BACKGROUND
tiling-arrays are used for the identification of specific genomic dna regions that can be enriched using various procedures to study certain molecular biological features.
for example, dna fragments that are bound by a protein of interest, e.g., a transcription factor, can be enriched by using chromatin immunoprecipitation .
when these enriched fragments are hybridized to an array, a genome wide protein binding profile can be obtained that is associated with this particular protein of interest in the cell type that was studied .
other applications of tiling-arrays  <cit>  are: methylated-dna immunoprecipitation , transcriptome mapping  <cit> , recognition of hypersensitive sites such as segments of open chromatin that are cleaved more readily by dnasei , or identification of copy number variations or breakpoints .
the use of tiling-arrays to detect enriched dna regions has several advantages such as i) high sensitivity, which allows the detection of small dna fragments associating with rare molecules and, ii) high probe-resolution, which results in accurate acquisition of unbiased data.
a tiling-array is an array of short dna fragments, which represent 'probes' that cover the entire genome, or contigs of the genome.
the hybridization of labeled dna to an array , will produce a quantitative signal intensity for each probe.
multiple contiguous probes with increased signal intensity across a particular genomic region, is a putative region-of-interest, and suggests the presence of a protein binding site.
as there are no standard criteria to accurately define a region-of-interest, a major challenge in the analysis of tiling-array data is to define such a region, and discriminate a positive signal from non-specific signals  <cit> .
defining regions-of-interest requires intensity thresholds on continuous probe intensity levels.
following this, the decision of the number of consecutive probes above the threshold needs to be made before a region-of-interest is called.
this threshold, and the number of probes above the threshold, directly influence the size of the region-of-interest that can be detected.
as biologically relevant regions may vary in intensity, employing a single threshold is insufficient.
additionally, as the probe-resolution varies across the genome, and across different tiling-array platforms, choosing a fixed number of consecutive probes as a region-of-interest is also inadequate.
various methods have been developed to detect regions-of-interest in chip-on-chip data such as welch t-test, hmm, tilemap, mat, mixture model approach, cmarrt, starr and ringo  <cit> .
mat   <cit>  is one of the most cited methods for analyzing chip-on-chip data and it has been shown to outperform welch t-test, hmm and tilemap  <cit> .
mat uses various user-defined parameters to model a region-of-interest, such as maximum bandwidth, maximum gap size between probes, the minimum number of probes in a region and the use of a fixed threshold.
a major limitation of this method is that it assumes a uniform probe-resolution across the genome, and depends on many user-defined parameters.
here, we propose a statistical framework  to identify regions-of-interest in tiling-array data.
hat has several advantages over previous methods including mat: i) as there is no single cut-off level for probe-intensity, hat can detect regions-of-interest for a large number of thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome and across tiling-array platforms and iv) it employs only a single user defined parameter: the significance level.
hat can be seen as a generalization of the transcript discovery approach used in bertone et al  <cit> .
a detailed description of our framework  can be found in the method section.
briefly, instead of a single probe-intensity cut-off level, hat evaluates a large number of thresholds.
each threshold transforms the continuous signal intensity levels into discrete calls for each probe; referred to as positive probes where the probe intensity exceeds the threshold, and negative probes where it does not.
in order to define regions-of-interest, all probes within the window of each positive probe are evaluated and the p-value is defined based on the ratio of both positive and negative probes using the hypergeometric distribution.
to detect regions-of-interest of any size, the width of the window is also varied across all relevant window widths, where a relevant window is defined by the expected fragment size in the experimental procedure .
the resulting regions-of-interest for each setting of the threshold and each window width are combined by taking the union of the significant window positions.
the family wise error  is controlled by employing a bonferroni correction.
we have used two datasets using promoter tiling-arrays to evaluate hat.
in the first assay, tiling-array data was employed to identify genome-wide dna binding profiles of the transcription factor cebpa, in a cell line model.
using these data, we have shown that although hat detected fewer regions-of-interest than mat, the detected regions are more highly enriched for cebp binding motifs, and include known cebpa target genes.
in the second experiment, a retroviral insertional mutagenesis assay, hat identified novel putative transforming loci that may play a role in tumor development.
two of these loci were subsequently validated using pcr.
hat can also detect and compare regions-of-interest across multiple samples.
each sample is analyzed independently, but when multiple samples within one experiment are used, detected regions-of-interest at the same genomic location among different samples are combined into 'common regions-of-interest', thereby increasing the confidence.
in addition, hat can incorporate sequence information for the detection of pre-defined sequences .
these are highlighted in the graphical output for every detected region-of-interest and indicated in the output file.
