spots are all identified and confirmed and are therefore noted on the virtual profile.
spot a  represents a spot for which identification of a good candidate sequence is extremely difficult because of extra spots  seen on the virtual profile complicating the virtual pattern near spot a. however, when we applied the filter described above to remove all extra spots based on the sequence environment of the noti site, the best candidate sequence for spot a became immediately obvious .
we note that the filtering criteria identified are similar to criteria used to define cpg islands.
we find that noti sites in regions that look less like cpg islands are more likely to be extra spots.
this is consistent with the idea that cpg islands are protected from methylation in normal cells in most cases but cpg dinucleotides outside cpg islands are highly methylated, and also explains why even though only 89% of noti sites in the human genome reside in cpg islands, 96% of identified rlgs spots are in cpg islands .
interestingly, as described below and shown in figure  <dig>  it is often true that rlgs loci found to exhibit tissue specific methylation arise from noti sites that we would filter out , and are therefore less "cpg island-like."
it was recently found that there are many regions of hypomethylation in mouse testis dna compared to dna in somatic tissues and that these chromosomal regions are defined by lower gc content  <cit> .
these observations suggest that there may be a relationship between the potential of a locus to exhibit tissue specific methylation and the "cpg island-like" characteristics of the sequence.
efficacy of vrlgs for novel spot identification
using the human noti-ecorv-hinfi vrlgs profile produced with the migration algorithms derived from  <dig> spots cloned from the library mixing gel approach and incorporating dna curvature and gc ratio, we attempted to identify  <dig> unknown rlgs spots.
we identified a top candidate sequence for each spot and designed pcr primers that fall within the 2nd dimension fragment.
for  <dig> spots, we chose  <dig> candidate sequences to test because one candidate appeared only slightly better than the other.
we then used the primers for the candidate sequence to amplify dna eluted from the spot in question, as well as a nearby spot.
accurate prediction of a spot sequence was determined if a strong pcr product was obtained when that spot was used as template, but no product or a very weak product was detected using a nearby spot as template.
application of vrlgs to other genomes and enzyme combinations
we have demonstrated excellent utility and pattern correlation of the vrlgs profile with the human noti-ecorv-hinfi enzyme combination from which the migration algorithms and extra spot filtering were derived.
to address whether these improvements to vrlgs were based on sound principles or were simply due to over fitting of the data, we applied the same migration factors to create a human asci-ecorv-hinfi vrlgs profile .
in addition we created vrlgs profiles for the mouse genome using the noti-ecorv-hinfi .
we used cloned spots as anchor points for each and calculated the formula coefficients as described in the materials and methods.
table  <dig> shows the error of the prediction formulas for the cloned spots in the two human profiles and the noti-ecorv-hinfi mouse profile with  <dig>   <dig>  and  <dig> spots cloned, respectively.
these data demonstrate that the migration algorithms developed for the human noti-ecorv-hinfi profile incorporating fragment length as well as information about the gc content of each fragment are based on sound principles and can be broadly applied to different enzyme combinations and genomes.
as a further test of the utility of the vrlgs system in the mouse genome, we used vrlgs to identify the sequence of  <dig> spots from the mouse noti-ecorv-hinfi master profile.
in addition, turning on or off the filtering out of extra spots can allow for use of vrlgs to identify tissue specific methylation spots.
figure  <dig> shows section 2f from the mouse noti-ecorv-hinfi profile with spots  <dig>   <dig>  and  <dig> previously identified and the indicated spot not present in the master profile or in a mouse liver profile.
this spot was, however, found on a mouse intestine profile .
when using the vrlgs profile where we filtered out extra spots, the intestine specific spot of interest was not present on the virtual profile , however, when we did not filter out the extra spots, the virtual spot was present .
the third panel of fig 5b shows the composite image of the vrlgs profile with the extra spots retained aligned with the master profile and the candidate virtual spot for the intestine specific spot of interest indicated.
we confirmed the correct identification of this intestine specific spot by bac clone mixing gel .
these data demonstrate that although in most cases filtering out the extra spots helps with the cloning of a particular spot of interest by reducing the complexity of the virtual pattern , for some specific spots in special situations  it is necessary to keep the extra spots in order to identify the spot of interest.
