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Comparing Copy Number Variations and SNPs

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Updated: Dec 10, 2025

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing

Published on: October 18, 2013

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection.

Tingting Gong^1,2, Vanessa M Hayes^1,2,3, Eva K F Chan^1,3

¹Garvan Institute of Medical Research, Darlinghurst, New South Wales, Australia.

|August 28, 2020

Summary

This summary is machine-generated.

Shiny-SoSV predicts somatic structural variant detection performance using simulation data. This tool guides study design by evaluating sequencing depth, variant allele fraction, and tool choice to optimize cancer research.

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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

Published on: January 16, 2019

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Somatic structural variants are key drivers of cancer development.
Accurate detection of these variants from whole genome sequencing (WGS) data is challenging due to numerous influencing parameters.
Existing tools lack guidance for study design and performance prediction in somatic structural variant detection.

Purpose of the Study:

To develop Shiny-SoSV, a web-based calculator for predicting somatic structural variant detection performance.
To assess the impact of variables like sequencing depth, variant allele fraction, and breakpoint resolution on detection sensitivity, precision, and F1 score.
To provide a tool for optimizing study design in cancer genomics research.

Main Methods:

Utilized simulation studies to evaluate singular and combined effects of key parameters on somatic structural variant detection.
Employed a generalized additive model to predict detection performance based on predictor variables.
Developed an interactive, user-friendly web application (Shiny-SoSV) for performance prediction.

Main Results:

Quantified the impact of variant detection tool choice, sequencing depth, variant allele fraction, and breakpoint resolution on detection metrics.
Modeled the relationship between these parameters and detection performance, enabling prediction for various study designs.
Demonstrated the utility of Shiny-SoSV in comparing parameter impacts and predicting performance.

Conclusions:

Shiny-SoSV offers a valuable platform for researchers to predict and optimize somatic structural variant detection study designs.
The tool facilitates informed decisions regarding sequencing parameters and variant callers before experimental work.
This enhances the efficiency and accuracy of cancer genomics research by improving structural variant detection.