RESULTS
data
two distinct experimental datasets were used in this study: chip-on-chip data derived from an inducible cebpa expressing myeloid cell line model and data obtained from genomic dna from retrovirus induced murine leukemias.
data were generated using the affymetrix genechip mouse promoter  <dig>  array.
this chip generates  <dig>  million perfect match probes over  <dig> mouse promoter regions.
promoter regions cover  <dig> kb upstream to  <dig>  kb downstream of 5' transcription start sites.
each probe has a size of  <dig> nt.
detection of regions-of-interest for cebpa chromatin immunoprecipitation by applying hat
to compare different methods and to analyze the promoter array data, we made use of a dataset that was obtained from a chip of beta-estradiol induced cebpa in a myeloid cell line, 32d, followed by promoter array hybridizations.
the data were used to examine the validity of detected regions-of-interest in two ways: i) at the 'ccaat' binding level; cebpa interacts with the nucleotide sequence 'ccaat' within the promoter regions represented on the chip, therefore cebp binding motifs are expected to be enriched, and ii) at the gene level; examination of the presence of known cebpa target genes, by taking the genes flanking the detected region-of-interest into account.
furthermore, one selected region-of-interest was validated by real time quantitative pcr .
the experimental setup was as follows: clones were derived from a myeloid cell line model , that expresses either beta-estradiol inducible cebpa-er  or control er .
chromatin immunoprecipitations were carried out using an antibody directed against er in the beta-estradiol treated cells and the dna obtained from these cells, after immunoprecipitation, was hybridized to affymetrix promoter chips.
for method comparison we used model-based analysis of tiling-arrays for chip-chip , with the default parameters for the detection of regions-of-interest .
the default settings agree with the average sonicated fragment sizes, being  <dig> bp, and the distance between two consecutive probes being approximately  <dig> bp.
using the default criteria in mat,  <dig> unique regions-of-interest were detected in at least one of the 32d-cebpa-er clones  and absent in control samples 32d-er .
using hat, the same significance level and maximum fragment size  were chosen to detect statistically significant regions-of-interest.
applying these parameters,  <dig> statistically significant regions-of-interest were detected in any of the 32d-cebpa-er clones; 80%  of these regions were detected in two or more clones .
this corresponds to  <dig> unique chromosomal regions-of-interest.
hat detected approximately one fifth of the regions-of-interest in comparison with mat for the same significance level, and  <dig> %  of these unique detected regions in hat overlapped with the regions detected by mat .
to investigate the validity of these detected regions-of-interest  on the sequence level, a motif enrichment analysis was performed.
this was carried out using the cis-regulatory element annotation system , where a p-value is computed for each known motif, and the motifs that are significantly enriched in the regions-of-interest are reported.
the top  <dig> enriched motifs are indicated in table  <dig> for both methods.
these data showed that hat detects regions that are highly enriched for the cebp motif binding sites, whereas mat does not show a clear enrichment for these sites.
note that the detected regions-of-interest by hat, are a subset of mat.
the top  <dig> motifs enriched in the detected regions-of-interest  by hat and mat for the cebpa-study .
among the top  <dig> motifs enriched in the regions-of-interest detected with hat, seven contained the cebp binding motif whereas for mat, only one contained the cebp binding motif.
for each reported motif, the number of hits within the regions-of-interest are counted, their fold change computed, and the p-value derived using the binomial test.
to investigate detected regions-of-interest based on their flanking genes, regions-of-interest were mapped to the closest 5' transcriptional start site of a gene.
mapping is applied on the forward and reverse dna strands, with a maximum distance of  <dig> kb up- and down-stream .
this resulted in  <dig> unique genes for the  <dig> unique detected regions-of-interest using hat .
these mouse genes were subsequently overlayed with  <dig> known homologous human cebpa target genes , demonstrating that  <dig> cebpa target genes being detected by hat  and  <dig> by mat .
