RefCNV: Identification of Gene-Based Copy Number Variants Using Whole Exome Sequencing

Chang, Lun Ching; Das, Biswajit; Lih, Chih Jian; Si, Han; Camalier, Corinne E.; McGregor, Paul M.; Polley, Eric C.

doi:10.4137/cin.s36612

Cited by 7 publications

(11 citation statements)

References 25 publications

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“…(v1.1), CNVnator[74] (v0.3.3), Delly2[75], DWAC-Seq(v0.7), GenomeSTRiP[76] (v2.0), Meerkat[77](v0.189), MetaSV[78] (v0.5.2), read depth (v0.9.8.4), svclassify[79], and Pindel[80] (v0.2.0). CNV estimation for the WES datasets was performed with CNVkit[81] (v0.8.5), CODEX[82] (v1.6.0), CopywriteR[83] (v2.6.0), DeAnnCNV[84], EXCAVATOR2[85] (v1.1.2), ExomeDepth[86] (v1.1.10), GATK[58] 4 Alpha, RefCNV[87], SAAS-CNV[88] (v0.3.4), and VarScan2[61] (v2.4.2). All results were output as VCF files.…”

mentioning

confidence: 99%

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

Jones¹,

Gong²,

Novoradovskaya³

et al. 2021

Genome Biol

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Background Oncopanel genomic testing, which identifies important somatic variants, is increasingly common in medical practice and especially in clinical trials. Currently, there is a paucity of reliable genomic reference samples having a suitably large number of pre-identified variants for properly assessing oncopanel assay analytical quality and performance. The FDA-led Sequencing and Quality Control Phase 2 (SEQC2) consortium analyze ten diverse cancer cell lines individually and their pool, termed Sample A, to develop a reference sample with suitably large numbers of coding positions with known (variant) positives and negatives for properly evaluating oncopanel analytical performance. Results In reference Sample A, we identify more than 40,000 variants down to 1% allele frequency with more than 25,000 variants having less than 20% allele frequency with 1653 variants in COSMIC-related genes. This is 5–100× more than existing commercially available samples. We also identify an unprecedented number of negative positions in coding regions, allowing statistical rigor in assessing limit-of-detection, sensitivity, and precision. Over 300 loci are randomly selected and independently verified via droplet digital PCR with 100% concordance. Agilent normal reference Sample B can be admixed with Sample A to create new samples with a similar number of known variants at much lower allele frequency than what exists in Sample A natively, including known variants having allele frequency of 0.02%, a range suitable for assessing liquid biopsy panels. Conclusion These new reference samples and their admixtures provide superior capability for performing oncopanel quality control, analytical accuracy, and validation for small to large oncopanels and liquid biopsy assays.

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mentioning

confidence: 99%

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

Jones¹,

Gong²,

Novoradovskaya³

et al. 2021

Genome Biol

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“…Fc receptor, observed in various cells such as B lymphocytes and macrophages, is able to bind to the Fc region of antibodies and plays a protective role the immune system 39–41 . It is known that Fc receptor targets the antibodies that are attached to invading pathogens or infected cells, and induces destruction of microbes or infected cells via phagocytosis or cytotoxicity 27–29 . Therefore, we hypothesized that radiation therapy mainly contributed to arrestment of cell cycle and activation of the immune system in lung tissue.…”

Section: Discussionmentioning

confidence: 99%

“…We used the DESseq. 2 29 package in the R software to screen differentially expressed genes between radiotherapy group and controls. Data were normalized by a negative binomial distribution statistical method.…”