virtual spot identification of genetically mapped loci
as a further test of the general applicability of our vrlgs system, we applied it to the mouse genome for two enzyme combinations where significant genetic mapping of rlgs spots had been previously published.
importantly, these were completely naïve data sets to test the vrlgs application since we had no previously cloned spots to use to derive coefficients for the dna fragment migration formulas.
we used the exact same formulas that were used for the mouse noti-ecorv-hinfi vrlgs profiles, again demonstrating that the accuracy of this system is due to the migration formulas being based on sound principles.
a set of  <dig> rlgs spots from the enzyme combination noti-psti-pvuii that vary between c57bl/6j and dba/2j have been previously mapped using recombinant inbred strains   <cit> .
for the noti-pvuii-psti enzyme combination, a set of  <dig> rlgs loci were mapped in a c57bl/ <dig> × m. spretus interspecific backcross   <cit> .
we aligned the vrlgs profiles for both enzyme combinations with actual gels as shown in figure  <dig>  excellent pattern matching was observed and we attempted to identify a total of  <dig> mapped c57bl/ <dig> spots on the vrlgs profiles.
we identified obvious candidate virtual spots for 60/ <dig> spots mapped in the bxd data  and 140/ <dig> spots mapped with the bss data  for a total of  <dig> c57bl/ <dig> spot candidate virtual sequences.
we then looked at the position of the noti site in the mouse genome for each of the candidate sequences and compared that with the genetic mapping data available for  <dig> of these rlgs spots .
we found that 92% of the virtual rlgs spot candidate sequences were consistent with the genetic mapping bxd data and 98% were consistent with the genetic mapping bss data .
these data provide an independent proof of the accuracy and utility of our vrlgs system and demonstrate that this is not merely due to over fitting of the migration algorithms.
rlgs spot sequence characteristics
through a combination of methods including boundary library mixing gels, bac clone mixing gels, and vrlgs we have identified and confirmed the sequence of  <dig> human noti-ecorv-hinfi spots  and  <dig> mouse noti-ecorv-hinfi spots .
to study the sequence characteristics and genomic context of each of these rlgs spots, we looked at the noti site plus or minus  <dig> bp and asked if this  <dig> bp sequence fell within a cpg island and if the cpg island was within  <dig> kb of the transcriptional start site of a known gene or mrna sequence .
we found that 96% of the human rlgs spots and 87% of the mouse rlgs spots were located in cpg islands.
greater than 90% of these were associated with genes in both genomes and were found at the 5' ends of genes for 76% in the human genome and 87% in the mouse genome.
these data confirm the findings of multiple smaller data sets demonstrating the strong bias of rlgs data to focus on cpg islands in the regulatory regions of genes where the best understood biological consequences of aberrant hypermethylation arise.
CONCLUSIONS
we have presented a novel bioinformatics tool to rapidly identify rlgs spot sequences by creating a virtual rlgs profile to connect the position of spots with genomic sequences.
this system of vrlgs builds upon previous systems by incorporating migration algorithms for both dimension that include consideration of the sequence characteristics of every restriction fragment, as well as the size.
in addition, this vrlgs system predicts which virtual spots will not be present on most normal profiles and can filter these extra spots out, or leave them in the profile if the research being done dictates a need to identify such loci.
the accuracy and utility of this vrlgs system was demonstrated on both the human and mouse genomes and for multiple enzyme combinations, demonstrating the broad applicability of the principals behind the migration algorithms.
although this system is still not able to perfectly mimic an actual gel, the data demonstrate an excellent ability to easily identify the most likely candidate sequence for a spot of interest in most cases.
as more rlgs spot clone information becomes confirmed, the new data can be incorporated into the migration algorithms to improve the overall fitting of the virtual profile to the actual profile.
the reporting of the confirmed identification of  <dig> human rlgs spots and  <dig> mouse rlgs spots provides a valuable tool for researchers using rlgs on human samples or in mouse models.
the characteristics of these identified rlgs spots confirm the value of the rlgs approach to focus aberrant methylation studies on cpg islands.
the vrlgs tool can now be used to identify the top candidate sequences for rlgs spots whose pattern of methylation in cancers is of interest.
confirmation of the accuracy of the prediction allows for rapid identification of novel targets of cpg island hypermethylation.

