PocaCNV: A Tool to Detect Copy Number Variants from Population-Scale Genome Sequencing Data

AbstractCopy number variants are insertions and deletions of 1 kb or larger in a genome that play an important role in phenotypic changes and human disease. Many software applications have been developed to detect copy number variants using either whole-genome sequencing or whole-exome sequencing data. However, there is poor agreement in the results from these applications. Simulated datasets containing copy number variants allow comprehensive comparisons of the operating characteristics of existing and novel copy number variant detection methods. Several software applications have been developed to simulate copy number variants and other structural variants in whole-genome sequencing data. However, none of the applications reliably simulate copy number variants in whole-exome sequencing data. We have developed and tested SECNVs (Simulator of Exome Copy Number Variants), a fast, robust and customizable software application for simulating copy number variants and whole-exome sequences from a reference genome. SECNVs is easy to install, implements a wide range of commands to customize simulations, can output multiple samples at once, and incorporates a pipeline to output rearranged genomes, short reads and BAM files in a single command. Variants generated by SECNVs are detected with high sensitivity and precision by tools commonly used to detect copy number variants. SECNVs is publicly available at https://github.com/YJulyXing/SECNVs.

Download Full-text

Performance of copy number variants detection based on whole-genome sequencing by DNBSEQ platforms

BMC Bioinformatics ◽

10.1186/s12859-020-03859-x ◽

2020 ◽

Vol 21 (1) ◽

Author(s):

Junhua Rao ◽

Lihua Peng ◽

Xinming Liang ◽

Hui Jiang ◽

Chunyu Geng ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Technology Use ◽

Copy Number ◽

Massively Parallel Sequencing ◽

Copy Number Variants ◽

Whole Genome ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Cnv Detection

Abstract Background DNBSEQ™ platforms are new massively parallel sequencing (MPS) platforms that use DNA nanoball technology. Use of data generated from DNBSEQ™ platforms to detect single nucleotide variants (SNVs) and small insertions and deletions (indels) has proven to be quite effective, while the feasibility of copy number variants (CNVs) detection is unclear. Results Here, we first benchmarked different CNV detection tools based on Illumina whole-genome sequencing (WGS) data of NA12878 and then assessed these tools in CNV detection based on DNBSEQ™ sequencing data from the same sample. When the same tool was used, the CNVs detected based on DNBSEQ™ and Illumina data were similar in quantity, length and distribution, while great differences existed within results from different tools and even based on data from a single platform. We further estimated the CNV detection power based on available CNV benchmarks of NA12878 and found similar precision and sensitivity between the DNBSEQ™ and Illumina platforms. We also found higher precision of CNVs shorter than 1 kbp based on DNBSEQ™ platforms than those based on Illumina platforms by using Pindel, DELLY and LUMPY. We carefully compared these two available benchmarks and found a large proportion of specific CNVs between them. Thus, we constructed a more complete CNV benchmark of NA12878 containing 3512 CNV regions. Conclusions We assessed and benchmarked CNV detections based on WGS with DNBSEQ™ platforms and provide guidelines for future studies.

Download Full-text

ClinSV: Clinical grade structural and copy number variant detection from whole genome sequencing data

10.1101/2020.06.30.20143453 ◽

2020 ◽

Author(s):

Andre E Minoche ◽

Ben Lundie ◽

Greg B Peters ◽

Thomas Ohnesorg ◽

Mark Pinese ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

False Positive ◽

Copy Number ◽

False Positive Rate ◽

Copy Number Variants ◽

Copy Number Variant ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

AbstractWhole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritisation and visualisation method, which identified 99.8% of pathogenic ClinVar CNVs >10kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5–4.5%) and reproducibility high (95–99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35–63% were not detectable by current clinical microarray designs.

Download Full-text

ClinSV: clinical grade structural and copy number variant detection from whole genome sequencing data

Genome Medicine ◽

10.1186/s13073-021-00841-x ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Andre E. Minoche ◽

Ben Lundie ◽

Greg B. Peters ◽

Thomas Ohnesorg ◽

Mark Pinese ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

False Positive ◽

Copy Number ◽

False Positive Rate ◽

Copy Number Variants ◽

Copy Number Variant ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data

AbstractWhole genome sequencing (WGS) has the potential to outperform clinical microarrays for the detection of structural variants (SV) including copy number variants (CNVs), but has been challenged by high false positive rates. Here we present ClinSV, a WGS based SV integration, annotation, prioritization, and visualization framework, which identified 99.8% of simulated pathogenic ClinVar CNVs > 10 kb and 11/11 pathogenic variants from matched microarrays. The false positive rate was low (1.5–4.5%) and reproducibility high (95–99%). In clinical practice, ClinSV identified reportable variants in 22 of 485 patients (4.7%) of which 35–63% were not detectable by current clinical microarray designs. ClinSV is available at https://github.com/KCCG/ClinSV.

Download Full-text

0306 Exploring the feasibility of using copy number variants as genetic markers through large-scale whole genome sequencing experiments

Journal of Animal Science ◽

10.2527/jam2016-0306 ◽

2016 ◽

Vol 94 (suppl_5) ◽

pp. 146-146

Author(s):

D. M. Bickhart ◽

L. Xu ◽

J. L. Hutchison ◽

J. B. Cole ◽

D. J. Null ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genetic Markers ◽

Genome Sequencing ◽

Copy Number ◽

Large Scale ◽

Copy Number Variants ◽

Whole Genome

Download Full-text

Detection and characterization of copy number variants based on whole-genome sequencing by DNBSEQ platforms

10.1101/786962 ◽

2019 ◽

Author(s):

Junhua Rao ◽

Lihua Peng ◽

Fang Chen ◽

Hui Jiang ◽

Chunyu Geng ◽

...

Keyword(s):

Whole Genome Sequencing ◽

Genome Sequencing ◽

Copy Number ◽

Copy Number Variants ◽

Copy Number Variant ◽

Whole Genome ◽

Genome Wide ◽

Wide Range ◽

Distribution Sensitivity ◽

Cnv Detection

AbstractBackgroundNext-generation sequence (NGS) has rapidly developed in past years which makes whole-genome sequencing (WGS) becoming a more cost- and time-efficient choice in wide range of biological researches. We usually focus on some variant detection via WGS data, such as detection of single nucleotide polymorphism (SNP), insertion and deletion (Indel) and copy number variant (CNV), which playing an important role in many human diseases. However, the feasibility of CNV detection based on WGS by DNBSEQ™ platforms was unclear. We systematically analysed the genome-wide CNV detection power of DNBSEQ™ platforms and Illumina platforms on NA12878 with five commonly used tools, respectively.ResultsDNBSEQ™ platforms showed stable ability to detect slighter more CNVs on genome-wide (average 1.24-fold than Illumina platforms). Then, CNVs based on DNBSEQ™ platforms and Illumina platforms were evaluated with two public benchmarks of NA12878, respectively. DNBSEQ™ and Illumina platforms showed similar sensitivities and precisions on both two benchmarks. Further, the difference between tools for CNV detection was analyzed, and indicated the selection of tool for CNV detection could affected the CNV performance, such as count, distribution, sensitivity and precision.ConclusionThe major contribution of this paper is providing a comprehensive guide for CNV detection based on WGS by DNBSEQ™ platforms for the first time.

Download Full-text