a multi-sample based method for identifying common cnvs in normal human genomic structure using high-resolution acgh data基于多试样的方法识别常见的基因拷贝数异变在正常的人类基因组结构使用高分辨率acgh数据.pdfVIP

A Multi-Sample Based Method for Identifying Common CNVs in Normal Human Genomic Structure Using High- Resolution aCGH Data 1 1 2 1 Chihyun Park , Jaegyoon Ahn , Youngmi Yoon , Sanghyun Park * 1 Department of Computer Science, Yonsei University, Seoul, South Korea, 2 Division of Information Engineering, Gachon University of Medicine and Science, Incheon, South Korea Abstract Background: It is difficult to identify copy number variations (CNV) in normal human genomic data due to noise and non- linear relationships between different genomic regions and signal intensity. A high-resolution array comparative genomic hybridization (aCGH) containing 42 million probes, which is very large compared to previous arrays, was recently published. Most existing CNV detection algorithms do not work well because of noise associated with the large amount of input data and because most of the current methods were not designed to analyze normal human samples. Normal human genome analysis often requires a joint approach across multiple samples. However, the majority of existing methods can only identify CNVs from a single sample. Methodology and Principal Findings: We developed a multi-sample-based genomic variations detector (MGVD) that uses segmentation to identify common breakpoints across multiple samples and a k-means-based clustering strategy. Unlike previous methods, MGVD simultaneously considers multiple samples with different genomic intensities and identifies CNVs and CNV zones (CNVZs); CNVZ is a more precise measure of the location of a genomic variant than the CNV region (CNVR). Conclusions and Significance: We designed a specialized algorithm to detect common CNVs from extremely high- resolution multi-sample aCGH data. MGVD