 restriction landmark genomic scanning has been used to study aberrant cpg island methylation in cancer for more than ten years. this approach remains one of the most reliable ways to characterize cpg island hypermethylation in cancer and has been used both to characterize differences in aberrant methylation phenotypes and also to identify tumor suppressor genes. not only have known tumor suppressor genes like cdkn2a, itga, and igfbp been identified as targets of aberrant methylation in cancer by rlgs, but also novel tumor suppressor genes such as tcf, slc5a, id, bmp3b, and socs have been identified by rlgs. rlgs is a two-dimensional gel electrophoresis method that allows detection of dna methylation if a methylation sensitive landmark enzyme such as noti is used. up to end-labeled landmark sites are displayed in a single rlgs profile. the labeling of the sites is based on incorporation of radionucleotides into the noti half-site by dna polymerase. methylated sites are not digested and are therefore not labeled, thus they do not contribute to the two-dimensional pattern of rlgs fragments. spots present in a normal profile, but absent from a tumor profile are indicative of methylation of the landmark site. furthermore, the profiles are quantitative such that spot intensity directly correlates with the degree of methylation of a locus, with partial methylation representing the cellular heterogeneity of the cancer both among the malignant cells and the associated non-malignant cells. rlgs profiles are highly reproducible allowing for comparison of different tissues or of different individuals. although this approach does not cover every cpg island in the genome, as microarray-based approaches may achieve, rlgs provides highly reliable data due to the molecular simplicity of the assay. unlike microarray-based techniques that depend upon molecularly complex manipulations such as whole genome pcr, hybridization, and immunoprecipitation of methylated dna, rlgs simply relies upon restriction digestion of dna and gel electrophoresis. crucial to the successful implementation of rlgs for a cpg island methylation profiling study is the ability to identify the sequence of the targets of aberrant methylation, which has been a bottleneck in the flow of these types of studies. significant improvements were made in rlgs spot cloning ability with the advent of arrayed boundary libraries, where restriction fragments from the arrayed library were mapped to the pattern of rlgs spots. although highly successful, this method is limited by the tremendous investment in effort required for each genome of interest and each enzyme combination of interest. with the completion of the human and mouse genome sequences, a bioinformatics approach to rlgs fragment identification became possible where each restriction fragment's migration in both dimensions could be predicted to create a virtual rlgs profile. such an approach could be applied to any sequenced genome, using any enzyme combination, and is unaffected by any potential tissue specific methylation. furthermore, such an approach should be far less labor intensive to develop and use than the arrayed boundary library spot cloning method. the relative ease of setup and use, plus the incredible flexibility in the virtual approach, makes such a development highly attractive, allowing any researcher the ability to identify rlgs spots without the creation of specialized reagents. this idea was partially realized in with a first generation version of a virtual rlgs gel that was called "virtual genome scan" and applied to the human genome. in this system, the restriction fragment migration prediction formula was based solely on fragment length. based on known fragment lengths and spot positions in both dimensions of rdna and ebv genome derived spots, a cubic polynomial was derived giving fragment length as a function of spot location. by comparing the known positions of previously identified spots with the positions of their corresponding fragments on the virtual gel, it was reported that 75% of fragments were in a × pixel window around the predicted location, with the remaining fragments in a × pixel window. this approach was used to identify one previously unknown rlgs spot methylated in neuroblastoma and found to be in the 5' end of the alx gene. taking a similar approach, but incorporating the known chromosome of origin of the rlgs spots into the predictions, zardo et al determined previously unknown spot identities, and hong et al determined approximately additional new spot identities. a second virtual rlgs system was reported in and applied to the arabidopsis and mouse genomes. the migration algorithms used were similarly based solely on the length of the dna fragments. in the mouse virtual profile it was observed that there were more spots on the virtual profile than are found on the same region of the actual profile. given that in the mouse genome, unlike the human genome, many of the landmark sites are within repetitive elements like ltrs, repeat masker was able to remove nearly of these extra spots from the virtual profile. this approach was used to identify tissue specifically methylated rlgs spots in mouse and to identify cpg islands that become hypomethylated in mouse es cells when dna methyltransferases are knocked