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Related Concept Videos

Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

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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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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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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.
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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: Apr 19, 2026

Detecting Somatic Genetic Alterations in Tumor Specimens by Exon Capture and Massively Parallel Sequencing
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Using VarScan 2 for Germline Variant Calling and Somatic Mutation Detection.

Daniel C Koboldt1, David E Larson2, Richard K Wilson2

  • 1The Genome Institute at Washington University in St. Louis, Missouri 63108, USA.

Current Protocols in Bioinformatics
|January 2, 2015
PubMed
Summary
This summary is machine-generated.

VarScan 2 identifies genetic variants from next-generation sequencing data. This guide details its use for germline, somatic, and trio analyses, including artifact filtering.

Keywords:
indelsmutation detectionnext-generation sequencingsnvstrio callingvariant callingvarscan 2

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Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Accurate identification of small sequence variants is crucial for next-generation sequencing (NGS) data analysis.
  • Existing variant calling methods often require specific input formats and may struggle with complex sample types.

Purpose of the Study:

  • To provide comprehensive guidelines for using VarScan 2, a variant calling tool.
  • To detail protocols for analyzing germline variants, somatic mutations, copy number alterations, and LOH events.
  • To describe a strategy for filtering false positive variants.

Main Methods:

  • Utilizes SAMtools mpileup data as input for variant calling.
  • Employs heuristic and statistical thresholds based on user-defined criteria.
  • Describes protocols for individual samples, tumor-normal pairs, and family trios.

Main Results:

  • VarScan 2 successfully identifies germline variants in individual samples.
  • The tool effectively calls somatic mutations, copy number alterations, and LOH events in tumor-normal pairs.
  • VarScan 2 identifies germline variants, de novo mutations, and Mendelian inheritance errors in family trios.
  • A filtering strategy is presented to remove common sequencing and alignment artifacts, reducing false positives.

Conclusions:

  • VarScan 2 is a versatile tool for variant calling across diverse genomic analyses.
  • The provided guidelines and protocols facilitate accurate variant identification and robust data interpretation.
  • Effective filtering strategies enhance the reliability of variant calls by minimizing artifacts.