Free-access copy-number variant detection tools for targeted next-generation sequencing data

Comparison of exon-level copy number variants in CytoScan XON assay and next-generation sequencing in clinical samples

BACKGROUND AND AIMS

Next-generation sequencing (NGS)-based copy number variants (CNVs) have high false-positive rates. The fewer the exons involved, the higher the false-positive rate. A CytoScan XON assay was developed to assess exon-level CNVs.

MATERIALS AND METHODS

Twenty-three clinically relevant exon-level CNVs in 20 patient blood samples found in previous NGS studies were compared with the results from the CytoScan XON and multiplex ligation-dependent probe amplification (MLPA).

RESULTS

Fifteen of the 23 exon-level CNVs were consistent with the NGS results. Among these, eight were confirmed using MLPA. In six out of eight discrepancies between the CytoScan Xon and NGS, MLPA was performed, and three were negative, indicating that the CNVs in NGS were false positives. The CytoScan XON exhibits a sensitivity of 72.7% for small exon-level CNVs, along with a specificity of 100%. The assay could not detect the three exon-level CNVs in PKD1 and TSC2 that were detected using both NGS and MLPA. This could be due to the distribution of the probes in some areas, and the CNV-calling regions containing multiple exons.

CONCLUSION

The CytoScan XON assay is a promising complementary tool for the detection of exon-level CNVs, provided that the users carefully examine the distribution of probes and calling regions.

Enhancing the Reliability of PMP22 Copy Number Variation Detection with an Inherited Peripheral Neuropathy Panel

The utility of the next-generation sequencing (NGS) panel could be increased in hereditary peripheral neuropathies, given that the duplication of PMP22 is a major abnormality. In the present study, the analytical performance of an algorithm for detecting PMP22 copy number variation (CNV) from the NGS panel data was evaluated. The NGS panel covers 141 genes, including PMP22 and five genes within 1.5-megabase duplicated region at 17p11.2. CNV calling was performed using a laboratory-developed algorithm. Among the 92 cases subjected to targeted NGS panel from March 2018 to January 2021, 26 were suggestive of PMP22 CNV. Multiplex ligation-dependent probe amplification analysis was performed in 58 cases, and the results were 100% concordant with the NGS data (23 duplications, 2 deletions, and 33 negatives). Analytical performance of the pipeline was further validated by another blind data set, including 14 positive and 20 negative samples. Reliable detection of PMP22 CNV was possible by analyzing not only PMP22 but also the adjacent genes within the 1.5-megabase region of 17p11.2. On the basis of the high accuracy of CNV calling for PMP22, the testing strategy for diagnosis of peripheral polyneuropathies could be simplified by reducing the need for multiplex ligation-dependent probe amplification.

Comprehensive Analysis of Clinically Relevant Copy Number Alterations (CNAs) Using a 523-Gene Next-Generation Sequencing Panel and NxClinical Software in Solid Tumors

Copy number alterations (CNAs) are significant in tumor initiation and progression. Identifying these aberrations is crucial for targeted therapies and personalized cancer diagnostics. Next-generation sequencing (NGS) methods present advantages in scalability and cost-effectiveness, surpassing limitations associated with reference assemblies and probe capacities in traditional laboratory approaches. This retrospective study evaluated CNAs in 50 FFPE tumor samples (breast cancer, ovarian carcinoma, pancreatic cancer, melanoma, and prostate carcinoma) using Illumina's TruSight Oncology 500 (TSO500) and the Affymetrix Oncoscan Molecular Inversion Probe (OS-MIP) (ThermoFisher Scientific, Waltham, MA, USA). NGS analysis with the NxClinical 6.2 software demonstrated a high sensitivity and specificity (100%) for CNA detection, with a complete concordance rate as compared to the OS-MIP. All 54 known CNAs were identified by NGS, with gains being the most prevalent (63%). Notable CNAs were observed in MYC (18%), TP53 (12%), BRAF (8%), PIK3CA, EGFR, and FGFR1 (6%) genes. The diagnostic parameters exhibited high accuracy, including a positive predictive value, negative predictive value, and overall diagnostic accuracy. This study underscores NxClinical as a reliable software for identifying clinically relevant gene alterations using NGS TSO500, offering valuable insights for personalized cancer treatment strategies based on CNA analysis.

Diagnostic Yield of Epilepsy-Genes Sequencing and Chromosomal Microarray in Pediatric Epilepsy

BACKGROUND

Around 40% of individuals with epilepsy have an underlying identifiable genetic etiology. Common methods for epilepsy genetic testing are chromosomal microarray (CMA) and epilepsy-genes sequencing (EGS). Historically, CMA was the first-line test for patients with epilepsy, but recent studies have shown that EGS has a superior diagnostic yield. To further optimize testing algorithms for epilepsy, we compared these tests' diagnostic yields and explored how they are influenced by age of onset and phenotype complexity.

METHODS

Genetic test results from a cohort of patients with epilepsy were used to determine the diagnostic yield of CMA (n = 366) versus EGS (n = 370) for genetic epilepsy etiologies. Further analysis examined the probability of diagnostic results based on age of seizure onset and patients' phenotype complexity.

RESULTS

For patients who underwent CMA, causative variants were found in 28 of 366 cases (7.7%), and 60 of 366 patients (16.4%) had at least one variant of uncertain significance (VUS). For EGS, 65 of 370 (17.6%) cases had causative variants, whereas 155 of 370 (41.9%) had at least one VUS. EGS had a significantly higher diagnostic yield than CMA (odds ratio [OR] = 2.63, P < 0.001). This difference in diagnostic yield was further pronounced among patients with infantile seizure onset (OR = 4.69, P < 0.001) and patients with additional neurological findings (OR = 2.99, P < 0.001).

CONCLUSION

To minimize the time and resources required to reach a diagnosis, clinicians and insurers alike should consider using EGS as an initial diagnostic tool.

DOCEST-fast and accurate estimator of human NGS sequencing depth and error rate

Motivation

Accurate estimation of next-generation sequencing depth of coverage is needed for detecting the copy number of repeated elements in the human genome. The common methods for estimating sequencing depth are based on counting the number of reads mapped to the genome or subgenomic regions. Such methods are sensitive to the mapping quality. The presence of contamination or the large deviance of an individual genome from the reference may introduce bias in depth estimation.