out. with approximately spots on a × cm gel in the human noti-ecorv-hinfi enzyme combination most commonly used, the average distance between neighboring spots is cm. to generate a virtual image that approximates the true image, such that the relative predicted positions of any two neighboring spots does not flip-flop, the window of error for spot prediction should be, at most, half this average distance or cm. one confounding issue is that others have demonstrated a considerable correlation between dna fragment curvature and gel mobility. thus, it is clear that, while a simple fragment migration model based upon fragment length alone is a good start, it must be improved upon. in addition, since in the human genome noti sites rarely cut in repetitive elements, use of a repeat masker does not help to remove the extra spots in a human virtual rlgs profile. we have developed a novel system of virtual rlgs using first and second dimension migration algorithms that use the sequence characteristics of each fragment along with the fragment length to determine spot position. these algorithms were determined based on the identification of rlgs spots in the human noti-ecorv-hinfi profile. in addition, we have defined a filter to remove extra spots from both the human and mouse vrlgs profiles based on gc content. we report greatly improved accuracy of pattern prediction even when the migration algorithms are applied to different enzyme combinations and different genomes from which they were derived. these improvements are combined with a novel graphic interface that allows for visualization of virtual spots that 1) look like real spots, and 2) allow the user to overlap the virtual and real profiles. these changes make the bioinformatics approach to identifying rlgs spots of interest a highly viable and effective alternative to more complex rlgs spot cloning methods.  spot location prediction vrlgs profiles for the human noti-ecorv-hinfi, asci-ecorv-hinfi, and the mouse noti-ecorv-hinfi genome and enzyme combinations were generated as described in the materials and methods. these virtual profiles were then compared to the laboratory gels that serve as our "master profiles" for each. fig 1a contains a sample screen shot of conime. the upper panel shows the overlap of the two gels and is colorized with the laboratory gel contributing blue and the virtual gel contributing red, while areas of overlap appear dark purple. the strongest spots on the laboratory gel arise from the high copy number rdna sequences. some of these spots line up quite well with the virtual spots, but others do not due to polymorphism in subsets of these highly repeated units. the virtual profile does not have polymorphic information for these subsets of the repeats, explaining why some of the darkest spots do not align. the image in fig 1a is zoomed out to show the entire gel, while in fig 1b the image is focused on a specific region of interest. this region of the gel is a portion of section 2d from the master pbl profile previously described. spots and have been identified and confirmed, and their positions on both the actual rlgs gel and the virtual gel are shown. the overlay in the top panel shows how closely the migration algorithms correctly predict the positions of the spots in both dimensions. in order to identify a top candidate sequence for the spot adjacent to with the arrow, the virtual profile is clickable. this clickable window presents information about the restriction fragment mapping to that position in the vrlgs profile, as shown in fig 1b. each vrlgs spot has a unique identifier, giving the chromosome followed by the base position on that chromosome. the unique identifier for each virtual spot is the position of either the noti site or the hinfi site in the 2nd dimension fragment, depending on the orientation of the sequence in the database with the 3' end of the restriction fragment used. therefore, even though a single noti site can potentially generate two spots, each will get a unique identifier with the spot generated by the fragment 5' to the noti site named by the position of the noti site, and the spot generated by the fragment 3' to the noti site named by the position of the hinfi site. additional information concerning the cpg characteristics of the region around the noti site are also included and discussed in more detail below. using sets of confirmed cloned spots for each gel, we computed the distance between the predicted x and y position and each spots' actual x and y positions on the master profiles. we also computed the euclidean distance between the spots' predicted and actual location on the gel. these distances represent the spot prediction error and give an objective measure of the accuracy of the dna fragment migration algorithms in each dimension. table presents the results of the prediction formulas applied to the three gels. we give the median error, the mean error, and the root mean squared error for the x and y dimensions as well as the euclidean prediction errors, the 90'th percentile error, and the root mean square error of the 90'th percentile of spots with the least error. the large improvement in the rms of the 90th percentile spots compared to the entire data set indicates that a relatively few number of outliers are strongly