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Related Concept Videos

Sanger Sequencing01:57

Sanger Sequencing

DNA sequencing is a fundamental technique that is routinely used in the biological sciences. This method can be applied to a range of questions at different scales - from the sequencing of a cloned DNA fragment or the study of a mutation in a gene up to whole-genome sequencing. However, despite the widespread use of sequencing today, it was not until 1977 that Fredrick Sanger and his collaborators developed the chain-termination method to decode DNA sequences. It relies on the separation of a...
Next-generation Sequencing03:00

Next-generation Sequencing

The first human genome sequencing project cost $2.7 billion and was declared complete in 2003, after 15 years of international cooperation and collaboration between several research teams and funding agencies. Today, with the advent of next-generation sequencing technologies, the cost and time of sequencing a human genome have dropped over 100 fold.
Next-Generation Sequencing Methods
Although all next-generation methods use different technologies, they all share a set of standard features.
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
Comparing Copy Number Variations and SNPs02:26

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%...
Genomics02:02

Genomics

Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...

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Updated: Jun 13, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

gSV: a general structural variant detector using the third-generation sequencing data.

Jingyu Hao1, Jiandong Shi2, Sheng Lian1

  • 1Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong SAR, 999077, China.

Briefings in Bioinformatics
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

gSV is a novel tool for detecting structural variants (SVs) in genomes. It improves accuracy for complex SVs missed by other methods, aiding cancer research and population genomics.

Keywords:
complex structural variantstructural variant detectionthird-generation sequencing

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Related Experiment Videos

Last Updated: Jun 13, 2026

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

Following the Dynamics of Structural Variants in Experimentally Evolved Populations
04:52

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'
08:31

Isolation and Genome Analysis of Single Virions using 'Single Virus Genomics'

Published on: May 26, 2013

Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Structural variants (SVs) significantly impact genome diversity and disease susceptibility, especially in cancer.
  • Third-generation sequencing enhances SV detection but struggles with complex SVs due to alignment challenges and reliance on predefined models.

Purpose of the Study:

  • To introduce gSV, a general structural variant detector designed to overcome limitations in complex SV detection.
  • To provide a unified framework for comprehensive SV discovery in research and clinical settings.

Main Methods:

  • gSV integrates alignment-based and assembly-based approaches with a maximum exact match strategy.
  • It resolves SVs with complex or atypical alignment signatures without predefined assumptions on SV types.
  • Benchmarking involved simulated datasets and real long-read sequencing data.

Main Results:

  • gSV demonstrated improved sensitivity and overall performance compared to state-of-the-art SV callers, particularly for complex SV events.
  • Unique SVs were discovered in cancer-associated genes within breast cancer cell lines, indicating biological relevance.
  • gSV proved useful for population-scale genomic studies, as shown in a Chinese breast cancer cohort.

Conclusions:

  • gSV offers a robust and generalizable approach for comprehensive structural variant detection.
  • The tool enhances the ability to identify SVs, including complex ones often missed by conventional methods.
  • gSV has significant potential for both research and clinical genomics applications.