Results

Here, we present an algorithm and implementation for estimating both the sequencing depth and error rate from unmapped reads using a uniquely filtered k-mer set. On simulated reads with 20× coverage, the margin of error was less than 0.01%. At 0.01× coverage and the presence of 10-fold contamination, the precision was within 2% for depth and within 10% for error rate.

Availability and implementation

DOCEST program and database can be downloaded from https://bioinfo.ut.ee/docest/.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Clinical utility of periodic reinterpretation of CNVs of uncertain significance: an 8-year retrospective study

BACKGROUND

Array-CGH is the first-tier genetic test both in pre- and postnatal developmental disorders worldwide. Variants of uncertain significance (VUS) represent around 10~15% of reported copy number variants (CNVs). Even though VUS reanalysis has become usual in practice, no long-term study regarding CNV reinterpretation has been reported.

METHODS

This retrospective study examined 1641 CGH arrays performed over 8 years (2010-2017) to demonstrate the contribution of periodically re-analyzing CNVs of uncertain significance. CNVs were classified using AnnotSV on the one hand and manually curated on the other hand. The classification was based on the 2020 American College of Medical Genetics (ACMG) criteria.

RESULTS

Of the 1641 array-CGH analyzed, 259 (15.7%) showed at least one CNV initially reported as of uncertain significance. After reinterpretation, 106 of the 259 patients (40.9%) changed categories, and 12 of 259 (4.6%) had a VUS reclassified to likely pathogenic or pathogenic. Six were predisposing factors for neurodevelopmental disorder/autism spectrum disorder (ASD). CNV type (gain or loss) does not seem to impact the reclassification rate, unlike the length of the CNV: 75% of CNVs downgraded to benign or likely benign are less than 500 kb in size.

CONCLUSIONS

This study's high rate of reinterpretation suggests that CNV interpretation has rapidly evolved since 2010, thanks to the continuous enrichment of available databases. The reinterpreted CNV explained the phenotype for ten patients, leading to optimal genetic counseling. These findings suggest that CNVs should be reinterpreted at least every 2 years.

The First Compound Heterozygous Mutations of DMP1 Causing Rare Autosomal Recessive Hypophosphatemic Rickets Type 1

CONTEXT

Hereditary hypophosphatemic rickets (HR) consists of a group of inherited hypophosphatemia due to mutations of different genes, which need genetic analysis to make a differential diagnosis. Among them, autosomal recessive hypophosphatemic rickets type 1 (ARHR1), caused by a homozygous mutation of dentin matrix protein 1 (DMP1), is extremely rare, with only 30 reported patients. To date, there has been no case with compound heterozygous DMP1 mutations.

OBJECTIVE

To report the first compound heterozygous mutations of DMP1 causing ARHR1 and confirm the effect of the mutation on DMP1 protein.

METHODS

We report the clinical features of a Chinese patient with HR. Whole-exome sequencing (WES) was performed on the proband. Then, Cytoscan HD array, Sanger sequencing, and genomic quantitative PCR (qPCR) were used to confirm the mutations. A cell experiment was conducted to explore the effect of the mutation.

RESULTS

The proband is a 4-year-old boy, who developed genu varum when he was able to walk at age 1 year and tooth loss after a mild hit at age 3.5 years. Physical examination, biochemical measurement, and imaging finding indicated HR. Family history was negative. WES performed on the proband revealed a novel start codon mutation (c.1A > T, p.Met1Leu) in DMP1 and a large deletion involving most of the small integrin-binding ligand N-linked glycoprotein (SIBLING) family gene, including DSPP, DMP1, IBSP, and MEPE. The novel paternally inherited start codon mutation, which resulted in decreased expression of DMP1 protein with smaller molecular weight and cleavage defect, was confirmed by Sanger sequencing. The maternally inherited deletion was validated by Cytoscan and qPCR, and the breakpoint was finally identified by long-range PCR and Sanger sequencing. Manifestation of dentin dysplasia (DD) or dentinogenesis imperfecta (DGI) caused by DSPP mutations was absent in the patient and his mother, confirming that haploinsufficiency could not lead to DD or DGI.

CONCLUSION

We report for the first time compound heterozygous DMP1 mutations consisting of a large deletion and a novel start codon mutation (c.1A > T, p.Met1Leu) in a Chinese patient with ARHR1.

Copy-number analysis by base-level normalization: An intuitive visualization tool for evaluating copy number variations

Next-generation sequencing (NGS) facilitates comprehensive molecular analyses that help with diagnosing unsolved disorders. In addition to detecting single-nucleotide variations and small insertions/deletions, bioinformatics tools can identify copy number variations (CNVs) in NGS data, which improves the diagnostic yield. However, due to the possibility of false positives, subsequent confirmation tests are generally performed. Here, we introduce Copy-number Analysis by BAse-level NormAlization (CABANA), a visualization tool that allows users to intuitively identify candidate CNVs using the normalized single-base-level read depth calculated from NGS data. To demonstrate how CABANA works, NGS data were obtained from 474 patients with neuromuscular disorders. CNVs were screened using a conventional bioinformatics tool, ExomeDepth, and then we normalized and visualized those data at the single-base level using CABANA, followed by manual inspection by geneticists to filter out false positives and determine candidate CNVs. In doing so, we identified 31 candidate CNVs (7%) in 474 patients and subsequently confirmed all of them to be true using multiplex ligation-dependent probe amplification. The performance of CABANA was deemed acceptable by comparing its diagnostic yield with previous data about neuromuscular disorders. Despite some limitations, we expect CABANA to help researchers accurately identify CNVs and reduce the need for subsequent confirmation testing.

EOSAL-CNV for Easy and Rapid Detection of CNVs by Fragment Analysis : EOSAL: A Fast and Reliable New Method for CNV Detection

Copy number variations (CNVs) are a type of genetic variation involving from 50 base pairs (bps) to millions of bps and, in a general point of view, can include alterations of complete chromosomes. As CNVs mean the gain or loss of DNA sequences, their detection requires specific techniques and analysis. We have developed Easy One-Step Amplification and Labeling for CNV Detection (EOSAL-CNV) by fragment analysis in a DNA sequencer. The procedure is based on a single PCR reaction for amplification and labeling of all fragments included. The protocol includes specific primers for the amplification of the regions of interest with a tail in each of the primers (one for forward and another for the reverse primers) together with primers for tail amplification. One of the primers for tail amplification is labeled with a fluorophore, allowing the amplification and labeling in the same reaction. Combination of several tail pairs and labels allows the detection of DNA fragment by different fluorophores and increases the number of fragments that can be analyzed in one reaction. PCR products can be analyzed without any purification on a DNA sequencer for fragment detection and quantification. Finally, simple and easy calculations allow the detection of fragments with deletions or extra copies. The use of EOSAL-CNV allows simplifying and reducing costs in sample analysis for CNV detection.

