Abstract
BackgroundDNA copy number aberration (CNA) is one of the key characteristics of cancer cells. Recent studies demonstrated the feasibility of utilizing high density single nucleotide polymorphism (SNP) genotyping arrays to detect CNA. Compared with the two-color array-based comparative genomic hybridization (array-CGH), the SNP arrays offer much higher probe density and lower signal-to-noise ratio at the single SNP level. To accurately identify small segments of CNA from SNP array data, segmentation methods that are sensitive to CNA while resistant to noise are required.ResultsWe have developed a highly sensitive algorithm for the edge detection of copy number data which is especially suitable for the SNP array-based copy number data. The method consists of an over-sensitive edge-detection step and a test-based forward-backward edge selection step.ConclusionUsing simulations constructed from real experimental data, the method shows high sensitivity and specificity in detecting small copy number changes in focused regions. The method is implemented in an R package FASeg, which includes data processing and visualization utilities, as well as libraries for processing Affymetrix SNP array data.
Highlights
DNA copy number aberration (CNA) is one of the key characteristics of cancer cells
High-density array platforms, e.g. single nucleotide polymorphism (SNP) array, provide the opportunity to identify genomic aberrations that localize to small segments of the chromosome, which we refer to as focused CNA in this paper
While most methods designed for array-comparative genomic hybridization (CGH) data can potentially be applied, their parameters may need to be fine-tuned to adapt to the different characteristics of the SNP array data
Summary
A forward-backward fragment assembling algorithm for the identification of genomic amplification and deletion breakpoints using high-density single nucleotide polymorphism (SNP) array. Tianwei Yu*1, Hui Ye2,3, Wei Sun, Ker-Chau Li4, Zugen Chen, Sharoni Jacobs, Dione K Bailey, David T Wong and Xiaofeng Zhou*2,8. Address: 1Department of Biostatistics, Rollins School of Public Health, Emory University, Atlanta, GA, USA, 2Center for Molecular Biology of Oral Diseases, College of Dentistry, University of Illinois at Chicago, Chicago, IL, USA, 3Shanghai Children's Medical Center, Shanghai Jiao-Tong University, Shanghai, China, 4Department of Statistics, University of California at Los Angeles, CA, USA, 5Department of Human Genetics & Microarray Core, University of California at Los Angeles, Los Angeles, CA, USA, 6Affymetrix, Inc., 3420 Central Expressway, Santa Clara, CA, USA, 7Dental Research Institute, School of Dentistry, David Geffen School of Medicine & Henry Samueli School of Engineering & Jonsson Comprehensive Cancer Center, University of California at Los Angeles, Los Angeles, CA, USA and 8Guanghua School & Research Institute of Stomatology, Sun Yat-Sen University, Guangzhou, China. Published: 3 May 2007 BMC Bioinformatics 2007, 8:145 doi:10.1186/1471-2105-8-145
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