Hypergeometric Analysis of Tiling-arrays

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BMC Bioinformatics 2010, 11:275
Published: 21 May 2010


Institutions
Delft Bioinformatics Lab (DBL), Delft University of Technology, Delft, 2628 CD, The Netherlands
Department of Hematology, Erasmus University Medical Center, Rotterdam, 3015 GE, The Netherlands
Netherlands Bioinformatics Centre (NBIC), The Netherlands


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Introduction

We have demonstrated that HAT has increased specificity for analysis of tiling-array data in comparison with alternative methods, and that it accurately detected regions-of-interest in 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, 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 model 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.

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