HER2 amplification by next-generation sequencing to identify HER2-positive invasive breast cancer with negative HER2 immunohistochemistry

Background

Human epidermal growth factor receptor 2 (HER2) positive breast carcinomas due to HER2 amplification are associated with aggressive behavior and a poor prognosis. Anti-HER2-targeted therapies are widely used to treat HER2-positive breast carcinomas with excellent outcomes. Accurate identification of HER2 amplification status in breast carcinomas is of important diagnostic and treatment value. Currently, HER2 amplification status is routinely determined by immunohistochemistry (IHC) and/or fluorescence in situ hybridization (FISH) testing. This study will review our past HER2 data to determine and characterize discordant results between HER2 IHC and FISH. It will also determine a potential impact of HER2 amplification status by next-generation sequencing (NGS) on these patients.

Methods

We reviewed a total of 4884 breast carcinomas with coexisting HER2 IHC and HER2 FISH performed at our institution from 2010 to 2022. 57 cases also had a Next-Generation-Sequencing-based (NGS) gene panel performed. Given the advances in biostatic analysis pipelines, NGS methods were utilized to provide results on HER2 amplification status along with somatic mutations.

Results

While the majority (ranging from 98.5% with IHC score of 0 and 93.1% with IHC score of 1 +) of 4884 breast carcinomas had concordant results from HER2 IHC and HER2 FISH testing, a small percentage of patients (ranging from 1.5% in those with IHC score of 0, to 6.9% with IHC score of 1 +) had discordant results, with negative HER2 IHC and positive HER2 FISH results. These patients could be reported as HER2-negative breast carcinomas if only HER2 IHC testing has been performed according to a current cost-effective HER2 test strategy. 57 patients had HER2 amplification status determined by NGS, and all patients had concordant results between HER2 NGS and FISH tests. A HER2-amplified breast carcinoma by NGS had a negative IHC and a positive HER2 FISH result. This case was classified as a HER2-positive breast carcinoma, had anti-HER2-targeted therapy, and achieved a complete clinical response.

Conclusions

A small percentage of HER2-positive breast carcinomas are unidentified because of a negative HER2 IHC based on our current cost-effective HER2 test strategy. It is not feasible and affordable in routine clinical practice to perform HER2 FISH for the cases with negative HER2 IHC (IHC score 0 and 1 +). Therefore, NGS assays capable of simultaneously detecting both somatic mutations and HER2 amplification could provide a more comprehensive genetic profiling for breast carcinomas in a clinical setting. Identification of HER2 amplification by NGS in HER2-positive breast carcinomas with negative HER2 IHC results is important since these cases are concealed by our current cost-effective HER2 test strategy with IHC first (for all cases) and FISH reflex (only for cases with IHC score of 2 +), and would offer the opportunity for potentially beneficial anti-HER2-targeted therapies for these patients.

Bioinformatics roadmap for therapy selection in cancer genomics

Tumour heterogeneity is one of the main characteristics of cancer and can be categorised into inter- or intratumour heterogeneity. This heterogeneity has been revealed as one of the key causes of treatment failure and relapse. Precision oncology is an emerging field that seeks to design tailored treatments for each cancer patient according to epidemiological, clinical and omics data. This discipline relies on bioinformatics tools designed to compute scores to prioritise available drugs, with the aim of helping clinicians in treatment selection. In this review, we describe the current approaches for therapy selection depending on which type of tumour heterogeneity is being targeted and the available next-generation sequencing data. We cover intertumour heterogeneity studies and individual treatment selection using genomics variants, expression data or multi-omics strategies. We also describe intratumour dissection through clonal inference and single-cell transcriptomics, in each case providing bioinformatics tools for tailored treatment selection. Finally, we discuss how these therapy selection workflows could be integrated into the clinical practice.

PEcnv: accurate and efficient detection of copy number variations of various lengths

Copy number variation (CNV) is a class of key biomarkers in many complex traits and diseases. Detecting CNV from sequencing data is a substantial bioinformatics problem and a standard requirement in clinical practice. Although many proposed CNV detection approaches exist, the core statistical model at their foundation is weakened by two critical computational issues: (i) identifying the optimal setting on the sliding window and (ii) correcting for bias and noise. We designed a statistical process model to overcome these limitations by calculating regional read depths via an exponentially weighted moving average strategy. A one-run detection of CNVs of various lengths is then achieved by a dynamic sliding window, whose size is self-adopted according to the weighted averages. We also designed a novel bias/noise reduction model, accompanied by the moving average, which can handle complicated patterns and extend training data. This model, called PEcnv, accurately detects CNVs ranging from kb-scale to chromosome-arm level. The model performance was validated with simulation samples and real samples. Comparative analysis showed that PEcnv outperforms current popular approaches. Notably, PEcnv provided considerable advantages in detecting small CNVs (1 kb–1 Mb) in panel sequencing data. Thus, PEcnv fills the gap left by existing methods focusing on large CNVs. PEcnv may have broad applications in clinical testing where panel sequencing is the dominant strategy. Availability and implementation: Source code is freely available at https://github.com/Sherwin-xjtu/PEcnv

The hemoglobinopathies, molecular disease mechanisms and diagnostics

Hemoglobinopathies are the most common monogenic disorders in the world with an ever increasing global disease burden each year. As most hemoglobinopathies show recessive inheritance carriers are usually clinically silent. Programmes for preconception and antenatal carrier screening, with the option of prenatal diagnosis are considered beneficial in many endemic countries. With the development of genetic tools such as Array analysis and Next Generation Sequencing in addition to state of the art screening at the hematologic, biochemic and genetic level, have contributed to the discovery of an increasing number of rare rearrangements and novel factors influencing the disease severity over the recent years. This review summarizes the basic requirements for adequate carrier screening analysis, the importance of genotype–phenotype correlation and how this may lead to the unrevealing exceptional interactions causing a clinically more severe phenotype in otherwise asymptomatic carriers. A special group of patients are β‐thalassemia carriers presenting with features of β‐thalassemia intermedia of various clinical severity. The disease mechanisms may involve duplicated α‐globin genes, mosaic partial Uniparental Isodisomy of chromosome 11p15.4 where the HBB gene is located or haplo‐insufficiency of a non‐linked gene SUPT5H on chromosome 19q, first described in two Dutch families with β‐thalassemia trait without variants in the HBB gene.

