Personalized Copy-Number and Segmental Duplication Maps using Next-Generation Sequencing

By Can Alkan, J. Kidd, Tomas Marques-Bonet, Gozde Aksay, F. Antonacci, Fereydoun Hormozdiari, J. Kitzman, Carl A. Baker, Maika Malig, Onur Mutlu and 3 more. Nature Genetics, 2009

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Despite their importance in gene innovation and phenotypic variation, duplicated regions have remained largely intractable owing to difficulties in accurately resolving their structure, copy number and sequence content. We present an algorithm (mrFAST) to comprehensively map next-generation sequence reads, which allows for the prediction of absolute copy-number variation of duplicated segments and genes. We examine three human genomes and experimentally validate genome-wide copy number differences. We estimate that, on average, 73–87 genes vary in copy number between any two individuals and find that these genic differences overwhelmingly correspond to segmental duplications (odds ratio = 135; P < 2.2 × 10−16). Our method can distinguish between different copies of highly identical genes, providing a more accurate assessment of gene content and insight into functional constraint without the limitations of array-based technology.

Subfields: Genetics, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/7b27a91e8bca7fc900c5b2b8365c819530c87000

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Citations in the corpus

VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. (2012, background): “Most of these approaches (Campbell et al. 2008; Alkan et al. 2009; Chiang et al. 2009; Yoon et al. 2009; Abyzov et al. 2011) would be confounded by exome data sets, because of the biases introduced by hybridization and the sparse and uneven coverages throughout the genome.”
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “net/ C SAM A tool to detect large segmental duplications and insertions [35]”
Statistical Design and Analysis of RNA Sequencing Data (2010, background): “2008), epigenetic events (P ark 2009), copy number variants (A lkan et al .”
Next-Generation Genomics: an Integrative Approach (2010, neutral): “NGS can also detect copy-number variants and gene-fusion event”
cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012, methodology): “MOFDOC: we implemented MOFDOC using the CNV calling criterion of Alkan et al. (6).”
Copy number variation detection and genotyping from exome sequence data (2012, background): “These biases, as well as the sparse nature of the capture, make exome sequencing unsuitable for ‘‘traditional’’ CNV detection algorithms, such as raw read-depth (Alkan et al. 2009; Chiang et al. 2008; Yoon et al. 2009), read-pair alignment (Korbel et al.”
A survey of tools for variant analysis of next-generation genome sequencing data (2013, background): “Bowtie/Bowtie2 [68, 69], BWA [70, 71], MAQ [72], mrFAST [73], Novoalign (http://novocraft.”
Detection of Chromosomal Alterations in the Circulation of Cancer Patients with Whole-Genome Sequencing (2012, methodology): “Although each of these approaches has certain advantages, we chose to analyze chromosome arms because these were frequently altered in breast and colorectal cancer samples previously analyzed for copy number alterations and would be expected to be altered in most human cancers (see Materials and Met”