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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.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>We have developed Hypergeometric Analysis of Tiling-arrays (HAT), and first evaluated its performance for tiling-array datasets from a Chromatin Immunoprecipitation study on chip (ChIP-on-chip) for the identification of genome-wide DNA binding profiles of transcription factor Cebpa (used for method comparison). 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 (MAT). 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 (q)PCR.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>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: <jats:italic>i)<\/jats:italic> as there is no single cut-off level for probe-intensity, HAT can detect regions-of-interest at various thresholds, <jats:italic>ii)<\/jats:italic> it can detect regions-of-interest of any size, <jats:italic>iii)<\/jats:italic> it is independent of probe-resolution across the genome, and across tiling-array platforms and <jats:italic>iv)<\/jats:italic> 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.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1186\/1471-2105-11-275","type":"journal-article","created":{"date-parts":[[2010,5,22]],"date-time":"2010-05-22T06:13:45Z","timestamp":1274508825000},"update-policy":"http:\/\/dx.doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["HAT: Hypergeometric Analysis of Tiling-arrays with application to promoter-GeneChip data"],"prefix":"10.1186","volume":"11","author":[{"given":"Erdogan","family":"Taskesen","sequence":"first","affiliation":[]},{"given":"Renee","family":"Beekman","sequence":"additional","affiliation":[]},{"given":"Jeroen","family":"de Ridder","sequence":"additional","affiliation":[]},{"given":"Bas J","family":"Wouters","sequence":"additional","affiliation":[]},{"given":"Justine K","family":"Peeters","sequence":"additional","affiliation":[]},{"given":"Ivo P","family":"Touw","sequence":"additional","affiliation":[]},{"given":"Marcel JT","family":"Reinders","sequence":"additional","affiliation":[]},{"given":"Ruud","family":"Delwel","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2010,5,21]]},"reference":[{"key":"3732_CR1","first-page":"Unit 17.7","volume":"Chapter 17","author":"O Aparicio","year":"2004","unstructured":"Aparicio O, Geisberg JV, Struhl K: Chromatin immunoprecipitation for determining the association of proteins with specific genomic sequences in vivo. 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