Bioinformatics: From NGS Data to Biological Complexity in Variant Detection and Oncological Clinical Practice

The use of next-generation sequencing (NGS) techniques for variant detection has become increasingly important in clinical research and in clinical practice in oncology. Many cancer patients are currently being treated in clinical practice or in clinical trials with drugs directed against specific genomic alterations. In this scenario, the development of reliable and reproducible bioinformatics tools is essential to derive information on the molecular characteristics of each patient’s tumor from the NGS data. The development of bioinformatics pipelines based on the use of machine learning and statistical methods is even more relevant for the determination of complex biomarkers. In this review, we describe some important technologies, computational algorithms and models that can be applied to NGS data from Whole Genome to Targeted Sequencing, to address the problem of finding complex cancer-associated biomarkers. In addition, we explore the future perspectives and challenges faced by bioinformatics for precision medicine both at a molecular and clinical level, with a focus on an emerging complex biomarker such as homologous recombination deficiency (HRD).

Detection of Structural Variants by NGS: Revealing Missing Alleles in Lysosomal Storage Diseases

Lysosomal storage diseases (LSDs) are a heterogeneous group of rare multisystem metabolic disorders occurring mostly in infancy and childhood, characterized by a gradual accumulation of non-degraded substrates inside the cells. Although biochemical enzymatic assays are considered the gold standard for diagnosis of symptomatic patients, genotyping is a requirement for inclusion in enzyme replacement programs and is a prerequisite for carrier tests in relatives and DNA-based prenatal diagnosis. The emerging next-generation sequencing (NGS) technologies are now offering a powerful diagnostic tool for genotyping LSDs patients by providing faster, cheaper, and higher-resolution testing options, and are allowing to unravel, in a single integrated workflow SNVs, small insertions and deletions (indels), as well as major structural variations (SVs) responsible for the pathology. Here, we summarize the current knowledge about the most recurrent and private SVs involving LSDs-related genes, review advantages and drawbacks related to the use of the NGS in the SVs detection, and discuss the challenges to bring this type of analysis in clinical diagnostics.

Case Report: Whole-Exome Sequencing-Based Copy Number Variation Analysis Identified a Novel DRC1 Homozygous Exon Deletion in a Patient With Primary Ciliary Dyskinesia

Objective: Whole-exome sequencing (WES) based copy number variation (CNV) analysis has been reported to improve the diagnostic rate in rare genetic diseases. In this study, we aim to find the disease-associated variants in a highly suspected primary ciliary dyskinesia (PCD) patient without a genetic diagnosis by routine WES analysis.

Methods: We identified the CNVs using the “Exomedepth” package in an undiagnosed PCD patient with a negative result through routine WES analysis. RNA isolation, PCR amplification, and Sanger sequencing were used to confirm the variant. High-speed video microscopy analysis (HSVA) and immunofluorescence analysis were applied to detect the functional and structural deficiency of nasal cilia and sperm flagella. Papanicolaou staining was employed to characterize the morphology of sperm flagella.

Results: NC_000002.11(NM_145038.5): g.26635488_26641606del, c.156-1724_244-2550del, r.156_243del, p. (Glu53Asnfs*13), a novel DRC1 homozygous CNV, was identified by WES-based CNV analysis rather than routine variants calling, in a patient from a non-consanguineous family. HSVA results showed no significant change in ciliary beating frequency but with reduced beating amplitude compared with normal control, and his spermatozoa were almost immotile. The diagnosis of multiple morphological abnormalities of the sperm flagella (MMAF) was established through sperm motility and morphology analysis. PCR amplification and Sanger sequencing confirmed the novel variant of DRC1. Immunofluorescence showed that both cilia and sperm flagella were deficient in protein expression related to the dynein regulatory complex.

Conclusion: This report identifies a novel DRC1 disease-associated variant by WES-based CNV analysis from a highly suspected PCD patient with MMAF. Our findings not only expand the genetic spectrum of PCD with MMAF but suggest that in combination with CNV analysis might improve the efficiency of genetic tests.

Single-Cell RNA-Seq Reveals the Heterogeneity of Cell Communications between Schwann Cells and Fibroblasts within the Microenvironment in Vestibular Schwannoma

Vestibular schwannomas (VSs), which develop from Schwann cells (SCs) of the vestibular nerve, are the most prevalent benign tumors of the cerebellopontine angle and internal auditory canal. Despite advances in treatment, the cellular components and mechanisms of VS tumor progression remain unclear. Here, we performed single-cell RNA-sequencing on clinically surgically isolated VS samples and clarified their cellular composition in a sophisticated manner, including the heterogeneous SC subtypes. Advanced bioinformatics analysis also revealed the associated biological functions, pseudotime trajectory, and transcriptional network of the SC subgroups. We also found that there was tight intercellular communication between SCs and tumor-associated fibroblasts via integrin and growth factor signaling and that the gene expression differences in SCs and fibroblasts determined the heterogeneity of cellular communication in different individuals. Our findings suggest a microenvironmental mechanism underlying the development of VS.

Determining the Accuracy of Next Generation Sequencing Based Copy Number Variation Analysis in Hereditary Breast and Ovarian Cancer

BACKGROUND

Copy number variations (CNVs) are commonly associated with malignancies, including hereditary breast and ovarian cancers. Next generation sequencing (NGS) provides solutions for CNV detection in a single run. This study aimed to compare the accuracy of CNV detection by NGS analysing tool against Multiplex Ligation Dependent Probe Amplification (MLPA).

RESEARCH DESIGN AND METHODS

In total, 1276 cases were studied by targeted NGS panels and 691 cases (61 calls in 58 NGS-CNV positive and 633 NGS-CNV negative cases) were validated by MLPA.

RESULTS

Twenty-eight (46%) NGS-CNV positive calls were consistent, whereas 33 (54%) calls showed discordance with MLPA. Two cases were detected as SNV by the NGS and CNV by the MLPA analysis. In total, 2% of the cases showed an MLPA confirmed CNV region in BRCA1/2. The results of this study showed that despite the high false positive call rate of the NGS-CNV algorithm, there were no false negative calls. The cases that were determined to be negative by the NGS and positive by the MLPA were actually carrying SNVs that were located on the MLPA probe binding sites.