note that mat has detected approximately five times more regions-of-interest  resulting in  <dig> unique genes .
some of the detected cebpa target genes have previously been described, such as: myc, hp, mpo and il6ra  <cit> .
enrichment of the il- <dig> receptor alpha  transcriptional start site  was subsequently validated by qpcr.
an alternative comparison can be performed using the number of regions-of-interest, instead of the significance level.
for hat;  <dig> unique regions-of-interest were detected with a significance level α =  <dig> × 10- <dig>  to gain approximately the same number of regions-of-interest using mat, we would need to set the α level at  <dig> × 10- <dig>  resulting in  <dig> regions-of-interest.
the regions-of-interest detected by hat showed 84%  overlap with mat whereas the overlap of detected regions of mat with hat was 83% .
both methods show a high enrichment for the cebp binding motifs.
comparing the detected regions-of-interest with respect to mat , we need to set the α level higher than  <dig>  in hat, but this may compromise the reliability of detected regions-of-interest.
for this reason, we have set the α level at  <dig>  and hereby detected  <dig> unique regions-of-interest.
these were highly enriched for cebp binding motifs based on the motif enrichment analysis , whereas the detected regions-of-interest by mat were not highly enriched for cebp binding motifs .
the regions-of-interest detected by hat showed 98%  overlap with mat whereas the overlap of detected regions of mat with hat was 39% .
the top  <dig> motifs enriched in the  <dig> detected regions-of-interest using hat  in the cebpa-study.
there is a high enrichment for binding motif cebp.
for each reported motif, the number of hits within the regions-of-interest are counted, their fold change computed, and the p-value derived using the binomial test.
in addition, the hat and mat results were also compared with the detected regions of starr  <cit> .
starr implements the cmarrt algorithm  <cit>  and thereby incorporates the correlation structure for the identification of regions-of-interest in tiling-array data.
for the detection of regions-of-interest, we have utilized similar parameter settings  as used in hat and mat.
using these parameter settings, starr detected  <dig> regions-of-interest and showed high enrichment for cebp binding motifs .
following this, we have examined the overlap of regions-of-interest detected by all methods as depicted in figure  <dig>  all regions-of-interest detected by hat  were also detected by mat alone or together with starr .
note that the number of overlapping regions can contain multiple regions-of-interest detected by a single method.
to asses the validity of the detected regions-of-interest by hat, starr and mat, we have examined the enrichment for cebp binding motifs for the different parts in the venn diagram, depicted as different colors in figure  <dig> .
high enrichment for cebp motifs are found for; i) the overlap of hat with the other two methods , ii) the overlap of hat with mat  and, iii) the overlap between starr and mat .
no significant enriched motifs are found in the regions detected only by starr  and limited motifs are enriched for cebp in the regions detected only by mat .
therefore we can conclude that hat had the highest specificity as it was able to detect regions-of-interest highly enriched for cebp binding motifs.
detection of retroviral insertion sites by hat
retroviral integration mutagenesis  in mice is a powerful tool to identify new genes playing an important role in oncogenesis.
mice are injected with retroviruses that potentially integrate into the murine genome upon infection.
viral integration can lead to gene deregulation, and depending on the genes affected, tumors may develop.
genes located proximal to viral integration sites are potentially oncogenic, leading to tumor development.
genomic regions that have been targeted by proviral dna in multiple tumors are called common viral integration sites , and are likely driving tumor development.
using retroviral insertional mutagenesis, many oncogenes have been identified using large sequencing screens in multiple tumors  <cit> .
we hypothesise that within tumors, genes may be silenced as a result of proviral integration caused by hypermethylation of the cpgs in the viral long terminal repeat, and subsequently in the promoters of their target genes.
the identification of methylated genes by means of retroviral insertional mutagenesis may be studied by methyl-dna immunopreciptitation , followed by inverse pcr, using long terminal repeat  specific primers.
after combining these two technologies, we hybridized samples to affymetrix promoter chips to identify genomic locations involved in viral integration that potentially harbour new tumor suppressor genes .
regions-of-interest within this dataset differ from the cebpa-study as they have; i) a higher variability in fragment sizes and, ii) contain specific sequences within the identified regions.
