scholarly journals CoDEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

2017 ◽  
Author(s):  
Yuchao Jiang ◽  
Rujin Wang ◽  
Eugene Urrutia ◽  
Ioannis N. Anastopoulos ◽  
Katherine L. Nathanson ◽  
...  
Keyword(s):  
Dna Sequencing ◽  
High Throughput ◽  
Copy Number ◽  
Copy Number Variations ◽  
Negative Control ◽  
Sequencing Data ◽  
Full Spectrum ◽  
Number Variation ◽  
High Throughput Dna Sequencing ◽  
Low Sensitivity

AbstractHigh-throughput DNA sequencing enables detection of copy number variations (CNVs) on the genome-wide scale with finer resolution compared to array-based methods, but suffers from biases and artifacts that lead to false discoveries and low sensitivity. We describe CODEX2, a statistical framework for full-spectrum CNV profiling that is sensitive for variants with both common and rare population frequencies and that is applicable to study designs with and without negative control samples. We demonstrate and evaluate CODEX2 on whole-exome and targeted sequencing data, where biases are the most prominent. CODEX2 outperforms existing methods and, in particular, significantly improves sensitivity for common CNVs.

scholarly journals CODEX2: full-spectrum copy number variation detection by high-throughput DNA sequencing

2018 ◽  
Vol 19 (1) ◽  
Author(s):  
Yuchao Jiang ◽  
Rujin Wang ◽  
Eugene Urrutia ◽  
Ioannis N. Anastopoulos ◽  
Katherine L. Nathanson ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Dna Sequencing ◽  
High Throughput ◽  
Copy Number ◽  
Full Spectrum ◽  
Number Variation ◽  
High Throughput Dna Sequencing ◽  
Copy Number Variation Detection

Application of Copy Number Variation Sequencing in Genetic Analysis of Miscarriages in Early and Middle Pregnancy

2020 ◽  
Vol 160 (11-12) ◽  
pp. 634-642
Author(s):  
Shiqiang Luo ◽  
Xingyuan Chen ◽  
Tizhen Yan ◽  
Jiaolian Ya ◽  
Zehui Xu ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
High Throughput ◽  
Copy Number ◽  
High Throughput Sequencing ◽  
Chromosomal Abnormalities ◽  
Pregnancy Termination ◽  
Mendelian Inheritance ◽  
Copy Number Variations ◽  
Abnormal Chromosome ◽  
Number Variation

High-throughput sequencing based on copy number variation (CNV-seq) is commonly used to detect chromosomal abnormalities. This study identifies chromosomal abnormalities in aborted embryos/fetuses in early and middle pregnancy and explores the application value of CNV-seq in determining the causes of pregnancy termination. High-throughput sequencing was used to detect chromosome copy number variations (CNVs) in 116 aborted embryos in early and middle pregnancy. The detection data were compared with the Database of Genomic Variants (DGV), the Database of Chromosomal Imbalance and Phenotype in Humans using Ensemble Resources (DECIPHER), and the Online Mendelian Inheritance in Man (OMIM) database to determine the CNV type and the clinical significance. High-throughput sequencing results were successfully obtained in 109 out of 116 specimens, with a detection success rate of 93.97%. In brief, there were 64 cases with abnormal chromosome numbers and 23 cases with CNVs, in which 10 were pathogenic mutations and 13 were variants of uncertain significance. An abnormal chromosome number is the most important reason for embryo termination in early and middle pregnancy, followed by pathogenic chromosome CNVs. CNV-seq can quickly and accurately detect chromosome abnormalities and identify microdeletion and microduplication CNVs that cannot be detected by conventional chromosome analysis, which is convenient and efficient for genetic etiology diagnosis in miscarriage.

Hadoop-CNV-RF: a clinically validated and scalable copy number variation detection tool for next-generation sequencing data

2020 ◽  
Author(s):  
Getiria Onsongo ◽  
Ham Ching Lam ◽  
Matthew Bower ◽  
Bharat Thyagarajan
Keyword(s):  
Copy Number ◽  
Copy Number Variations ◽  
Next Generation Sequencing Data ◽  
Sequencing Data ◽  
Large Gene ◽  
Data Framework ◽  
Number Variation ◽  
Targeted Capture ◽  
Objective Detection ◽  
Gene Panels

Abstract Objective : Detection of small copy number variations (CNVs) in clinically relevant genes is routinely being used to aid diagnosis. We recently developed a tool, CNV-RF , capable of detecting small clinically relevant CNVs. CNV-RF was designed for small gene panels and did not scale well to large gene panels. On large gene panels, CNV-RF routinely failed due to memory limitations. When successful, it took about 2 days to complete a single analysis, making it impractical for routinely analyzing large gene panels. We need a reliable tool capable of detecting CNVs in the clinic that scales well to large gene panels. Results : We have developed Hadoop-CNV-RF, a scalable implementation of CNV-RF . Hadoop-CNV-RF is a freely available tool capable of rapidly analyzing large gene panels. It takes advantage of Hadoop, a big data framework developed to analyze large amounts of data. Preliminary results show it reduces analysis time from about 2 days to less than 4 hours and can seamlessly scale to large gene panels. Hadoop-CNV-RF has been clinically validated for targeted capture data and is currently being used in a CLIA molecular diagnostics laboratory. Its availability and usage instructions are publicly available at: https://github.com/getiria-onsongo/hadoop-cnvrf-public .

scholarly journals Efficient storage of high throughput DNA sequencing data using reference-based compression

2011 ◽  
Vol 21 (5) ◽  
pp. 734-740 ◽  
Author(s):  
M. Hsi-Yang Fritz ◽  
R. Leinonen ◽  
G. Cochrane ◽  
E. Birney
Keyword(s):  
Dna Sequencing ◽  
High Throughput ◽  
Sequencing Data ◽  
Efficient Storage ◽  
High Throughput Dna Sequencing

scholarly journals Comparative Study of Exome Copy Number Variation Estimation Tools Using Array Comparative Genomic Hybridization as Control

2013 ◽  
Vol 2013 ◽  
pp. 1-7 ◽  
Author(s):  
Yan Guo ◽  
Quanghu Sheng ◽  
David C. Samuels ◽  
Brian Lehmann ◽  
Joshua A. Bauer ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Exome Sequencing ◽  
Copy Number ◽  
Array Cgh ◽  
False Positive Rate ◽  
Comparative Genomic ◽  
Comparative Genome Hybridization ◽  
Sequencing Data ◽  
Number Variation ◽  
Low Sensitivity

Exome sequencing using next-generation sequencing technologies is a cost-efficient approach to selectively sequencing coding regions of the human genome for detection of disease variants. One of the lesser known yet important applications of exome sequencing data is to identify copy number variation (CNV). There have been many exome CNV tools developed over the last few years, but the performance and accuracy of these programs have not been thoroughly evaluated. In this study, we systematically compared four popular exome CNV tools (CoNIFER, cn.MOPS, exomeCopy, and ExomeDepth) and evaluated their effectiveness against array comparative genome hybridization (array CGH) platforms. We found that exome CNV tools are capable of identifying CNVs, but they can have problems such as high false positives, low sensitivity, and duplication bias when compared to array CGH platforms. While exome CNV tools do serve their purpose for data mining, careful evaluation and additional validation is highly recommended. Based on all these results, we recommend CoNIFER and cn.MOPs for nonpaired exome CNV detection over the other two tools due to a low false-positive rate, although none of the four exome CNV tools performed at an outstanding level when compared to array CGH.

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