CONCLUSION

The diagnostic performance of NGS-CNV analysis is promising; however, the need for confirmation by different methods remains.

Progress in Methods for Copy Number Variation Profiling

Copy number variations (CNVs) are the predominant class of structural genomic variations involved in the processes of evolutionary adaptation, genomic disorders, and disease progression. Compared with single-nucleotide variants, there have been challenges associated with the detection of CNVs owing to their diverse sizes. However, the field has seen significant progress in the past 20–30 years. This has been made possible due to the rapid development of molecular diagnostic methods which ensure a more detailed view of the genome structure, further complemented by recent advances in computational methods. Here, we review the major approaches that have been used to routinely detect CNVs, ranging from cytogenetics to the latest sequencing technologies, and then cover their specific features.

Case report: Compound heterozygosity in PKLR gene with a large exon deletion and a novel rare p.Gly536Asp variant as a cause of severe pyruvate kinase deficiency

Red cell pyruvate kinase (PK) deficiency is the most common cause of hereditary nonspherocytic hemolytic anemia and the most frequent enzyme abnormality of the glycolytic pathway. To the best of our knowledge, this is the first Korean PK deficiency study that analyzes copy number variation (CNV) using next-generation sequencing (NGS). A 7-year-old girl with jaundice was admitted for evaluation of a persistent hemolytic anemia. The proband appeared chronically ill, showing a yellowish skin color, icteric sclera, hepatomegaly, and splenomegaly on physical examination. Sequence variants and CNV generated from NGS data were estimated to determine if there was a potential genetic cause. As a result, compound heterozygosity in the PKLR gene for a large exon deletion between exon 3 and exon 9 accompanied with a novel rare p.Gly536Asp variant located on exon 10 was identified as a cause of severe PK deficiency in the proband. The PK activity of the proband had been measured at the time of day 1, 21, and 28 after receiving transfusion to indirectly assume the effect of the transfused blood, and the results were 100.9%, 73.0%, and 48.5%, compared with average of normal controls, respectively. Our report emphasizes the need to perform complete CNV analysis of NGS data and gene dosage assays such as multiplex ligation-dependent probe amplification to evaluate large deletions or duplications/insertions of the PKLR gene in patients with suspected PK deficiency.

An Easy-to-Use Approach to Detect CNV From Targeted NGS Data: Identification of a Novel Pathogenic Variant in MO Disease

BACKGROUND

Despite the new next-generation sequencing (NGS) molecular approaches implemented the genetic testing in clinical diagnosis, copy number variation (CNV) detection from NGS data remains difficult mainly in the absence of bioinformatics personnel (not always available among laboratory resources) and when using very small gene panels that do not meet commercial software criteria. Furthermore, not all large deletions/duplications can be detected with the Multiplex Ligation-dependent Probe Amplification (MLPA) technique due to both the limitations of the methodology and no kits available for the most of genes.

AIM

We propose our experience regarding the identification of a novel large deletion in the context of a rare skeletal disease, multiple osteochondromas (MO), using and validating a user-friendly approach based on NGS coverage data, which does not require any dedicated software or specialized personnel.

METHODS

The pipeline uses a simple algorithm comparing the normalized coverage of each amplicon with the mean normalized coverage of the same amplicon in a group of "wild-type" samples representing the baseline. It has been validated on 11 samples, previously analyzed by MLPA, and then applied on 20 patients with MO but negative for the presence of pathogenic variants in EXT1 or EXT2 genes. Sensitivity, specificity, and accuracy were evaluated.

RESULTS

All the 11 known CNVs (exon and multi-exon deletions) have been detected with a sensitivity of 97.5%. A novel EXT2 partial exonic deletion c. (744-122)-?_804+?del -out of the MLPA target regions- has been identified. The variant was confirmed by real-time quantitative Polymerase Chain Reaction (qPCR).

CONCLUSION

In addition to enhancing the variant detection rate in MO molecular diagnosis, this easy-to-use approach for CNV detection can be easily extended to many other diagnostic fields-especially in resource-limited settings or very small gene panels. Notably, it also allows partial-exon deletion detection.

Evaluation of copy number variants for genetic hearing loss: a review of current approaches and recent findings

Structural variation includes a change in copy number, orientation, or location of a part of the genome. Copy number variants (CNVs) are a common cause of genetic hearing loss, comprising nearly 20% of diagnosed cases. While large deletions involving the gene STRC are the most common pathogenic CNVs, a significant proportion of known hearing loss genes also contain pathogenic CNVs. In this review, we provide an overview of currently used methods for detection of CNVs in genes known to cause hearing loss including molecular techniques such as multiplex ligation probe amplification (MLPA) and digital droplet polymerase chain reaction (ddPCR), array-CGH and single-nucleotide polymorphism (SNP) arrays, as well as techniques for detection of CNVs using next-generation sequencing data analysis including targeted gene panel, exome, and genome sequencing data. In addition, in this review, we compile published data on pathogenic hearing loss CNVs to provide an up-to-date overview. We show that CNVs have been identified in 29 different non-syndromic hearing loss genes. An understanding of the contribution of CNVs to genetic hearing loss is critical to the current diagnosis of hearing loss and is crucial for future gene therapies. Thus, evaluation for CNVs is required in any modern pipeline for genetic diagnosis of hearing loss.

isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data

Background

Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole‐exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches.

Results

We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms: CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets.

Conclusions

isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https://gitlab.com/sequentiateampublic/isocnv.

