CoverageMaster: comprehensive CNV detection and visualization from NGS short reads for genetic medicine applications

CoverageMaster (CoM) is a Copy Number Variation (CNV) calling algorithm based on depth-of-coverage maps designed to detect CNVs of any size in exome (WES) and genome (WGS) data. The core of the algorithm is the compression of sequencing coverage data in a multiscale Wavelet space and the analysis through an iterative Hidden Markov Model (HMM). CoM processes WES and WGS data at nucleotide scale resolution and accurately detect and visualize full size range CNVs, including single or partial exon deletions and duplications. The results obtained with this approach support the possibility for coverage-based CNV callers to replace probe-based methods such array CGH and MLPA in the near future.

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

Bioinformatics ◽

10.1093/bioinformatics/btaa208 ◽

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.

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Emerging Technologies and Organizational Transformation

Advances in Business Information Systems and Analytics - Technology, Innovation, and Enterprise Transformation ◽

10.4018/978-1-4666-6473-9.ch002 ◽

2015 ◽

pp. 20-34 ◽

Cited By ~ 1

Author(s):

Albena Antonova

Keyword(s):

Business Models ◽

Emerging Technologies ◽

Organizational Transformation ◽

Business Practices ◽

Business Transformation ◽

The Core ◽

Emerging Trends ◽

Business Changes ◽

The Impact ◽

Near Future

Technologies continue to evolve and to largely transform business practices. In the near future, a few technologies, such as Internet of Things (IoT), Augmented Reality (AR), additive manufacturing (3D printing), and robots, can substantially influence businesses. The reason to focus specifically on these technologies as leading factors for organizational change is twofold: first, there already exist many prototypes and pilot experiments; and second, these technologies have the potential to provoke substantial breakthroughs, leading to substantial business changes. The chapter proposes an overall vision about the impact of these four emerging technologies on business practices and how they will fuel substantial business transformation. The chapter starts with a short analysis how IT influences the core business models and value formation. Then, the authors present the state of the art in e-business technologies and current emerging trends. Finally, the authors propose a detailed overview and discussion of the newly emerging bridge technologies, illustrating with examples their role and economic potential.

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Epilogue

Occupying Schools, Occupying Land ◽

10.1093/oso/9780190870324.003.0009 ◽

2019 ◽

pp. 303-318

Author(s):

Rebecca Tarlau

Keyword(s):

Organizational Structure ◽

Federal Government ◽

Presidential Election ◽

Political Mobilization ◽

Political Struggle ◽

Far Right ◽

Resource Base ◽

The Core ◽

Near Future ◽

Internal Capacity

The Epilogue reflects on the future of the MST’s political struggle—and that of the Brazilian left more broadly—in the context of the 2016 ousting of the Workers’ Party (PT) from the federal government and the 2018 presidential election of ultra-right conservative Jair Bolsonaro. Although Bolsonaro’s rise to power is a serious setback for the movement, the core argument of the book still holds: the MST’s thirty-five-year strategic engagement with the Brazilian state significantly expanded its internal capacity, including its organizational structure, resource base, and collective leadership, and the movement is unlikely to disappear in the near future. This long march through the institutions was only possible because activists engaged in contentious political mobilization, while also prefiguring their social and economic vision within a variety of state spheres and under a diversity of political regimes. Even in the new political context, activists will be able to defend many of their institutional gains, helping the movement withstand, if not fully deflect, this far-right resurgence.

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Investigation of Redox-Transmetallation Process for Coating of Gold(Au)-Shell on Nickel(Ni)-Core Nanoparticles

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.607.55 ◽

2014 ◽

Vol 607 ◽

pp. 55-60

Author(s):

N.R. Nik Roselina ◽

Azizan Aziz ◽

Koay Mei Hyie ◽

Che Murad Mardziah ◽

Zuraida Salleh

Keyword(s):

Shell Structure ◽

Sodium Citrate ◽

Size Range ◽

Reducing Agent ◽

Core Shell ◽

Ni Nanoparticles ◽

Spectra Analysis ◽

The Core ◽

Core Shell Structure ◽

Core Nanoparticles

Core-shell nanoparticle has created great interest among researchers due to their various unique properties. The new properties created are combination of both the core and the shell. In this work, pre-prepared Nickel (Ni) nanoparticles were coated with Gold (Au) to produce core-shell structure. Formation mechanism of the core-shell structure is investigated via UV-Vis spectrum of the as-synthesized particles and its supernatants. Ni nanoparticles were synthesized using polyol method with hydrazine as the reducing agent. Coating of Au was conducted using sodium citrate as the reducing agent and acid citric to control the pH of the mixture. UV-Vis absorption spectra analysis of as-synthesized nanoparticles and the supernatant results revealed that the coating happened via redox-transmetallation process with a very broad peak at about 540 nm and size range of 26 - 40 nm. XPS results suggest that the coating consists of Au compound.

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Ready to clone: CNV detection and breakpoint fine-mapping in breast and ovarian cancer susceptibility genes by high-resolution array CGH

Breast Cancer Research and Treatment ◽

10.1007/s10549-016-3956-z ◽

2016 ◽

Vol 159 (3) ◽

pp. 585-590 ◽

Cited By ~ 12

Author(s):

Karl Hackmann ◽

Franziska Kuhlee ◽

Elitza Betcheva-Krajcir ◽

Anne-Karin Kahlert ◽

Luisa Mackenroth ◽

...

Keyword(s):

Ovarian Cancer ◽

High Resolution ◽

Fine Mapping ◽

Array Cgh ◽

Cancer Susceptibility ◽

Susceptibility Genes ◽

Breast And Ovarian Cancer ◽

Cancer Susceptibility Genes ◽

Cnv Detection

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

BioMed Research International ◽

10.1155/2013/915636 ◽

2013 ◽

Vol 2013 ◽

pp. 1-7 ◽

Cited By ~ 23

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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Hidden Markov Model-Based CNV Detection Algorithms for Illumina Genotyping Microarrays

Cancer Informatics ◽

10.4137/cin.s16345 ◽

2014 ◽

Vol 13s7 ◽

pp. CIN.S16345 ◽

Cited By ~ 7

Author(s):

Eric L. Seiser ◽

Federico Innocenti

Keyword(s):

Copy Number ◽

Markov Models ◽

Hidden Markov ◽

Copy Number Variant ◽

Data Sets ◽

Dna Copy Number ◽

Detection Algorithms ◽

Number Variation ◽

Low Sensitivity ◽

Cnv Detection

Somatic alterations in DNA copy number have been well studied in numerous malignancies, yet the role of germline DNA copy number variation in cancer is still emerging. Genotyping microarrays generate allele-specific signal intensities to determine genotype, but may also be used to infer DNA copy number using additional computational approaches. Numerous tools have been developed to analyze Illumina genotype microarray data for copy number variant (CNV) discovery, although commonly utilized algorithms freely available to the public employ approaches based upon the use of hidden Markov models (HMMs). QuantiSNP, PennCNV, and GenoCN utilize HMMs with six copy number states but vary in how transition and emission probabilities are calculated. Performance of these CNV detection algorithms has been shown to be variable between both genotyping platforms and data sets, although HMM approaches generally outperform other current methods. Low sensitivity is prevalent with HMM-based algorithms, suggesting the need for continued improvement in CNV detection methodologies.

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