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Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customizable parameters. ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using targeted next-generation sequencing (NGS) datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity, and accuracy. ifCNV is a publicly available open-source software (https://github.com/SimCab-CHU/ifCNV) that allows the detection of CNVs in many clinical situations.

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. [ABSTRACT FROM AUTHOR]

BackgroundDespite 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.AimWe 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.MethodsThe 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.ResultsAll 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).ConclusionIn 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.

Gains and losses of large segments of genomic DNA, known as copy number variants (CNVs) gained considerable interest in clinical diagnostics lately, as particular forms may lead to inherited genetic diseases. In recent decades, researchers developed a wide variety of cytogenetic and molecular methods with different detection capabilities to detect clinically relevant CNVs. In this review, we summarize methodological progress from conventional approaches to current state of the art techniques capable of detecting CNVs from a few bases up to several megabases. Although the recent rapid progress of sequencing methods has enabled precise detection of CNVs, determining their functional effect on cellular and whole-body physiology remains a challenge. Here, we provide a comprehensive list of databases and bioinformatics tools that may serve as useful assets for researchers, laboratory diagnosticians, and clinical geneticists facing the challenge of CNV detection and interpretation. [ABSTRACT FROM AUTHOR]

Summary: Large phenotypic differences have been observed between Tongcheng and Large White pigs. However, little is known about their genetic basis. This study performed a genome‐wide comparison of CNVs between Tongcheng and Large White pigs using genome sequencing data. By combining the advantages of three different strategies (read depth, paired‐end mapping and split read), we detected in total 18 687 CNVs that covered approximately 3.5% of the pig genome length for Tongcheng and Large White pigs. We identified 1864 breed‐stratified CNVs (top 10%) by performing VST statistics. Functional enrichment analyses for genes located in breed‐stratified CNVs were found to be involved in pigmentation, behavior, immune system and reproductive processes, which coincide with phenotypic differences between the two breeds. Using a systematic analysis of the genome and transcriptome data, we further identified four novel breed‐differential CNVs on the functional genes (disease‐resistant, DCUN1D2 and SPARCL1; lipid metabolism, PLEKHA2 and SLCO1A2). Subsequent PCR validation confirmed their accurate breakpoint positions in 33 Tongcheng pigs and 33 Large White pigs. This study provides essential information on differential CNVs for further research on the genetic basis of phenotypic differences between Tongcheng and Large White pigs. [ABSTRACT FROM AUTHOR]

Genetic laboratories use custom-commercial targeted next-generation sequencing (tg-NGS) assays to identify disease-causing variants. Although the high coverage achieved with these tests allows for the detection of copy number variants (CNVs), which account for an important proportion of the genetic burden in human diseases, an easy-to-use tool for automatic CNV detection is still lacking. This article presents a new CNV detection tool optimized for tg-NGS data: PattRec. PattRec was evaluated using a wide range of data, and its performance compared with those of other CNV detection tools. The software includes features for selecting optimal controls, discarding polymorphic CNVs prior to analysis, and filtering out deletions based on SNV zygosity, and automatically creates an in-house CNV database. There is no need for high level bioinformatic expertise and users can choose color-coded xlsx output that helps to prioritize potentially pathogenic CNVs. PattRec is presented as a Java based GUI, freely available online: https://github.com/irotero/PattRec. [ABSTRACT FROM AUTHOR]

Copy number variations (CNVs) represent a type of structural variant involving alterations in the number of copies of specific regions of DNA that can either be deleted or duplicated. CNVs contribute substantially to normal population variability, however, abnormal CNVs cause numerous genetic disorders. At present, several methods for CNV detection are applied, ranging from the conventional cytogenetic analysis, through microarray-based methods (aCGH), to next-generation sequencing (NGS). In this paper, we present GenomeScreen, an NGS-based CNV detection method for low-coverage, whole-genome sequencing. We determined the theoretical limits of its accuracy and obtained confirmation in an extensive in silico study and in real patient samples with known genotypes. In theory, at least 6 M uniquely mapped reads are required to detect a CNV with the length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in silico analysis required at least 8 M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has mean resolution of 200 kb. GenomeScreen and aCGH both detected 59 deviations, while GenomeScreen furthermore detected 134 other (usually) smaller variations. When compared to aCGH, overall performance of the proposed GenemoScreen tool is comparable or superior in terms of accuracy, turn-around time, and cost-effectiveness, thus providing reasonable benefits, particularly in a prenatal diagnosis setting.