therefore these data are used to examine the performance and broad applicability of hat among different applications of tiling-array data.
using hat, we have identified candidate tsgs in mouse tumors by considering regions with a maximum fragment size of  <dig> bp and a significance level α =  <dig> .
with these parameters, we detected  <dig> methylated viral integration sites ; of which one appeared to be a common methylated vis  among two samples .
besides the detection of candidate regions based on a statistical framework, we have attached additional mouse genomic sequence information  to the model, in order to determine the sequence-of-interest based on the restriction enzyme used in the inverse pcr.
within this assay, a restriction enzyme  will cleave dna at sequence 'gatc', within the integrated viral sequence and the flanking genome.
note that because of this property, it is expected that every detected region must contain a nearby restriction site, which can easily be verified with hat.
hat showed that all detected mviss contain a nearby restriction site, conforming specificity of the identified region as being a viral insertion site.
for pcr validation of the method, two mviss were selected based on their location to a nearby 5' transcriptional start site, and confirmed.
one of the validated regions is illustrated in figure  <dig>
extended applications of hat
the scope of this method is not limited to the presented studies .
moreover, we have successfully applied hat for the detection of regions enriched for histone modifications such as, trimethylation of histone  <dig> at lysine  <dig> or lysine  <dig>  .
some of the detected regions-of-interest were selected for further validation and confirmed by qpcr.
regarding tiling-array data spanning the entire genome  <cit>  , we do not expect changes in algorithm performance  due to an increased variability in hybridization consistency because the applied normalization method  <cit>  corrects for two major causes of differences in hybridization consistency, i.e., probe sequence and presence of repeats within the genome.
furthermore, in addition to one-color arrays  we envision that hat can also be applied on data stemming from two-color arrays , because data structure remains similar.
we stress however that the normalization procedure is an important step and strongly depends on the type of tiling-array dataset.
CONCLUSIONS
here we propose a statistical framework; hat  to analyze tiling-array data.
we showed that the method is robust and has increased specificity in the detection of regions-of-interest in comparison with two alternative methods.
this is achieved by computing the hypergeometric-probability for every detected region-of-interest, among different threshold levels of probe-intensities and window sizes, while keeping control of the family wise error  by employing bonferroni correction.
besides the detection of regions-of-interest, hat also determines sequences-of-interest, flanking genes and the distances to 5' transcriptional start sites on both dna strands.
we describe the performance of hat, when applied to different experimental tiling-array datasets.
for each experimental dataset, the selected downstream genes flanking the detected regions-of-interest were successfully confirmed by pcr.
we compared the detected regions-of-interest of hat with two other methods , and showed that hat resulted in a reduced number of detected regions-of-interest using the same significance for both mat and starr.
however, using motif enrichment analysis we showed that the regions-of-interest detected by hat were more enriched for the expected binding motifs of cebp compared to mat and showed similar enrichment for starr, illustrating increased specificity using hat.
besides analyzing chip-on-chip data, hat is also suitable for the analysis of other types of tiling-array data.
applying hat to the data from the medip inverse-pcr and promoter-genechip hybridization experiment, we discovered mviss and cmvis that are subject to dna methylation and identified the genes  that flank these methylated viral integration sites .
hat is applicable to detect regions-of-interest among the different applications of tiling-arrays, and has the advantage of being independent for thresholds, number of probes in a region and probe-resolution.
it does not depend on setting various user defined parameters, except for the significance level and an optional maximum fragment size.