Next-generation gene panel testing in adolescents and adults in a medical neuropsychiatric genetics clinic

Intellectual disability (ID) encompasses a clinically and genetically heterogeneous group of neurodevelopmental disorders that may present with psychiatric illness in up to 40% of cases. Despite the evidence for clinical utility of genetic panels in pediatrics, there are no published studies in adolescents/adults with ID or autism spectrum disorder (ASD). This study was approved by our institutional research ethics board. We retrospectively reviewed the medical charts of all patients evaluated between January 2017 and December 2019 in our adult neuropsychiatric genetics clinic at the McGill University Health Centre (MUHC), who had undergone a comprehensive ID/ASD gene panel. Thirty-four patients aged > 16 years, affected by ID/ASD and/or other neuropsychiatric/behavioral disorders, were identified. Pathogenic or likely pathogenic variants were identified in one-third of our cohort (32%): 8 single-nucleotide variants in 8 genes (CASK, SHANK3, IQSEC2, CHD2, ZBTB20, TREX1, SON, and TUBB2A) and 3 copy number variants (17p13.3, 16p13.12p13.11, and 9p24.3p24.1). The presence of psychiatric/behavioral disorders, regardless of the co-occurrence of ID, and, at a borderline level, the presence of ID alone were associated with positive genetic findings (p = 0.024 and p = 0.054, respectively). Moreover, seizures were associated with positive genetic results (p = 0.024). One-third of individuals presenting with psychiatric illness who met our red flags for Mendelian diseases have pathogenic or likely pathogenic variants which can be identified using a comprehensive ID/ASD gene panel (~ 2500 genes) performed on an exome backbone.

Next Generation Sequencing Based Multiplex Long-Range PCR for Routine Genotyping of Autoinflammatory Disorders

Background

During the last decade, remarkable progress with massive sequencing has been made in the identification of disease-associated genes for AIDs using next-generation sequencing technologies (NGS). An international group of experts described the ideal genetic screening method which should give information about SNVs, InDels, Copy Number Variations (CNVs), GC rich regions. We aimed to develop and validate a molecular diagnostic method in conjunction with the NGS platform as an inexpensive, extended and uniform coverage and fast screening tool which consists of nine genes known to be associated with various AIDs.

Methods

For the validation of basic and expanded panels, long-range multiplex models were setup on healthy samples without any known variations for MEFV, MVK, TNFRSF1A, NLRP3, PSTPIP1, IL1RN, NOD2, NLRP12 and LPIN2 genes. Patients with AIDs who had already known causative variants in these genes were sequenced for analytical validation. As a last step, multiplex models were validated on patients with pre-diagnosis of AIDs. All sequencing steps were performed on the Illumina NGS platform. Validity steps included the selection of related candidate genes, primer design, development of screening methods, validation and verification of the product. The GDPE (Gentera) bioinformatics pipeline was followed.

Results

Although there was no nonsynonymous variation in 21 healthy samples, 107 synonymous variant alleles and some intronic and UTR variants were detected. In 10 patients who underwent analytical validation, besides the 11 known nonsynonymous variant alleles, 11 additional nonsynonymous variant alleles and a total of 81 synonymous variants were found. In the clinical validation phase, 46 patients sequenced with multiplex panels, genetic and clinical findings were combined for diagnosis.

Conclusion

In this study, we describe the development and validation of an NGS-based multiplex array enabling the "long-amplicon" approach for targeted sequencing of nine genes associated with common AIDs. This screening tool is less expensive and more comprehensive compared to other methods and more informative than traditional sequencing. The proposed panel offers advantages to WES or hybridization probe equivalents in terms of CNV analysis, high sensitivity and uniformity, GC-rich region sequencing, InDel detection and intron covering.

Analysis of Copy Number Variations in Solid Tumors Using a Next Generation Sequencing Custom Panel

Somatic copy number variations (CNV; i.e., amplifications and deletions) have been implicated in the origin and development of multiple cancers and some of these aberrations are designated targets for therapies. Although FISH is still considered the gold standard for CNV detection, the increasing number of potentially druggable amplifications to be assessed makes a gene-by-gene approach time- and tissue-consuming. Here we investigated the potential of next generation sequencing (NGS) custom panels to simultaneously determine CNVs across FFPE solid tumor samples. DNA was purified from cell lines and FFPE samples and analyzed by NGS sequencing using a 20-gene custom panel in the GeneReader Platform®. CNVs were identified using an in-house algorithm based on the UMI read coverage. Retrospective validation of in-house algorithm to identify CNVs showed 97.1% concordance rate with the NGS custom panel. The prospective analysis was performed in a cohort of 243 FFPE samples from patients arriving at our hospital, which included 74 NSCLC tumors, 148 CRC tumors, and 21 other tumors. Of them, 33% presented CNVs by NGS and in 14 cases (5.9%) the CNV was the only alteration detected. We have identified CNV alterations in about one-third of our cohort, including FGFR1, CDK6, CDK4, EGFR, MET, ERBB2, BRAF, or KRAS. Our work highlights the need to include CNV testing as a part of routine NGS analysis in order to uncover clinically relevant gene amplifications that can guide the selection of therapies.

CNVfilteR: an R/bioconductor package to identify false positives produced by germline NGS CNV detection tools

 

Germline copy-number variants (CNVs) are relevant mutations for multiple genetics fields, such as the study of hereditary diseases. However, available benchmarks show that all next-generation sequencing (NGS) CNV calling tools produce false positives. We developed CNVfilteR, an R package that uses the single nucleotide variant calls usually obtained in germline NGS pipelines to identify those false positives. The package can detect both false deletions and false duplications. We evaluated CNVfilteR performance on callsets generated by 13 CNV calling tools on 3 whole-genome sequencing and 541 panel samples, showing a decrease of up to 44.8% in false positives and consistent F1-score increase. Using CNVfilteR to detect false-positive calls can improve the overall performance of existing CNV calling pipelines.

AVAILABILITY

CNVfilteR is released under Artistic-2.0 License. Source code and documentation are freely available at Bioconductor (http://www.bioconductor.org/packages/CNVfilteR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Is Gene-Size an Issue for the Diagnosis of Skeletal Muscle Disorders?

Human genes have a variable length. Those having a coding sequence of extraordinary length and a high number of exons were almost impossible to sequence using the traditional Sanger-based gene-by-gene approach. High-throughput sequencing has partly overcome the size-related technical issues, enabling a straightforward, rapid and relatively inexpensive analysis of large genes.

Several large genes (e.g. TTN, NEB, RYR1, DMD) are recognized as disease-causing in patients with skeletal muscle diseases. However, because of their sheer size, the clinical interpretation of variants in these genes is probably the most challenging aspect of the high-throughput genetic investigation in the field of skeletal muscle diseases.

The main aim of this review is to discuss the technical and interpretative issues related to the diagnostic investigation of large genes and to reflect upon the current state of the art and the future advancements in the field.