Background: With the introduction of Olaparib treatment for BRCA-deficient recurrent ovarian cancer, testing for somatic and/or germline mutations in BRCA1/2 genes in tumor tissues became essential for treatment decisions. In most cases only formalin-fixed paraffin-embedded (FFPE) samples, containing fragmented and chemically modified DNA of minor quality, are available. Thus, multiplex PCR-based sequencing is most commonly applied in routine molecular testing, which is predominantly focused on the identification of known hot spot mutations in oncogenes.Methods: We compared the overall performance of an adjusted targeted capture-based enrichment protocol and a multiplex PCR-based approach for calling of pathogenic SNVs and InDels using DNA extracted from 13 FFPE tissue samples. We further applied both strategies to seven blood samples and five matched FFPE tumor tissues of patients with known germline exon-spanning deletions and gene-wide duplications in BRCA1/2 to evaluate CNV detection based solely on panel NGS data. Finally, we analyzed DNA from FFPE tissues of 11 index patients from families suspected of having hereditary breast and ovarian cancer, of whom no blood samples were available for testing, in order to identify underlying pathogenic germline BRCA1/2 mutations.Results: The multiplex PCR-based protocol produced inhomogeneous coverage among targets of each sample and between samples as well as sporadic amplicon drop out, leading to insufficiently or non-covered nucleotides, which subsequently hindered variant detection. This protocol further led to detection of PCR-artifacts that could easily have been misinterpreted as pathogenic mutations. No such limitations were observed by application of an adjusted targeted capture-based protocol, which allowed for CNV calling with 86% sensitivity and 100% specificity. All pathogenic CNVs were confirmed in the five matched FFPE tumor samples from patients carrying known pathogenic germline mutations and we additionally identified somatic loss of the second allele in BRCA1/2. Furthermore we detected pathogenic BRCA1/2 variants in four the eleven FFPE samples from patients of whom no blood was available for analysis.Conclusions: We demonstrate that an adjusted targeted capture-based enrichment protocol is superior to commonly applied multiplex PCR-based protocols for reliable BRCA1/2 variant detection, including CNV-detection, using FFPE tumor samples. [ABSTRACT FROM AUTHOR]

Gains and losses of large segments of genomic DNA, known as copy number variants (CNVs) gained considerable interest in clinical diagnostics lately, as particular forms may lead to inherited genetic diseases. In recent decades, researchers developed a wide variety of cytogenetic and molecular methods with different detection capabilities to detect clinically relevant CNVs. In this review, we summarize methodological progress from conventional approaches to current state of the art techniques capable of detecting CNVs from a few bases up to several megabases. Although the recent rapid progress of sequencing methods has enabled precise detection of CNVs, determining their functional effect on cellular and whole-body physiology remains a challenge. Here, we provide a comprehensive list of databases and bioinformatics tools that may serve as useful assets for researchers, laboratory diagnosticians, and clinical geneticists facing the challenge of CNV detection and interpretation.

Detection of copy number variants as an integral part of noninvasive prenatal testing is increasingly used in clinical practice worldwide. We performed validation on plasma samples from 34 pregnant women with known aberrations using cell-free DNA sequencing to evaluate the sensitivity for copy number variants (CNV) detection using an in-house CNV fraction-based detection algorithm. The sensitivity for CNVs smaller than 3 megabases (Mb), larger than 3Mb, and overall was 78.57%, 100%, and 90.6%, respectively. Regarding the fetal fraction, detection sensitivity in the group with a fetal fraction of less than 10% was 57.14%, whereas there was 100% sensitivity in the group with fetal fraction exceeding 10%. The assay is also capable of indicating whether the origin of an aberration is exclusively fetal or fetomaternal/maternal. This validation demonstrated that a CNV fraction-based algorithm was applicable and feasible in clinical settings as a supplement to testing for common trisomies 21, 18, and 13.

