scholarly journals A Simple, Universal, and Cost-Efficient Digital PCR Method for the Targeted Analysis of Copy Number Variations

2019 ◽  
Vol 65 (9) ◽  
pp. 1153-1160 ◽  
Author(s):  
Kévin Cassinari ◽  
Olivier Quenez ◽  
Géraldine Joly-Hélas ◽  
Ludivine Beaussire ◽  
Nathalie Le Meur ◽  
...  
Keyword(s):  
Copy Number ◽  
Segregation Analysis ◽  
Genetic Diseases ◽  
Genomic Medicine ◽  
Digital Pcr ◽  
Copy Number Variations ◽  
Locked Nucleic Acid ◽  
Targeted Analysis ◽  
Cost Efficient ◽  
High Flexibility

Abstract BACKGROUND Rare copy number variations (CNVs) are a major cause of genetic diseases. Simple targeted methods are required for their confirmation and segregation analysis. We developed a simple and universal CNV assay based on digital PCR (dPCR) and universal locked nucleic acid (LNA) hydrolysis probes. METHODS We analyzed the mapping of the 90 LNA hydrolysis probes from the Roche Universal ProbeLibrary (UPL). For each CNV, selection of the optimal primers and LNA probe was almost automated; probes were reused across assays and each dPCR assay included the CNV amplicon and a reference amplicon. We assessed the assay performance on 93 small and large CNVs and performed a comparative cost-efficiency analysis. RESULTS UPL-LNA probes presented nearly 20000000 occurrences on the human genome and were homogeneously distributed with a mean interval of 156 bp. The assay accurately detected all the 93 CNVs, except one (<200 bp), with coefficient of variation <10%. The assay was more cost-efficient than all the other methods. CONCLUSIONS The universal dPCR CNV assay is simple, robust, and cost-efficient because it combines a straightforward design allowed by universal probes and end point PCR, the advantages of a relative quantification of the target to the reference within the same reaction, and the high flexibility of the LNA hydrolysis probes. This method should be a useful tool for genomic medicine, which requires simple methods for the interpretation and segregation analysis of genomic variations.

scholarly journals CNV-P: a machine-learning framework for predicting high confident copy number variations

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12564
Author(s):  
Taifu Wang ◽  
Jinghua Sun ◽  
Xiuqing Zhang ◽  
Wen-Jing Wang ◽  
Qing Zhou
Keyword(s):  
Machine Learning ◽  
False Positive ◽  
Copy Number ◽  
Genetic Disorders ◽  
Genetic Diseases ◽  
Basic Research ◽  
Read Depth ◽  
Copy Number Variations ◽  
Sequencing Data ◽  
Learning Framework

Background Copy-number variants (CNVs) have been recognized as one of the major causes of genetic disorders. Reliable detection of CNVs from genome sequencing data has been a strong demand for disease research. However, current software for detecting CNVs has high false-positive rates, which needs further improvement. Methods Here, we proposed a novel and post-processing approach for CNVs prediction (CNV-P), a machine-learning framework that could efficiently remove false-positive fragments from results of CNVs detecting tools. A series of CNVs signals such as read depth (RD), split reads (SR) and read pair (RP) around the putative CNV fragments were defined as features to train a classifier. Results The prediction results on several real biological datasets showed that our models could accurately classify the CNVs at over 90% precision rate and 85% recall rate, which greatly improves the performance of state-of-the-art algorithms. Furthermore, our results indicate that CNV-P is robust to different sizes of CNVs and the platforms of sequencing. Conclusions Our framework for classifying high-confident CNVs could improve both basic research and clinical diagnosis of genetic diseases.

scholarly journals The ELIXIR Human Copy Number Variations Community: building bioinformatics infrastructure for research

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 1229
Author(s):  
David Salgado ◽  
Irina M. Armean ◽  
Michael Baudis ◽  
Sergi Beltran ◽  
Salvador Capella-Gutierrez ◽  
...  
Keyword(s):  
Copy Number ◽  
High Throughput Sequencing ◽  
Population Genomics ◽  
Genetic Diseases ◽  
White Paper ◽  
Copy Number Variations ◽  
Direct Result ◽  
Sequencing Technologies ◽  
Wide Range ◽  
Definition Of

Copy number variations (CNVs) are major causative contributors both in the genesis of genetic diseases and human neoplasias. While “High-Throughput” sequencing technologies are increasingly becoming the primary choice for genomic screening analysis, their ability to efficiently detect CNVs is still heterogeneous and remains to be developed. The aim of this white paper is to provide a guiding framework for the future contributions of ELIXIR’s recently established human CNV Community, with implications beyond human disease diagnostics and population genomics. This white paper is the direct result of a strategy meeting that took place in September 2018 in Hinxton (UK) and involved representatives of 11 ELIXIR Nodes. The meeting led to the definition of priority objectives and tasks, to address a wide range of CNV-related challenges ranging from detection and interpretation to sharing and training. Here, we provide suggestions on how to align these tasks within the ELIXIR Platforms strategy, and on how to frame the activities of this new ELIXIR Community in the international context.

