scholarly journals Easy One-Step Amplification and Labeling Procedure for Copy Number Variation Detection

2020 ◽  
Vol 66 (3) ◽  
pp. 463-473 ◽  
Author(s):  
Sebastián Blesa ◽  
María D Olivares ◽  
Andy S Alic ◽  
Alicia Serrano ◽  
Verónica Lendinez ◽  
...  
Keyword(s):  
Dna Repair ◽  
Copy Number ◽  
Simple Procedure ◽  
Receptor Gene ◽  
Density Lipoprotein ◽  
Copy Number Variations ◽  
Chromosome 17 ◽  
Reaction Products ◽  
One Step ◽  
Cnv Detection

Abstract Background The specific characteristics of copy number variations (CNVs) require specific methods of detection and characterization. We developed the Easy One-Step Amplification and Labeling procedure for CNV detection (EOSAL-CNV), a new method based on proportional amplification and labeling of amplicons in 1 PCR. Methods We used tailed primers for specific amplification and a pair of labeling probes (only 1 labeled) for amplification and labeling of all amplicons in just 1 reaction. Products were loaded directly onto a capillary DNA sequencer for fragment sizing and quantification. Data obtained could be analyzed by Microsoft Excel spreadsheet or EOSAL-CNV analysis software. We developed the protocol using the LDLR (low density lipoprotein receptor) gene including 23 samples with 8 different CNVs. After optimizing the protocol, it was used for genes in the following multiplexes: BRCA1 (BRCA1 DNA repair associated), BRCA2 (BRCA2 DNA repair associated), CHEK2 (checkpoint kinase 2), MLH1 (mutL homolog 1) plus MSH6 (mutS homolog 6), MSH2 (mutS homolog 2) plus EPCAM (epithelial cell adhesion molecule) and chromosome 17 (especially the TP53 [tumor protein 53] gene). We compared our procedure with multiplex ligation-dependent probe amplification (MLPA). Results The simple procedure for CNV detection required 150 min, with <10 min of handwork. After analyzing >240 samples, EOSAL-CNV excluded the presence of CNVs in all controls, and in all cases, results were identical using MLPA and EOSAL-CNV. Analysis of the 17p region in tumor samples showed 100% similarity between fluorescent in situ hybridization and EOSAL-CNV. Conclusions EOSAL-CNV allowed reliable, fast, easy detection and characterization of CNVs. It provides an alternative to targeted analysis methods such as MLPA.

scholarly journals Copy number variant detection with low-coverage whole-genome sequencing is a viable replacement for the traditional array-CGH

2020 ◽  
Author(s):  
Marcel Kucharik ◽  
Jaroslav Budis ◽  
Michaela Hyblova ◽  
Gabriel Minarik ◽  
Tomas Szemes
Keyword(s):  
In Silico ◽  
Copy Number ◽  
Normal Population ◽  
Genetic Disorders ◽  
Prenatal Testing ◽  
In Silico Analysis ◽  
Copy Number Variant ◽  
Detection Algorithm ◽  
Copy Number Variations ◽  
Cnv Detection

Copy number variations (CNVs) are a type of structural variant involving alterations in the number of copies of specific regions of DNA, which can either be deleted or duplicated. CNVs contribute substantially to normal population variability; however, abnormal CNVs cause numerous genetic disorders. Nowadays, several methods for CNV detection are used, from the conventional cytogenetic analysis through microarray-based methods (aCGH) to next-generation sequencing (NGS). We present GenomeScreen - NGS based CNV detection method based on a previously described CNV detection algorithm used for non-invasive prenatal testing (NIPT). We determined theoretical limits of its accuracy and confirmed it with extensive in-silico study and already genotyped samples. Theoretically, at least 6M uniquely mapped reads are required to detect CNV with a length of 100 kilobases (kb) or more with high confidence (Z-score > 7). In practice, the in-silico analysis showed the requirement at least 8M to obtain >99% accuracy (for 100 kb deviations). We compared GenomeScreen with one of the currently used aCGH methods in diagnostic laboratories, which has a 200 kb mean resolution. GenomeScreen and aCGH both detected 59 deviations, GenomeScreen furthermore detected 134 other (usually) smaller variations. Furthermore, the overall cost per sample is about 2-3x lower in the case of GenomeScreen.

