scholarly journals CNV-BAC: Copy number Variation Detection in Bacterial Circular Genome

2020 ◽  
Vol 36 (12) ◽  
pp. 3890-3891
Author(s):  
Linjie Wu ◽  
Han Wang ◽  
Yuchao Xia ◽  
Ruibin Xi
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Genome Structure ◽  
Real Data ◽  
Read Depth ◽  
Supplementary Information ◽  
Circular Genome ◽  
Number Variation ◽  
Copy Number Variation Detection ◽  
Cnv Detection

Abstract Motivation Whole-genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias. Results We show that the replication bias is widespread using ∼200 bacterial WGS data. We develop CNV-BAC (CNV-Bacteria) that can properly normalize the replication bias and other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms. Availability and implementation CNV-BAC is available at https://github.com/XiDsLab/CNV-BAC. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CNV-BAC: Copy Number Variation Detection in Bacterial Circular Genome

2019 ◽  
Author(s):  
Linjie Wu ◽  
Han Wang ◽  
Yuchao Xia ◽  
Ruibin Xi
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Genome Structure ◽  
Real Data ◽  
Read Depth ◽  
Replication Rate ◽  
Circular Genome ◽  
Number Variation ◽  
Copy Number Variation Detection ◽  
Cnv Detection

AbstractMotivationWhole genome sequencing (WGS) is widely used for copy number variation (CNV) detection. However, for most bacteria, their circular genome structure and high replication rate make reads more enriched near the replication origin. CNV detection based on read depth could be seriously influenced by such replication bias.ResultsWe show that the replication bias is widespread using ~200 bacterial WGS data. We develop CNV-BAC that can properly normalize the replication bias as well as other known biases in bacterial WGS data and can accurately detect CNVs. Simulation and real data analysis show that CNV-BAC achieves the best performance in CNV detection compared with available algorithms.Availability and implementationCNV-BAC is available at https://github.com/LinjieWu/[email protected]

scholarly journals An accurate and powerful method for copy number variation detection

2019 ◽  
Vol 35 (17) ◽  
pp. 2891-2898
Author(s):  
Feifei Xiao ◽  
Xizhi Luo ◽  
Ning Hao ◽  
Yue S Niu ◽  
Xiangjun Xiao ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Complex Traits ◽  
Copy Number ◽  
Statistical Power ◽  
Copy Number Variants ◽  
Supplementary Information ◽  
External Information ◽  
Number Variation ◽  
Normal Mean ◽  
Copy Number Variation Detection

Abstract Motivation Integration of multiple genetic sources for copy number variation detection (CNV) is a powerful approach to improve the identification of variants associated with complex traits. Although it has been shown that the widely used change point based methods can increase statistical power to identify variants, it remains challenging to effectively detect CNVs with weak signals due to the noisy nature of genotyping intensity data. We previously developed modSaRa, a normal mean-based model on a screening and ranking algorithm for copy number variation identification which presented desirable sensitivity with high computational efficiency. To boost statistical power for the identification of variants, here we present a novel improvement that integrates the relative allelic intensity with external information from empirical statistics with modeling, which we called modSaRa2. Results Simulation studies illustrated that modSaRa2 markedly improved both sensitivity and specificity over existing methods for analyzing array-based data. The improvement in weak CNV signal detection is the most substantial, while it also simultaneously improves stability when CNV size varies. The application of the new method to a whole genome melanoma dataset identified novel candidate melanoma risk associated deletions on chromosome bands 1p22.2 and duplications on 6p22, 6q25 and 19p13 regions, which may facilitate the understanding of the possible roles of germline copy number variants in the etiology of melanoma. Availability and implementation http://c2s2.yale.edu/software/modSaRa2 or https://github.com/FeifeiXiaoUSC/modSaRa2. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Modified screening and ranking algorithm for copy number variation detection

2014 ◽  
Vol 31 (9) ◽  
pp. 1341-1348 ◽  
Author(s):  
Feifei Xiao ◽  
Xiaoyi Min ◽  
Heping Zhang
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Ranking Algorithm ◽  
Number Variation ◽  
Copy Number Variation Detection

scholarly journals An Extended Targeted Copy Number Variation Detection Array Including 187 Genes for the Diagnostics of Neuromuscular Disorders

2018 ◽  
Vol 5 (3) ◽  
pp. 307-314 ◽  
Author(s):  
Lydia Sagath ◽  
Vilma-Lotta Lehtokari ◽  
Salla Välipakka ◽  
Bjarne Udd ◽  
Carina Wallgren-Pettersson ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Neuromuscular Disorders ◽  
Number Variation ◽  
Copy Number Variation Detection

scholarly journals 28: Copy Number Variation Detection Using Next-Generation Sequencing

2015 ◽  
Vol 143 (suppl_1) ◽  
pp. A013-A013
Author(s):  
Linda B. Baughn ◽  
Getiria Onsongo ◽  
Matthew Bower ◽  
Christine Henzler ◽  
Kevin A.T. Silverstein ◽  
...  
Keyword(s):  
Next Generation Sequencing ◽  
Copy Number Variation ◽  
Copy Number ◽  
Next Generation ◽  
Number Variation ◽  
Copy Number Variation Detection ◽  
Generation Sequencing

Mini-Exome Coupled to Read-Depth Based Copy Number Variation Analysis in Patients with Inherited Ataxias

2016 ◽  
Vol 37 (12) ◽  
pp. 1340-1353 ◽  
Author(s):  
Cecilia Marelli ◽  
Claire Guissart ◽  
Cecile Hubsch ◽  
Mathilde Renaud ◽  
Jean-Philippe Villemin ◽  
...  
Keyword(s):  
Copy Number Variation ◽  
Copy Number ◽  
Read Depth ◽  
Variation Analysis ◽  
Copy Number Variation Analysis ◽  
Number Variation ◽  
Inherited Ataxias
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