Thompson Reid F, Reimers Mark, Khulan Batbayar, Gissot Mathieu, Richmond Todd A, Chen Quan, Zheng Xin, Kim Kami, Greally John M
Department of Molecular Genetics, Albert Einstein College of Medicine, 1300 Morris Park Avenue, Bronx, NY 10461, USA.
Bioinformatics. 2008 May 1;24(9):1161-7. doi: 10.1093/bioinformatics/btn096. Epub 2008 Mar 18.
Representations of the genome can be generated by the selection of a subpopulation of restriction fragments using ligation-mediated PCR. Such representations form the basis for a number of high-throughput assays, including the HELP assay to study cytosine methylation. We find that HELP data analysis is complicated not only by PCR amplification heterogeneity but also by a complex and variable distribution of cytosine methylation. To address this, we created an analytical pipeline and novel normalization approach that improves concordance between microarray-derived data and single locus validation results, demonstrating the value of the analytical approach. A major influence on the PCR amplification is the size of the restriction fragment, requiring a quantile normalization approach that reduces the influence of fragment length on signal intensity. Here we describe all of the components of the pipeline, which can also be applied to data derived from other assays based on genomic representations.
通过使用连接介导的PCR选择限制性片段的亚群,可以生成基因组的代表性片段。这些代表性片段构成了许多高通量检测的基础,包括用于研究胞嘧啶甲基化的HELP检测。我们发现,HELP数据分析不仅因PCR扩增异质性而复杂,还因胞嘧啶甲基化的复杂且可变的分布而复杂。为了解决这个问题,我们创建了一个分析流程和新颖的标准化方法,该方法提高了微阵列衍生数据与单基因座验证结果之间的一致性,证明了该分析方法的价值。对PCR扩增的一个主要影响是限制性片段的大小,这需要一种分位数标准化方法来减少片段长度对信号强度的影响。在这里,我们描述了该流程的所有组件,这些组件也可应用于基于基因组代表性片段的其他检测所获得的数据。