CNVnator: an approach to discover, genotype, and characterize typical and atypical CNVs from family and population genome sequencing.

By Alexej Abyzov, Alexander E. Urban, Michael Snyder, Mark Gerstein. Genome Research, 2011

1,610 citations recorded by the source corpus.

Copy number variation (CNV) in the genome is a complex phenomenon, and not completely understood. We have developed a method, CNVnator, for CNV discovery and genotyping from read-depth (RD) analysis of personal genome sequencing. Our method is based on combining the established mean-shift approach with additional refinements (multiple-bandwidth partitioning and GC correction) to broaden the range of discovered CNVs. We calibrated CNVnator using the extensive validation performed by the 1000 Genomes Project. Because of this, we could use CNVnator for CNV discovery and genotyping in a population and characterization of atypical CNVs, such as de novo and multi-allelic events. Overall, for CNVs accessible by RD, CNVnator has high sensitivity (86%-96%), low false-discovery rate (3%-20%), high genotyping accuracy (93%-95%), and high resolution in breakpoint discovery (<200 bp in 90% of cases with high sequencing coverage). Furthermore, CNVnator is complementary in a straightforward way to split-read and read-pair approaches: It misses CNVs created by retrotransposable elements, but more than half of the validated CNVs that it identifies are not detected by split-read or read-pair. By genotyping CNVs in the CEPH, Yoruba, and Chinese-Japanese populations, we estimated that at least 11% of all CNV loci involve complex, multi-allelic events, a considerably higher estimate than reported earlier. Moreover, among these events, we observed cases with allele distribution strongly deviating from Hardy-Weinberg equilibrium, possibly implying selection on certain complex loci. Finally, by combining discovery and genotyping, we identified six potential de novo CNVs in two family trios.

Subfields: Genetics, Plant Science

View Paper: https://www.semanticscholar.org/paper/c11ccf3399c7e8473d81643265bae66f5164e06e

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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.”
Whole genome sequencing resource identifies 18 new candidate genes for autism spectrum disorder (2017, positive): “For CNVnator, we removed calls with >50% of q0 (zero mapping quality) reads within the CNV regions (q0 filter), except for the homozygous autosomal deletions or hemizygous X-linked deletions in males (with normalized average read depth; NRD < 0.03).”
Genome Sequencing of Pediatric Medulloblastoma Links Catastrophic DNA Rearrangements with TP53 Mutations (2012, neutral): “, 2009), and read-depth analysis (Abyzov et al., 2011; Chiang et al., 2009; Waszak et al., 2010).”
AlleleSeq: analysis of allele-specific expression and binding in a network framework (2011, methodology): “SNP positions that by read-depth analysis (Abyzov et al , 2011) are determined to be potentially in a CNV (the read depth of genomic DNA reads in a 1-kb window around the SNP is either o 1 or 4 3) are excluded (see Materials and methods).”
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.”
Comprehensive evaluation of structural variation detection algorithms for whole genome sequencing (2019, methodology): “Many projects use popular SV detection algorithms, including BreakDancer [30], CNVnator [31], DELLY [32], GenomeSTRiP [33], Pindel [34], and Lumpy [35], which give calls with relatively high accuracy.”
SpeedSeq: Ultra-fast personal genome analysis and interpretation (2014, methodology): “SpeedSeq detects genome structural variation (SV) using a combination of read-pair and split-read data using LUMPY[18] and read-depth analysis using CNVnator[19], and includes a novel SV breakpoint genotyper (SVTyper).”
High-Throughput Sequencing Technologies (2015, background): “These approaches include depth of read coverage (Abyzov et al., 2011), mapping of paired end reads that are discordant from the reference genome (Korbel et al.”
High-coverage whole-genome sequencing of the expanded 1000 Genomes Project cohort including 602 trios (2021, positive): “, 2015), followed by copy-number annotation with CNVnator (Abyzov et al., 2011) and reclassification of variants based on concordance of read-depth with breakpoint orientation.”
The Sequence of 1504 Mutants in the Model Rice Variety Kitaake Facilitates Rapid Functional Genomic Studies (2017, background): “, 2009), CNVnator (Abyzov et al., 2011), and DELLY (Rausch et al.”
Natural variation in genome architecture among 205 Drosophila melanogaster Genetic Reference Panel lines (2014, methodology): “4d, CNVnator (Abyzov et al., 2011) v0.”
LUMPY: a probabilistic framework for structural variant discovery (2012, methodology): “To demonstrate this capability, we included results for four different LUMPY configurations: 1) the standard configuration of read-pair and split-read signals from NA12878; 2) read-pair and split-read signals from NA12878, as well as prior knowledge (using LUMPY’s generic evidence module) of deletio”
Personal Omics Profiling Reveals Dynamic Molecular and Medical Phenotypes (2012, methodology): “, 2009), 2) Read depth (Abyzov et al., 2011), 3) Split reads Chen et al.”