 tiling-arrays are used for the identification of specific genomic dna regions that can be enriched using various procedures to study certain molecular biological features. for example, dna fragments that are bound by a protein of interest, e.g., a transcription factor, can be enriched by using chromatin immunoprecipitation. when these enriched fragments are hybridized to an array, a genome wide protein binding profile can be obtained that is associated with this particular protein of interest in the cell type that was studied. other applications of tiling-arrays are: methylated-dna immunoprecipitation, transcriptome mapping, recognition of hypersensitive sites such as segments of open chromatin that are cleaved more readily by dnasei, or identification of copy number variations or breakpoints. the use of tiling-arrays to detect enriched dna regions has several advantages such as i) high sensitivity, which allows the detection of small dna fragments associating with rare molecules and, ii) high probe-resolution, which results in accurate acquisition of unbiased data. a tiling-array is an array of short dna fragments, which represent'probes' that cover the entire genome, or contigs of the genome. the hybridization of labeled dna to an array, will produce a quantitative signal intensity for each probe. multiple contiguous probes with increased signal intensity across a particular genomic region, is a putative region-of-interest, and suggests the presence of a protein binding site. as there are no standard criteria to accurately define a region-of-interest, a major challenge in the analysis of tiling-array data is to define such a region, and discriminate a positive signal from non-specific signals. defining regions-of-interest requires intensity thresholds on continuous probe intensity levels. following this, the decision of the number of consecutive probes above the threshold needs to be made before a region-of-interest is called. this threshold, and the number of probes above the threshold, directly influence the size of the region-of-interest that can be detected. as biologically relevant regions may vary in intensity, employing a single threshold is insufficient. additionally, as the probe-resolution varies across the genome, and across different tiling-array platforms, choosing a fixed number of consecutive probes as a region-of-interest is also inadequate. various methods have been developed to detect regions-of-interest in chip-on-chip data such as welch t-test, hmm, tilemap, mat, mixture model approach, cmarrt, starr and ringo. mat is one of the most cited methods for analyzing chip-on-chip data and it has been shown to outperform welch t-test, hmm and tilemap. mat uses various user-defined parameters to model a region-of-interest, such as maximum bandwidth, maximum gap size between probes, the minimum number of probes in a region and the use of a fixed threshold. a major limitation of this method is that it assumes a uniform probe-resolution across the genome, and depends on many user-defined parameters. here, we propose a statistical framework to identify regions-of-interest in tiling-array data. hat has several advantages over previous methods including mat: i) as there is no single cut-off level for probe-intensity, hat can detect regions-of-interest for a large number of thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome and across tiling-array platforms and iv) it employs only a single user defined parameter: the significance level. hat can be seen as a generalization of the transcript discovery approach used in bertone et al. a detailed description of our framework can be found in the method section. briefly, instead of a single probe-intensity cut-off level, hat evaluates a large number of thresholds. each threshold transforms the continuous signal intensity levels into discrete calls for each probe; referred to as positive probes where the probe intensity exceeds the threshold, and negative probes where it does not. in order to define regions-of-interest, all probes within the window of each positive probe are evaluated and the p-value is defined based on the ratio of both positive and negative probes using the hypergeometric distribution. to detect regions-of-interest of any size, the width of the window is also varied across all relevant window widths, where a relevant window is defined by the expected fragment size in the experimental procedure. the resulting regions-of-interest for each setting of the threshold and each window width are combined by taking the union of the significant window positions. the family wise error is controlled by employing a bonferroni correction. we have used two datasets using promoter tiling-arrays to evaluate hat. in the first assay, tiling-array data was employed to identify genome-wide dna binding profiles of the transcription factor cebpa, in a cell line model. using these data, we have shown that although hat detected fewer regions-of-interest than mat, the detected regions are more highly enriched for cebp binding motifs, and include known cebpa target genes. in the second experiment, a retroviral insertional mutagenesis assay, hat identified novel putative transforming loci that may play a role in tumor development. two of these loci were subsequently validated using pcr. hat can also detect and compare regions-of-interest across multiple samples. each sample is analyzed independently, but when multiple samples within one experiment are used, detected regions-of-interest at the same genomic location among different samples are combined into'common regions-of-interest', thereby increasing the confidence. in addition, hat can incorporate sequence information for the detection of pre-defined sequences. these are