Assessment Of Sfmbt1, Prkra, And Erlin1 Copy Number Variations In Patients With Carotid Atherosclerosis Using Droplet Digital Pcr

2019 ◽  
Vol 287 ◽  
pp. e165-e166
Author(s):  
A. Sleptcov ◽  
M. Nazarenko ◽  
A. Kazantsev ◽  
I. Lebedev ◽  
O. Barbarash ◽  
...  
Keyword(s):  
Copy Number ◽  
Carotid Atherosclerosis ◽  
Digital Pcr ◽  
Copy Number Variations ◽  
Droplet Digital Pcr

scholarly journals Digital PCR for determination of cytochrome P450 2D6 and sulfotransferase 1A1 gene copy number variations

2017 ◽  
Vol 11 (6) ◽  
pp. 336-341 ◽  
Author(s):  
Yutaro Motoi ◽  
Kazufumi Watanabe ◽  
Hiroyuki Honma ◽  
Yousuke Tadano ◽  
Hiroshi Hashimoto ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Copy Number ◽  
Gene Copy Number ◽  
Digital Pcr ◽  
Copy Number Variations ◽  
Gene Copy ◽  
Cytochrome P450 2D6 ◽  
Sulfotransferase 1A1 ◽  
P450 2D6

scholarly journals Droplet digital PCR for identifying copy number variations in patients with primary immunodeficiency disorders

2021 ◽  
Author(s):  
See-Tarn Woon ◽  
Julia Mayes ◽  
Alexander Quach ◽  
Hilary Longhurst ◽  
Antonio Ferrante ◽  
...  
Keyword(s):  
Primary Immunodeficiency ◽  
Copy Number ◽  
Sanger Sequencing ◽  
Cost Effective ◽  
Digital Pcr ◽  
Copy Number Variations ◽  
Droplet Digital Pcr ◽  
Inborn Errors ◽  
Primary Immunodeficiency Disorders ◽  
Large Deletions

Abstract Primary immunodeficiency disorders comprise a rare group of mostly monogenic disorders caused by inborn errors of immunity. The majority can be identified by either Sanger sequencing or Next Generation Sequencing. Some disorders result from large insertions or deletions leading to copy number variations (CNV). Sanger sequencing may not identify these mutations. Here we present droplet digital PCR as an alternative cost-effective diagnostic method to identify CNV in these genes. The data from patients with large deletions of NFKB1, SERPING1 and SH2D1A are presented.

scholarly journals Automated prediction of the clinical impact of structural copy number variations

2020 ◽  
Author(s):  
Michaela Gaziova ◽  
Tomas Sladecek ◽  
Ondrej Pos ◽  
Martin Stevko ◽  
Werner Krampl ◽  
...  
Keyword(s):  
Copy Number ◽  
Genetic Diseases ◽  
Copy Number Variants ◽  
Copy Number Gain ◽  
Regulatory Elements ◽  
Copy Number Variations ◽  
Clinical Impact ◽  
Copy Number Loss ◽  
Pathogenicity Prediction

Introduction: Copy number variants (CNVs) play an important role in many biological processes, including the development of genetic diseases, making them attractive targets for genetic analyses. The interpretation of the effect of structural variants is a challenging problem due to highly variable numbers of gene, regulatory or other genomic elements affected by the CNV. This led to the demand for the interpretation tools that would relieve researchers, laboratory diagnosticians, genetic counselors, and clinical geneticists from the laborious process of annotation and classification of CNVs. Materials and Methods: We designed a classifier method based on the annotations of CNVs from several publicly available databases. The attributes take into account gene elements, regulatory elements affected by the CNV, as well as other CNVs with known clinical significance that overlap the candidate CNV. We also describe the process of model selection and the construction of training, validation, and test set. Results: The presented approach achieved more than 98% prediction accuracy on both copy number loss and copy number gain variants and can be improved by imposing probability thresholds to eliminate low confidence predictions. Discussion: Method has shown considerable performance in predicting the clinical impact of CNVs and therefore has a great potential to guide users to more precise conclusions. The CNV annotation and pathogenicity prediction can be fully automated, relieving users of tedious interpretation processes. Availability and Implementation: The results can be reproduced by following instructions at {{https://github.com/tsladecek/isv}}.

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