Abstract
Tiling arrays are useful for exploring local functions of regions of the genome in an unbiased fashion. The exact determination of those genomic regions based on tiling-array data, e.g., generated by means of hybridization with immunopreciptated DNA-fragments to the arrays is a challenge. Many different statistical methodologies have been developed to find biological relevant regions-of-interest (ROI) by using the quantitative signal intensity of each probe. We previously developed a method called Hypergeometric Analysis of Tiling arrays (HAT) for the analysis of tiling-array data, but it is developed such that it can also be used to study data derived by genome-wide deep sequencing approaches. Here we applied HAT to analyze two publicly available tiling-array data sets. After the detection of statistically significant ROI, these are often used in additional analysis for hypothesis testing. We therefore discuss, by using the results of the tiling-array experiment, pathway and motif analyses.
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Acknowledgment
We wish to thank Claudia Erpelinck-Verschueren for technical assistance in the preparation of the CEBPA C-terminal mutant samples.
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Taskesen, E., Wouters, B., Delwel, R. (2013). HAT: A Novel Statistical Approach to Discover Functional Regions in the Genome. In: Lee, TL., Shui Luk, A. (eds) Tiling Arrays. Methods in Molecular Biology, vol 1067. Humana Press, Totowa, NJ. https://doi.org/10.1007/978-1-62703-607-8_9
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DOI: https://doi.org/10.1007/978-1-62703-607-8_9
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