Abstract 5271: Optimization and detection of focal somatic copy number variants in whole genome, whole exome and panel sequencing for tumor/normal matched pairs and tumor only analysis

Author(s):  
Jessica Aldrich ◽  
Jonathan J. Keats ◽  
Austin Christofferson ◽  
Winnie S. Liang ◽  
John D. Carpten ◽  
...  
2019 ◽  
Author(s):  
Yue Xing ◽  
Alan R. Dabney ◽  
Xiao Li ◽  
Guosong Wang ◽  
Clare A. Gill ◽  
...  

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.


Author(s):  
Marie Coutelier ◽  
Manuel Holtgrewe ◽  
Marten Jäger ◽  
Ricarda Flöttman ◽  
Martin A. Mensah ◽  
...  

AbstractCopy Number Variants (CNVs) are deletions, duplications or insertions larger than 50 base pairs. They account for a large percentage of the normal genome variation and play major roles in human pathology. While array-based approaches have long been used to detect them in clinical practice, whole-genome sequencing (WGS) bears the promise to allow concomitant exploration of CNVs and smaller variants. However, accurately calling CNVs from WGS remains a difficult computational task, for which a consensus is still lacking. In this paper, we explore practical calling options to reach the best compromise between sensitivity and sensibility. We show that callers based on different signal (paired-end reads, split reads, coverage depth) yield complementary results. We suggest approaches combining four selected callers (Manta, Delly, ERDS, CNVnator) and a regenotyping tool (SV2), and show that this is applicable in everyday practice in terms of computation time and further interpretation. We demonstrate the superiority of these approaches over array-based Comparative Genomic Hybridization (aCGH), specifically regarding the lack of resolution in breakpoint definition and the detection of potentially relevant CNVs. Finally, we confirm our results on the NA12878 benchmark genome, as well as one clinically validated sample. In conclusion, we suggest that WGS constitutes a timely and economically valid alternative to the combination of aCGH and whole-exome sequencing.


2016 ◽  
Vol 94 (suppl_5) ◽  
pp. 146-146
Author(s):  
D. M. Bickhart ◽  
L. Xu ◽  
J. L. Hutchison ◽  
J. B. Cole ◽  
D. J. Null ◽  
...  

2019 ◽  
Author(s):  
Junhua Rao ◽  
Lihua Peng ◽  
Fang Chen ◽  
Hui Jiang ◽  
Chunyu Geng ◽  
...  

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.


2016 ◽  
Vol 15 ◽  
pp. CIN.S36612 ◽  
Author(s):  
Lun-Ching Chang ◽  
Biswajit Das ◽  
Chih-Jian Lih ◽  
Han Si ◽  
Corinne E. Camalier ◽  
...  

With rapid advances in DNA sequencing technologies, whole exome sequencing (WES) has become a popular approach for detecting somatic mutations in oncology studies. The initial intent of WES was to characterize single nucleotide variants, but it was observed that the number of sequencing reads that mapped to a genomic region correlated with the DNA copy number variants (CNVs). We propose a method RefCNV that uses a reference set to estimate the distribution of the coverage for each exon. The construction of the reference set includes an evaluation of the sources of variability in the coverage distribution. We observed that the processing steps had an impact on the coverage distribution. For each exon, we compared the observed coverage with the expected normal coverage. Thresholds for determining CNVs were selected to control the false-positive error rate. RefCNV prediction correlated significantly ( r = 0.96–0.86) with CNV measured by digital polymerase chain reaction for MET (7q31), EGFR (7p12), or ERBB2 (17q12) in 13 tumor cell lines. The genome-wide CNV analysis showed a good overall correlation (Spearman's coefficient = 0.82) between RefCNV estimation and publicly available CNV data in Cancer Cell Line Encyclopedia. RefCNV also showed better performance than three other CNV estimation methods in genome-wide CNV analysis.


2017 ◽  
Vol 94 (1) ◽  
Author(s):  
Zirui Dong ◽  
Weiwei Xie ◽  
Haixiao Chen ◽  
Jinjin Xu ◽  
Huilin Wang ◽  
...  

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