Section: Methodsmentioning

confidence: 99%

Transcriptome alteration spectrum in rat lung induced by radiotherapy

Zhang

Cheng

Sun

et al. 2019

Sci Rep

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Radiation therapy is crucial for curative treatment of lung cancer, which frequently leads to lung injury. Long non-coding RNAs (lncRNAs) are a group of RNAs longer than 200 nucleotides and lack protein-coding capacity. Increasing evidences demonstrate the important roles of lncRNAs in biological processes. However, the mechanism underlying the association of ionizing radiation with alterations in mRNA and lncRNA expression and lung injury remains unclear. In our study, the male Sprague-Dawley (SD) rats were exposed to a dose of 18 Gy of 6 MV X-ray and the transcriptome spectrum was studied. To identify the differentially expressed mRNAs and lncRNAs induced by X-ray, the RNA sequencing data of lung tissues from irradiated and normal rats for 4, 8, and 16 weeks were analyzed, using |log2_ratio| ≥ 1 and q ≤ 0.05 as thresholds for significantly differential expression. The number of differentially expressed mRNAs was 1097 (686 up- and 411 down-) for 4-week radiotherapy group, 3006 (1935 up- and 1071 down-) for 8-week group and 1838 (1178 up- and 660 down-) for 16-week group. There were 606 (279 up- and 327 down-) differentially expressed lncRNAs in 4-week group, 1715 (831 up- and 884 down-) in 8-week group and 1043 (656 up- and 387 down-) in 16-week group. The differentially expressed mRNAs were mainly involved in cell cycle regulation and Fc receptor pathway, while the lncRNA target genes were significantly enriched in cellular stress response and regulation of cell migration. Moreover, compared with the control group, the irradiated group presented higher tissue specificity of lncRNAs. Radiation-induced lung injury, especially the dynamic network of lncRNAs and mRNAs, is worthy of study. Investigation on the regulatory details of related pathways is significant for the prevention of radiation-related lung injury, as well as the improvement of radiation therapy.

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“…The deletion 'truth-set' was obtained from the Genome in a Bottle (GIAB) Consortium [23] benchmark SV calls resource, as called by svclassify, a machine learning based approach [24]. This dataset was generated using one-class Support These CNVs have been made available by GIAB to use as a reference standard and have been used in this capacity in a number of studies both in software development and validation [25,26], as well as the recent read depth WGS deletion detection software evaluation [16]. For this report deletions ≥ 1kb were considered for statistical analysis; consisting of 612 of the 2744 total CNVs reported by GIAB.…”

Section: Deletion Detectionmentioning

confidence: 99%

Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data

Whitford

Lehnert

Snell

et al. 2018

Preprint

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Background: Whole genome sequencing (WGS) has increased in popularity and decreased in cost over the past decade, rendering this approach as a viable and sensitive method for variant detection. In addition to its utility for single nucleotide variant detection, WGS data has the potential to detect Copy Number Variants (CNV) to fine resolution. Many CNV detection software packages have been developed exploiting four main types of data: read pair, split read, read depth, and assembly based methods. The aim of this study was to evaluate the efficiency of each of these main approaches in detecting deletions.• Methods: WGS data and high confidence deletion calls for the individual NA12878 from the Genome in a Bottle consortium were the benchmark dataset. The performance of Breakdancer, CNVnator, Delly, FermiKit, and Pindel was assessed by comparing the accuracy and sensitivity of each software package in detecting deletions exceeding 1kb.• Results: There was considerable variability in the outputs of the different WGS CNV detection programs. The best performance was seen from Breakdancer and Delly, with 92.6% and 96.7% sensitivity, respectively and 34.5% and 68.5% false discovery rate (FDR), respectively. In comparison, Pindel, CNVnator, and FermiKit were less effective with sensitivities of 69.1%, 66.0%, and 15.8%, respectively and FDR of 91.3%, 69.0%, and 31.7%, respectively. Concordance across software packages was poor, with only 27 of the total 612 benchmark deletions identified by all five methodologies. • Conclusions: The WGS based CNV detection tools evaluated show disparate performance in identifying deletions ≥ 1kb, particularly those utilising different input data characteristics.Software that exploits read pair based data had the highest sensitivity, namely Breakdancer and Delly. Breakdancer also had the second lowest false discovery rate. Therefore, in this analysis read pair methods (Breakdancer in particular) were the best performing approaches for the identification of deletions ≥ 1kb, balancing accuracy and sensitivity. There is potential for improvement in the detection algorithms, particularly for reducing FDR. This analysis has validated the utility of WGS based CNV detection software to reliably identify deletions, and these findings will be of use when choosing appropriate software for deletion detection, in both research and diagnostic medicine.

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RefCNV: Identification of Gene-Based Copy Number Variants Using Whole Exome Sequencing

Cited by 7 publications

References 25 publications

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

A verified genomic reference sample for assessing performance of cancer panels detecting small variants of low allele frequency

Transcriptome alteration spectrum in rat lung induced by radiotherapy

Evaluation of the performance of copy number variant prediction tools for the detection of deletions from whole genome sequencing data

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