CNV-seq, a new method to detect copy number variation using high-throughput sequencing
By Chao Xie, Martti T. Tammi. BMC Bioinformatics, 2009
573 citations recorded by the source corpus.
BackgroundDNA copy number variation (CNV) has been recognized as an important source of genetic variation. Array comparative genomic hybridization (aCGH) is commonly used for CNV detection, but the microarray platform has a number of inherent limitations.ResultsHere, we describe a method to detect copy number variation using shotgun sequencing, CNV-seq. The method is based on a robust statistical model that describes the complete analysis procedure and allows the computation of essential confidence values for detection of CNV. Our results show that the number of reads, not the length of the reads is the key factor determining the resolution of detection. This favors the next-generation sequencing methods that rapidly produce large amount of short reads.ConclusionSimulation of various sequencing methods with coverage between 0.1× to 8× show overall specificity between 91.7 – 99.9%, and sensitivity between 72.2 – 96.5%. We also show the results for assessment of CNV between two individual human genomes.
Subfields: Cancer Research, Genetics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/54d26e8c8ff638d6ffd9ac483b0057d507c399bb
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Citations in the corpus
- Computational tools for copy number variation (CNV) detection using next-generation sequencing data: features and perspectives (2013, methodology): “sg/cnv-seq/ Perl, R Aligned read positions Identifying CNVs using the difference of observed copy number ratios [31]”
- Trigenomic Bridges for Brassica Improvement (2011, methodology): “, 2006) or shotgun sequencing (Xie and Tammi, 2009) for analysis of copy number variation, as applied in human genetics (Itsara et al.”
- A robust model for read count data in exome sequencing experiments and implications for copy number variant calling (2012, methodology): “, 2010) and read depth approaches (Krumm et al., 2012; Sathirapongsasuti et al., 2011; Xie and Tammi, 2009).”
- cn.MOPS: mixture of Poissons for discovering copy number variations in next-generation sequencing data with a low false discovery rate (2012, methodology): “The lengths of the CNV regions were chosen randomly from the interval 75–200 kb, which, according to Xie and Tammi (20), is the range of accurate detection for the given coverage.”