Identification of MECP2 Duplication Using Low-Depth Whole-Genome Sequencing-Based Copy Number Variation Analysis

2020 ◽  
Vol 10 (2) ◽  
pp. 165
Author(s):  
Mi-Ae Jang ◽  
Soyoung Park ◽  
Jong Eun Park ◽  
Young-Eun Kim ◽  
Chang-Seok Ki
Keyword(s):  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome ◽  
Variation Analysis ◽  
Copy Number Variation Analysis ◽  
Number Variation

scholarly journals Whole-genome copy number variation analysis in anophthalmia and microphthalmia

2013 ◽  
Vol 84 (5) ◽  
pp. 473-481 ◽  
Author(s):  
KF Schilter ◽  
LM Reis ◽  
A Schneider ◽  
TM Bardakjian ◽  
O Abdul-Rahman ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Whole Genome ◽  
Variation Analysis ◽  
Genome Copy ◽  
Copy Number Variation Analysis ◽  
Number Variation ◽  
Genome Copy Number

scholarly journals CNView: a visualization and annotation tool for copy number variation from whole-genome sequencing

2016 ◽  
Author(s):  
Ryan L. Collins ◽  
Matthew R. Stone ◽  
Harrison Brand ◽  
Joseph T. Glessner ◽  
Michael E. Talkowski
Keyword(s):  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Software Tool ◽  
Whole Genome ◽  
Data Types ◽  
Recent Emergence ◽  
Number Variation ◽  
Population Scale

AbstractSummaryCopy number variation (CNV) is a major component of structural differences between individual genomes. The recent emergence of population-scale whole-genome sequencing (WGS) datasets has enabled genome-wide CNV delineation. However, molecular validation at this scale is impractical, so visualization is an invaluable preliminary screening approach when evaluating CNVs. Standardized tools for visualization of CNVs in large WGS datasets are therefore in wide demand.Methods & ResultsTo address this demand, we developed a software tool, CNView, for normalized visualization, statistical scoring, and annotation of CNVs from population-scale WGS datasets. CNView surmounts challenges of sequencing depth variability between individual libraries by locally adapting to cohort-wide variance in sequencing uniformity at any locus. Importantly, CNView is broadly extensible to any reference genome assembly and most current WGS data types.Availability and ImplementationCNView is written in R, is supported on OS X, MS Windows, and Linux, and is freely distributed under the MIT license. Source code and documentation are available from https://github.com/RCollins13/[email protected]

scholarly journals Effective normalization for copy number variation detection from whole genome sequencing

BMC Genomics ◽  
2012 ◽  
Vol 13 (Suppl 6) ◽  
pp. S16 ◽  
Author(s):  
Angel Janevski ◽  
Vinay Varadan ◽  
Sitharthan Kamalakaran ◽  
Nilanjana Banerjee ◽  
Nevenka Dimitrova
Keyword(s):  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Whole Genome ◽  
Number Variation ◽  
Copy Number Variation Detection

scholarly journals Identification of Copy Number Variation in Domestic Chicken Using Whole-Genome Sequencing Reveals Evidence of Selection in the Genome

Animals ◽  
2019 ◽  
Vol 9 (10) ◽  
pp. 809
Author(s):  
Donghyeok Seol ◽  
Byung June Ko ◽  
Bongsang Kim ◽  
Han-Ha Chai ◽  
Dajeong Lim ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Whole Genome Sequencing ◽  
Genome Sequencing ◽  
Copy Number ◽  
Breeding Systems ◽  
Whole Genome Sequencing Data ◽  
Whole Genome ◽  
Genetic Characteristics ◽  
Red Jungle Fowl ◽  
Number Variation

Copy number variation (CNV) has great significance both functionally and evolutionally. Various CNV studies are in progress to find the cause of human disease and to understand the population structure of livestock. Recent advances in next-generation sequencing (NGS) technology have made CNV detection more reliable and accurate at whole-genome level. However, there is a lack of CNV studies on chickens using NGS. Therefore, we obtained whole-genome sequencing data of 65 chickens including Red Jungle Fowl, Cornish (broiler), Rhode Island Red (hybrid), and White Leghorn (layer) from the public databases for CNV region (CNVR) detection. Using CNVnator, a read-depth based software, a total of 663 domesticated-specific CNVRs were identified across autosomes. Gene ontology analysis of genes annotated in CNVRs showed that mainly enriched terms involved in organ development, metabolism, and immune regulation. Population analysis revealed that CN and RIR are closer to each other than WL, and many genes (LOC772271, OR52R1, RD3, ADH6, TLR2B, PRSS2, TPK1, POPDC3, etc.) with different copy numbers between breeds found. In conclusion, this study has helped to understand the genetic characteristics of domestic chickens at CNV level, which may provide useful information for the development of breeding systems in chickens.

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