Taskesen Erdogan, Wouters Bas, Delwel Ruud
Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands.
Methods Mol Biol. 2013;1067:125-41. doi: 10.1007/978-1-62703-607-8_9.
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.
平铺阵列有助于以无偏的方式探索基因组区域的局部功能。基于平铺阵列数据（例如通过免疫沉淀的DNA片段与阵列杂交产生的数据）精确确定那些基因组区域是一项挑战。已经开发了许多不同的统计方法，通过使用每个探针的定量信号强度来找到生物学相关的感兴趣区域（ROI）。我们之前开发了一种名为平铺阵列超几何分析（HAT）的方法来分析平铺阵列数据，但它也可以用于研究通过全基因组深度测序方法获得的数据。在这里，我们应用HAT分析了两个公开可用的平铺阵列数据集。在检测到具有统计学意义的ROI后，这些ROI通常用于假设检验的额外分析。因此，我们通过使用平铺阵列实验的结果来讨论通路和基序分析。