highlighted in the graphical output for every detected region-of-interest and indicated in the output file.  data two distinct experimental datasets were used in this study: chip-on-chip data derived from an inducible cebpa expressing myeloid cell line model and data obtained from genomic dna from retrovirus induced murine leukemias. data were generated using the affymetrix genechip mouse promoter array. this chip generates million perfect match probes over mouse promoter regions. promoter regions cover kb upstream to kb downstream of 5' transcription start sites. each probe has a size of nt. detection of regions-of-interest for cebpa chromatin immunoprecipitation by applying hat to compare different methods and to analyze the promoter array data, we made use of a dataset that was obtained from a chip of beta-estradiol induced cebpa in a myeloid cell line, 32d, followed by promoter array hybridizations. the data were used to examine the validity of detected regions-of-interest in two ways: i) at the'ccaat' binding level; cebpa interacts with the nucleotide sequence'ccaat' within the promoter regions represented on the chip, therefore cebp binding motifs are expected to be enriched, and ii) at the gene level; examination of the presence of known cebpa target genes, by taking the genes flanking the detected region-of-interest into account. furthermore, one selected region-of-interest was validated by real time quantitative pcr. the experimental setup was as follows: clones were derived from a myeloid cell line model, that expresses either beta-estradiol inducible cebpa-er or control er. chromatin immunoprecipitations were carried out using an antibody directed against er in the beta-estradiol treated cells and the dna obtained from these cells, after immunoprecipitation, was hybridized to affymetrix promoter chips. for method comparison we used model-based analysis of tiling-arrays for chip-chip, with the default parameters for the detection of regions-of-interest. the default settings agree with the average sonicated fragment sizes, being bp, and the distance between two consecutive probes being approximately bp. using the default criteria in mat, unique regions-of-interest were detected in at least one of the 32d-cebpa-er clones and absent in control samples 32d-er. using hat, the same significance level and maximum fragment size were chosen to detect statistically significant regions-of-interest. applying these parameters, statistically significant regions-of-interest were detected in any of the 32d-cebpa-er clones; 80% of these regions were detected in two or more clones. this corresponds to unique chromosomal regions-of-interest. hat detected approximately one fifth of the regions-of-interest in comparison with mat for the same significance level, and % of these unique detected regions in hat overlapped with the regions detected by mat. to investigate the validity of these detected regions-of-interest on the sequence level, a motif enrichment analysis was performed. this was carried out using the cis-regulatory element annotation system, where a p-value is computed for each known motif, and the motifs that are significantly enriched in the regions-of-interest are reported. the top enriched motifs are indicated in table for both methods. these data showed that hat detects regions that are highly enriched for the cebp motif binding sites, whereas mat does not show a clear enrichment for these sites. note that the detected regions-of-interest by hat, are a subset of mat. the top motifs enriched in the detected regions-of-interest by hat and mat for the cebpa-study. among the top motifs enriched in the regions-of-interest detected with hat, seven contained the cebp binding motif whereas for mat, only one contained the cebp binding motif. for each reported motif, the number of hits within the regions-of-interest are counted, their fold change computed, and the p-value derived using the binomial test. to investigate detected regions-of-interest based on their flanking genes, regions-of-interest were mapped to the closest 5' transcriptional start site of a gene. mapping is applied on the forward and reverse dna strands, with a maximum distance of kb up- and down-stream. this resulted in unique genes for the unique detected regions-of-interest using hat. these mouse genes were subsequently overlayed with known homologous human cebpa target genes, demonstrating that cebpa target genes being detected by hat and by mat. note that mat has detected approximately five times more regions-of-interest resulting in unique genes. some of the detected cebpa target genes have previously been described, such as: myc, hp, mpo and il6ra. enrichment of the il- receptor alpha transcriptional start site was subsequently validated by qpcr. an alternative comparison can be performed using the number of regions-of-interest, instead of the significance level. for hat; unique regions-of-interest were detected with a significance level α = × 10- to gain approximately the same number of regions-of-interest using mat, we would need to set the α level at × 10- resulting in regions-of-interest. the regions-of-interest detected by hat showed 84% overlap with mat whereas the overlap of detected regions of mat with hat was 83%. both methods show a high enrichment for the cebp binding motifs. comparing the detected regions-of-interest with respect to mat, we need to set the α level higher than in hat, but this may compromise the reliability of detected regions-of-interest. for this reason, we have set the α level at and hereby detected unique regions-of-interest. these were highly enriched for cebp binding motifs based on the motif enrichment analysis, whereas the detected regions-of-interest by mat were not highly