influencing the mean error. importantly, the maximum difference between actual spot position and virtual spot position for the 75th percentile of spots is cm in the 1st dimension and cm in the 2nd dimension. this represents a significant improvement over the maximum error for the 75th percentile of spots of cm and cm previously reported for a virtual rlgs approach. asee materials and methods for descriptions of functions according to the number; ball values in cm; croot mean square; d maximum error of the 90th percentile of spots. e euclidean distance. factors other than length also contribute to migration spot locations in laboratory rlgs gels depend not only upon dna sequence properties, but also upon laboratory conditions. temperature, ionic strength of buffers, gel running times, and the physical handling of rlgs gels all affect the migration of dna fragments. exact spot locations on different gels vary slightly, even if the gels are produced from the same dna specimen, although their relative positions do not change. thus, our prediction formulas must model not only migration distance based on sequence properties, but also the effects of warping and distortion in the gel used to create the master profile. comparing the three formulas based on fragment length there is marked improvement from the logl formula to the cubic to the quintic polynomial. adding a linear curvature term to the cubic and quintic length polynomials improves both formulas, with the quintic polynomial retaining an advantage. replacing curvature by the gc ratio gives a slight, but non-significant, improvement. fragment gc ratio is highly correlated with curvature, suggesting that these terms are interchangeable. computing gc ratio is much simpler than computing curvature and is far preferable. each spot location is based on two fragments, one that migrates in the x-direction and a second that migrates in the y-direction. the x-direction formulas discussed above depend upon the sequence of the first fragment in the pair. the last x-direction formula adds a linear term proportional to the log of the length of the second sequence. this can be viewed as slightly adjusting the x-coordinate based on the y-coordinate of the spot. similarly, the y-direction formulas depend upon the sequence of the second fragment in the pair. the last y-direction formula adds a linear term proportional to the log of the length of the first sequence. the last formula gives the best fit on all three gels. the improvement in prediction formula using the quintic polynomial with gc ratio and the log of the other dimension fragment length can be easily seen by visually comparing computer generated vrlgs gels with laboratory gels. figure shows a region in section 2d of the master human noti-ecorv-hinfi profile and the corresponding regions in virtual profiles generated by various formulas. spots labeled through are previously cloned rlgs spots allowing the correct identification of each of the corresponding virtual spots, as shown in fig the swirl shape seen in the master profile is much more evident in fig 2c and 2d. of particular importance are the relative positions of spots and in fig 2a and 2b, the position of spot is below spots and in the virtual profiles, but in the master profile spot is above spots and this is remedied in fig 2c and 2d where adding in a sequence specific factor corrects for the slower migration of spot finally in fig 2d, although the absolute positions of spots and are not perfect in the virtual gel, their relative spacing is correct within a distance equal to the height of the spots. more broadly, table shows that by using the migration formulas shown in fig 2d, the median error between predicted position and actual position for all cloned spots is cm in the 1st dimension and cm in the 2nd. to address whether these data were due merely to over-fitting of the migration formulas to the spot migration data, we randomly selected half of the cloned spots to be used to derive the migration formulas and measured the accuracy of the other half. we found a median error in the 1st dimension of cm and of in the second dimension, demonstrating that the data shown in table is not due to over-fitting of the data. such improvement over previous virtual rlgs approaches dramatically improves the utility of virtual rlgs for spot identification since the error is less than half the average distance between spots. this makes it much more likely that the pattern of spots relative to each other will be faithfully preserved in the virtual image even if some small error remains in their absolute positions. there are two confounding factors in modeling dna fragment migration in these gels that make using a simple migration model based on the log of the fragment size insufficient. these are the physical imperfection of these gels as described above and the sequence dependent geometry of the dna fragments. we hypothesize that the improvement from the logarithmic to cubic to quintic formulas probably reflects the better ability of the quintic polynomial to model physical gel distortion. the variability in physical gel distortion from one master profile to the other can be accounted for by independent derivation of the coefficients based upon known spots. the improvements seen by adding in the gc ratio