Scacheri Peter C, Crawford Gregory E, Davis Sean
Department of Genetics, Case Western Reserve University, Cleveland, OH, USA.
Methods Enzymol. 2006;411:270-82. doi: 10.1016/S0076-6879(06)11014-9.
Data obtained from high-density oligonucleotide tiling arrays present new computational challenges for users. This chapter presents ACME (Algorithm for Capturing Microarray Enrichment), a computer program developed for the analysis of data obtained using NimbleGen-tiled microarrays. ACME identifies signals or "peaks" in tiled array data using a simple sliding window and threshold strategy and assigns a probability value (p value) to each and every probe on the array. We present data indicating that this approach can be applied successfully to at least two different genomic applications involving tiled arrays: ChIP-chip and DNase-chip. In addition to highlighting previously described methods for analyzing tiled array data, we provide recommendations for assessing the quality of ChIP-chip and DNase-chip data, suggestions for optimizing the use of ACME, and descriptions of several of ACME features designed to facilitate interpretation of processed tiled array data. ACME is written in R language and is freely available upon request or through Bioconductor.
从高密度寡核苷酸平铺阵列获得的数据给用户带来了新的计算挑战。本章介绍了ACME（捕获微阵列富集算法），这是一个为分析使用NimbleGen平铺微阵列获得的数据而开发的计算机程序。ACME使用简单的滑动窗口和阈值策略识别平铺阵列数据中的信号或“峰”，并为阵列上的每个探针分配一个概率值（p值）。我们展示的数据表明，这种方法可以成功应用于至少两种涉及平铺阵列的不同基因组应用：ChIP芯片和DNase芯片。除了强调先前描述的分析平铺阵列数据的方法外，我们还提供了评估ChIP芯片和DNase芯片数据质量的建议、优化ACME使用的建议，以及对ACME的几个旨在促进对处理后的平铺阵列数据进行解释的功能的描述。ACME用R语言编写，可应要求免费提供，也可通过Bioconductor获得。