scholarly journals Integrating genomic correlation structure improves copy number variations detection

2020 ◽  
Author(s):  
Xizhi Luo ◽  
Fei Qin ◽  
Guoshuai Cai ◽  
Feifei Xiao
Keyword(s):  
Copy Number ◽  
Snp Array ◽  
Copy Number Variations ◽  
Correlation Structure ◽  
Supplementary Information ◽  
Dependence Structure ◽  
Random Pattern ◽  
Genomic Correlation ◽  
Equal Chance ◽  
Cnv Detection

Abstract Motivation Copy number variation plays important roles in human complex diseases. The detection of copy number variants (CNVs) is identifying mean shift in genetic intensities to locate chromosomal breakpoints, the step of which is referred to as chromosomal segmentation. Many segmentation algorithms have been developed with a strong assumption of independent observations in the genetic loci, and they assume each locus has an equal chance to be a breakpoint (i.e. boundary of CNVs). However, this assumption is violated in the genetics perspective due to the existence of correlation among genomic positions, such as linkage disequilibrium (LD). Our study showed that the LD structure is related to the location distribution of CNVs, which indeed presents a non-random pattern on the genome. To generate more accurate CNVs, we proposed a novel algorithm, LDcnv, that models the CNV data with its biological characteristics relating to genetic dependence structure (i.e. LD). Results We theoretically demonstrated the correlation structure of CNV data in SNP array, which further supports the necessity of integrating biological structure in statistical methods for CNV detection. Therefore, we developed the LDcnv that integrated the genomic correlation structure with a local search strategy into statistical modeling of the CNV intensities. To evaluate the performance of LDcnv, we conducted extensive simulations and analyzed large-scale HapMap datasets. We showed that LDcnv presented high accuracy, stability and robustness in CNV detection and higher precision in detecting short CNVs compared to existing methods. This new segmentation algorithm has a wide scope of potential application with data from various high-throughput technology platforms. Availability and implementation https://github.com/FeifeiXiaoUSC/LDcnv. Supplementary information Supplementary data are available at Bioinformatics online.

Copy number variations in AR-associated and DNA repair genes from plasma cell-free DNA of metastatic CRPC patients.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 281-281 ◽  
Author(s):  
Ratish Gambhira ◽  
Elisa M. Ledet ◽  
Aryeneesh Dotiwala ◽  
Diptasri Mandal ◽  
A. Oliver Sartor
Keyword(s):  
Prostate Cancer ◽  
Dna Repair ◽  
Cancer Progression ◽  
Copy Number ◽  
Genetic Alterations ◽  
Copy Number Variations ◽  
Dna Repair Genes ◽  
Prostate Cancer Progression ◽  
Free Dna ◽  
Repair Genes

281 Background: Cell-free DNA (cfDNA) present in the plasma of advanced cancer patients can reflect tumor related genetic alterations. Recent data suggests copy number variations (CNVs) in AR-associated and DNA repair pathway genes play a potential role in prostate cancer progression. Here, we performed sequencing of cfDNA from 13 mCRPC patients to evaluate its potential in elucidating tumor related genetic variations. The long-term goal of our project is to correlate cfDNA derived genetic alterations with prostate cancer progression and/or therapeutic resistance/responses. Methods: cfDNA was isolated from 13 advanced mCRPC patient plasma samples using the Qiagen circulating nucleic acid kit. 100ng of cfDNA was utilized for library construction; and the libraries were paired-end sequenced on the Illumina HiSeq 2000. The resulting data was analyzed using the GATK best practices bioinformatics pipeline and the visualized using the SNP & Variation Suite v8.x. Results: The bioanalyzer profiles of cfDNA derived from mCRPC patients is highly fragmented with an average fragment size of 306-605bp. Although, several CNVs were found across the genome, we focused analysis on CNVs related to AR associated and DNA repair genes. Our preliminary analysis of cfDNA, despite low sequencing depth, shows full or partial amplifications in AR (13/13), and other genes including FOXA1, NCOR1, NCOR2 and/or PIK3CA (7/13) and NCOR2 (10/13). For DNA repair genes partial/full amplifications were present in BRAC1, BRAC2, ATM, CDK12, MLH1 and/or MSH2 (7/13). Deletions are less reliably detected in the highly fragmented cfDNA. The majority of these CNVs have been reported in the WGS studies from metastatic CRPC tissue derived genomic DNA (cBioPortal). We are currently validating cfDNA genomic alterations by comparing it to germ line DNA derived via qPCR. Conclusions: Our preliminary study indicates that AR and DNA repair related genetic alterations could be found in the cfDNA derived from metastatic CRPC patients. This warrants more detailed examination of these cfDNA genetic alterations for identifying clinically relevant issues in mCRPC patients.