factor reflect the sequence dependent influence of dna fragment geometry on fragment migration. although this is an indirect measure of geometry, it performed similarly to the use of the geometry prediction program curva. we believe that this factor is the most difficult to model properly and is the major reason some error remains in these models. the improvement in the final formula, adding in the log of the length of the other dimension fragment, reflects the better ability of this formula to model physical gel distortion and possibly the influence of dna fragment geometry. the fact that vertical migration in the y-direction depends somewhat on the size of the horizontal fragment is not so surprising since vertical migration follows horizontal migration. more remarkable is that the x-location seems to depend upon the size of the vertical fragment, even though horizontal migration precedes vertical migration. we hypothesize that vertical fragments do not necessarily migrate in a perfect vertical line through a gel, but actually move slightly along the x-direction, explaining why the x-location would depend upon the vertical fragment size. filtering extra spots from the vrlgs rlgs is typically applied using a methylation sensitive landmark enzyme such as noti or asci, with the goal of identifying methylation differences at cpg islands between state a and state b. upon close examination of the vrlgs profile it became clear that there were significantly more virtual spots than real spots. this can be explained by the fact that certain noti sites are methylated, even in normal tissues, and this may be tissue specific. the presence of these extra spots on the virtual profile makes the pattern correlations with the actual rlgs profile much more difficult to discern. we investigated whether a difference in the sequence surrounding the noti sites could be found between real rlgs spots and extra spots seen only on the virtual profile. we identified extra spots from the virtual profile in regions of the gel where most of the actual rlgs spots had already been identified. we compared the sequence of the noti site +/- bp from the extra virtual spots to cloned and confirmed actual rlgs spots. figure shows the distributions of gc%, observed over expected cpg ratio, and cpg count in the extra virtual spots and the actual rlgs spots. we tested various combinations of these three factors that could best distinguish the two groups in order to maximize the number of extra spots identified, yet minimize the number of actual spots identified. we found that if we filtered out noti sites whose surrounding bp of sequence had less than 50% gc, a cpg ratio of less than, and a cpg count of less than we could eliminate 46% of our extra virtual spots, yet retain 96% of our actual spots. the results of this filtering can be seen in figure 4a and 4b. in each, the lower left is the master rlgs profile zoomed in to section 3d, with the corresponding region of the virtual gel shown in the right panel. the numbered spots are all identified and confirmed and are therefore noted on the virtual profile. spot a represents a spot for which identification of a good candidate sequence is extremely difficult because of extra spots seen on the virtual profile complicating the virtual pattern near spot a. however, when we applied the filter described above to remove all extra spots based on the sequence environment of the noti site, the best candidate sequence for spot a became immediately obvious. we note that the filtering criteria identified are similar to criteria used to define cpg islands. we find that noti sites in regions that look less like cpg islands are more likely to be extra spots. this is consistent with the idea that cpg islands are protected from methylation in normal cells in most cases but cpg dinucleotides outside cpg islands are highly methylated, and also explains why even though only 89% of noti sites in the human genome reside in cpg islands, 96% of identified rlgs spots are in cpg islands. interestingly, as described below and shown in figure it is often true that rlgs loci found to exhibit tissue specific methylation arise from noti sites that we would filter out, and are therefore less "cpg island-like." it was recently found that there are many regions of hypomethylation in mouse testis dna compared to dna in somatic tissues and that these chromosomal regions are defined by lower gc content. these observations suggest that there may be a relationship between the potential of a locus to exhibit tissue specific methylation and the "cpg island-like" characteristics of the sequence. efficacy of vrlgs for novel spot identification using the human noti-ecorv-hinfi vrlgs profile produced with the migration algorithms derived from spots cloned from the library mixing gel approach and incorporating dna curvature and gc ratio, we attempted to identify unknown rlgs spots. we identified a top candidate sequence for each spot and designed pcr primers that fall within the 2nd dimension fragment. for spots, we chose candidate sequences to test because one candidate appeared only slightly better than the other. we then used the primers for the candidate sequence to amplify dna eluted from the spot in question, as well as a nearby spot. accurate prediction of a spot sequence was