Genomic Signature of Oral Squamous Cell Carcinomas from Non-Smoking Non-Drinking Patients

Simple Summary

A clinically distinct cohort of non-smoking non-drinking patients who develop oral cavity squamous cell carcinomas has been identified, with previous work suggesting that these patients tend to be older, female, and have poor outcomes. Our study characterised tumour molecular alterations in these patients, identifying differences in genomic profiles as compared to patients who smoke and/or drink. Associations between molecular alterations and other clinical and pathological characteristics were also explored.

Abstract

Molecular alterations in 176 patients with oral squamous cell carcinomas (OSCC) were evaluated to delineate differences in non-smoking non-drinking (NSND) patients. Somatic mutations and DNA copy number variations (CNVs) in a 68-gene panel and human papilloma virus (HPV) status were interrogated using targeted next-generation sequencing. In the entire cohort, TP53 (60%) and CDKN2A (24%) were most frequently mutated, and the most common CNVs were EGFR amplifications (9%) and deletions of BRCA2 (5%) and CDKN2A (4%). Significant associations were found for TP53 mutation and nodal disease, lymphovascular invasion and extracapsular spread, CDKN2A mutation or deletion with advanced tumour stage, and EGFR amplification with perineural invasion and extracapsular spread. PIK3CA mutation, CDKN2A deletion, and EGFR amplification were associated with worse survival in univariate analyses (p < 0.05 for all comparisons). There were 59 NSND patients who tended to be female and older than patients who smoke and/or drink, and showed enrichment of CDKN2A mutations, EGFR amplifications, and BRCA2 deletions (p < 0.05 for all comparisons), with a younger subset showing higher mutation burden. HPV was detected in three OSCC patients and not associated with smoking and drinking habits. NSND OSCC exhibits distinct genomic profiles and further exploration to elucidate the molecular aetiology in these patients is warranted.

Immune deficiency, autoimmune disease and intellectual disability: A pleiotropic disorder caused by biallelic variants in the TPP2 gene

Four individuals from two families presented with a multisystemic condition of suspected genetic origin that was diagnosed only after genome analysis. The main phenotypic features were immune system dysregulation (severe immunodeficiency with autoimmunity) and intellectual disability. The four individuals were found to be homozygous for a 4.4 Kb deletion removing exons 20-23 (NM_003291.4) of the TPP2 gene, predicting a frameshift with premature termination of the protein. The deletion was located on a shared chromosome 13 haplotype indicating a Swiss founder mutation. Tripeptidyl peptidase 2 (TPP2) is a protease involved in HLA/antigen complex processing and amino acid homeostasis. Biallelic variants in TPP2 have been described in 10 individuals with variable features including immune deficiency, autoimmune cytopenias, and intellectual disability or chronic sterile brain inflammation mimicking multiple sclerosis. Our observations further delineate this severe condition not yet included in the OMIM catalog. Timely recognition of TPP2 deficiency is crucial since (1) immune surveillance is needed and hematopoietic stem cell transplantation may be necessary, and (2) for provision of genetic counselling. Additionally, enzyme replacement therapy, as already established for TPP1 deficiency, might be an option in the future.

Genetic alterations in squamous cell lung cancer associated with idiopathic pulmonary fibrosis

Patients with idiopathic pulmonary fibrosis (IPF) are at higher risk of developing lung cancers including squamous cell lung carcinoma (SCC), which typically carries a poor prognosis. Although the molecular basis of cancer development subsequent to IPF has not been fully investigated, we recently reported two epigenetic phenotypes characterized by frequent and infrequent DNA hypermethylation in SCC, and an association of the infrequent hypermethylation phenotype with IPF-associated SCCs. Here, we conducted targeted exon sequencing in SCCs with and without IPF using the Human Lung Cancer Panel to investigate the genetic basis of IPF-associated SCC. SCCs with and without IPF displayed comparable numbers of total mutations (137 ± 22 vs 131 ± 27, P = .5), nonsynonymous mutations (72 ± 14 vs 69 ± 16, P = .5), indels (3.0 ± 3.5 vs 3.0 ± 3.9, P = 1) and synonymous mutations (62 ± 9.1 vs 60 ± 12, P = .5). Signature 1 was the predominant signature in SCCs with and without IPF. SETD2 and NFE2L2 mutations were significantly associated with IPF (44% vs 13%, P = .03 for SETD2; 38% vs 10%, P = .04 for NFE2L2). MYC amplification, assessed by copy number variant analysis, was also significantly associated with IPF (18.8% vs 0%, P = .04). Mutations in TP53 and CDKN2A were observed relatively frequently in SCCs with frequent hypermethylation (P = .02 for TP53 and P = .06 for CDKN2A). Survival analysis revealed that the SETD2 mutation was significantly associated with worse prognosis (P = .04). Collectively, we found frequent involvement of SETD2 and NFE2L2 mutations and MYC amplification in SCCs with IPF, and an association of a SETD2 mutation with poorer prognosis.

Estimating Copy-Number Proportions: The Comeback of Sanger Sequencing

Determination of the relative copy numbers of mixed molecular species in nucleic acid samples is often the objective of biological experiments, including Single-Nucleotide Polymorphism (SNP), indel and gene copy-number characterization, and quantification of CRISPR-Cas9 base editing, cytosine methylation, and RNA editing. Standard dye-terminator chromatograms are a widely accessible, cost-effective information source from which copy-number proportions can be inferred. However, the rate of incorporation of dye terminators is dependent on the dye type, the adjacent sequence string, and the secondary structure of the sequenced strand. These variable rates complicate inferences and have driven scientists to resort to complex and costly quantification methods. Because these complex methods introduce their own biases, researchers are rethinking whether rectifying distortions in sequencing trace files and using direct sequencing for quantification will enable comparable accurate assessment. Indeed, recent developments in software tools (e.g., TIDE, ICE, EditR, BEEP and BEAT) indicate that quantification based on direct Sanger sequencing is gaining in scientific acceptance. This commentary reviews the common obstacles in quantification and the latest insights and developments relevant to estimating copy-number proportions based on direct Sanger sequencing, concluding that bidirectional sequencing and sophisticated base calling are the keys to identifying and avoiding sequence distortions.

Performance of In Silico Prediction Tools for the Detection of Germline Copy Number Variations in Cancer Predisposition Genes in 4208 Female Index Patients with Familial Breast and Ovarian Cancer

Simple Summary

The identification of germline copy number variants (CNVs) by targeted nextgeneration sequencing frequently relies on in silico prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools in 17 cancer predisposition genes in a large series of 4208 female index patients with familial breast and/or ovarian cancer. We identified 77 CNVs in 76 out of 4208 patients; six CNVs were missed by at least one of the prediction tools. Experimental verification of in silico predicted CNVs is required due to high frequencies of false positive predictions. For female index patients with familial breast and/or ovarian cancer, CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further cancer predisposition genes.