Characterization of copy number variations (CNV) in the NKG2C receptor gene

2015 ◽  
Vol 76 ◽  
pp. 135
Author(s):  
Kai Cao ◽  
Weicheng Zhao ◽  
Nathaniel Smith ◽  
Yudith Carmazzi ◽  
Elizabeth Shpall ◽  
...  
Keyword(s):  
Copy Number ◽  
Receptor Gene ◽  
Copy Number Variations

scholarly journals Improving high-resolution copy number variation analysis from next generation sequencing using unique molecular identifiers

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Pierre-Julien Viailly ◽  
Vincent Sater ◽  
Mathieu Viennot ◽  
Elodie Bohers ◽  
Nicolas Vergne ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
B Cell Lymphoma ◽  
Copy Number Variations ◽  
Comparative Genomic ◽  
Detection Accuracy ◽  
Next Generation ◽  
Tumor Suppressor Gene Inactivation ◽  
Generation Sequencing ◽  
Cnv Detection

Abstract Background Recently, copy number variations (CNV) impacting genes involved in oncogenic pathways have attracted an increasing attention to manage disease susceptibility. CNV is one of the most important somatic aberrations in the genome of tumor cells. Oncogene activation and tumor suppressor gene inactivation are often attributed to copy number gain/amplification or deletion, respectively, in many cancer types and stages. Recent advances in next generation sequencing protocols allow for the addition of unique molecular identifiers (UMI) to each read. Each targeted DNA fragment is labeled with a unique random nucleotide sequence added to sequencing primers. UMI are especially useful for CNV detection by making each DNA molecule in a population of reads distinct. Results Here, we present molecular Copy Number Alteration (mCNA), a new methodology allowing the detection of copy number changes using UMI. The algorithm is composed of four main steps: the construction of UMI count matrices, the use of control samples to construct a pseudo-reference, the computation of log-ratios, the segmentation and finally the statistical inference of abnormal segmented breaks. We demonstrate the success of mCNA on a dataset of patients suffering from Diffuse Large B-cell Lymphoma and we highlight that mCNA results have a strong correlation with comparative genomic hybridization. Conclusion We provide mCNA, a new approach for CNV detection, freely available at https://gitlab.com/pierrejulien.viailly/mcna/ under MIT license. mCNA can significantly improve detection accuracy of CNV changes by using UMI.

scholarly journals A Cluster-Based Approach for the Discovery of Copy Number Variations From Next-Generation Sequencing Data

2021 ◽  
Vol 12 ◽  
Author(s):  
Guojun Liu ◽  
Junying Zhang
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number ◽  
Read Depth ◽  
Copy Number Variations ◽  
Experimental Results ◽  
Data Sets ◽  
Next Generation ◽  
Sequencing Data ◽  
Generation Sequencing ◽  
Cnv Detection

The next-generation sequencing technology offers a wealth of data resources for the detection of copy number variations (CNVs) at a high resolution. However, it is still challenging to correctly detect CNVs of different lengths. It is necessary to develop new CNV detection tools to meet this demand. In this work, we propose a new CNV detection method, called CBCNV, for the detection of CNVs of different lengths from whole genome sequencing data. CBCNV uses a clustering algorithm to divide the read depth segment profile, and assigns an abnormal score to each read depth segment. Based on the abnormal score profile, Tukey’s fences method is adopted in CBCNV to forecast CNVs. The performance of the proposed method is evaluated on simulated data sets, and is compared with those of several existing methods. The experimental results prove that the performance of CBCNV is better than those of several existing methods. The proposed method is further tested and verified on real data sets, and the experimental results are found to be consistent with the simulation results. Therefore, the proposed method can be expected to become a routine tool in the analysis of CNVs from tumor-normal matched samples.

