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Related Concept Videos

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%...
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...
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,...
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...
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...

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Related Experiment Video

Updated: May 15, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

A model-based clustering method for genomic structural variant prediction and genotyping using paired-end sequencing

Matthew Hayes1, Yoon Soo Pyon, Jing Li

  • 1Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, Ohio, United States of America.

Plos One
|January 10, 2013
PubMed
Summary

Structural variations (SVs) linked to diseases like cancer can now be identified with next-generation sequencing (NGS). Our SVMiner tool accurately detects these genomic changes and predicts deletion heterozygosity, outperforming existing methods.

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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Last Updated: May 15, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Published on: June 23, 2012

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)
11:35

Screening for Functional Non-coding Genetic Variants Using Electrophoretic Mobility Shift Assay (EMSA) and DNA-affinity Precipitation Assay (DAPA)

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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
05:53

Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry

Published on: June 21, 2018

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Structural variations (SVs) are implicated in various diseases, including cancer.
  • Next-generation sequencing (NGS) technologies enable the identification of diverse SV types.
  • Accurate SV detection is crucial for understanding disease mechanisms.

Purpose of the Study:

  • To develop a novel computational method for identifying structural variations (SVs) from sequencing data.
  • To enhance the accuracy of SV detection and characterization, including heterozygosity prediction for genomic deletions.
  • To provide a robust and superior alternative to existing SV detection tools.

Main Methods:

  • A model-based clustering approach was developed, termed SVMiner.
  • The method utilizes a set of defined features specific to different SV event types.
  • SVMiner calculates probability scores for candidate SVs and predicts deletion heterozygosity.

Main Results:

  • SVMiner demonstrated robustness against variations in single cluster features.
  • The tool significantly outperformed several commonly used SV detection programs in extensive experiments.
  • Experiments were conducted on genome-wide deep sequencing data, validating the method's performance.

Conclusions:

  • SVMiner offers a reliable and accurate method for structural variation detection using NGS data.
  • The tool's ability to predict deletion heterozygosity adds significant value for genomic analysis.
  • SVMiner represents a substantial advancement in the field of genomic variation analysis and disease association studies.