enriched for cebp binding motifs. the regions-of-interest detected by hat showed 98% overlap with mat whereas the overlap of detected regions of mat with hat was 39%. the top motifs enriched in the detected regions-of-interest using hat in the cebpa-study. there is a high enrichment for binding motif cebp. for each reported motif, the number of hits within the regions-of-interest are counted, their fold change computed, and the p-value derived using the binomial test. in addition, the hat and mat results were also compared with the detected regions of starr. starr implements the cmarrt algorithm and thereby incorporates the correlation structure for the identification of regions-of-interest in tiling-array data. for the detection of regions-of-interest, we have utilized similar parameter settings as used in hat and mat. using these parameter settings, starr detected regions-of-interest and showed high enrichment for cebp binding motifs. following this, we have examined the overlap of regions-of-interest detected by all methods as depicted in figure all regions-of-interest detected by hat were also detected by mat alone or together with starr. note that the number of overlapping regions can contain multiple regions-of-interest detected by a single method. to asses the validity of the detected regions-of-interest by hat, starr and mat, we have examined the enrichment for cebp binding motifs for the different parts in the venn diagram, depicted as different colors in figure. high enrichment for cebp motifs are found for; i) the overlap of hat with the other two methods, ii) the overlap of hat with mat and, iii) the overlap between starr and mat. no significant enriched motifs are found in the regions detected only by starr and limited motifs are enriched for cebp in the regions detected only by mat. therefore we can conclude that hat had the highest specificity as it was able to detect regions-of-interest highly enriched for cebp binding motifs. detection of retroviral insertion sites by hat retroviral integration mutagenesis in mice is a powerful tool to identify new genes playing an important role in oncogenesis. mice are injected with retroviruses that potentially integrate into the murine genome upon infection. viral integration can lead to gene deregulation, and depending on the genes affected, tumors may develop. genes located proximal to viral integration sites are potentially oncogenic, leading to tumor development. genomic regions that have been targeted by proviral dna in multiple tumors are called common viral integration sites, and are likely driving tumor development. using retroviral insertional mutagenesis, many oncogenes have been identified using large sequencing screens in multiple tumors. we hypothesise that within tumors, genes may be silenced as a result of proviral integration caused by hypermethylation of the cpgs in the viral long terminal repeat, and subsequently in the promoters of their target genes. the identification of methylated genes by means of retroviral insertional mutagenesis may be studied by methyl-dna immunopreciptitation, followed by inverse pcr, using long terminal repeat specific primers. after combining these two technologies, we hybridized samples to affymetrix promoter chips to identify genomic locations involved in viral integration that potentially harbour new tumor suppressor genes. regions-of-interest within this dataset differ from the cebpa-study as they have; i) a higher variability in fragment sizes and, ii) contain specific sequences within the identified regions. therefore these data are used to examine the performance and broad applicability of hat among different applications of tiling-array data. using hat, we have identified candidate tsgs in mouse tumors by considering regions with a maximum fragment size of bp and a significance level α =. with these parameters, we detected methylated viral integration sites; of which one appeared to be a common methylated vis among two samples. besides the detection of candidate regions based on a statistical framework, we have attached additional mouse genomic sequence information to the model, in order to determine the sequence-of-interest based on the restriction enzyme used in the inverse pcr. within this assay, a restriction enzyme will cleave dna at sequence'gatc', within the integrated viral sequence and the flanking genome. note that because of this property, it is expected that every detected region must contain a nearby restriction site, which can easily be verified with hat. hat showed that all detected mviss contain a nearby restriction site, conforming specificity of the identified region as being a viral insertion site. for pcr validation of the method, two mviss were selected based on their location to a nearby 5' transcriptional start site, and confirmed. one of the validated regions is illustrated in figure extended applications of hat the scope of this method is not limited to the presented studies. moreover, we have successfully applied hat for the detection of regions enriched for histone modifications such as, trimethylation of histone at lysine or lysine. some of the detected regions-of-interest were selected for further validation and confirmed by qpcr. regarding tiling-array data spanning the entire genome, we do not expect changes in algorithm performance due to an increased variability in hybridization consistency because the applied normalization method corrects for two major causes of differences in hybridization consistency, i.e., probe sequence and presence of repeats within the genome. furthermore, in addition to one-color arrays we envision that hat can also be applied on data stemming from two-color arrays, because data structure remains similar. we stress however that the normalization procedure is an important step and strongly depends on the type of tiling-array dataset.  