scholarly journals The utility of MLPA in Familial Hypercholesterolemia diagnosis

2021 ◽  
Vol 67 (3) ◽  
pp. 162-166
Author(s):  
George Valeriu Moldovan ◽  
Adina Huțanu ◽  
Liliana Demian ◽  
Laszlo Hadadi ◽  
Bogdan Mănescu ◽  
...  
Keyword(s):  
Coronary Heart Disease ◽  
Heart Disease ◽  
Familial Hypercholesterolemia ◽  
Copy Number ◽  
Low Density Lipoprotein ◽  
Density Lipoprotein ◽  
Copy Number Variations ◽  
Lipoprotein Cholesterol ◽  
Low Density ◽  
Premature Coronary Heart Disease

Abstract Background: Familial Hypercholesterolemia (FH) is an inherited disease, associated with an increased risk of atherosclerosis, manifested clinically as premature coronary heart disease. FH is biochemically characterized by increased Cholesterol and Low-density Lipoprotein Cholesterol serum levels. The diagnosis is often made using clinical scores however, the definitive FH diagnosis should point out the underlying molecular change, which can be: a point mutation within the three major genes, a number of single nucleotide polymorphisms determining the polygenic etiology, or copy number variations in the Low-density lipoprotein receptor gene. Objective: In the present study we investigated copy number variations as a possible etiological factor for FH in a cohort of patients with documented premature coronary heart disease. Methods: The study population consisted of 150 patients with premature coronary heart disease documented by angiography, all being under lipid-lowering therapy, and 20 apparently healthy controls. Serum lipids were assessed using the Cobas Integra 400 plus and commercial reagents. Copy number variations were evaluated with the SALSA MLPA Probemix P062 LDLR kit. Results: Cholesterol, Triglycerides, Low-density Lipoprotein Cholesterol and High-density Lipoprotein Cholesterol showed no difference between patients and controls. No copy number variations were detected in the investigated regions, namely all 18 exons and the promoter region of the Low-density lipoprotein receptor gene. Conclusions: Even in the presence of negative results, the Familial Hypercholesterolemia genetic diagnosis has to be further pursued in the presence of a clinical diagnosis, as the identification of the molecular etiology may bring additional clinical and therapeutical benefits, as well as open the possibility for “cascade screening”.

scholarly journals Copy-number variation contributes 9% of pathogenicity in the inherited retinal degenerations

2019 ◽  
Author(s):  
Erin Zampaglione ◽  
Benyam Kinde ◽  
Emily M. Place ◽  
Daniel Navarro-Gomez ◽  
Matthew Maher ◽  
...  
Keyword(s):  
Copy Number ◽  
Snp Array ◽  
Copy Number Variations ◽  
Detection Methods ◽  
Single Nucleotide Variants ◽  
Targeted Next Generation Sequencing ◽  
Small Indels ◽  
Pathogenic Variants ◽  
Number Variation ◽  
Cnv Detection

ABSTRACTPurposeCurrent sequencing strategies can genetically solve 55-60% of inherited retinal degeneration (IRD) cases, despite recent progress in sequencing. This can partially be attributed to elusive pathogenic variants (PVs) in known IRD genes, including copy number variations (CNVs), which we believe are a major contributor to unsolved IRD cases.MethodsFive hundred IRD patients were analyzed with targeted next generation sequencing (NGS). The NGS data was used to detect CNVs with ExomeDepth and gCNV and the results were compared to CNV detection with a SNP-Array. Likely causal CNV predictions were validated by quantitative (q)PCR.ResultsLikely disease-causing single nucleotide variants (SNVs) and small indels were found in 55.8% of subjects. PVs in USH2A (11.6%), RPGR (4%) and EYS (4%) were the most common. Likely causal CNVs were found in an additional 8.8% of patients. Of the three CNV detection methods, gCNV showed the highest accuracy. Approximately 30% of unsolved subjects had a single likely PV in a recessive IRD gene.ConclusionsCNV detection using NGS-based algorithms is a reliable method that greatly increases the genetic diagnostic rate of IRDs. Experimentally validating CNVs helps estimate the rate at which IRDs might be solved by a CNV plus a more elusive variant.

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