Sensitive and accurate detection of copy number variants using read depth of coverage.

By Seungtai Yoon, Zhenyu Xuan, Vladimir Makarov, Kenny Q. Ye, Jonathan Sebat. Genome Research, 2009

633 citations recorded by the source corpus.

Methods for the direct detection of copy number variation (CNV) genome-wide have become effective instruments for identifying genetic risk factors for disease. The application of next-generation sequencing platforms to genetic studies promises to improve sensitivity to detect CNVs as well as inversions, indels, and SNPs. New computational approaches are needed to systematically detect these variants from genome sequence data. Existing sequence-based approaches for CNV detection are primarily based on paired-end read mapping (PEM) as reported previously by Tuzun et al. and Korbel et al. Due to limitations of the PEM approach, some classes of CNVs are difficult to ascertain, including large insertions and variants located within complex genomic regions. To overcome these limitations, we developed a method for CNV detection using read depth of coverage. Event-wise testing (EWT) is a method based on significance testing. In contrast to standard segmentation algorithms that typically operate by performing likelihood evaluation for every point in the genome, EWT works on intervals of data points, rapidly searching for specific classes of events. Overall false-positive rate is controlled by testing the significance of each possible event and adjusting for multiple testing. Deletions and duplications detected in an individual genome by EWT are examined across multiple genomes to identify polymorphism between individuals. We estimated error rates using simulations based on real data, and we applied EWT to the analysis of chromosome 1 from paired-end shotgun sequence data (30x) on five individuals. Our results suggest that analysis of read depth is an effective approach for the detection of CNVs, and it captures structural variants that are refractory to established PEM-based methods.

Subfields: Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/22944e3c9e00137c85163ad9a9915632bcb65b18

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Citations in the corpus

A survey of tools for variant analysis of next-generation genome sequencing data (2013, methodology): “This results in a precision and recall of 0.74 and 0.69, as well as 1 and 0.1 for CNVnator and RDXplorer, respectively.”
Exome sequencing-based copy-number variation and loss of heterozygosity detection: ExomeCNV (2011, methodology): “While methods have been developed for CNV estimation in whole-genome sequencing (Chiang et al., 2009; Yoon et al., 2009), these methods make key assumptions that fail to hold in the exome sequencing setting.”
Genome structural variation discovery and genotyping (2011, neutral): “Methods that attempt to discover smaller deletions and duplications at better breakpoint resolution include the event-wise-testing (EWT) algorith”
Summarizing and correcting the GC content bias in high-throughput sequencing (2012, positive): “shtml) and studies (4,5) using high-throughput sequencing.”
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.”
GC-Content Normalization for RNA-Seq Data (2011, methodology): “[18] propose a GC-content normalization method for DNA copy number studies, which involves binning reads in 100-bp windows and scaling bin-level read counts by the ratio between the overall median and the median for bins with the same GC-content.”
CNVkit: Genome-Wide Copy Number Detection and Visualization from Targeted DNA Sequencing (2016, methodology): “More recently, methods have been developed to obtain copy number information from whole-genome sequencing data ([3]; reviewed by [4]).”
CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing. (2011, methodology): “…to only unique regions of the genome (Bentley et al. 2008; Campbell et al. 2008; Chiang et al. 2009), discovered only large CNVs with poor breakpoint resolution (Bentley et al. 2008; Campbell et al. 2008; Alkan et al. 2009; Chiang et al. 2009), or could not perform genotyping (Yoon et al. 2009).”
cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012, methodology): “EWT (‘Event-Wise Testing’), introduced by Yoon et al. (15), is identical to MOFDOC except for the ﬁnal segmentation algorithm.”
Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “Taking advantage of the simultaneous analyses of multiple samples, JointSLM works better than the EWT algorithm implemented in RDXplorer [28] in terms of detecting small CNV regions as short as 500 bp.”
Copy number variation detection and genotyping from exome sequence data (2012, background): “…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. 2007, 2009; Hormozdiari et al. 2009), or splitread mapping (Ye et al. 2009; Karakoc et al.…”
Estimation of 18S Gene Copy Number in Marine Eukaryotic Plankton Using a Next-Generation Sequencing Approach (2019, background): “GC% Correction and 18S Gene Copy Number Estimation GC% in sequences can affect sequencing depth and consequently lead to a biased copy number estimation (Yoon et al., 2009).”