here we propose a statistical framework; hat to analyze tiling-array data. we showed that the method is robust and has increased specificity in the detection of regions-of-interest in comparison with two alternative methods. this is achieved by computing the hypergeometric-probability for every detected region-of-interest, among different threshold levels of probe-intensities and window sizes, while keeping control of the family wise error by employing bonferroni correction. besides the detection of regions-of-interest, hat also determines sequences-of-interest, flanking genes and the distances to 5' transcriptional start sites on both dna strands. we describe the performance of hat, when applied to different experimental tiling-array datasets. for each experimental dataset, the selected downstream genes flanking the detected regions-of-interest were successfully confirmed by pcr. we compared the detected regions-of-interest of hat with two other methods, and showed that hat resulted in a reduced number of detected regions-of-interest using the same significance for both mat and starr. however, using motif enrichment analysis we showed that the regions-of-interest detected by hat were more enriched for the expected binding motifs of cebp compared to mat and showed similar enrichment for starr, illustrating increased specificity using hat. besides analyzing chip-on-chip data, hat is also suitable for the analysis of other types of tiling-array data. applying hat to the data from the medip inverse-pcr and promoter-genechip hybridization experiment, we discovered mviss and cmvis that are subject to dna methylation and identified the genes that flank these methylated viral integration sites. hat is applicable to detect regions-of-interest among the different applications of tiling-arrays, and has the advantage of being independent for thresholds, number of probes in a region and probe-resolution. it does not depend on setting various user defined parameters, except for the significance level and an optional maximum fragment size. 
hat has several advantages over previous methods: i) as there is no single cut-off level for probe-intensity, hat can detect regions-of-interest at various thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome, and across tiling-array platforms and iv) it employs a single user defined parameter: the significance level. a major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity in a region. we have developed hypergeometric analysis of tiling-arrays, and first evaluated its performance for tiling-array datasets from a chromatin immunoprecipitation study on chip for the identification of genome-wide dna binding profiles of transcription factor cebpa. subsequently, data from a retroviral insertional mutagenesis screen were used to examine the performance of hat among different applications of tiling-array datasets. we demonstrate that hat has increased specificity for analysis of tiling-array data in comparison with the alternative method, and that it accurately detects regions-of-interest in two different applications of tiling-arrays. regions-of-interest are detected by computing the hypergeometric-probability, while controlling the family wise error. currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width.
 tiling-arrays are applicable to multiple types of biological research questions. due to its advantages, the technology is often employed in genome-wide investigations. a major challenge in the analysis of tiling-array data is to define regions-of-interest, i.e., contiguous probes with increased signal intensity in a region. currently, no standard criteria are available to define these regions-of-interest as there is no single probe intensity cut-off level, different regions-of-interest can contain various numbers of probes, and can vary in genomic width. furthermore, the chromosomal distance between neighboring probes can vary across the genome among different arrays.  we have developed hypergeometric analysis of tiling-arrays, and first evaluated its performance for tiling-array datasets from a chromatin immunoprecipitation study on chip for the identification of genome-wide dna binding profiles of transcription factor cebpa. using this assay, we can refine the detection of regions-of-interest by illustrating that regions detected by hat are more highly enriched for expected motifs in comparison with an alternative detection method. subsequently, data from a retroviral insertional mutagenesis screen were used to examine the performance of hat among different applications of tiling-array datasets. in both studies, detected regions-of-interest have been validated with pcr.  we demonstrate that hat has increased specificity for analysis of tiling-array data in comparison with the alternative method, and that it accurately detects regions-of-interest in two different applications of tiling-arrays. hat has several advantages over previous methods: i) as there is no single cut-off level for probe-intensity, hat can detect regions-of-interest at various thresholds, ii) it can detect regions-of-interest of any size, iii) it is independent of probe-resolution across the genome, and across tiling-array platforms and iv) it employs a single user defined parameter: the significance level. regions-of-interest are detected by computing the hypergeometric-probability, while controlling the family wise error. furthermore, the method does not require experimental replicates, common regions-of-interest are indicated, a sequence-of-interest can be examined for every detected region-of-interest, and flanking genes can be reported. 