Diagnostic Genetic Unit, Careggi Hospital, AOUC, University of Florence, Florence, Italy.
Biostatistics. 2010 Apr;11(2):265-80. doi: 10.1093/biostatistics/kxp051. Epub 2009 Nov 30.
Array comparative genomic hybridization (aCGH) is a microarray technology that allows one to detect and map genomic alterations. The goal of aCGH analysis is to identify the boundaries of the regions where the number of DNA copies changes (breakpoint identification) and then to label each region as loss, neutral, or gain (calling). In this paper, we introduce a new algorithm, based on the shifting level model (SLM), with the aim of locating regions with different means of the log(2) ratio in genomic profiles obtained from aCGH data. We combine the SLM algorithm with the CGHcall calling procedure and compare their performances with 5 state-of-the-art methods. When dealing with synthetic data, our method outperforms the other 5 algorithms in detecting the change in the number of DNA copies in the most challenging situations. For real aCGH data, SLM is able to locate all the cytogenetically mapped aberrations giving a smaller number of false-positive breakpoints than the compared methods. The application of the SLM algorithm is not limited to aCGH data. Our approach can also be used for the analysis of several emerging experimental strategies such as high-resolution tiling array.
阵列比较基因组杂交（aCGH）是一种微阵列技术，可用于检测和绘制基因组改变。aCGH 分析的目的是识别 DNA 拷贝数量发生变化的区域的边界（断点识别），然后将每个区域标记为缺失、中性或增益（调用）。在本文中，我们引入了一种新的算法，基于平移水平模型（SLM），旨在定位从 aCGH 数据获得的基因组谱中具有不同对数比均值的区域。我们将 SLM 算法与 CGHcall 调用程序相结合，并将其性能与 5 种最先进的方法进行比较。在处理合成数据时，我们的方法在最具挑战性的情况下检测 DNA 拷贝数量变化的性能优于其他 5 种算法。对于真实的 aCGH 数据，SLM 能够定位所有细胞遗传学映射的异常，与比较方法相比，假阳性断点的数量更少。SLM 算法的应用不仅限于 aCGH 数据。我们的方法还可以用于分析几种新兴的实验策略，如高分辨率平铺阵列。
