scholarly journals isoCNV: in silico optimization of copy number variant detection from targeted or exome sequencing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rosa Barcelona-Cabeza ◽  
Walter Sanseverino ◽  
Riccardo Aiese Cigliano
Keyword(s):  
Exome Sequencing ◽  
In Silico ◽  
Copy Number ◽  
Copy Number Variant ◽  
Comparative Genomic ◽  
Sequencing Data ◽  
Whole Exome ◽  
Orthogonal Methods ◽  
Ngs Data ◽  
Dependent Probe

Abstract Background Accurate copy number variant (CNV) detection is especially challenging for both targeted sequencing (TS) and whole‐exome sequencing (WES) data. To maximize the performance, the parameters of the CNV calling algorithms should be optimized for each specific dataset. This requires obtaining validated CNV information using either multiplex ligation-dependent probe amplification (MLPA) or array comparative genomic hybridization (aCGH). They are gold standard but time-consuming and costly approaches. Results We present isoCNV which optimizes the parameters of DECoN algorithm using only NGS data. The parameter optimization process is performed using an in silico CNV validated dataset obtained from the overlapping calls of three algorithms: CNVkit, panelcn.MOPS and DECoN. We evaluated the performance of our tool and showed that increases the sensitivity in both TS and WES real datasets. Conclusions isoCNV provides an easy-to-use pipeline to optimize DECoN that allows the detection of analysis-ready CNV from a set of DNA alignments obtained under the same conditions. It increases the sensitivity of DECoN without the need for orthogonal methods. isoCNV is available at https://gitlab.com/sequentiateampublic/isocnv.

scholarly journals Evaluation of somatic copy number estimation tools for whole-exome sequencing data

2015 ◽  
Vol 17 (2) ◽  
pp. 185-192 ◽  
Author(s):  
Jae-Yong Nam ◽  
Nayoung K. D. Kim ◽  
Sang Cheol Kim ◽  
Je-Gun Joung ◽  
Ruibin Xi ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Copy Number ◽  
Sequencing Data ◽  
Copy Number Estimation ◽  
Exome Sequencing Data ◽  
Whole Exome ◽  
Whole Exome Sequencing Data

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.

scholarly journals Simulation of heterogeneous tumour genomes with HeteroGenesis and in silico whole exome sequencing

2019 ◽  
Vol 35 (16) ◽  
pp. 2850-2852
Author(s):  
Georgette Tanner ◽  
David R Westhead ◽  
Alastair Droop ◽  
Lucy F Stead
Keyword(s):  
Exome Sequencing ◽  
In Silico ◽  
Evolutionary Dynamics ◽  
Treatment Resistance ◽  
Supplementary Information ◽  
Sequencing Data ◽  
High Coverage ◽  
Whole Exome ◽  
Cancer Phenotypes ◽  
Genetic Events

Abstract Summary Tumour evolution results in progressive cancer phenotypes such as metastatic spread and treatment resistance. To better treat cancers, we must characterize tumour evolution and the genetic events that confer progressive phenotypes. This is facilitated by high coverage genome or exome sequencing. However, the best approach by which, or indeed whether, these data can be used to accurately model and interpret underlying evolutionary dynamics is yet to be confirmed. Establishing this requires sequencing data from appropriately heterogeneous tumours in which the exact trajectory and combination of events occurring throughout its evolution are known. We therefore developed HeteroGenesis: a tool to generate realistically evolved tumour genomes, which can be sequenced using weighted-Wessim (w-Wessim), an in silico exome sequencing tool that we have adapted from previous methods. HeteroGenesis simulates more complex and realistic heterogeneous tumour genomes than existing methods, can model different evolutionary dynamics, and enables the creation of multi-region and longitudinal data. Availability and implementation HeteroGenesis and w-Wessim are freely available under the GNU General Public Licence from https://github.com/GeorgetteTanner, implemented in Python and supported on linux and MS Windows. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DeAnnCNV: a tool for online detection and annotation of copy number variations from whole-exome sequencing data

2015 ◽  
Vol 43 (W1) ◽  
pp. W289-W294 ◽  
Author(s):  
Yuanwei Zhang ◽  
Zhenhua Yu ◽  
Rongjun Ban ◽  
Huan Zhang ◽  
Furhan Iqbal ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Copy Number ◽  
Copy Number Variations ◽  
Online Detection ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Whole Exome ◽  
Whole Exome Sequencing Data

scholarly journals CEQer: A Graphical Tool for Copy Number and Allelic Imbalance Detection from Whole-Exome Sequencing Data

PLoS ONE ◽  
2013 ◽  
Vol 8 (10) ◽  
pp. e74825 ◽  
Author(s):  
Rocco Piazza ◽  
Vera Magistroni ◽  
Alessandra Pirola ◽  
Sara Redaelli ◽  
Roberta Spinelli ◽  
...  
Keyword(s):  
Exome Sequencing ◽  
Whole Exome Sequencing ◽  
Copy Number ◽  
Allelic Imbalance ◽  
Sequencing Data ◽  
Exome Sequencing Data ◽  
Whole Exome ◽  
Graphical Tool ◽  
Whole Exome Sequencing Data

scholarly journals Polishing Copy Number Variant Calls on Exome Sequencing Data via Deep Learning

2020 ◽  
Author(s):  
Furkan Özden ◽  
Can Alkan ◽  
A. Ercüment Çiçek
Keyword(s):  
Deep Learning ◽  
Exome Sequencing ◽  
Copy Number ◽  
Gc Content ◽  
Copy Number Variant ◽  
Copy Number Variations ◽  
Exome Capture ◽  
Sequencing Data ◽  
Independent Manner ◽  
Efficient Detection

AbstractAccurate and efficient detection of copy number variants (CNVs) is of critical importance due to their significant association with complex genetic diseases. Although algorithms working on whole genome sequencing (WGS) data provide stable results with mostly-valid statistical assumptions, copy number detection on whole exome sequencing (WES) data has mostly been a losing game with extremely high false discovery rates. This is unfortunate as WES data is cost efficient, compact and is relatively ubiquitous. The bottleneck is primarily due to non-contiguous nature of the targeted capture: biases in targeted genomic hybridization, GC content, targeting probes, and sample batching during sequencing. Here, we present a novel deep learning model, DECoNT, which uses the matched WES and WGS data and learns to correct the copy number variations reported by any over-the-shelf WES-based germline CNV caller. We train DECoNT on the 1000 Genomes Project data, and we show that (i) we can efficiently triple the duplication call precision and double the deletion call precisions of the state-of-the-art algorithms. We also show that model consistently improves the performance in a (i) sequencing technology, (ii) exome capture kit and (iii) CNV caller independent manner. Using DECoNT as a universal exome CNV call polisher has the potential to improve the reliability of germline CNV detection on WES data sets and surge its application. The code and the models are available at https://github.com/ciceklab/DECoNT.

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