determined if a strong pcr product was obtained when that spot was used as template, but no product or a very weak product was detected using a nearby spot as template. application of vrlgs to other genomes and enzyme combinations we have demonstrated excellent utility and pattern correlation of the vrlgs profile with the human noti-ecorv-hinfi enzyme combination from which the migration algorithms and extra spot filtering were derived. to address whether these improvements to vrlgs were based on sound principles or were simply due to over fitting of the data, we applied the same migration factors to create a human asci-ecorv-hinfi vrlgs profile. in addition we created vrlgs profiles for the mouse genome using the noti-ecorv-hinfi. we used cloned spots as anchor points for each and calculated the formula coefficients as described in the materials and methods. table shows the error of the prediction formulas for the cloned spots in the two human profiles and the noti-ecorv-hinfi mouse profile with and spots cloned, respectively. these data demonstrate that the migration algorithms developed for the human noti-ecorv-hinfi profile incorporating fragment length as well as information about the gc content of each fragment are based on sound principles and can be broadly applied to different enzyme combinations and genomes. as a further test of the utility of the vrlgs system in the mouse genome, we used vrlgs to identify the sequence of spots from the mouse noti-ecorv-hinfi master profile. in addition, turning on or off the filtering out of extra spots can allow for use of vrlgs to identify tissue specific methylation spots. figure shows section 2f from the mouse noti-ecorv-hinfi profile with spots and previously identified and the indicated spot not present in the master profile or in a mouse liver profile. this spot was, however, found on a mouse intestine profile. when using the vrlgs profile where we filtered out extra spots, the intestine specific spot of interest was not present on the virtual profile, however, when we did not filter out the extra spots, the virtual spot was present. the third panel of fig 5b shows the composite image of the vrlgs profile with the extra spots retained aligned with the master profile and the candidate virtual spot for the intestine specific spot of interest indicated. we confirmed the correct identification of this intestine specific spot by bac clone mixing gel. these data demonstrate that although in most cases filtering out the extra spots helps with the cloning of a particular spot of interest by reducing the complexity of the virtual pattern, for some specific spots in special situations it is necessary to keep the extra spots in order to identify the spot of interest. virtual spot identification of genetically mapped loci as a further test of the general applicability of our vrlgs system, we applied it to the mouse genome for two enzyme combinations where significant genetic mapping of rlgs spots had been previously published. importantly, these were completely naïve data sets to test the vrlgs application since we had no previously cloned spots to use to derive coefficients for the dna fragment migration formulas. we used the exact same formulas that were used for the mouse noti-ecorv-hinfi vrlgs profiles, again demonstrating that the accuracy of this system is due to the migration formulas being based on sound principles. a set of rlgs spots from the enzyme combination noti-psti-pvuii that vary between c57bl/6j and dba/2j have been previously mapped using recombinant inbred strains. for the noti-pvuii-psti enzyme combination, a set of rlgs loci were mapped in a c57bl/ × m. spretus interspecific backcross. we aligned the vrlgs profiles for both enzyme combinations with actual gels as shown in figure excellent pattern matching was observed and we attempted to identify a total of mapped c57bl/ spots on the vrlgs profiles. we identified obvious candidate virtual spots for 60/ spots mapped in the bxd data and 140/ spots mapped with the bss data for a total of c57bl/ spot candidate virtual sequences. we then looked at the position of the noti site in the mouse genome for each of the candidate sequences and compared that with the genetic mapping data available for of these rlgs spots. we found that 92% of the virtual rlgs spot candidate sequences were consistent with the genetic mapping bxd data and 98% were consistent with the genetic mapping bss data. these data provide an independent proof of the accuracy and utility of our vrlgs system and demonstrate that this is not merely due to over fitting of the migration algorithms. rlgs spot sequence characteristics through a combination of methods including boundary library mixing gels, bac clone mixing gels, and vrlgs we have identified and confirmed the sequence of human noti-ecorv-hinfi spots and mouse noti-ecorv-hinfi spots. to study the sequence characteristics and genomic context of each of these rlgs spots, we looked at the noti site plus or minus bp and asked if this bp sequence fell within a cpg island and if the cpg island was within kb of the transcriptional start site of a known gene or mrna sequence. we found that 96% of the human rlgs spots and 87% of the mouse rlgs spots were located in cpg islands. greater than 90% of these were associated with genes in both genomes and were found at the 5' ends of genes for 76% in the human genome and 87% in the mouse genome. these data confirm the findings of multiple smaller data sets demonstrating the strong bias of rlgs data to focus on cpg islands in the regulatory regions of genes where the best understood biological consequences of aberrant hypermethylation arise.  