Abstract

The identification of germline copy number variants (CNVs) by targeted next-generation sequencing (NGS) frequently relies on in silico CNV prediction tools with unknown sensitivities. We investigated the performances of four in silico CNV prediction tools, including one commercial (Sophia Genetics DDM) and three non-commercial tools (ExomeDepth, GATK gCNV, panelcn.MOPS) in 17 cancer predisposition genes in 4208 female index patients with familial breast and/or ovarian cancer (BC/OC). CNV predictions were verified via multiplex ligation-dependent probe amplification. We identified 77 CNVs in 76 out of 4208 patients (1.81%); 33 CNVs were identified in genes other than BRCA1/2, mostly in ATM, CHEK2, and RAD51C and less frequently in BARD1, MLH1, MSH2, PALB2, PMS2, RAD51D, and TP53. The Sophia Genetics DDM software showed the highest sensitivity; six CNVs were missed by at least one of the non-commercial tools. The positive predictive values ranged from 5.9% (74/1249) for panelcn.MOPS to 79.1% (72/91) for ExomeDepth. Verification of in silico predicted CNVs is required due to high frequencies of false positive predictions, particularly affecting target regions at the extremes of the GC content or target length distributions. CNV detection should not be restricted to BRCA1/2 due to the relevant proportion of CNVs in further BC/OC predisposition genes.

EMQN best practice guidelines for genetic testing in dystrophinopathies

Dystrophinopathies are X-linked diseases, including Duchenne muscular dystrophy and Becker muscular dystrophy, due to DMD gene variants. In recent years, the application of new genetic technologies and the availability of new personalised drugs have influenced diagnostic genetic testing for dystrophinopathies. Therefore, these European best practice guidelines for genetic testing in dystrophinopathies have been produced to update previous guidelines published in 2010.These guidelines summarise current recommended technologies and methodologies for analysis of the DMD gene, including testing for deletions and duplications of one or more exons, small variant detection and RNA analysis. Genetic testing strategies for diagnosis, carrier testing and prenatal diagnosis (including non-invasive prenatal diagnosis) are then outlined. Guidelines for sequence variant annotation and interpretation are provided, followed by recommendations for reporting results of all categories of testing. Finally, atypical findings (such as non-contiguous deletions and dual DMD variants), implications for personalised medicine and clinical trials and incidental findings (identification of DMD gene variants in patients where a clinical diagnosis of dystrophinopathy has not been considered or suspected) are discussed.

Detection of copy-number variations from NGS data using read depth information: a diagnostic performance evaluation

The detection of copy-number variations (CNVs) from NGS data is underexploited as chip-based or targeted techniques are still commonly used. We assessed the performances of a workflow centered on CANOES, a bioinformatics tool based on read depth information. We applied our workflow to gene panel (GP) and whole-exome sequencing (WES) data, and compared CNV calls to quantitative multiplex PCR of short fluorescent fragments (QMSPF) or array comparative genomic hybridization (aCGH) results. From GP data of 3776 samples, we reached an overall positive predictive value (PPV) of 87.8%. This dataset included a complete comprehensive QMPSF comparison of four genes (60 exons) on which we obtained 100% sensitivity and specificity. From WES data, we first compared 137 samples with aCGH and filtered comparable events (exonic CNVs encompassing enough aCGH probes) and obtained an 87.25% sensitivity. The overall PPV was 86.4% following the targeted confirmation of candidate CNVs from 1056 additional WES. In addition, our CANOES-centered workflow on WES data allowed the detection of CNVs with a resolution of single exons, allowing the detection of CNVs that were missed by aCGH. Overall, switching to an NGS-only approach should be cost-effective as it allows a reduction in overall costs together with likely stable diagnostic yields. Our bioinformatics pipeline is available at: https://gitlab.bioinfo-diag.fr/nc4gpm/canoes-centered-workflow .

Evaluation of CNV detection tools for NGS panel data in genetic diagnostics

Although germline copy-number variants (CNVs) are the genetic cause of multiple hereditary diseases, detecting them from targeted next-generation sequencing data (NGS) remains a challenge. Existing tools perform well for large CNVs but struggle with single and multi-exon alterations. The aim of this work is to evaluate CNV calling tools working on gene panel NGS data and their suitability as a screening step before orthogonal confirmation in genetic diagnostics strategies. Five tools (DECoN, CoNVaDING, panelcn.MOPS, ExomeDepth, and CODEX2) were tested against four genetic diagnostics datasets (two in-house and two external) for a total of 495 samples with 231 single and multi-exon validated CNVs. The evaluation was performed using the default and sensitivity-optimized parameters. Results showed that most tools were highly sensitive and specific, but the performance was dataset dependant. When evaluating them in our diagnostics scenario, DECoN and panelcn.MOPS detected all CNVs with the exception of one mosaic CNV missed by DECoN. However, DECoN outperformed panelcn.MOPS specificity achieving values greater than 0.90 when using the optimized parameters. In our in-house datasets, DECoN and panelcn.MOPS showed the highest performance for CNV screening before orthogonal confirmation. Benchmarking and optimization code is freely available at https://github.com/TranslationalBioinformaticsIGTP/CNVbenchmarkeR .

Copy Number Variation Detection Using Total Variation

Next-generation sequencing (NGS) technologies offer new opportunities for precise and accurate identification of genomic aberrations, including copy number variations (CNVs). For high-throughput NGS data, using depth of coverage has become a major approach to identify CNVs, especially for whole exome sequencing (WES) data. Due to the high level of noise and biases of read-count data and complexity of the WES data, existing CNV detection tools identify many false CNV segments. Besides, NGS generates a huge amount of data, requiring to use effective and efficient methods. In this work, we propose a novel segmentation algorithm based on the total variation approach to detect CNVs more precisely and efficiently using WES data. The proposed method also filters out outlier read-counts and identifies significant change points to reduce false positives. We used real and simulated data to evaluate the performance of the proposed method and compare its performance with those of other commonly used CNV detection methods. Using simulated and real data, we show that the proposed method outperforms the existing CNV detection methods in terms of accuracy and false discovery rate and has a faster runtime compared to the circular binary segmentation method.