Abstract Given that all forms of Usher syndrome (USH) present with hearing loss in advance of retinal disease, the syndromic nature of the disorder is rarely appreciated when critical management decisions are being made. As a result, molecular diagnostics are crucial in guiding the management of USH patients. While 11 genes have been associated with USH, the USH2A gene is one of the largest contributors. Approximately 20% of suspected USH probands that undergo USH2A sequencing at our laboratory receive an inconclusive result due to the identification of a monoallelic disease-causing variant in USH2A. Many studies suggest that intragenic deletions and duplications represent an important USH2A variant type that can be missed by sequencing assays if supplemental algorithms or testing methods are not applied. To gain a comprehensive view of the contribution of USH2A CNVs to USH, we conducted prospective and retrospective screening in 700 hearing loss probands. Fourteen individuals with 11 unique USH2A CNVs are reported, including one pathogenic multi-exon duplication. Additionally, we mapped deletion breakpoints and performed a meta-analysis of USH2A CNVs to evaluate recurrence and underlying mechanisms. This analysis revealed breakpoint grouping within three introns, raising the possibility of CNV-susceptible regions within the gene. Overall, our data highlight the diversity of pathogenic CNVs in this gene, demonstrating that the comprehensive, high-resolution USH2A CNV analysis methods employed here are essential components of clinical genetic testing for USH. [ABSTRACT FROM AUTHOR]

Summary: Beak deformity (crossed beaks) is found in several indigenous chicken breeds including Beijing‐You studied here. Birds with deformed beaks have reduced feed intake and poor production performance. Recently, copy number variation (CNV) has been examined in many species and is recognized as a source of genetic variation, especially for disease phenotypes. In this study, to unravel the genetic mechanisms underlying beak deformity, we performed genome‐wide CNV detection using Affymetrix chicken high‐density 600K data on 48 deformed‐beak and 48 normal birds using penncnv. As a result, two and eight CNV regions (CNVRs) covering 0.32 and 2.45 Mb respectively on autosomes were identified in deformed‐beak and normal birds respectively. Further RT‐qPCR studies validated nine of the 10 CNVRs. The ratios of six CNVRs were significantly different between deformed‐beak and normal birds (P < 0.01). Within these six regions, three and 21 known genes were identified in deformed‐beak and normal birds respectively. Bioinformatics analysis showed that these genes were enriched in six GO terms and one KEGG pathway. Five candidate genes in the CNVRs were further validated using RT‐qPCR. The expression of LRIG2 (leucine rich repeats and immunoglobulin like domains 2) was lower in birds with deformed beaks (P < 0.01). Therefore, the LRIG2 gene could be considered a key factor in view of its known functions and its potential roles in beak deformity. Overall, our results will be helpful for future investigations of the genomic structural variations underlying beak deformity in chickens. [ABSTRACT FROM AUTHOR]

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., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background In Multiplex Ligation-dependent Probe Amplification (MLPA), copy number variants (CNVs) for specific genes are identified after normalization of the amounts of PCR products from ligated reference probes hybridized to genomic regions that are ideally free from normal variation. However, we observed ambiguous calls for two reference probes in an investigation of the human 15q11.2 region by MLPA among 20 controls, due to the presence of single nucleotide polymorphisms (SNPs) in the probe-binding regions. Further in silico analysis revealed that 18 out of 19 reference probes hybridize to regions subject to variation, underlining the requirement for designing new reference probes against variation-free regions. Methods An improved MLPA ( i -MLPA) method was developed by generating a new set of reference probes to reduce the chances of ambiguous calls and new reagents that reduce hybridization times to 30 min from 16 h to obtain MLPA ratio data within 6 h. Using i- MLPA, we screened 240 schizophrenia patients for CNVs in 15q11.2 region. Conclusion Three deletions and two duplications were identified among the 240 schizophrenia patients. No variation was observed for the new reference probes. Taken together, i -MLPA procedure helps obtaining non-ambiguous CNV calls within 6 h without compromising accuracy. [ABSTRACT FROM AUTHOR]