we have presented a novel bioinformatics tool to rapidly identify rlgs spot sequences by creating a virtual rlgs profile to connect the position of spots with genomic sequences. this system of vrlgs builds upon previous systems by incorporating migration algorithms for both dimension that include consideration of the sequence characteristics of every restriction fragment, as well as the size. in addition, this vrlgs system predicts which virtual spots will not be present on most normal profiles and can filter these extra spots out, or leave them in the profile if the research being done dictates a need to identify such loci. the accuracy and utility of this vrlgs system was demonstrated on both the human and mouse genomes and for multiple enzyme combinations, demonstrating the broad applicability of the principals behind the migration algorithms. although this system is still not able to perfectly mimic an actual gel, the data demonstrate an excellent ability to easily identify the most likely candidate sequence for a spot of interest in most cases. as more rlgs spot clone information becomes confirmed, the new data can be incorporated into the migration algorithms to improve the overall fitting of the virtual profile to the actual profile. the reporting of the confirmed identification of human rlgs spots and mouse rlgs spots provides a valuable tool for researchers using rlgs on human samples or in mouse models. the characteristics of these identified rlgs spots confirm the value of the rlgs approach to focus aberrant methylation studies on cpg islands. the vrlgs tool can now be used to identify the top candidate sequences for rlgs spots whose pattern of methylation in cancers is of interest. confirmation of the accuracy of the prediction allows for rapid identification of novel targets of cpg island hypermethylation. 
restriction landmark genomic scanning is one of the most successfully applied methods for the identification of aberrant cpg island hypermethylation in cancer, as well as the identification of tissue specific methylation of cpg islands. we demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation. we report the confirmed identification of human and mouse rlgs spots for the most commonly used enzyme combinations. however, a limitation to the utility of this method has been the ability to assign specific genomic sequences to rlgs spots, a process commonly referred to as "rlgs spot cloning." the new vrlgs system reported here is highly robust for the identification of novel rlgs spots. the identification of hundreds of mouse and human rlgs spot loci confirms the strong bias of rlgs studies to focus on cpg islands and provides a valuable resource to rapidly study their methylation. we report the development of a virtual rlgs method that allows for rlgs spot identification in any sequenced genome and with any enzyme combination. the migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. we report significant improvements in predicting dna fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median euclidian distance between actual and predicted spot migration of centimeters for the most complex human rlgs pattern. we also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes.
 restriction landmark genomic scanning is one of the most successfully applied methods for the identification of aberrant cpg island hypermethylation in cancer, as well as the identification of tissue specific methylation of cpg islands. however, a limitation to the utility of this method has been the ability to assign specific genomic sequences to rlgs spots, a process commonly referred to as "rlgs spot cloning."  we report the development of a virtual rlgs method that allows for rlgs spot identification in any sequenced genome and with any enzyme combination. we report significant improvements in predicting dna fragment migration patterns by incorporating sequence information into the migration models, and demonstrate a median euclidian distance between actual and predicted spot migration of centimeters for the most complex human rlgs pattern. we report the confirmed identification of human and mouse rlgs spots for the most commonly used enzyme combinations. we also developed a method to filter the virtual spots to reduce the number of extra spots seen on a virtual profile for both the mouse and human genomes. we demonstrate use of this filter to simplify spot cloning and to assist in the identification of spots exhibiting tissue-specific methylation.  the new vrlgs system reported here is highly robust for the identification of novel rlgs spots. the migration models developed are not specific to the genome being studied or the enzyme combination being used, making this tool broadly applicable. the identification of hundreds of mouse and human rlgs spot loci confirms the strong bias of rlgs studies to focus on cpg islands and provides a valuable resource to rapidly study their methylation. 