Abstract Background One of the main types of genetic variations in cancer is Copy Number Variations (CNV). Whole exome sequenicng (WES) is a popular alternative to whole genome sequencing (WGS) to study disease specific genomic variations. However, finding CNV in Cancer samples using WES data has not been fully explored. Results We present a new method, called CoNVEX, to estimate copy number variation in whole exome sequencing data. It uses ratio of tumour and matched normal average read depths at each exonic region, to predict the copy gain or loss. The useful signal produced by WES data will be hindered by the intrinsic noise present in the data itself. This limits its capacity to be used as a highly reliable CNV detection source. Here, we propose a method that consists of discrete wavelet transform (DWT) to reduce noise. The identification of copy number gains/losses of each targeted region is performed by a Hidden Markov Model (HMM). Conclusion HMM is frequently used to identify CNV in data produced by various technologies including Array Comparative Genomic Hybridization (aCGH) and WGS. Here, we propose an HMM to detect CNV in cancer exome data. We used modified data from 1000 Genomes project to evaluate the performance of the proposed method. Using these data we have shown that CoNVEX outperforms the existing methods significantly in terms of precision. Overall, CoNVEX achieved a sensitivity of more than 92% and a precision of more than 50%.

Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customizable parameters. ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using targeted next-generation sequencing (NGS) datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity, and accuracy. ifCNV is a publicly available open-source software (https://github.com/SimCab-CHU/ifCNV) that allows the detection of CNVs in many clinical situations., Competing Interests: The authors do not have any conflicts of interest., (© 2022 The Authors.)

Copy number variations (CNVs) represent a type of structural variant involving alterations in the number of copies of specific regions of DNA that can either be deleted or duplicated. CNVs contribute substantially to normal population variability, however, abnormal CNVs cause numerous genetic disorders. At present, several methods for CNV detection are applied, ranging from the conventional cytogenetic analysis, through microarray-based methods (aCGH), to next-generation sequencing (NGS). In this paper, we present GenomeScreen, an NGS-based CNV detection method for low-coverage, whole-genome sequencing. We determined the theoretical limits of its accuracy and obtained confirmation in an extensive in silico study and in real patient samples with known genotypes. In theory, at least 6 M uniquely mapped reads are required to detect a CNV with the length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in silico analysis required at least 8 M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has mean resolution of 200 kb. GenomeScreen and aCGH both detected 59 deviations, while GenomeScreen furthermore detected 134 other (usually) smaller variations. When compared to aCGH, overall performance of the proposed GenemoScreen tool is comparable or superior in terms of accuracy, turn-around time, and cost-effectiveness, thus providing reasonable benefits, particularly in a prenatal diagnosis setting. [ABSTRACT FROM AUTHOR]

Detection of copy number variants as an integral part of noninvasive prenatal testing is increasingly used in clinical practice worldwide. We performed validation on plasma samples from 34 pregnant women with known aberrations using cell-free DNA sequencing to evaluate the sensitivity for copy number variants (CNV) detection using an in-house CNV fraction-based detection algorithm. The sensitivity for CNVs smaller than 3 megabases (Mb), larger than 3Mb, and overall was 78.57%, 100%, and 90.6%, respectively. Regarding the fetal fraction, detection sensitivity in the group with a fetal fraction of less than 10% was 57.14%, whereas there was 100% sensitivity in the group with fetal fraction exceeding 10%. The assay is also capable of indicating whether the origin of an aberration is exclusively fetal or fetomaternal/maternal. This validation demonstrated that a CNV fraction-based algorithm was applicable and feasible in clinical settings as a supplement to testing for common trisomies 21, 18, and 13. [ABSTRACT FROM AUTHOR]

Pheochromocytomas and paragangliomas (PPGL) are rare neuroendocrine tumors with a strong hereditary background and a large genetic heterogeneity. Identification of the underlying genetic cause is crucial for the management of patients and their families as it aids differentiation between hereditary and sporadic cases. To improve diagnostics and clinical management we tailored an enrichment based comprehensive multi-gene next generation sequencing panel applicable to both analyses of tumor tissue and blood samples. We applied this panel to tumor samples and compared its performance to our current routine diagnostic approach. Routine diagnostic sequencing of 11 PPGL susceptibility genes was applied to blood samples of 65 unselected PPGL patients at a single center in Dresden, Germany. Predisposing germline mutations were identified in 19 (29.2%) patients. Analyses of 28 PPGL tumor tissues using the dedicated PPGL panel revealed pathogenic or likely pathogenic variants in known PPGL susceptibility genes in 21 (75%) cases, including mutations in IDH2, ATRX and HRAS . These mutations suggest sporadic tumor development. Our results imply a diagnostic benefit from extended molecular tumor testing of PPGLs and consequent improvement of patient management. The approach is promising for determination of prognostic biomarkers that support therapeutic decision-making.